1
|
Chen Y, Sang Y, Li S, Xue J, Chen M, Hong S, Lv W, Sehgal K, Xiao H, Liu R. The ERK inhibitor GDC-0994 selectively inhibits growth of BRAF mutant cancer cells. Transl Oncol 2024; 45:101991. [PMID: 38728872 DOI: 10.1016/j.tranon.2024.101991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024] Open
Abstract
BRAF or RAS mutation-induced aberrant activation of the mitogen-activated protein kinase (MAPK) pathway is frequently observed in human cancers. As the key downstream node of MAPK pathway, ERK1/2 is as an important therapeutic target. GDC-0994 (ravoxertinib), an orally bioavailable, highly selective small-molecule inhibitor of ERK1/2, showed acceptable safety and pharmacodynamic profile in a recent phase I clinical trial. In this study, we investigated dependence of the anti-tumor effect of ERK inhibitor GDC-0994 on genetic alterations in the MAPK pathway. The results showed that GDC-0994 sharply inhibited cell proliferation and colony formation and induced remarkable G1 phase cell-cycle arrest in cancer cells harboring BRAF mutation but had little effect on cell behaviors in most RAS mutant or wild-type cell lines. The expression of a large number of genes, particularly the genes in the cell cycle pathway, were significantly changed after GDC-0994 treatment in BRAF mutant cells, while no remarkable expression change of such genes was observed in wild-type cells. Moreover, GDC-0994 selectively inhibited tumor growth in a BRAF mutant xenograft mice model. Our findings demonstrate a BRAF mutation-dependent anti-tumor effect of GDC-0994 and provide a rational strategy for patient selection for ERK1/2 inhibitor treatment.
Collapse
Affiliation(s)
- Yulu Chen
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Ye Sang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Shiyong Li
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Junyu Xue
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Mengke Chen
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China
| | - Weiming Lv
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Kartik Sehgal
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China.
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, Guangdong 510080, China.
| |
Collapse
|
2
|
Rosenberg E, Andersen TI, Samajdar R, Petukhov A, Hoke JC, Abanin D, Bengtsson A, Drozdov IK, Erickson C, Klimov PV, Mi X, Morvan A, Neeley M, Neill C, Acharya R, Allen R, Anderson K, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bilmes A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Campero J, Chang HS, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Barba ADT, Demura S, Di Paolo A, Dunsworth A, Earle C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Garcia G, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hill G, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Mandrà S, Martin O, Martin S, McClean JR, McEwen M, Meeks S, Miao KC, Mieszala A, Montazeri S, Movassagh R, Mruczkiewicz W, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Omonije S, Opremcak A, Potter R, Pryadko LP, Quintana C, Rhodes DM, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Sivak V, Skruzny J, Smith WC, Somma RD, Sterling G, Strain D, Szalay M, Thor D, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Khemani V, Gopalakrishnan S, Prosen T, Roushan P. Dynamics of magnetization at infinite temperature in a Heisenberg spin chain. Science 2024; 384:48-53. [PMID: 38574139 DOI: 10.1126/science.adi7877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/01/2024] [Indexed: 04/06/2024]
Abstract
Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
Collapse
Affiliation(s)
- E Rosenberg
- Google Research, Mountain View, CA, USA
- Department of Physics, Cornell University, Ithaca, NY, USA
| | | | - R Samajdar
- Department of Physics, Princeton University, Princeton, NJ, USA
- Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA
| | | | - J C Hoke
- Department of Physics, Stanford University, Stanford, CA, USA
| | - D Abanin
- Google Research, Mountain View, CA, USA
| | | | - I K Drozdov
- Google Research, Mountain View, CA, USA
- Department of Physics, University of Connecticut, Storrs, CT, USA
| | | | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - A Morvan
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - R Allen
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - A Bilmes
- Google Research, Mountain View, CA, USA
| | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - J Campero
- Google Research, Mountain View, CA, USA
| | - H-S Chang
- Google Research, Mountain View, CA, USA
| | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | - C Earle
- Google Research, Mountain View, CA, USA
| | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - G Garcia
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | - G Hill
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- QSI, Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - S Mandrà
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | - S Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | - S Meeks
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - S Omonije
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - V Sivak
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | | | - R D Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - D Thor
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | | | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - V Khemani
- Department of Physics, Stanford University, Stanford, CA, USA
| | | | - T Prosen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - P Roushan
- Google Research, Mountain View, CA, USA
| |
Collapse
|
3
|
Mi X, Michailidis AA, Shabani S, Miao KC, Klimov PV, Lloyd J, Rosenberg E, Acharya R, Aleiner I, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Genois É, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Malone FD, Martin O, McClean JR, McEwen M, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Roushan P, Smelyanskiy V, Abanin DA. Stable quantum-correlated many-body states through engineered dissipation. Science 2024; 383:1332-1337. [PMID: 38513021 DOI: 10.1126/science.adh9932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 02/13/2024] [Indexed: 03/23/2024]
Abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
Collapse
Affiliation(s)
- X Mi
- Google Research, Mountain View, CA, USA
| | - A A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - S Shabani
- Google Research, Mountain View, CA, USA
| | - K C Miao
- Google Research, Mountain View, CA, USA
| | | | - J Lloyd
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | | - R Acharya
- Google Research, Mountain View, CA, USA
| | - I Aleiner
- Google Research, Mountain View, CA, USA
| | | | - M Ansmann
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - D Chik
- Google Research, Mountain View, CA, USA
| | - C Chou
- Google Research, Mountain View, CA, USA
| | - J Cogan
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | - A G Dau
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - É Genois
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | - R Gosula
- Google Research, Mountain View, CA, USA
| | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | - M C Hamilton
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
| | - M Hansen
- Google Research, Mountain View, CA, USA
| | | | | | - P Heu
- Google Research, Mountain View, CA, USA
| | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Research, Mountain View, CA, USA
| | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | | | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Software and Information (QSI), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Kitaev
- Google Research, Mountain View, CA, USA
| | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
- Department of Chemistry, Columbia University, New York, NY, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | | | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
| | | | | | - A Morvan
- Google Research, Mountain View, CA, USA
| | | | | | - M Neeley
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - J H Ng
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | | | | | - R Potter
- Google Research, Mountain View, CA, USA
| | - L P Pryadko
- Google Research, Mountain View, CA, USA
- Department of Physics and Astronomy, University of California, Riverside, CA, USA
| | | | - C Rocque
- Google Research, Mountain View, CA, USA
| | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - N Shutty
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - R Somma
- Google Research, Mountain View, CA, USA
| | | | - D Strain
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - B W K Woo
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z J Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - G Young
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - N Zobrist
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | - P Roushan
- Google Research, Mountain View, CA, USA
| | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
- Department of Physics, Princeton University, Princeton, NJ, USA
| |
Collapse
|
4
|
Sang Y, Hu G, Xue J, Chen M, Hong S, Liu R. Risk stratification by combining common genetic mutations and TERT promoter methylation in papillary thyroid cancer. Endocrine 2024:10.1007/s12020-024-03722-6. [PMID: 38356100 DOI: 10.1007/s12020-024-03722-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Risk stratification based on somatic mutations in TERT promoter and BRAF/RAS has been well established for papillary thyroid cancer (PTC), and there is emerging evidence showed that TERT promoter methylation was frequently observed in thyroid cancer patients with adverse features. This study was aimed to comprehensive explore the prognostic value of BRAF/RAS mutations, TERT promoter mutations, and TERT promoter methylation in PTC. METHODS The relationships of BRAF/RAS mutations, TERT promoter mutations, and TERT promoter methylation with clinical characteristics and outcomes of PTC were analyzed in 382 patients with PTC. RESULTS TERT promoter mutation and hypermethylation were collectively observed in 52 (13.6%) samples and associated with BRAF/RAS mutation, aggressive clinical characteristics, and poor clinical outcomes of PTC. Coexistence of BRAF/RAS and TERT alterations was found in 45 of 382 (11.8%) PTC patients and strongly associated with old patient age, extrathyroidal extension, advanced pathologic T stage and metastasis. Importantly, patients with both BRAF/RAS and TERT alterations had higher rates of tumor recurrence (13.6% vs 1.5%, P = 0.042) and disease progression (24.4% vs 3.3%, P < 0.001) than patients without any alterations, and cox regression analysis revealed that the coexistence of BRAF/RAS and TERT alterations, but not BRAF/RAS or TERT alterations alone, increased the risk of progression-free interval with an adjusted HR of 10.35 (95% CI: 1.79-59.81, P = 0.009). CONCLUSIONS This study suggested that comprehensively analysis of BRAF/RAS mutations, TERT promoter mutation and methylation is an effective strategy to identify high-risk patients with PTC.
Collapse
Affiliation(s)
- Ye Sang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China
| | - Guanghui Hu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China
| | - Junyu Xue
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China
| | - Mengke Chen
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, China.
| |
Collapse
|
5
|
Xian W, Liu B, Li J, Yang Y, Hong S, Xiao H, Wu D, Li Y. Graves' disease and systemic lupus erythematosus: a Mendelian randomization study. Front Immunol 2024; 15:1273358. [PMID: 38352885 PMCID: PMC10863043 DOI: 10.3389/fimmu.2024.1273358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 01/12/2024] [Indexed: 02/16/2024] Open
Abstract
Introduction Previous observational studies have established a correlation between Graves' disease(GD) and systemic lupus erythematosus(SLE). However, whether a causal relationship exists between these two diseases remains unknown.We utilized Mendelian randomization to infer the causal association between GD and SLE. Methods This study employed GWAS summary statistics of GD and SLE in individuals of Asian descent. The random effect inverse variance weighted (IVW) method was utilized to aggregate the causal effect estimates of all SNPs. Cochran's Q values were computed to evaluate the heterogeneity among instrumental variables. Sensitivity analyses such as MR-Egger method, median weighting method, leave-one-out method, and MR-PRESSO method were used to test whether there was horizontal pleiotropy of instrumental variables. Results Our study found genetically predicted GD may increase risk of SLE (OR=1.17, 95% CI 0.99-1.40, p=0.069). Additionally, genetically predicted SLE elevated the risk of developing GD by 15% (OR=1.15, 95% CI 1.05-1.27, p= 0.004). After correcting for possible horizontal pleiotropy by excluding outlier SNPs, the results suggested that GD increased the risk of SLE (OR=1.27, 95% CI 1.09-1.48, p =0.018), while SLE also increased the risk of developing GD (OR=1.13, 95% CI 1.05-1.22, p =0.003). Conclusion The findings of the study indicate that there may be a correlation between GD and SLE, with each potentially increasing the risk of the other. These results have important implications for the screening and treatment of patients with co-morbidities in clinical settings, as well as for further research into the molecular mechanisms underlying the relationship between GD and SLE.
Collapse
Affiliation(s)
- Wei Xian
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- Department of Pediatric Allergy, Immunology & Rheumatology, Guangzhou Women and Children’s Medical Center, Guangzhou, Guangdong, China
| | - Boyuan Liu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Jinjian Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuxin Yang
- Zhongshan School of Medicine, Sun Yat Sen University, Guangzhou, Guangdong, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Dide Wu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| |
Collapse
|
6
|
Cao S, Yin Y, Hu H, Hong S, He W, Lv W, Liu R, Li Y, Yu S, Xiao H. CircGLIS3 inhibits thyroid cancer invasion and metastasis through miR-146b-3p/AIF1L axis. Cell Oncol (Dordr) 2023; 46:1777-1789. [PMID: 37610691 DOI: 10.1007/s13402-023-00845-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 08/24/2023] Open
Abstract
PURPOSE Studies have shown that circRNA is involved in the occurrence and development of human cancers. However, it remains unclear that the contribution of circRNA in thyroid carcinoma and its role in the process of tumorigenesis. METHODS The expression profile of circRNA-miRNA-mRNA in thyroid carcinoma was detected by RNA sequencing and verified by qRT-PCR. The characteristics of circGLIS3 were verified by RNase R and actinomycin assays, subcellular fractionation, and fluorescence in situ hybridization. The functions of circGLIS3 and AIF1L were detected by wound healing, transwell, 3D culture and Western blot. RNA Immunoprecipitation (RIP), RNA pulldown and dual-luciferase reporter assays were used to verify the target genes of circGLIS3 and downstream miRNAs. Functional rescue experiments were performed by transfecting miRNA mimics or siRNA of target genes. Finally, metastatic mouse models were used to investigate circGLIS3 function in vivo. RESULTS In this study, we discovered a novel circRNA (has_circ_0007368, named as circGLIS3) by RNA sequencing. CircGLIS3 was down-regulated in thyroid carcinoma tissues and cells line, and was negatively associated with malignant clinical features of thyroid carcinoma. Functional studies found that circGLIS3 could inhibit the migration and invasion of thyroid carcinoma cells, and was related to the EMT process. Mechanistically, circGLIS3 can upregulate the expression of the AIF1L gene by acting as a miR-146b-3p sponge to inhibit the progression of thyroid carcinoma. CONCLUSION Our study identified circGLIS3 as a novel tumor suppressor in thyroid cancer, indicating the potential of circGLIS3 as a promising diagnostic and prognostic marker for thyroid cancer.
Collapse
Affiliation(s)
- Siting Cao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Yali Yin
- Department of Endocrinology, Peking University Shenzhen Hospital, Shenzhen, 518036, China
| | - Huijuan Hu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Weiman He
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Weiming Lv
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
| |
Collapse
|
7
|
Zhu H, Zou M, Wu D, Li B, Su Y, Li Y, Hong S, Yang Z. Quantitative assessment of extraocular muscles in Graves' ophthalmopathy using T1 mapping. Eur Radiol 2023; 33:9074-9083. [PMID: 37466707 DOI: 10.1007/s00330-023-09931-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 05/03/2023] [Accepted: 05/14/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVE To evaluate the performance of T1 mapping in the characterization of extraocular muscles (EOMs) of Graves' ophthalmopathy (GO) patients and investigate its feasibility in assessing the response to glucocorticoid therapy in active GO patients. METHODS A total of 133 participants (78 active GO, 23 inactive GO, 18 Graves' disease (GD) patients, and 14 healthy volunteers) were consecutively enrolled from July 2018 to December 2020. Native T1 (nT1) and postcontrast T1 (cT1) values of EOMs were measured and compared. The variations in T1 mapping metrics of EOMs were compared pre/post glucocorticoid treatment in 23 follow-up active GO patients. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. RESULTS The nT1 of EOMs in GO patients was higher than that in GD patients and healthy volunteers. The nT1 of superior rectus (SR) in active GO was higher than that in inactive GO patients, and it could be used as a potential marker of GO activity (OR: 1.003; 95% CI: 1.001, 1.004), with a diagnostic sensitivity of 86.3% and specificity of 43.7%. Meanwhile, the cT1 of SR, inferior rectus (IR), and medial rectus (MR) in inactive GO patients were higher than those in active GO patients. The nT1 of EOMs achieved sufficient diagnostic performance in evaluating the response to glucocorticoid therapy for follow-up active GO patients (AUC, 0.797; sensitivity, 71.9%; specificity, 85.7%). CONCLUSIONS T1 mapping could quantitatively assess the activity of GO and the response to glucocorticoid therapy in active GO patients and may even potentially reflect the fibrosis of EOMs. CLINICAL RELEVANCE STATEMENT T1 values can reflect the pathological status of the extraocular muscle. T1 mapping could help to quantitatively assess the clinical activity of GO and the response to glucocorticoid therapy in active GO patients. KEY POINTS • Graves' ophthalmopathy patients had greater nT1 of extraocular muscles than Graves' disease patients and healthy volunteers, and nT1 of the superior rectus could be a potential marker of Graves' ophthalmopathy activity. • The cT1 of extraocular muscles in inactive Graves' ophthalmopathy patients was higher than that in active Graves' ophthalmopathy patients, and it might be associated with muscle fibrosis. • nT1 of extraocular muscles could offer sufficient diagnostic performance in evaluating the response to glucocorticoid therapy for follow-up active Graves' ophthalmopathy patients.
Collapse
Affiliation(s)
- Hongzhang Zhu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Mengsha Zou
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Dide Wu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bin Li
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yihua Su
- Department of Ophthalmology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
8
|
Wu D, Liu B, Xian W, Yang Y, Li J, Hong S, Li Y, Xiao H. New insight into the causal relationship between Graves' disease liability and drug eruption: a Mendelian randomization study. Front Immunol 2023; 14:1267814. [PMID: 38077385 PMCID: PMC10703291 DOI: 10.3389/fimmu.2023.1267814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
Background Graves' disease (GD) and drug eruption are closely associated and frequently observed in the clinical setting. However, it remains unclear whether a causal relationship exists between these two conditions. The aim of the study is to investigate whether GD is causal to drug eruptions using two-sample Mendelian randomization. Methods We launched a two-sample MR to investigate whether GD is causal to drug eruption using Genome-wide association study (GWAS) summary data from Biobank Japan and FinnGen. Genetic variants were used as instrumental variables to avoid confounding bias. Statistical methods including inverse variance weighted (IVW), weighted median, MR-Egger, and MR-PRESSO were conducted to identify the robustness of the causal effect. Results Genetically predicted GD may increase the risk of drug eruption by 30.3% (OR=1.303, 95% CI 1.119-1.516, p<0.001) in the Asian population. In European populations, GD may increase the generalized drug eruption by 15.9% (OR=1.159, 95%CI 0.982-1.367, p=0.080). Conclusions We found GD is potentially causal to drug eruption. This finding expanded the view of the frequently observed co-existence of GD and adverse drug reactions involving the skin. The mechanism remains for further investigation.
Collapse
Affiliation(s)
- Dide Wu
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Boyuan Liu
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wei Xian
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuxin Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jinjian Li
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shubin Hong
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanbing Li
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Haipeng Xiao
- Department of Endocrinology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| |
Collapse
|
9
|
Zhang L, Yu S, Hong S, Xiao X, Liao Z, Li Y, Xiao H. Comprehensive analysis of BTNL9 as a prognostic biomarker correlated with immune infiltrations in thyroid cancer. BMC Med Genomics 2023; 16:234. [PMID: 37798795 PMCID: PMC10552425 DOI: 10.1186/s12920-023-01676-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Thyroid cancer (THCA) is the most common type of endocrine cancers, and the disease recurrences were usually associated with the risks of metastasis and fatality. Butyrophilin-like protein 9 (BTNL9) is a member of the immunoglobulin families. This study investigated the prognostic role of BTNL9 in THCA. METHODS Gene enhancers of BTNL9 were identified by interrogating H3K27ac ChIP-seq and RNA-seq data of papillary thyroid cancer (PTC) and benign thyroid nodule (BTN) tissues. Meanwhile, BTNL9 expression level was verified by qRT-PCR in 30 pairs of primary THCA and adjacent normal tissues. Clinicopathological and RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) to analyze the relations between BTNL9 expression and immune cell infiltration, chemokines/cytokines, immune checkpoint genes, clinical parameters and prognosis values. Besides, survival analysis combining BTNL9 expression and immune cell infiltration scores was conducted. Functional enrichment analysis was performed to investigate the potential biological mechanisms. Cox regression analyses were used to explore independent clinical indicators, and a nomogram model incorporating BTNL9 expression with clinical parameters was established. RESULTS BTNL9 showed significantly stronger H3K27ac modifications in BTN than PTC tissues at the promoter region (chr5: 181,035,673-181,047,436) and gene body (chr5: 181,051,544-181,054,849). The expression levels of BTNL9 were significantly down-regulated in THCA samples compared to normal tissues, and were strongly associated with different tumor stages, immune cell infiltrations, chemokines/cytokines and immune checkpoint genes in THCA. Functional enrichment analyses indicated that BTNL9 was involved in immune-related and cancer-related pathways. The Kaplan-Meier analysis showed lower BTNL9 expression was associated with poorer progression-free interval (PFI). BTNL9 expression and pathologic stages were independent prognostic indicators of PFI in THCA. CONCLUSIONS The results implied an important role of BTNL9 in the tumor progression, with the possibility of serving as a novel prognostic biomarker and a potential therapeutic target for THCA.
Collapse
Affiliation(s)
- Luyao Zhang
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Xi Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Zhihong Liao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
| |
Collapse
|
10
|
Ward MC, Atlas JL, Carrizosa DR, Milas ZL, Brickman DS, Frenkel CH, Hong S, Heinzerling JH, Prabhu RS, Moeller BJ. Weekly vs. Bolus Cisplatin Concurrent with Definitive Radiotherapy for Squamous Carcinoma of the Head and Neck: A Systematic Review and Network Meta-Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e632-e633. [PMID: 37785889 DOI: 10.1016/j.ijrobp.2023.06.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) The optimal schedule for cisplatin delivered concurrently with definitive radiation for squamous carcinoma of the head and neck remains controversial. Randomized data in the postoperative setting is mixed, and definitive studies are ongoing. Meanwhile, multiple trials have already compared cetuximab to cisplatin in the definitive setting. Across these trials, the cetuximab dosing was identical, but cisplatin dosing was variable and can be categorized as weekly (40 mg/m2 q1 week) or bolus (100 mg/m2 q3 weeks). We indirectly compared these two cisplatin schedules by performing a network meta-analysis of cetuximab trials. MATERIALS/METHODS We performed a PRISMA-concordant systematic review to identify randomized controlled trials comparing cisplatin to cetuximab for patients with non-metastatic squamous carcinoma of the head and neck treated with definitive radiation therapy. Trials of primary surgery, incorporating induction therapy, or mixing other therapeutics were excluded. The analysis was pre-registered with the Open Science Foundation. Individual patient survival data was extracted from the published overall survival curves using a digitizer, and outcomes were validated against published point-estimates and hazard ratios. A random effects Cox regression was used to perform the individual-patient analysis using a one-step approach under a frequentist framework. Random effects were applied to model heterogeneity in the baseline hazard function and treatment effect. Models were adjusted by HPV and smoking status, which were trial-level covariates. Alternative endpoints (DFS, LRF, DM, etc.) were analyzed qualitatively. IRB approval was not required. RESULTS Five randomized trials were identified, including 1,678 patients. Bolus cisplatin was delivered to 572 patients in 2 trials, and weekly to 271 in 3 trials. The risk of bias was low. Relative to cetuximab, both bolus and weekly cisplatin reduced the risk of death (adjusted HR 0.63, 95% CI 0.46-0.87, p = 0.004 & HR 0.56, 95% CI 0.37-0.86, p = 0.008 respectively). No interaction was identified between regimen and HPV or smoking status. Between-study heterogeneity (δ2) was 0.148 and treatment effect heterogeneity (τ2) was small (<0.0002). There was no statistical difference in OS between bolus vs. weekly regimens (weekly vs. bolus HR 0.90, 95% CI 0.53-1.52, p = 0.345). This Cox model therefore suggested that on average, the absolute difference in 5-year OS is <1.5% between the two regimens, which was not statistically significant. Secondary endpoints and toxicity were not obviously different by regimen, qualitatively. CONCLUSION Using cetuximab as a common reference, there was no significant difference in survival between weekly and bolus cisplatin schedules.
Collapse
Affiliation(s)
- M C Ward
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - J L Atlas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D R Carrizosa
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - Z L Milas
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - D S Brickman
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - C H Frenkel
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - S Hong
- Levine Cancer Institute, Atrium Health, Charlotte, NC
| | - J H Heinzerling
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - R S Prabhu
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| | - B J Moeller
- Levine Cancer Institute, Atrium Health and Southeast Radiation Oncology Group, Charlotte, NC
| |
Collapse
|
11
|
Hoke JC, Ippoliti M, Rosenberg E, Abanin D, Acharya R, Andersen TI, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Dau AG, Debroy DM, Del Toro Barba A, Demura S, Di Paolo A, Drozdov IK, Dunsworth A, Eppens D, Erickson C, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hoffmann MR, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Kechedzhi K, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lensky YD, Lester BJ, Lill AT, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Miao KC, Mieszala A, Montazeri S, Morvan A, Movassagh R, Mruczkiewicz W, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Opremcak A, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Babbush R, Bacon D, Boixo S, Hilton J, Lucero E, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Mi X, Khemani V, Roushan P. Measurement-induced entanglement and teleportation on a noisy quantum processor. Nature 2023; 622:481-486. [PMID: 37853150 PMCID: PMC10584681 DOI: 10.1038/s41586-023-06505-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/01/2023] [Indexed: 10/20/2023]
Abstract
Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.
Collapse
|
12
|
Sud S, Poellmann M, Garg V, King T, Casey DL, Wang AZ, Hong S, Weiner AA. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Localized Lung Cancer Treated with Radiotherapy or Chemoradiotherapy with Definitive Intent. Int J Radiat Oncol Biol Phys 2023; 117:e60. [PMID: 37785811 DOI: 10.1016/j.ijrobp.2023.06.778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To characterize circulating tumor cell (CTC) kinetics in response to definitive therapy in patients with local or locoregional lung cancer and identify CTC kinetic profiles associated with favorable disease response versus progression. MATERIALS/METHODS In this single-institution prospective correlative biomarker study, we enrolled patients receiving definitive intent radiotherapy (RT) or chemoradiotherapy for non-metastatic lung cancer. Blood specimens were collected prior to RT (baseline), during RT and at follow up visits up to 24 months post RT. Subsequent lines of therapy were administered per standard of care. CTCs were captured and enumerated using a previously reported nanotechnology-based assay functionalized with aEpCAM, aHER-2, and aEGFR to facilitate biomimetic cell rolling and dendrimer-mediated multivalent binding. Disease status was assessed per RECIST 1.1 criteria. CTC kinetics and absolute values were analyzed to identify patterns associated with disease control versus progression. RESULTS We enrolled 24 patients with median follow up of 8 months corresponding to 114 CTC measurements. Seven patients (30%) had biopsy proven disease, while 17 (70%) were diagnosed based on clinical and radiographic features alone. Nineteen patients (79%) received stereotactic body radiation therapy. Median baseline CTC count was 12.6 CTCs/ml (range 0-290) and post RT decreased to median 4 CTCs/ml (0-42.7). For 95% of patients, a favorable kinetic profile (defined as stable CTC count, decreased CTC count or <24 CTCs/ml corresponding to the 80th percentile) during radiotherapy or at the time of first follow up corresponded to local control of the irradiated lesion. Five patients (20%) experienced disease progression within the follow up period. In the two patients with local progression of the irradiated lesion, the CTC count rose >10 fold prior to or at the time of radiographic detection of progression. In the three patients with systemic progression, CTC count rose 1.46-5.8-fold at the time of progression. Notably, four of the five patients with disease progression did not have initial biopsy confirmation of disease but did experience a CTC elevation at the time of progression. CONCLUSION Our data suggests CTCs may serve as a biomarker for response to therapy in patients being treated with RT with definitive intent for early stage or locally advanced lung cancer. This finding is of importance given important limitations in obtaining pathologic confirmation of disease in select patients and challenges distinguishing disease progression versus benign post radiotherapy radiographic changes. Further studies are needed to characterize the predictive and prognostic value of circulating biomarker levels and kinetics in lung cancer.
Collapse
Affiliation(s)
- S Sud
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - M Poellmann
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - V Garg
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - T King
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - D L Casey
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| | - A Z Wang
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC; UT Southwestern Department of Radiation Oncology, Dallas, TX
| | - S Hong
- Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI
| | - A A Weiner
- Department of Radiation Oncology, University of North Carolina, Chapel Hill, NC
| |
Collapse
|
13
|
Smitherman EA, Chahine RA, Beukelman T, Lewandowski LB, Rahman AKMF, Wenderfer SE, Curtis JR, Hersh AO, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar‐Smiley F, Barillas‐Arias L, Basiaga M, Baszis K, Becker M, Bell‐Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang‐Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel‐Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie‐Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui‐Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein‐Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PM, McGuire S, McHale I, McMonagle A, McMullen‐Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O'Brien B, O'Brien T, Okeke O, Oliver M, Olson J, O'Neil K, Onel K, Orandi A, Orlando M, Osei‐Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan‐Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas‐Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth‐Wojcicki E, Rouster – Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert‐Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner‐Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Childhood-Onset Lupus Nephritis in the Childhood Arthritis and Rheumatology Research Alliance Registry: Short-Term Kidney Status and Variation in Care. Arthritis Care Res (Hoboken) 2023; 75:1553-1562. [PMID: 36775844 PMCID: PMC10500561 DOI: 10.1002/acr.25002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 07/14/2022] [Accepted: 08/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The goal was to characterize short-term kidney status and describe variation in early care utilization in a multicenter cohort of patients with childhood-onset systemic lupus erythematosus (cSLE) and nephritis. METHODS We analyzed previously collected prospective data from North American patients with cSLE with kidney biopsy-proven nephritis enrolled in the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry from March 2017 through December 2019. We determined the proportion of patients with abnormal kidney status at the most recent registry visit and applied generalized linear mixed models to identify associated factors. We also calculated frequency of medication use, both during induction and ever recorded. RESULTS We identified 222 patients with kidney biopsy-proven nephritis, with 64% class III/IV nephritis on initial biopsy. At the most recent registry visit at median (interquartile range) of 17 (8-29) months from initial kidney biopsy, 58 of 106 patients (55%) with available data had abnormal kidney status. This finding was associated with male sex (odds ratio [OR] 3.88, 95% confidence interval [95% CI] 1.21-12.46) and age at cSLE diagnosis (OR 1.23, 95% CI 1.01-1.49). Patients with class IV nephritis were more likely than class III to receive cyclophosphamide and rituximab during induction. There was substantial variation in mycophenolate, cyclophosphamide, and rituximab ever use patterns across rheumatology centers. CONCLUSION In this cohort with predominately class III/IV nephritis, male sex and older age at cSLE diagnosis were associated with abnormal short-term kidney status. We also observed substantial variation in contemporary medication use for pediatric lupus nephritis between pediatric rheumatology centers. Additional studies are needed to better understand the impact of this variation on long-term kidney outcomes.
Collapse
|
14
|
Andersen TI, Lensky YD, Kechedzhi K, Drozdov IK, Bengtsson A, Hong S, Morvan A, Mi X, Opremcak A, Acharya R, Allen R, Ansmann M, Arute F, Arya K, Asfaw A, Atalaya J, Babbush R, Bacon D, Bardin JC, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Chik D, Chou C, Cogan J, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Ferreira VS, Burgos LF, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Gosula R, Dau AG, Gross JA, Habegger S, Hamilton MC, Hansen M, Harrigan MP, Harrington SD, Heu P, Hilton J, Hoffmann MR, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev A, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Lucero E, Malone FD, Martin O, McClean JR, McCourt T, McEwen M, Miao KC, Mieszala A, Mohseni M, Montazeri S, Mount E, Movassagh R, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Nersisyan A, Newman M, Ng JH, Nguyen A, Nguyen M, Niu MY, O’Brien TE, Omonije S, Petukhov A, Potter R, Pryadko LP, Quintana C, Rocque C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shutty N, Shvarts V, Skruzny J, Smith WC, Somma R, Sterling G, Strain D, Szalay M, Torres A, Vidal G, Villalonga B, Heidweiller CV, White T, Woo BWK, Xing C, Yao ZJ, Yeh P, Yoo J, Young G, Zalcman A, Zhang Y, Zhu N, Zobrist N, Neven H, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Kim EA, Aleiner I, Roushan P. Non-Abelian braiding of graph vertices in a superconducting processor. Nature 2023; 618:264-269. [PMID: 37169834 DOI: 10.1038/s41586-023-05954-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/14/2023] [Indexed: 06/09/2023]
Abstract
Indistinguishability of particles is a fundamental principle of quantum mechanics1. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons9,10, we implement a generalized stabilizer code and unitary protocol23 to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.
Collapse
|
15
|
Chen M, Xue J, Sang Y, Jiang W, He W, Hong S, Lv W, Xiao H, Liu R. Highly sensitive droplet digital PCR for detection of RET fusion in papillary thyroid cancer. BMC Cancer 2023; 23:363. [PMID: 37081420 PMCID: PMC10120194 DOI: 10.1186/s12885-023-10852-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 04/15/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Thyroid cancer is the most frequent malignancy of the endocrine system, of which papillary thyroid cancer (PTC) is the predominant form with a rapid increasing incidence worldwide. Rearranged during transfection (RET) fusions are common genetic drivers of PTC and the potent RET inhibitor selpercatinib has been recently approved for treating advanced or metastatic RET fusion-positive thyroid cancer. In this study we aimed to develop a droplet digital PCR (ddPCR) system to accurately detect RET fusion in PTC samples. METHODS The frequency and distribution of RET fusions in PTC were analyzed using genomic data of 402 PTC patients in The Cancer Genome Atlas (TCGA) database. To establish the ddPCR system for detecting CCDC6::RET fusion, a plasmid containing CCDC6::RET infusion fragment was constructed as standard template, the annealing temperature and concentrations of primers and probe were optimized. The analytical performance of ddPCR and quantitative reverse transcription PCR (qRT-PCR) were assessed in standard templates and tissue samples from 112 PTC patients. Sanger sequencing was performed in all the RET fusion-positive samples identified by ddPCR. RESULTS RET fusions were observed in 25 (6.2%) of the 402 TCGA samples, and 15 (60%) of the RET fusion-positive patients had the CCDC6::RET fusion. Compared with qRT-PCR, the ddPCR method showed a lower limit of detection (128.0 and 430.7 copies/reaction for ddPCR and qRT-PCR, respectively). When applying the two methods to 112 tissue samples of PTC, eleven (9.8%) CCDC6::RET fusion-positive samples were detected by qRT-PCR, while ddPCR identified 4 additional positive samples (15/112, 13.4%). All the CCDC6::RET fusion-positive cases identified by ddPCR were confirmed by Sanger sequencing except for one case with 0.14 copies/uL of the fusion. CONCLUSION The accurate and sensitive ddPCR method reported here is powerful to detection CCDC6::RET fusion in PTC samples, application of this method would benefit more RET fusion-positive patients in the clinic.
Collapse
Affiliation(s)
- Mengke Chen
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Junyu Xue
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Ye Sang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Wenting Jiang
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Weiman He
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Weiming Lv
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China.
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Second Road, Guangzhou, 510080, China.
| |
Collapse
|
16
|
Akhtar M, Bonus F, Lebrun-Gallagher FR, Johnson NI, Siegele-Brown M, Hong S, Hile SJ, Kulmiya SA, Weidt S, Hensinger WK. A high-fidelity quantum matter-link between ion-trap microchip modules. Nat Commun 2023; 14:531. [PMID: 36754957 PMCID: PMC9908934 DOI: 10.1038/s41467-022-35285-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/25/2022] [Indexed: 02/10/2023] Open
Abstract
System scalability is fundamental for large-scale quantum computers (QCs) and is being pursued over a variety of hardware platforms. For QCs based on trapped ions, architectures such as the quantum charge-coupled device (QCCD) are used to scale the number of qubits on a single device. However, the number of ions that can be hosted on a single quantum computing module is limited by the size of the chip being used. Therefore, a modular approach is of critical importance and requires quantum connections between individual modules. Here, we present the demonstration of a quantum matter-link in which ion qubits are transferred between adjacent QC modules. Ion transport between adjacent modules is realised at a rate of 2424 s-1 and with an infidelity associated with ion loss during transport below 7 × 10-8. Furthermore, we show that the link does not measurably impact the phase coherence of the qubit. The quantum matter-link constitutes a practical mechanism for the interconnection of QCCD devices. Our work will facilitate the implementation of modular QCs capable of fault-tolerant utility-scale quantum computation.
Collapse
Affiliation(s)
- M. Akhtar
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - F. Bonus
- Universal Quantum Ltd, Brighton, BN1 6SB UK ,grid.83440.3b0000000121901201Department of Physics and Astronomy, University College London, London, WC1E 6BT UK
| | - F. R. Lebrun-Gallagher
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - N. I. Johnson
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - M. Siegele-Brown
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. Hong
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. J. Hile
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK
| | - S. A. Kulmiya
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,grid.5337.20000 0004 1936 7603Quantum Engineering Centre for Doctoral Training, University of Bristol, Bristol, BS8 1TH UK
| | - S. Weidt
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| | - W. K. Hensinger
- grid.12082.390000 0004 1936 7590Sussex Centre for Quantum Technologies, University of Sussex, Brighton, BN1 9QH UK ,Universal Quantum Ltd, Brighton, BN1 6SB UK
| |
Collapse
|
17
|
Bang S, Kwon H, Yoon C, Rhew S, Shin D, Moon H, Cho H, Ha U, Lee J, Hong S. Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors. Eur Urol 2023. [DOI: 10.1016/s0302-2838(23)01313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
|
18
|
Ma X, Yan H, Hong S, Yu S, Gong Y, Wu D, Li Y, Xiao H. Gamma-Aminobutyric Acid Promotes Beige Adipocyte Reconstruction by Modulating the Gut Microbiota in Obese Mice. Nutrients 2023; 15:nu15020456. [PMID: 36678326 PMCID: PMC9864545 DOI: 10.3390/nu15020456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
Given the increasing prevalence of obesity, the white-to-beige adipocyte conversion has attracted interest as a target for obesity treatment. Gamma-aminobutyric acid (GABA) treatment can reduce obesity, but the underlying mechanism remains unclear. Here, we aimed to investigate the mechanism by which GABA triggers weight loss by improving the beiging of inguinal white adipose tissue (iWAT) and the role of gut microbiota in this process. The results showed that GABA reduced body weight and adipose inflammation and promoted the expression of thermogenic genes in the iWAT. The 16S rRNA sequence analysis of gut microbiota showed that GABA treatment increased the relative abundance of Bacteroidetes, Akkermansia, and Romboutsia and reduced that of Firmicutes and Erysipelatoclostridium in obese mice. Additionally, serum metabolomic analysis revealed that GABA treatment increased 3-hydroxybutyrate and reduced oxidized lipid levels in obese mice. Spearman's correlation analysis showed that Akkermansia and Romboutsia were negatively associated with the levels of oxidized lipids. Fecal microbiota transplantation analysis confirmed that the gut microbiota was involved in the white-to-beige adipocyte reconstruction by GABA. Overall, our findings suggest that GABA treatment may promote iWAT beiging through the gut microbiota in obese mice. GABA may be utilized to protect obese people against metabolic abnormalities brought on by obesity and gut dysbiosis.
Collapse
Affiliation(s)
- Xiaoyi Ma
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Huanhuan Yan
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yingying Gong
- Department of Geriatrics, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Dide Wu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
- Correspondence:
| |
Collapse
|
19
|
He W, Cheng Z, Huo Z, Lin B, Wang X, Sun Y, Yu S, Cao S, Xue J, Liu R, Lv W, Li Y, Hong S, Xiao H. STRA6 Promotes Thyroid Carcinoma Progression via Activation of the ILK/AKT/mTOR Axis in Cells and Female Nude Mice. Endocrinology 2023; 164:6967061. [PMID: 36592123 DOI: 10.1210/endocr/bqac215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
BACKGROUND Metastasis has emerged to be an important cause for poor prognosis of thyroid carcinoma (TC) and its molecular mechanisms are not fully understood. STRA6 is a multifunctional membrane protein widely expressed in embryonic and adult tissues. The function and mechanism of STRA6 in TC remain elusive. OBJECTIVE We aimed to explore the role of STRA6 in TC progression and provide a therapeutic target for TC. METHODS The expression and clinicopathological relevance of STRA6 were explored in TC. Stable STRA6-knockdown TC cells were established and used to determine the biological function of STRA6 in vitro and in vivo. RNA sequencing and co-immunoprecipitation were performed to unveil the molecular mechanism of STRA6 in TC progression. The potential of STRA6 as a therapeutic target was evaluated by lipid nanoparticles (LNPs) containing siRNA. RESULTS STRA6 was upregulated in TC and correlated with aggressive clinicopathological features, including extrathyroidal extension and lymph node metastasis, which contributed to the poor prognosis of TC. STRA6 facilitated TC progression by enhancing proliferation and metastasis in vitro and in vivo. Mechanistically, STRA6 could interact with integrin-linked kinase (ILK) and subsequently activate the protein kinase B/mechanistic target of rapamycin (AKT/mTOR) signaling pathway. We further unveiled that STRA6 reprogrammed lipid metabolism through SREBP1, which was crucial for the metastasis of TC. Moreover, STRA6 siRNA delivered by LNPs significantly inhibited cell growth in xenograft tumor models. CONCLUSIONS Our study demonstrates the critical roles of STRA6 contributing to TC progression via the ILK/AKT/mTOR axis, which may provide a novel prognostic marker as well as a promising therapeutic target for aggressive TC.
Collapse
Affiliation(s)
- Weiman He
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Zhen Cheng
- Department of Pulmonary Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524000, China
| | - Zijun Huo
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Bo Lin
- Department of Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xuejie Wang
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yijia Sun
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Siting Cao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Junyu Xue
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Weiming Lv
- Department of Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| |
Collapse
|
20
|
He W, Sun Y, Ge J, Wang X, Lin B, Yu S, Li Y, Hong S, Xiao H. STRA6 regulates tumor immune microenvironment and is a prognostic marker in BRAF-mutant papillary thyroid carcinoma. Front Endocrinol (Lausanne) 2023; 14:1076640. [PMID: 36843593 PMCID: PMC9950572 DOI: 10.3389/fendo.2023.1076640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/31/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND BRAF mutation is one of the most common genetic alterations contributing to the initiation and progression of papillary thyroid carcinoma (PTC). However, the prognostic value of BRAF mutation for PTC is limited. Novel markers are needed to identify BRAF-mutant patients with poor prognosis. METHODS Transcriptional expression data were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Pathway enrichment was performed by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA). Protein-protein interaction networks were predicted by the GeneMANIA. The correlation between STRA6 expression and immune infiltration was analyzed by tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB). Immunohistochemistry was used to detect the STRA6 protein expression level of PTC. Infiltration of regulatory T cells (Tregs) and CD8+ T cells in tumor samples were analyzed by fluorescent multiplex immunohistochemistry. RESULTS In BRAF-mutant PTC, STRA6 was extremely upregulated and predicted unfavorable survival, which was an independent risk factor for increased mortality risk. Bioinformatic analyses indicated that STRA6 might activate the MAPK pathway synergistically with BRAFV600E. The expression of STRA6 was associated with immune infiltrates and T cell exhaustion. Fluorescent multiplex immunohistochemistry showed that STRA6 increased Tregs abundance and decreased CD8+ T cells infiltration in PTC. Moreover, STRA6 promoted epithelial-mesenchymal transition via increased cancer-associated fibroblasts infiltration. CONCLUSIONS Our study demonstrates STRA6 may serve as a prognostic marker for BRAF-mutated PTC, which may drive thyroid carcinogenesis via activation of oncogenic pathway and regulation of tumor immunosuppressive microenvironment.
Collapse
Affiliation(s)
- Weiman He
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yijia Sun
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiawei Ge
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xuejie Wang
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bo Lin
- Department of Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- *Correspondence: Haipeng Xiao, ; Shubin Hong,
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- *Correspondence: Haipeng Xiao, ; Shubin Hong,
| |
Collapse
|
21
|
Xian W, Wu D, Liu B, Hong S, Huo Z, Xiao H, Li Y. Graves' disease and inflammatory bowel disease: A bidirectional Mendelian randomization. J Clin Endocrinol Metab 2022; 108:1075-1083. [PMID: 36459455 PMCID: PMC10099169 DOI: 10.1210/clinem/dgac683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/17/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
CONTEXT Both Graves' disease (GD) and inflammatory bowel disease (IBD) are common autoimmune diseases that severely damage patients' quality of life. Previous epidemiological studies have suggested associations between GD and IBD. However, whether a causal relationship exists between these two diseases remains unknown. OBJECTIVE To infer a causal relationship between GD and IBD using bidirectional two-sample Mendelian randomization(MR). METHODS We performed bidirectional two-sample MR to infer a causal relationship between GD and IBD using GWAS summary data obtained from Biobank Japan (BBJ) and the International Inflammatory Bowel Disease Genetic Consortium (IIBDGC). Several methods (random-effect inverse variance weighted, weighted median, MR‒Egger regression, and MR-PRESSO) were used to ensure the robustness of the causal effect. Heterogeneity was measured based on Cochran's Q value. Horizontal pleiotropy was evaluated by MR‒Egger regression and leave-one-out analysis. RESULTS Genetically predicted IBD may increase the risk of GD by 24% (OR 1.24, 95% CI 1.01-1.52, p = 0.041). Crohn's disease (CD) may increase the risk of GD, whereas ulcerative colitis (UC) may prevent patients from developing GD. Conversely, genetically predicted GD may slightly increase the risk of CD, although evidence indicating that the presence of GD increased the risk of UC or IBD was lacking. Outlier-corrected results were consistent with raw causal estimates. CONCLUSIONS Our study revealed a potentially higher comorbidity rate for GD and CD. However, UC might represent a protective factor for GD. The underlying mechanism and potential common pathways await discovery.
Collapse
Affiliation(s)
- Wei Xian
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Dide Wu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Boyuan Liu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Zijun Huo
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
| |
Collapse
|
22
|
Choi H, Pyo KH, Lim S, Cho B, Hong S. PP223 Single-cell RNA sequencing in metastatic lung cancer uncovers the efficacy of PD-1/PD-L1 inhibitors on immune cell population. ESMO Open 2022. [DOI: 10.1016/j.esmoop.2022.100719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
|
23
|
Knight, Imwattana K, Lim SC, Hong S, Putsathit P, Collins DA, Riley TV. WS1.6: GENOMIC EPIDEMIOLOGY OF RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN WESTERN AUSTRALIA. J Glob Antimicrob Resist 2022. [DOI: 10.1016/s2213-7165(22)00274-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
|
24
|
Morvan A, Andersen TI, Mi X, Neill C, Petukhov A, Kechedzhi K, Abanin DA, Michailidis A, Acharya R, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Bovaird J, Brill L, Broughton M, Buckley BB, Buell DA, Burger T, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Curtin B, Debroy DM, Del Toro Barba A, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores Burgos L, Forati E, Fowler AG, Foxen B, Giang W, Gidney C, Gilboa D, Giustina M, Grajales Dau A, Gross JA, Habegger S, Hamilton MC, Harrigan MP, Harrington SD, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Juhas P, Kafri D, Khattar T, Khezri M, Kieferová M, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Laws L, Lee J, Lee KW, Lester BJ, Lill AT, Liu W, Locharla A, Malone F, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Nersisyan A, Newman M, Nguyen A, Nguyen M, Niu MY, O'Brien TE, Olenewa R, Opremcak A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schurkus HF, Schuster C, Shearn MJ, Shorter A, Shvarts V, Skruzny J, Smith WC, Strain D, Sterling G, Su Y, Szalay M, Torres A, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Xing C, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Kelly J, Chen Y, Smelyanskiy V, Aleiner I, Ioffe LB, Roushan P. Formation of robust bound states of interacting microwave photons. Nature 2022; 612:240-245. [PMID: 36477133 PMCID: PMC9729104 DOI: 10.1038/s41586-022-05348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 12/12/2022]
Abstract
Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.
Collapse
Affiliation(s)
- A Morvan
- Google Research, Mountain View, CA, USA
| | | | - X Mi
- Google Research, Mountain View, CA, USA
| | - C Neill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - A Michailidis
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - R Acharya
- Google Research, Mountain View, CA, USA
| | - F Arute
- Google Research, Mountain View, CA, USA
| | - K Arya
- Google Research, Mountain View, CA, USA
| | - A Asfaw
- Google Research, Mountain View, CA, USA
| | - J Atalaya
- Google Research, Mountain View, CA, USA
| | - J C Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J Basso
- Google Research, Mountain View, CA, USA
| | | | - G Bortoli
- Google Research, Mountain View, CA, USA
| | | | - J Bovaird
- Google Research, Mountain View, CA, USA
| | - L Brill
- Google Research, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Research, Mountain View, CA, USA
| | - T Burger
- Google Research, Mountain View, CA, USA
| | - B Burkett
- Google Research, Mountain View, CA, USA
| | | | - Z Chen
- Google Research, Mountain View, CA, USA
| | - B Chiaro
- Google Research, Mountain View, CA, USA
| | - R Collins
- Google Research, Mountain View, CA, USA
| | - P Conner
- Google Research, Mountain View, CA, USA
| | | | - A L Crook
- Google Research, Mountain View, CA, USA
| | - B Curtin
- Google Research, Mountain View, CA, USA
| | | | | | - S Demura
- Google Research, Mountain View, CA, USA
| | | | - D Eppens
- Google Research, Mountain View, CA, USA
| | | | - L Faoro
- Google Research, Mountain View, CA, USA
| | - E Farhi
- Google Research, Mountain View, CA, USA
| | - R Fatemi
- Google Research, Mountain View, CA, USA
| | | | - E Forati
- Google Research, Mountain View, CA, USA
| | | | - B Foxen
- Google Research, Mountain View, CA, USA
| | - W Giang
- Google Research, Mountain View, CA, USA
| | - C Gidney
- Google Research, Mountain View, CA, USA
| | - D Gilboa
- Google Research, Mountain View, CA, USA
| | | | | | - J A Gross
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - S Hong
- Google Research, Mountain View, CA, USA
| | - T Huang
- Google Research, Mountain View, CA, USA
| | - A Huff
- Google Research, Mountain View, CA, USA
| | | | | | - J Iveland
- Google Research, Mountain View, CA, USA
| | - E Jeffrey
- Google Research, Mountain View, CA, USA
| | - Z Jiang
- Google Research, Mountain View, CA, USA
| | - C Jones
- Google Research, Mountain View, CA, USA
| | - P Juhas
- Google Research, Mountain View, CA, USA
| | - D Kafri
- Google Research, Mountain View, CA, USA
| | - T Khattar
- Google Research, Mountain View, CA, USA
| | - M Khezri
- Google Research, Mountain View, CA, USA
| | - M Kieferová
- Google Research, Mountain View, CA, USA
- Centre for Quantum Computation and Communication Technology, Centre for Quantum Software and Information, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
| | - S Kim
- Google Research, Mountain View, CA, USA
| | - A Y Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | - A R Klots
- Google Research, Mountain View, CA, USA
| | - A N Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P Laptev
- Google Research, Mountain View, CA, USA
| | - K-M Lau
- Google Research, Mountain View, CA, USA
| | - L Laws
- Google Research, Mountain View, CA, USA
| | - J Lee
- Google Research, Mountain View, CA, USA
| | - K W Lee
- Google Research, Mountain View, CA, USA
| | | | - A T Lill
- Google Research, Mountain View, CA, USA
| | - W Liu
- Google Research, Mountain View, CA, USA
| | | | - F Malone
- Google Research, Mountain View, CA, USA
| | - O Martin
- Google Research, Mountain View, CA, USA
| | | | - M McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | - K C Miao
- Google Research, Mountain View, CA, USA
| | - M Mohseni
- Google Research, Mountain View, CA, USA
| | | | - E Mount
- Google Research, Mountain View, CA, USA
| | | | - O Naaman
- Google Research, Mountain View, CA, USA
| | - M Neeley
- Google Research, Mountain View, CA, USA
| | | | - M Newman
- Google Research, Mountain View, CA, USA
| | - A Nguyen
- Google Research, Mountain View, CA, USA
| | - M Nguyen
- Google Research, Mountain View, CA, USA
| | - M Y Niu
- Google Research, Mountain View, CA, USA
| | | | - R Olenewa
- Google Research, Mountain View, CA, USA
| | | | - R Potter
- Google Research, Mountain View, CA, USA
| | | | - N C Rubin
- Google Research, Mountain View, CA, USA
| | - N Saei
- Google Research, Mountain View, CA, USA
| | - D Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - A Shorter
- Google Research, Mountain View, CA, USA
| | - V Shvarts
- Google Research, Mountain View, CA, USA
| | - J Skruzny
- Google Research, Mountain View, CA, USA
| | - W C Smith
- Google Research, Mountain View, CA, USA
| | - D Strain
- Google Research, Mountain View, CA, USA
| | | | - Y Su
- Google Research, Mountain View, CA, USA
| | - M Szalay
- Google Research, Mountain View, CA, USA
| | - A Torres
- Google Research, Mountain View, CA, USA
| | - G Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T White
- Google Research, Mountain View, CA, USA
| | - C Xing
- Google Research, Mountain View, CA, USA
| | - Z Yao
- Google Research, Mountain View, CA, USA
| | - P Yeh
- Google Research, Mountain View, CA, USA
| | - J Yoo
- Google Research, Mountain View, CA, USA
| | - A Zalcman
- Google Research, Mountain View, CA, USA
| | - Y Zhang
- Google Research, Mountain View, CA, USA
| | - N Zhu
- Google Research, Mountain View, CA, USA
| | - H Neven
- Google Research, Mountain View, CA, USA
| | - D Bacon
- Google Research, Mountain View, CA, USA
| | - J Hilton
- Google Research, Mountain View, CA, USA
| | - E Lucero
- Google Research, Mountain View, CA, USA
| | - R Babbush
- Google Research, Mountain View, CA, USA
| | - S Boixo
- Google Research, Mountain View, CA, USA
| | - A Megrant
- Google Research, Mountain View, CA, USA
| | - J Kelly
- Google Research, Mountain View, CA, USA
| | - Y Chen
- Google Research, Mountain View, CA, USA
| | | | - I Aleiner
- Google Research, Mountain View, CA, USA.
| | - L B Ioffe
- Google Research, Mountain View, CA, USA.
| | - P Roushan
- Google Research, Mountain View, CA, USA.
| |
Collapse
|
25
|
Hahn T, Daymont C, Beukelman T, Groh B, Hays K, Bingham CA, Scalzi L, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Intraarticular steroids as DMARD-sparing agents for juvenile idiopathic arthritis flares: Analysis of the Childhood Arthritis and Rheumatology Research Alliance Registry. Pediatr Rheumatol Online J 2022; 20:107. [PMID: 36434731 PMCID: PMC9701017 DOI: 10.1186/s12969-022-00770-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 11/08/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Children with juvenile idiopathic arthritis (JIA) who achieve a drug free remission often experience a flare of their disease requiring either intraarticular steroids (IAS) or systemic treatment with disease modifying anti-rheumatic drugs (DMARDs). IAS offer an opportunity to recapture disease control and avoid exposure to side effects from systemic immunosuppression. We examined a cohort of patients treated with IAS after drug free remission and report the probability of restarting systemic treatment within 12 months. METHODS We analyzed a cohort of patients from the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry who received IAS for a flare after a period of drug free remission. Historical factors and clinical characteristics and of the patients including data obtained at the time of treatment were analyzed. RESULTS We identified 46 patients who met the inclusion criteria. Of those with follow up data available 49% had restarted systemic treatment 6 months after IAS injection and 70% had restarted systemic treatment at 12 months. The proportion of patients with prior use of a biologic DMARD was the only factor that differed between patients who restarted systemic treatment those who did not, both at 6 months (79% vs 35%, p < 0.01) and 12 months (81% vs 33%, p < 0.05). CONCLUSION While IAS are an option for all patients who flare after drug free remission, it may not prevent the need to restart systemic treatment. Prior use of a biologic DMARD may predict lack of success for IAS. Those who previously received methotrexate only, on the other hand, are excellent candidates for IAS.
Collapse
Affiliation(s)
- Timothy Hahn
- Department of Pediatrics, Penn State Children's Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA, 17033-0855, USA.
| | - Carrie Daymont
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Timothy Beukelman
- grid.265892.20000000106344187Department of Pediatrics, University of Alabama at Birmingham, CPPN G10, 1600 7th Ave South, Birmingham, AL 35233 USA
| | - Brandt Groh
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | - Catherine April Bingham
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | - Lisabeth Scalzi
- grid.240473.60000 0004 0543 9901Department of Pediatrics, Penn State Children’s Hospital, 500 University Dr, Hershey, 90 Hope Drive, P.O. Box 855, Hershey, PA 17033-0855 USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
26
|
He LN, Fu S, Ma H, Chen C, Zhang X, Li H, Du W, Chen T, Jiang Y, Wang Y, Wang Y, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang H, Zhang L, Hong S. Early on-treatment tumor growth rate (EOT-TGR) determines treatment outcomes of advanced non-small-cell lung cancer patients treated with programmed cell death protein 1 axis inhibitor. ESMO Open 2022; 7:100630. [PMID: 36442353 PMCID: PMC9808481 DOI: 10.1016/j.esmoop.2022.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/02/2022] [Accepted: 10/09/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Tumor growth rate (TGR), denoted as percentage change in tumor size per month, is a well-established indicator of tumor growth kinetics. The predictive value of early on-treatment TGR (EOT-TGR) for immunotherapy remains unclear. We sought to establish and validate the association of EOT-TGR with treatment outcomes in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 (programmed cell death protein 1/programmed death-ligand 1) therapy. PATIENTS AND METHODS This bicenter retrospective cohort study included a training cohort, a contemporaneously treated internal validation cohort, and an external validation cohort. Computed tomography images were retrieved to calculate EOT-TGR, denoted as tumor burden change per month during a period between baseline and the first imaging evaluation after immunotherapy. Kaplan-Meier methodology and Cox regression analysis were conducted for survival analyses. RESULTS In the pooled cohort (n = 172), 125 patients (72.7%) were males; median age at diagnosis was 58 (range 28-79) years. Based on the training cohort, we determined the optimal cut-off value for EOT-TGR as 10.4%/month. Higher EOT-TGR was significantly associated with inferior overall survival [OS; hazard ratio (HR) 2.93, 95% confidence interval (CI) 1.47-5.83; P = 0.002], worse progression-free survival (PFS; HR 2.44, 95% CI 1.46-4.08; P = 0.001), and lower objective response rate (3.3% versus 20.9%; P = 0.040) and durable clinical benefit rate (6.7% versus 41.9%; P = 0.001). Results were reproducible in the two validation cohorts for OS and PFS. Among 43 patients who had a best response of progressive disease in the training cohort, those with high EOT-TGR had worse OS (HR 2.64; P = 0.041) and were more likely to progress due to target lesions at the first tumor evaluation (85.2% versus 0.0%; P <0.001). CONCLUSIONS Higher EOT-TGR was associated with inferior OS and immunotherapeutic response in patients with aNSCLC undergoing anti-PD-1/PD-L1 therapy. This easy-to-calculate radiologic biomarker may help evaluate the abilities of immunotherapy to prolong survival and assist in tailoring patients' management. TRIAL REGISTRATION ClinicalTrials.govNCT04722406; https://clinicaltrials.gov/ct2/show/NCT04722406.
Collapse
Affiliation(s)
- L.-N. He
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - S. Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation of Sun Yat-Sen University; Department of Cellular & Molecular Diagnostics Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - H. Ma
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China
| | - C. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Departments of Radiation Oncology, Guangzhou, China
| | - X. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Li
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Du
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - T. Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Nuclear Medicine, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Endoscopy, Guangzhou, China
| | - Y. Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,VIP Region, Guangzhou, China
| | - Z. Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y. Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W. Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - H. Zhang
- Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China,Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou, China,Prof. Haibo Zhang, Department of Oncology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, 111 Dade Road, Guangzhou, Guangdong 510120, People’s Republic of China. Tel: +86-20-81887233-34830
| | - L. Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Prof. Li Zhang, MD, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87343458
| | - S. Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China,Collaborative Innovation Center for Cancer Medicine, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China,Correspondence to: Prof. Shaodong Hong, Department of Medical Oncology, Sun Yat-sen University Cancer Center, 651 Dongfeng East Road, Guangzhou, Guangdong 510060, People’s Republic of China. Tel: +86-20-87342480
| |
Collapse
|
27
|
Mi X, Sonner M, Niu MY, Lee KW, Foxen B, Acharya R, Aleiner I, Andersen TI, Arute F, Arya K, Asfaw A, Atalaya J, Bardin JC, Basso J, Bengtsson A, Bortoli G, Bourassa A, Brill L, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chen Z, Chiaro B, Collins R, Conner P, Courtney W, Crook AL, Debroy DM, Demura S, Dunsworth A, Eppens D, Erickson C, Faoro L, Farhi E, Fatemi R, Flores L, Forati E, Fowler AG, Giang W, Gidney C, Gilboa D, Giustina M, Dau AG, Gross JA, Habegger S, Harrigan MP, Hoffmann M, Hong S, Huang T, Huff A, Huggins WJ, Ioffe LB, Isakov SV, Iveland J, Jeffrey E, Jiang Z, Jones C, Kafri D, Kechedzhi K, Khattar T, Kim S, Kitaev AY, Klimov PV, Klots AR, Korotkov AN, Kostritsa F, Kreikebaum JM, Landhuis D, Laptev P, Lau KM, Lee J, Laws L, Liu W, Locharla A, Martin O, McClean JR, McEwen M, Meurer Costa B, Miao KC, Mohseni M, Montazeri S, Morvan A, Mount E, Mruczkiewicz W, Naaman O, Neeley M, Neill C, Newman M, O’Brien TE, Opremcak A, Petukhov A, Potter R, Quintana C, Rubin NC, Saei N, Sank D, Sankaragomathi K, Satzinger KJ, Schuster C, Shearn MJ, Shvarts V, Strain D, Su Y, Szalay M, Vidal G, Villalonga B, Vollgraff-Heidweiller C, White T, Yao Z, Yeh P, Yoo J, Zalcman A, Zhang Y, Zhu N, Neven H, Bacon D, Hilton J, Lucero E, Babbush R, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Abanin DA, Roushan P. Noise-resilient edge modes on a chain of superconducting qubits. Science 2022; 378:785-790. [DOI: 10.1126/science.abq5769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the one-dimensional kicked Ising model, which exhibits nonlocal Majorana edge modes (MEMs) with
ℤ
2
parity symmetry. We find that any multiqubit Pauli operator overlapping with the MEMs exhibits a uniform late-time decay rate comparable to single-qubit relaxation rates, irrespective of its size or composition. This characteristic allows us to accurately reconstruct the exponentially localized spatial profiles of the MEMs. Furthermore, the MEMs are found to be resilient against certain symmetry-breaking noise owing to a prethermalization mechanism. Our work elucidates the complex interplay between noise and symmetry-protected edge modes in a solid-state environment.
Collapse
Affiliation(s)
- X. Mi
- Google Research, Mountain View, CA, USA
| | - M. Sonner
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | - M. Y. Niu
- Google Research, Mountain View, CA, USA
| | - K. W. Lee
- Google Research, Mountain View, CA, USA
| | - B. Foxen
- Google Research, Mountain View, CA, USA
| | | | | | | | - F. Arute
- Google Research, Mountain View, CA, USA
| | - K. Arya
- Google Research, Mountain View, CA, USA
| | - A. Asfaw
- Google Research, Mountain View, CA, USA
| | | | - J. C. Bardin
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - J. Basso
- Google Research, Mountain View, CA, USA
| | | | | | | | - L. Brill
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Chen
- Google Research, Mountain View, CA, USA
| | - B. Chiaro
- Google Research, Mountain View, CA, USA
| | | | - P. Conner
- Google Research, Mountain View, CA, USA
| | | | | | | | - S. Demura
- Google Research, Mountain View, CA, USA
| | | | - D. Eppens
- Google Research, Mountain View, CA, USA
| | | | - L. Faoro
- Google Research, Mountain View, CA, USA
| | - E. Farhi
- Google Research, Mountain View, CA, USA
| | - R. Fatemi
- Google Research, Mountain View, CA, USA
| | - L. Flores
- Google Research, Mountain View, CA, USA
| | - E. Forati
- Google Research, Mountain View, CA, USA
| | | | - W. Giang
- Google Research, Mountain View, CA, USA
| | - C. Gidney
- Google Research, Mountain View, CA, USA
| | - D. Gilboa
- Google Research, Mountain View, CA, USA
| | | | - A. G. Dau
- Google Research, Mountain View, CA, USA
| | | | | | | | | | - S. Hong
- Google Research, Mountain View, CA, USA
| | - T. Huang
- Google Research, Mountain View, CA, USA
| | - A. Huff
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - Z. Jiang
- Google Research, Mountain View, CA, USA
| | - C. Jones
- Google Research, Mountain View, CA, USA
| | - D. Kafri
- Google Research, Mountain View, CA, USA
| | | | | | - S. Kim
- Google Research, Mountain View, CA, USA
| | - A. Y. Kitaev
- Google Research, Mountain View, CA, USA
- Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA
| | | | | | - A. N. Korotkov
- Google Research, Mountain View, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | | | | | - P. Laptev
- Google Research, Mountain View, CA, USA
| | - K.-M. Lau
- Google Research, Mountain View, CA, USA
| | - J. Lee
- Google Research, Mountain View, CA, USA
| | - L. Laws
- Google Research, Mountain View, CA, USA
| | - W. Liu
- Google Research, Mountain View, CA, USA
| | | | - O. Martin
- Google Research, Mountain View, CA, USA
| | | | - M. McEwen
- Google Research, Mountain View, CA, USA
- Department of Physics, University of California, Santa Barbara, CA, USA
| | | | | | | | | | - A. Morvan
- Google Research, Mountain View, CA, USA
| | - E. Mount
- Google Research, Mountain View, CA, USA
| | | | - O. Naaman
- Google Research, Mountain View, CA, USA
| | - M. Neeley
- Google Research, Mountain View, CA, USA
| | - C. Neill
- Google Research, Mountain View, CA, USA
| | - M. Newman
- Google Research, Mountain View, CA, USA
| | | | | | | | - R. Potter
- Google Research, Mountain View, CA, USA
| | | | | | - N. Saei
- Google Research, Mountain View, CA, USA
| | - D. Sank
- Google Research, Mountain View, CA, USA
| | | | | | | | | | | | - D. Strain
- Google Research, Mountain View, CA, USA
| | - Y. Su
- Google Research, Mountain View, CA, USA
| | - M. Szalay
- Google Research, Mountain View, CA, USA
| | - G. Vidal
- Google Research, Mountain View, CA, USA
| | | | | | - T. White
- Google Research, Mountain View, CA, USA
| | - Z. Yao
- Google Research, Mountain View, CA, USA
| | - P. Yeh
- Google Research, Mountain View, CA, USA
| | - J. Yoo
- Google Research, Mountain View, CA, USA
| | | | - Y. Zhang
- Google Research, Mountain View, CA, USA
| | - N. Zhu
- Google Research, Mountain View, CA, USA
| | - H. Neven
- Google Research, Mountain View, CA, USA
| | - D. Bacon
- Google Research, Mountain View, CA, USA
| | - J. Hilton
- Google Research, Mountain View, CA, USA
| | - E. Lucero
- Google Research, Mountain View, CA, USA
| | | | - S. Boixo
- Google Research, Mountain View, CA, USA
| | | | - Y. Chen
- Google Research, Mountain View, CA, USA
| | - J. Kelly
- Google Research, Mountain View, CA, USA
| | | | - D. A. Abanin
- Google Research, Mountain View, CA, USA
- Department of Theoretical Physics, University of Geneva, Geneva, Switzerland
| | | |
Collapse
|
28
|
Hong S, Lee J, Heo J, Suh K, Kim S, Kim Y, Kim J, Lee JS. 413P Association of concomitant medications on survival outcomes in cancer patients treated with immune checkpoint inhibitors: Analysis of health insurance review and assessment database. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.10.444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
|
29
|
Hall J, Sud S, Casey D, Poellmann M, Bu J, Wang A, Hong S, Shen C. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients with Locoregional Head and Neck Cancer Receiving Definitive Therapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
30
|
Song S, Kim J, Nam J, Ko Y, Kim J, Jung S, Kang S, Park J, Seo H, Kim H, Jeong B, Kim T, Choi S, Nam J, Ku J, Joo K, Jang W, Yoon Y, Yun S, Hong S, Oh J. Stage matched head-to-head comparison between urachal carcinoma and urothelial bladder cancer: TNM-stage based analysis from a national multicenter database. EUR UROL SUPPL 2022. [DOI: 10.1016/s2666-1683(22)02591-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
|
31
|
Yu M, Yun M, Lee S, Rajasekaran N, Park K, Kim N, Hong S, Oh S, Lee Y, Lee E, Kim C, Lim S, Choi J, Cho B. 1174P The MET inhibitor ABN401 in combination with the third-generation EGFR-TKI is effective MET-amplified and EGFR-mutant NSCLC with acquired resistance to third-generation EGFR-TKI in preclinical models. Ann Oncol 2022. [DOI: 10.1016/j.annonc.2022.07.1297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
|
32
|
Ramesh P, Jaishankar D, Cosgrove C, Kosche C, Li A, Hong S, Shivde R, Munir S, Zhang H, Choi J, Le Poole I. 318 Skin rash composition after checkpoint inhibitor therapy varies by therapeutic regimen. J Invest Dermatol 2022. [DOI: 10.1016/j.jid.2022.05.326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
33
|
Kang E, Kim YG, Oh JS, Hong S, Lee CK, Yoo B, Ahn SM. POS1247 THE EFFECT OF IMMUNOSUPPRESSIVE AGENTS ON ANTIBODY FORMATION AFTER COVID-19 VACCINATION IN RHEUMATOID ARTHRITIS PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundThere is still controversy about the efficacy of COVID-19 vaccination and its extent in lowering immunogenicity of Rheumatoid Arthritis (RA) patients. The guideline in whether immunosuppressive agents need to be discontinued before the vaccination is continuously updated because it is considered to lower immunogenicity. Furthermore, there is great discussion on the effectiveness of the COVID-19 booster vaccine and interest in antibody generation in different types of vaccine, as in South Korea there are many patients who were prescribed the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine.ObjectivesThus, we investigated the differences of antibody production between patients who received only two doses of ChAdOx1-S nCoV-19 and those who received the mRNA booster vaccine. Also, antibody production under different types of immunosuppressive agents was analyzed.MethodsFrom October 14, 2021 to January 21, 2022 at a tertiary referral center, two patient groups diagnosed with RA were studied prospectively; one group that completed 1st and 2nd doses of ChAdOx1-S nCoV-19 vaccine, second group that completed mRNA booster vaccine as well as two doses of ChAdOx1-S nCoV-19 vaccine. SARS-CoV-2 antibody testing on the semiquantitative anti-SARS-CoV-2 S enzyme immunoassay was done, and differences in antibody titers were analyzed in patients who received different immunosuppressive agents such as csDMARD, TNF inhibitor, JAK inhibitor, Tocilizumab, Abatacept and Corticosteroid. Statistical analysis with a multivariate logistic regression model was performed.ResultsIn a total of 261 patients, 153 patients had completed two doses of ChAdOx1-S nCoV-19, 108 patients had completed third mRNA booster vaccine. Anti-SARS-CoV-2 RBD antibody positive rate (titer>0.8U/mL) was 97%(149/153) and 99%(107/108) respectively, and only 5 patients showed negative result. In the aspect of high antibody titer(>250U/mL), which is the upper limit of the RBD antibody immunoassay, the result showed rate of 31% (47/153) in the non-booster group and 94%(102/108) in the booster group respectively.Among the different immunosuppressive agents and other clinical aspects, multivariate analysis revealed that corticosteroid use (OR 0.91; 95% CI: 0.86-0.98), older age(OR 4.33; 95% CI: 1.34-13.91), and male gender(OR 0.35; 95% CI 0.16-0.75) were significantly associated with low rate of high antibody titer.Furthermore, out of 14 patients who underwent antibody test twice before and after the mRNA booster vaccine, other than four patients who already showed high titer of >250U/mL before the mRNA booster vaccine, 10 patients showed an increase in titer after the booster vaccine and 7 patients were acquired high titer of >250U/mL.Figure 1.Anti-SARS-CoV RBD antibody titer of two groupsTable 1.Analysis of immunosuppressive agents and other clinical aspects for high antibody titer(>250U/mL) after two doses of ChAdOx1-S nCoV-19Univariate analysisMultivariate analysisParameterOR95% CIp valueOR95% CIp valueClinical features Age0.9170.860-0.9780.0080.9170.857-0.9810.012 Sex3.6741.206-11.1910.0224.3301.348-13.9120.014 DAS 281.1440.670-1.9500.622 Duration0.9300.830-1.0430.214Medications csDMARD1.2730.639-2.5331.273 TNF inhibitor2.2110.795-6.1450.128 JAK inhibitor0.6650.275-1.6070.365 Abatacept0.3680.038-3.6020.391 Tocilizumab1.2640.438-3.6480.665 Corticosteroid0.4720.235-0.9490.0350.3490.163-0.7480.007Medication dose Methotrexate0.9930.919-1.0720.855 Corticosteroid0.8490.719-1.0030.054ConclusionAnti-SARS-CoV-2 RBD antibody positive rate was 97% or more regardless of the mRNA booster vaccination. However, patients who received the mRNA booster vaccine after two doses of ChAdOx1-S nCoV-19 vaccine showed high antibody titer (>250U/mL) three times more than those who did not receive the booster shot.Our findings also showed that corticosteroid use, old age, and male gender is significantly associated with low rate of acquiring high antibody titer.Disclosure of InterestsNone declared
Collapse
|
34
|
Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0476 PREDICTIVE FACTORS FOR THE DEVELOPMENT OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH IMMUNE THROMBOCYTOPENIA. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.1436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundPatients with immune thrombocytopenia (ITP) have a risk of developing systemic lupus erythematosus (SLE). We sought to examine the clinical characteristics of patients with primary ITP who later developed SLE, and identified the risk factors for the development of SLE.ObjectivesWe retrospectively examined patients who were diagnosed with primary ITP at a tertiary hospital between August 2001 and November 2019. We compared the clinical characteristics according to the development of SLE. Logistic regression analysis was performed to identify the factors associated with the development of SLE.MethodsOf 130 patients with primary ITP, 10 (7.7%) were later diagnosed with SLE during follow-up (median, 30 months [IQR, 15.5–105]). The presence of skin bleeding, organ bleeding, lymphopenia, anemia, and positive antinuclear antibody (ANA) titer (> 1:160) were more common among patients who later developed SLE than did those who did not develop SLE. Multivariate analysis showed that young age (< 40 years; odds ratio [OR], 8.359 [95% confidence interval (CI), 1.230–56.793]; p = 0.033), organ bleeding (OR, 18.349 [95% CI, 2.771–121.517]; p = 0.003), and ANA positivity (>1:160; OR, 7.692 [95% CI, 1.482–39.910]; p = 0.015) were significantly associated with the development of SLE.ResultsYoung age (< 40 years), organ bleeding, and ANA positivity (> 1:160) were risk factors for the development of SLE in patients with primary ITP.ConclusionThese results suggest that continued follow-up for the detection of SLE development is needed for patients with ITP, particularly those with young age, ANA positivity, or organ bleeding.References[1]Zhu, Fang-Xiao, et al. “Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.” Annals of the rheumatic diseases 79.6 (2020): 793-799.Table 1.Factors associated with the development of SLE in patients with primary ITPUnivariateMultivariateOR95% CIP valueOR95% CIP valueYoung agea5.4441.332–22.2500.0188.3591.230–56.7930.033Female4.3330.530–35.4220.17BMI0.8730.717–1.0700.20Skin bleeding8.4191.034–68.5330.046Mucosa bleeding1.2500.247–6.3300.79Organ bleeding14.8643.633–60.815< 0.00118.3492.771–121.5170.003Platelet counts0.9110.828–1.0020.06ANA positivityb16.5003.984–68.341< 0.0017.6921.482–39.9100.015Neutropeniac2.1110.229–19.4990.51Lymphopeniad4.8461.189–19.7590.028Anemiae10.1182.044–50.0910.005SLE: systemic lupus erythematosus, ITP: immune thrombocytopenia, BMI: body mass index, ANA: antinuclear antibody, OR: odds ratio, CI: confidence interval.aYoung age = age < 40 yearsbANA positivity ≥ 1:160cNeutropenia = Absolute neutrophil count < 1500 μLdLymphopenia = Absolute lymphocyte count < 1500 μLeAnemia = Hemoglobin < 12 g/dLDisclosure of InterestsNone declared
Collapse
|
35
|
Kim YE, Choi SJ, Lim DH, Ahn SM, Oh JS, Kim YG, Lee CK, Yoo B, Hong S. AB0456 DISEASE FLARE OF SYSTEMIC LUPUS ERYTHEMATOSUS IN PATIENTS WITH END-STAGE RENAL DISEASE ON DIALYSIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundThe systemic lupus erythematosus (SLE) disease activity in patients with lupus nephritis (LN) generally declines after the initiation of renal replacement therapy (RRT); this is known as the “burn out” phenomenon that possibly occurs due to the suppression of cellular and humoral immunity in the end-stage renal disease (ESRD) state and elimination of disease pathogenic factor by dialysis [1-4]. However, several studies showed that SLE flares could occur even during RRT [5-8]. Nevertheless, the details of disease flares of SLE in patients under dialysis have not been studied yet.ObjectivesThis study aimed to investigate the clinical features, risk factors, and treatment details of SLE patients experiencing disease flare under RRT.MethodsThe medical records of SLE patients who received dialysis at two tertiary referral hospitals in Seoul and Ulsan, South Korea were reviewed. All patients in this study were either clinically or histologically diagnosed with LNResultsOf a total of 121 patients with SLE on dialysis, 96 (79.3%) were on hemodialysis (HD) and 25 (20.6%) were on peritoneal dialysis (PD). During a median follow-up of 45 months (IQR, 23–120) after the initiation of dialysis, 32 (26.4%) patients experienced SLE flare (HD, n = 25; PD, n = 7). The most common features of SLE flare were hematologic (40.6%) and constitutional manifestations (40.6%). Treatments for disease flares were based on corticosteroids, and 11 (34.3%) patients required additional immunosuppressants including cyclophosphamide and mycophenolate mofetil. There was no case of severe adverse events related to medication. non-renal SLE Disease Activity Index (SLEDAI) score before dialysis initiation (HR 1.235; 95% CI, 1.122–1.359; P = 0.001) was a significant risk factor for disease flare during dialysis.Table 1.Multivariable analysis of factors associated with SLE flare under dialysisHazard ratio95% CIP-valueNon-renal SLEDAI at the initiation of dialysis1.2351.122–1.3590.001Hematologic manifestation prior to dialysis1.2560.690–2.8260.150Cumulative amount of steroid during 1 year prior to the initiation of dialysis1.0400.995–1.0870.086Dialysis modality: hemodialysis0.7660.262–2.2430.630ConclusionMore than one-quarter of SLE patients experienced disease flare during dialysis, which most commonly had hematologic manifestations. Continued follow-up and appropriate treatments including immunosuppressants should be considered for patients with SLE under dialysis.References[1]Coplon NS, Diskin CJ, Petersen J, Swenson RS. The Long-Term Clinical Course of Systemic Lupus Erythematosus in End-Stage Renal Disease. New England Journal of Medicine 1983;308:186-90.[2]Lee P-T, Fang H-C, Chen C-L, Chiou Y-H, Chou K-J, Chung H-M. Poor prognosis of end-stage renal disease in systemic lupus erythematosus: a cohort of Chinese patients. Lupus 2003;12:827-32.[3]Pahl MV, Gollapudi S, Sepassi L, Gollapudi P, Elahimehr R, Vaziri ND. Effect of end-stage renal disease on B-lymphocyte subpopulations, IL-7, BAFF and BAFF receptor expression. Nephrology Dialysis Transplantation 2010;25:205-12.[4]Ribeiro FM, Fabris CL, Bendet I, Lugon JR. Survival of lupus patients on dialysis: a Brazilian cohort. Rheumatology 2013;52:494-500.[5]Okano K, Yumura W, Nitta K et al. Analysis of Lupus Activity in End-Stage Renal Disease Treated by Hemodialysis. Internal Medicine 2001;40:598-602.[6]Barrera-Vargas A, Quintanar-Martínez M, Merayo-Chalico J, Alcocer-Varela J, Gómez-Martín D. Risk factors for systemic lupus erythematosus flares in patients with end-stage renal disease: a case–control study. Rheumatology 2015:kev349.[7]Cucchiari D, Graziani G, Ponticelli C. The dialysis scenario in patients with systemic lupus erythematosus. Nephrology Dialysis Transplantation 2014;29:1507-13.[8]Kang S-H, Chung B-H, Choi S-R et al. Comparison of Clinical Outcomes by Different Renal Replacement Therapy in Patients with End-Stage Renal Disease Secondary to Lupus Nephritis. The Korean Journal of Internal Medicine 2011;26:60.Disclosure of InterestsNone declared
Collapse
|
36
|
Nam SH, Ahn SM, Oh JS, Hong S, Lee CK, Yoo B, Kim YG. AB1273 MACROPHAGE ACTIVATION SYNDROME IN RHEUMATIC DISEASE: CLINICAL CHARACTERISTICS AND PROGNOSIS OF 20 PATIENTS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundMacrophage activation syndrome (MAS) is a hyperinflammatory condition that is known to be secondary hemophagocytic lymphohistiocytosis (HLH) in patients with rheumatic disease.ObjectivesThe aim of study was to evaluate the clinical manifestations and outcomes in patients with MAS with rheumatic disease.MethodsWe performed a retrospective study of 20 adult patients who were diagnosed with MAS from 2012 to 2020. MAS was classified according to the HLH-2004 criteria. Patients’ information, including clinical features, laboratory findings, and treatment regimens, was collected, and the overall survival rate was estimated by the Kaplan–Meier method.ResultsTwenty patients (18 women, 35.6 ± 18.3 years) who met the HLH-2004 criteria also fulfilled the 2016 EULAR/ACR/PRINTO classification criteria for MAS, and HScore was higher than 169 (median, 238.5). Fourteen patients with systemic lupus erythematosus and 6 patients with adult-onset Still’s disease were included. All patients were treated initially with corticosteroids, and 16 patients required additional immunosuppressants. The overall survival at 3 and 6 months was 75.2% and 64.3%. In survivors, renal impairment was less common (23.1% versus 42.9%, p = 0.007), the levels of AST (202.0 versus 72.0 IU/L, p = 0.006) and LDH (1144.0 versus 343.0IU/L, p = 0.001), and platelet count (90.0 versus 46.0 × 109/L, p = 0.016) were higher in compared to non-survivors. Nine patients had opportunistic infections, five of whom died during admission.ConclusionThe mortality of patients with MAS remains high. Renal impairment, levels of AST and LDH, and platelet count might be associated with prognosis.Table 1.Treatments and management characteristics of patients with MASNo.Age/sexDiseaseDisease duration (months)1st Treatment (corticosteroids)2nd Treatment3rd TreatmentCombined infectionAlive/dead119/FSLE11 mg/kgIVIG + PPTCZ, RTXBacteremiaDead220/MSLE01 mg/kg---Alive320/FAOSD11 mg/kgVP16--Alive422/FSLE1100 mgIVIG + PP-PneumoniaDead522/FAOSD0500 mgIVIG--Alive623/FSLE1821 mg/kg---Alive723/FSLE411 mg/kg---Alive830/FSLE1461 mg/kgIVIGCsA-Alive932/FSLE1271 mg/kgIVIG + PPCsA, TCZPneumoniaAlive1035/FAOSD01 mg/kgCsA-Viral infectionAlive1137/FSLE651 mg/kgCsA, VP16-BacteremiaAlive1238/FSLE01 mg/kgIVIG + PPRTX-Dead1340/FAOSD00.5 mg/kgCsA--Alive1443/FSLE601 mg/kgIVIG + PPTCZ, RTX, CsA,PCP,DeadVP16, IFXViral infection1549/FSLE01 mg/kgCYC-BacteremiaAlive1651/FAOSD01 mg/kg---Alive1757/FSLE01 mg/kgIVIG + PPCsA, VP16Fungal infectionDead1861/FSLE21 mg/kgIVIG + PPTCZ-Dead1968/FSLE21 mg/kgIVIG + PPCsAFungal infectionAlive2070/MAOSD01 mg/kgIVIG + PPCsA, VP16Fungal infectionDeadSLE: Systemic lupus erythematosus, IVIG: Intravenous immunoglobulin, PP: Plasmapheresis, TCZ: Tocilizumab, RTX: Rituximab, AOSD: Adult-onset still’s disease, VP16: Etoposide, PCP: Pneumocystis pneumonia, CsA: Cyclosporin, IFX: Infliximab, MCTD: Mixed connective tissue disease.Disclosure of InterestsNone declared
Collapse
|
37
|
Kang E, Hong S, Kim YG, Lee CK, Oh JS, Yoo B, Ahn SM. POS0762 LONG-TERM RENAL OUTCOMES OF PATIENTS WITH NON-PROLIFERATIVE LUPUS NEPHRITIS. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.3393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundAlthough proliferative (class III or IV) lupus nephritis (LN) is the most common finding in the classification of LN, pure membranous (class V) or mesangial (class I or II) LN can occur as a form of LN. Even though non-proliferative LN (class I, II, or V) is a less severe form with good outcomes, data on long-term renal prognosis are limited.ObjectivesThis study investigated the long-term outcomes and prognostic factors in non-proliferative LN.MethodsWe retrospectively reviewed the medical records of patients with systemic lupus erythematosus who were diagnosed with LN class I, II, V or II+IV by kidney biopsy between 1997 and 2021 at a tertiary referral center. Clinical and laboratory data were compared between patients with and without poor renal outcomes. Poor renal outcome was defined as an estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 or death due to renal cause. Univariate and multivariate analyses were performed with the Cox proportional hazard model to identify the factors associated with poor renal outcomes.ResultsWe included 71 patients with non-proliferative LN (4: class I; 17: class II; 48: class V, 17; 2: class II+V). Median follow-up duration was 103 months (interquartile range 27–185) and the overall rate of poor renal outcomes at last follow-up was 29% (21/71), including end-stage renal disease (n=2) and renal death (n=1).Univariate analysis indicated that older age (HR 1.05; 95% CI: 1.00–1.09), low eGFR (HR 0.97; 95% CI: 0.95–0.99) and failure to reach complete remission at 6 months (HR 0.332; 95% CI: 0.12–0.92) were significantly associated with poor renal outcomes. Multivariate analysis revealed that low eGFR at 6 months (HR 0.97; 95% CI: 0.95–0.99) was significantly associated with poor renal outcomes.Figure 1.Renal outcomes at last follow upeGFR, estimated glomerular filtration rate (ml/min/1.73m2)Table 1.Univariate and multivariate Cox proportional hazard regression analyses of the factor associated with poor renal outcomesParameterUnivariate analysisMultivariate analysisHR95% CIp valueHR95% CIp valueClinical features Age1.0461.003-1.0910.0361.0020.960-1.0470.921 Sex1.6540.375-7.2980.506 SLEDAI1.0360.965-1.1120.327 Extra renal SLEDAI1.0380.971-1.110.272Renal profiles eGFR at LN diagnosis0.9930.976-1.0110.456 Proteinuria at LN diagnosis1.0001.000-1.0000.444 > 1g/24 hours0.6690.243-1.8410.437 > 3g/24 hours0.6240.229-1.6990.356 eGFR at 6M0.9670.948-0.9860.0010.9680.948-0.9880.002 eGFR at 12M0.9640.947-0.9810.000 Complete remission at 6M0.3320.119-0.9240.0350.5530.179-1.7070.303 Complete remission at 12M0.6670.232-1.9140.451 Transformation1.2460.423-0.7010.692Laboratory data Anti-dsDNA1.0010.999-1.0030.196 C31.0201.000-1.0410.051 C41.0270.969-1.0890.367 Albumin1.1800.661-2.1090.576ClassificationaClass I0.8020.102-6.3030.834Class II1.2980.412-4.0880.656Class V0.8870.308-2.5570.824Class II+V0.0480.000-16850.837Medicationsb ACEi/ARB1.6520.603-4.5280.329 Hydroxychloroquine1.3260.414-4.2420.635 Corticosteroid1.1860.154-9.1080.870 CNI2.4390.464-12.8240.292 MMF3.7880.959-14.9650.057 AZA0.5890.133-2.6110.486a LN classifications were based on the International Society of Pathology/Renal Pathology Society (ISN/RPS) classification.b Medications maintained at least one year since Lupus Nephritis diagnosis.HR, hazard ratio; 95% CI, 95% confidence interval; SLEDAI, systemic lupus erythematosus disease activity index; eGFR, estimated glomerular filtration rate; LN, lupus nephritis; anti-dsDNA, anti-double strand DNA; C3/C4; complement 3/4; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CNI, carcineurin inhibitor; MMF, mycophenolate mofetil; AZA, azathioprine.ConclusionPoor renal outcomes occurred in approximately 30% of patients with non-proliferative LN (class I, II or V) after long-term follow-up.Our findings suggest that more active management may be needed for non-proliferative LN, particularly in patients with low eGFR at 6 months.Disclosure of InterestsNone declared
Collapse
|
38
|
Chidambaram S, Hong S, Simpson M, Osazuwa-Peters N, Ward G, Massa S. Temporal Trends in Oropharyngeal Cancer Incidence, Survival, and Cancer-Directed Surgery Among Elderly Americans. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2021.12.067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
39
|
Soulsby WD, Balmuri N, Cooley V, Gerber LM, Lawson E, Goodman S, Onel K, Mehta B, Abel N, Abulaban K, Adams A, Adams M, Agbayani R, Aiello J, Akoghlanian S, Alejandro C, Allenspach E, Alperin R, Alpizar M, Amarilyo G, Ambler W, Anderson E, Ardoin S, Armendariz S, Baker E, Balboni I, Balevic S, Ballenger L, Ballinger S, Balmuri N, Barbar-Smiley F, Barillas-Arias L, Basiaga M, Baszis K, Becker M, Bell-Brunson H, Beltz E, Benham H, Benseler S, Bernal W, Beukelman T, Bigley T, Binstadt B, Black C, Blakley M, Bohnsack J, Boland J, Boneparth A, Bowman S, Bracaglia C, Brooks E, Brothers M, Brown A, Brunner H, Buckley M, Buckley M, Bukulmez H, Bullock D, Cameron B, Canna S, Cannon L, Carper P, Cartwright V, Cassidy E, Cerracchio L, Chalom E, Chang J, Chang-Hoftman A, Chauhan V, Chira P, Chinn T, Chundru K, Clairman H, Co D, Confair A, Conlon H, Connor R, Cooper A, Cooper J, Cooper S, Correll C, Corvalan R, Costanzo D, Cron R, Curiel-Duran L, Curington T, Curry M, Dalrymple A, Davis A, Davis C, Davis C, Davis T, De Benedetti F, De Ranieri D, Dean J, Dedeoglu F, DeGuzman M, Delnay N, Dempsey V, DeSantis E, Dickson T, Dingle J, Donaldson B, Dorsey E, Dover S, Dowling J, Drew J, Driest K, Du Q, Duarte K, Durkee D, Duverger E, Dvergsten J, Eberhard A, Eckert M, Ede K, Edelheit B, Edens C, Edens C, Edgerly Y, Elder M, Ervin B, Fadrhonc S, Failing C, Fair D, Falcon M, Favier L, Federici S, Feldman B, Fennell J, Ferguson I, Ferguson P, Ferreira B, Ferrucho R, Fields K, Finkel T, Fitzgerald M, Fleming C, Flynn O, Fogel L, Fox E, Fox M, Franco L, Freeman M, Fritz K, Froese S, Fuhlbrigge R, Fuller J, George N, Gerhold K, Gerstbacher D, Gilbert M, Gillispie-Taylor M, Giverc E, Godiwala C, Goh I, Goheer H, Goldsmith D, Gotschlich E, Gotte A, Gottlieb B, Gracia C, Graham T, Grevich S, Griffin T, Griswold J, Grom A, Guevara M, Guittar P, Guzman M, Hager M, Hahn T, Halyabar O, Hammelev E, Hance M, Hanson A, Harel L, Haro S, Harris J, Harry O, Hartigan E, Hausmann J, Hay A, Hayward K, Heiart J, Hekl K, Henderson L, Henrickson M, Hersh A, Hickey K, Hill P, Hillyer S, Hiraki L, Hiskey M, Hobday P, Hoffart C, Holland M, Hollander M, Hong S, Horwitz M, Hsu J, Huber A, Huggins J, Hui-Yuen J, Hung C, Huntington J, Huttenlocher A, Ibarra M, Imundo L, Inman C, Insalaco A, Jackson A, Jackson S, James K, Janow G, Jaquith J, Jared S, Johnson N, Jones J, Jones J, Jones J, Jones K, Jones S, Joshi S, Jung L, Justice C, Justiniano A, Karan N, Kaufman K, Kemp A, Kessler E, Khalsa U, Kienzle B, Kim S, Kimura Y, Kingsbury D, Kitcharoensakkul M, Klausmeier T, Klein K, Klein-Gitelman M, Kompelien B, Kosikowski A, Kovalick L, Kracker J, Kramer S, Kremer C, Lai J, Lam J, Lang B, Lapidus S, Lapin B, Lasky A, Latham D, Lawson E, Laxer R, Lee P, Lee P, Lee T, Lentini L, Lerman M, Levy D, Li S, Lieberman S, Lim L, Lin C, Ling N, Lingis M, Lo M, Lovell D, Lowman D, Luca N, Lvovich S, Madison C, Madison J, Manzoni SM, Malla B, Maller J, Malloy M, Mannion M, Manos C, Marques L, Martyniuk A, Mason T, Mathus S, McAllister L, McCarthy K, McConnell K, McCormick E, McCurdy D, Stokes PMC, McGuire S, McHale I, McMonagle A, McMullen-Jackson C, Meidan E, Mellins E, Mendoza E, Mercado R, Merritt A, Michalowski L, Miettunen P, Miller M, Milojevic D, Mirizio E, Misajon E, Mitchell M, Modica R, Mohan S, Moore K, Moorthy L, Morgan S, Dewitt EM, Moss C, Moussa T, Mruk V, Murphy A, Muscal E, Nadler R, Nahal B, Nanda K, Nasah N, Nassi L, Nativ S, Natter M, Neely J, Nelson B, Newhall L, Ng L, Nicholas J, Nicolai R, Nigrovic P, Nocton J, Nolan B, Oberle E, Obispo B, O’Brien B, O’Brien T, Okeke O, Oliver M, Olson J, O’Neil K, Onel K, Orandi A, Orlando M, Osei-Onomah S, Oz R, Pagano E, Paller A, Pan N, Panupattanapong S, Pardeo M, Paredes J, Parsons A, Patel J, Pentakota K, Pepmueller P, Pfeiffer T, Phillippi K, Marafon DP, Phillippi K, Ponder L, Pooni R, Prahalad S, Pratt S, Protopapas S, Puplava B, Quach J, Quinlan-Waters M, Rabinovich C, Radhakrishna S, Rafko J, Raisian J, Rakestraw A, Ramirez C, Ramsay E, Ramsey S, Randell R, Reed A, Reed A, Reed A, Reid H, Remmel K, Repp A, Reyes A, Richmond A, Riebschleger M, Ringold S, Riordan M, Riskalla M, Ritter M, Rivas-Chacon R, Robinson A, Rodela E, Rodriquez M, Rojas K, Ronis T, Rosenkranz M, Rosolowski B, Rothermel H, Rothman D, Roth-Wojcicki E, Rouster-Stevens K, Rubinstein T, Ruth N, Saad N, Sabbagh S, Sacco E, Sadun R, Sandborg C, Sanni A, Santiago L, Sarkissian A, Savani S, Scalzi L, Schanberg L, Scharnhorst S, Schikler K, Schlefman A, Schmeling H, Schmidt K, Schmitt E, Schneider R, Schollaert-Fitch K, Schulert G, Seay T, Seper C, Shalen J, Sheets R, Shelly A, Shenoi S, Shergill K, Shirley J, Shishov M, Shivers C, Silverman E, Singer N, Sivaraman V, Sletten J, Smith A, Smith C, Smith J, Smith J, Smitherman E, Soep J, Son M, Spence S, Spiegel L, Spitznagle J, Sran R, Srinivasalu H, Stapp H, Steigerwald K, Rakovchik YS, Stern S, Stevens A, Stevens B, Stevenson R, Stewart K, Stingl C, Stokes J, Stoll M, Stringer E, Sule S, Sumner J, Sundel R, Sutter M, Syed R, Syverson G, Szymanski A, Taber S, Tal R, Tambralli A, Taneja A, Tanner T, Tapani S, Tarshish G, Tarvin S, Tate L, Taxter A, Taylor J, Terry M, Tesher M, Thatayatikom A, Thomas B, Tiffany K, Ting T, Tipp A, Toib D, Torok K, Toruner C, Tory H, Toth M, Tse S, Tubwell V, Twilt M, Uriguen S, Valcarcel T, Van Mater H, Vannoy L, Varghese C, Vasquez N, Vazzana K, Vehe R, Veiga K, Velez J, Verbsky J, Vilar G, Volpe N, von Scheven E, Vora S, Wagner J, Wagner-Weiner L, Wahezi D, Waite H, Walker J, Walters H, Muskardin TW, Waqar L, Waterfield M, Watson M, Watts A, Weiser P, Weiss J, Weiss P, Wershba E, White A, Williams C, Wise A, Woo J, Woolnough L, Wright T, Wu E, Yalcindag A, Yee M, Yen E, Yeung R, Yomogida K, Yu Q, Zapata R, Zartoshti A, Zeft A, Zeft R, Zhang Y, Zhao Y, Zhu A, Zic C. Social determinants of health influence disease activity and functional disability in Polyarticular Juvenile Idiopathic Arthritis. Pediatr Rheumatol Online J 2022; 20:18. [PMID: 35255941 PMCID: PMC8903717 DOI: 10.1186/s12969-022-00676-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Social determinants of health (SDH) greatly influence outcomes during the first year of treatment in rheumatoid arthritis, a disease similar to polyarticular juvenile idiopathic arthritis (pJIA). We investigated the correlation of community poverty level and other SDH with the persistence of moderate to severe disease activity and functional disability over the first year of treatment in pJIA patients enrolled in the Childhood Arthritis and Rheumatology Research Alliance Registry. METHODS In this cohort study, unadjusted and adjusted generalized linear mixed effects models analyzed the effect of community poverty and other SDH on disease activity, using the clinical Juvenile Arthritis Disease Activity Score-10, and disability, using the Child Health Assessment Questionnaire, measured at baseline, 6, and 12 months. RESULTS One thousand six hundred eighty-four patients were identified. High community poverty (≥20% living below the federal poverty level) was associated with increased odds of functional disability (OR 1.82, 95% CI 1.28-2.60) but was not statistically significant after adjustment (aOR 1.23, 95% CI 0.81-1.86) and was not associated with increased disease activity. Non-white race/ethnicity was associated with higher disease activity (aOR 2.48, 95% CI: 1.41-4.36). Lower self-reported household income was associated with higher disease activity and persistent functional disability. Public insurance (aOR 1.56, 95% CI 1.06-2.29) and low family education (aOR 1.89, 95% CI 1.14-3.12) was associated with persistent functional disability. CONCLUSION High community poverty level was associated with persistent functional disability in unadjusted analysis but not with persistent moderate to high disease activity. Race/ethnicity and other SDH were associated with persistent disease activity and functional disability.
Collapse
Affiliation(s)
- William Daniel Soulsby
- University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA, 94158, USA.
| | - Nayimisha Balmuri
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Victoria Cooley
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Linda M. Gerber
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Erica Lawson
- grid.266102.10000 0001 2297 6811University of California, San Francisco, 550 16th Street, 4th Floor, Box #0632, San Francisco, CA 94158 USA
| | - Susan Goodman
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Karen Onel
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | - Bella Mehta
- grid.239915.50000 0001 2285 8823Hospital for Special Surgery, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
40
|
Chen W, Li J, Peng S, Hong S, Xu H, Lin B, Liang X, Liu Y, Liang J, Zhang Z, Ye Y, Liu F, Lin C, Xiao H, Lv W. Association of Total Thyroidectomy or Thyroid Lobectomy With the Quality of Life in Patients With Differentiated Thyroid Cancer With Low to Intermediate Risk of Recurrence. JAMA Surg 2022; 157:200-209. [PMID: 34935859 PMCID: PMC8696698 DOI: 10.1001/jamasurg.2021.6442] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
IMPORTANCE Owing to the good prognosis of differentiated thyroid cancer (DTC), guidelines recommend total thyroidectomy (TT) or thyroid lobectomy (TL) as surgical treatment for DTC with low to intermediate risk of recurrence. However, the association of these surgeries with the health-related quality of life (HRQOL) of patients with DTC with low to intermediate risk of recurrence is unclear. OBJECTIVE To longitudinally compare the HRQOL of patients with DTC undergoing different surgeries. DESIGN, SETTING, AND PARTICIPANTS This prospective observational longitudinal cohort study enrolled patients diagnosed with DTC with low to intermediate risk of recurrence at the First Affiliated Hospital, Sun Yat-sen University, China, from October 1, 2018, to September 31, 2019. Eligible patients were categorized into TL and TT groups according to the surgery they underwent. They were evaluated preoperatively and followed up at 1, 3, 6, and 12 months postoperatively using 3 HRQOL-related questionnaires (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, version 3.0; Hospital Anxiety and Depression Scale; and Thyroid Cancer-Specific Quality of Life Questionnaire); serum thyrotropin levels, complications, and patient satisfaction were also monitored. Data were analyzed to compare the HRQOL of patients undergoing different surgeries at different time points. EXPOSURES Total thyroidectomy or TL. MAIN OUTCOMES AND MEASURES The primary end point was HRQOL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, version 3.0; Hospital Anxiety and Depression Scale; and Thyroid Cancer-Specific Quality of Life Questionnaire) at different time points, and the secondary end points were postoperative complications, thyrotropin level, and patient satisfaction. RESULTS Of the 1060 eligible patients, 563 underwent TL (438 women [77.8%]; median [IQR] age, 38 [31-45] years), and 497 underwent TT (390 women [78.5%]; median [IQR] age, 38 [32-48] years). Compared with the TL group, including the 1- to 4-cm tumor subgroup, the TT group experienced more postoperative HRQOL problems at 1 and 3 months postoperatively. However, nearly all the differences disappeared at 6 and 12 months postoperatively. CONCLUSIONS AND RELEVANCE Results of this study suggest that HRQOL of patients with DTC with low to intermediate risk of recurrence is not associated with the extent of surgery, and HRQOL may not be an important consideration when making surgical decisions. If better HRQOL is requested in the short term, TL may be preferred.
Collapse
Affiliation(s)
- Wanna Chen
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jie Li
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sui Peng
- Clinical Trial Unit, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shubin Hong
- Department of Endocrinology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Heyang Xu
- Department of Obstetrics and Gynecology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bo Lin
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaoli Liang
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yihao Liu
- Clinical Trial Unit, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiadong Liang
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhaoxi Zhang
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yingnan Ye
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Fang Liu
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Cuiyu Lin
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haipeng Xiao
- Department of Endocrinology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weiming Lv
- Department of Breast and Thyroid Surgery, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
41
|
Hong S, L R, Mclellan L, Dabney J, Gerds TA, Rotz S, Kalaycio M, Hanna R, Hamilton BK, Majhail N, Sobecks RM. Comparison of Quality of Life and Outcomes between Haploidentical and Matched Related/Unrelated Donor Allogeneic Hematopoietic Cell Transplantation. Transplant Cell Ther 2022; 28:217.e1-217.e6. [DOI: 10.1016/j.jtct.2022.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 11/17/2022]
|
42
|
Hong S, Xie Y, Cheng Z, Li J, He W, Guo Z, Zhang Q, Peng S, He M, Yu S, Xu L, Liu R, Xu T, Zhang Y, Li Y, Wang J, Lv W, Yu J, Xiao H. Distinct molecular subtypes of papillary thyroid carcinoma and gene signature with diagnostic capability. Oncogene 2022; 41:5121-5132. [PMID: 36253446 PMCID: PMC9674518 DOI: 10.1038/s41388-022-02499-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 12/24/2022]
Abstract
Papillary thyroid carcinoma (PTC) is heterogeneous and its molecular characteristics remain elusive. We integrated transcriptomic sequencing, genomic analysis and clinicopathologic information from 582 tissue samples of 216 PTC and 75 benign thyroid nodule (BTN) patients. We discovered four subtypes of PTC including Immune-enriched Subtype, BRAF-enriched Subtype, Stromal Subtype and CNV-enriched Subtype. Molecular subtypes were validated in an external cohort of 497 PTC cases from the TCGA. Tumors in the Immune-enriched Subtype showed higher immune infiltration and overexpression of immune checkpoints, whilst BRAF-enriched Subtype showed a higher tendency for extrathyroidal extension and more advanced TNM stage. Key oncogenes including LRRK2, SLC34A2, MUC1, FOXQ1 and KRT19 were overexpressed and enriched in oncogenic MAPK and PI3K/AKT signaling pathways in BRAF-enriched subtype. Further analysis of BRAF-enriched Subtype identified three subclasses with different degrees of malignancies. We also uncovered the molecular link of the initiation and progression from BTN to subtypes of PTC using trajectory analysis. Moreover, a 20-gene expression signature was generated for differential diagnosis of PTC from BTN patients. Together, our work identified previously unreported molecular subtypes of PTC, offering opportunities to stratify patients into optimal treatment plans based on molecular subtyping.
Collapse
Affiliation(s)
- Shubin Hong
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yubin Xie
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhen Cheng
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jie Li
- grid.412615.50000 0004 1803 6239Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Weiman He
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhuming Guo
- grid.488530.20000 0004 1803 6191Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Quan Zhang
- grid.488530.20000 0004 1803 6191Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sui Peng
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China ,grid.412615.50000 0004 1803 6239Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Minghui He
- grid.412615.50000 0004 1803 6239Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuang Yu
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Lixia Xu
- grid.412615.50000 0004 1803 6239Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Rengyun Liu
- grid.412615.50000 0004 1803 6239Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Tianyi Xu
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yunjian Zhang
- grid.412615.50000 0004 1803 6239Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanbing Li
- grid.412615.50000 0004 1803 6239Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiguang Wang
- grid.24515.370000 0004 1937 1450Division of Life Science, Department of Chemical and Biological Engineering, State Key Laboratory of Molecular Neuroscience and Hong Kong Center for Neurodegenerative Diseases, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Weiming Lv
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Jun Yu
- Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. .,Institute of Digestive Disease and Department of Medicine & Therapeutics, State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Haipeng Xiao
- Department of endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
43
|
Yang Y, Xian W, Wu D, Huo Z, Hong S, Li Y, Xiao H. The role of obesity, type 2 diabetes, and metabolic factors in gout: A Mendelian randomization study. Front Endocrinol (Lausanne) 2022; 13:917056. [PMID: 35992130 PMCID: PMC9388832 DOI: 10.3389/fendo.2022.917056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Several epidemiological studies have reported a possible correlation between risk of gout and metabolic disorders including type 2 diabetes, insulin resistance, obesity, dyslipidemia, and hypertension. However, it is unclear if this association is causal. METHODS We used Mendelian randomization (MR) to evaluate the causal relation between metabolic conditions and gout or serum urate concentration by inverse-variance-weighted (conventional) and weighted median methods. Furthermore, MR-Egger regression and MR-pleiotropy residual sum and outlier (PRESSO) method were used to explore pleiotropy. Genetic instruments for metabolic disorders and outcome (gout and serum urate) were obtained from several genome-wide association studies on individuals of mainly European ancestry. RESULTS Conventional MR analysis showed a robust causal association of increasing obesity measured by body mass index (BMI), high-density lipoprotein cholesterol (HDL), and systolic blood pressure (SBP) with risk of gout. A causal relationship between fasting insulin, BMI, HDL, triglycerides (TG), SBP, alanine aminotransferase (ALT), and serum urate was also observed. These results were consistent in weighted median method and MR-PRESSO after removing outliers identified. Our analysis also indicated that HDL and serum urate as well as gout have a bidirectional causal effect on each other. CONCLUSIONS Our study suggested causal effects between glycemic traits, obesity, dyslipidemia, blood pressure, liver function, and serum urate as well as gout, which implies that metabolic factors contribute to the development of gout via serum urate, as well as potential benefit of sound management of increased serum urate in patients with obesity, dyslipidemia, hypertension, and liver dysfunction.
Collapse
|
44
|
Satzinger KJ, Liu YJ, Smith A, Knapp C, Newman M, Jones C, Chen Z, Quintana C, Mi X, Dunsworth A, Gidney C, Aleiner I, Arute F, Arya K, Atalaya J, Babbush R, Bardin JC, Barends R, Basso J, Bengtsson A, Bilmes A, Broughton M, Buckley BB, Buell DA, Burkett B, Bushnell N, Chiaro B, Collins R, Courtney W, Demura S, Derk AR, Eppens D, Erickson C, Faoro L, Farhi E, Fowler AG, Foxen B, Giustina M, Greene A, Gross JA, Harrigan MP, Harrington SD, Hilton J, Hong S, Huang T, Huggins WJ, Ioffe LB, Isakov SV, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Khattar T, Kim S, Klimov PV, Korotkov AN, Kostritsa F, Landhuis D, Laptev P, Locharla A, Lucero E, Martin O, McClean JR, McEwen M, Miao KC, Mohseni M, Montazeri S, Mruczkiewicz W, Mutus J, Naaman O, Neeley M, Neill C, Niu MY, O'Brien TE, Opremcak A, Pató B, Petukhov A, Rubin NC, Sank D, Shvarts V, Strain D, Szalay M, Villalonga B, White TC, Yao Z, Yeh P, Yoo J, Zalcman A, Neven H, Boixo S, Megrant A, Chen Y, Kelly J, Smelyanskiy V, Kitaev A, Knap M, Pollmann F, Roushan P. Realizing topologically ordered states on a quantum processor. Science 2021; 374:1237-1241. [PMID: 34855491 DOI: 10.1126/science.abi8378] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
[Figure: see text].
Collapse
Affiliation(s)
| | - Y-J Liu
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - A Smith
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, UK.,Centre for the Mathematics and Theoretical Physics of Quantum Non-Equilibrium Systems, University of Nottingham, Nottingham NG7 2RD, UK
| | - C Knapp
- Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Newman
- Google Quantum AI, Mountain View, CA, USA
| | - C Jones
- Google Quantum AI, Mountain View, CA, USA
| | - Z Chen
- Google Quantum AI, Mountain View, CA, USA
| | - C Quintana
- Google Quantum AI, Mountain View, CA, USA
| | - X Mi
- Google Quantum AI, Mountain View, CA, USA
| | | | - C Gidney
- Google Quantum AI, Mountain View, CA, USA
| | - I Aleiner
- Google Quantum AI, Mountain View, CA, USA
| | - F Arute
- Google Quantum AI, Mountain View, CA, USA
| | - K Arya
- Google Quantum AI, Mountain View, CA, USA
| | - J Atalaya
- Google Quantum AI, Mountain View, CA, USA
| | - R Babbush
- Google Quantum AI, Mountain View, CA, USA
| | - J C Bardin
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA
| | - R Barends
- Google Quantum AI, Mountain View, CA, USA
| | - J Basso
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Bilmes
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - D A Buell
- Google Quantum AI, Mountain View, CA, USA
| | - B Burkett
- Google Quantum AI, Mountain View, CA, USA
| | - N Bushnell
- Google Quantum AI, Mountain View, CA, USA
| | - B Chiaro
- Google Quantum AI, Mountain View, CA, USA
| | - R Collins
- Google Quantum AI, Mountain View, CA, USA
| | - W Courtney
- Google Quantum AI, Mountain View, CA, USA
| | - S Demura
- Google Quantum AI, Mountain View, CA, USA
| | - A R Derk
- Google Quantum AI, Mountain View, CA, USA
| | - D Eppens
- Google Quantum AI, Mountain View, CA, USA
| | - C Erickson
- Google Quantum AI, Mountain View, CA, USA
| | - L Faoro
- Laboratoire de Physique Theorique et Hautes Energies, Sorbonne Université, 75005 Paris, France
| | - E Farhi
- Google Quantum AI, Mountain View, CA, USA
| | - A G Fowler
- Google Quantum AI, Mountain View, CA, USA
| | - B Foxen
- Google Quantum AI, Mountain View, CA, USA
| | - M Giustina
- Google Quantum AI, Mountain View, CA, USA
| | - A Greene
- Google Quantum AI, Mountain View, CA, USA.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - J A Gross
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Hilton
- Google Quantum AI, Mountain View, CA, USA
| | - S Hong
- Google Quantum AI, Mountain View, CA, USA
| | - T Huang
- Google Quantum AI, Mountain View, CA, USA
| | | | - L B Ioffe
- Google Quantum AI, Mountain View, CA, USA
| | - S V Isakov
- Google Quantum AI, Mountain View, CA, USA
| | - E Jeffrey
- Google Quantum AI, Mountain View, CA, USA
| | - Z Jiang
- Google Quantum AI, Mountain View, CA, USA
| | - D Kafri
- Google Quantum AI, Mountain View, CA, USA
| | | | - T Khattar
- Google Quantum AI, Mountain View, CA, USA
| | - S Kim
- Google Quantum AI, Mountain View, CA, USA
| | - P V Klimov
- Google Quantum AI, Mountain View, CA, USA
| | - A N Korotkov
- Google Quantum AI, Mountain View, CA, USA.,Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
| | | | - D Landhuis
- Google Quantum AI, Mountain View, CA, USA
| | - P Laptev
- Google Quantum AI, Mountain View, CA, USA
| | - A Locharla
- Google Quantum AI, Mountain View, CA, USA
| | - E Lucero
- Google Quantum AI, Mountain View, CA, USA
| | - O Martin
- Google Quantum AI, Mountain View, CA, USA
| | | | - M McEwen
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics, University of California, Santa Barbara, CA, USA
| | - K C Miao
- Google Quantum AI, Mountain View, CA, USA
| | - M Mohseni
- Google Quantum AI, Mountain View, CA, USA
| | | | | | - J Mutus
- Google Quantum AI, Mountain View, CA, USA
| | - O Naaman
- Google Quantum AI, Mountain View, CA, USA
| | - M Neeley
- Google Quantum AI, Mountain View, CA, USA
| | - C Neill
- Google Quantum AI, Mountain View, CA, USA
| | - M Y Niu
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Opremcak
- Google Quantum AI, Mountain View, CA, USA
| | - B Pató
- Google Quantum AI, Mountain View, CA, USA
| | - A Petukhov
- Google Quantum AI, Mountain View, CA, USA
| | - N C Rubin
- Google Quantum AI, Mountain View, CA, USA
| | - D Sank
- Google Quantum AI, Mountain View, CA, USA
| | - V Shvarts
- Google Quantum AI, Mountain View, CA, USA
| | - D Strain
- Google Quantum AI, Mountain View, CA, USA
| | - M Szalay
- Google Quantum AI, Mountain View, CA, USA
| | | | - T C White
- Google Quantum AI, Mountain View, CA, USA
| | - Z Yao
- Google Quantum AI, Mountain View, CA, USA
| | - P Yeh
- Google Quantum AI, Mountain View, CA, USA
| | - J Yoo
- Google Quantum AI, Mountain View, CA, USA
| | - A Zalcman
- Google Quantum AI, Mountain View, CA, USA
| | - H Neven
- Google Quantum AI, Mountain View, CA, USA
| | - S Boixo
- Google Quantum AI, Mountain View, CA, USA
| | - A Megrant
- Google Quantum AI, Mountain View, CA, USA
| | - Y Chen
- Google Quantum AI, Mountain View, CA, USA
| | - J Kelly
- Google Quantum AI, Mountain View, CA, USA
| | | | - A Kitaev
- Google Quantum AI, Mountain View, CA, USA.,Department of Physics and Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, CA, USA.,Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, CA, USA
| | - M Knap
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany.,Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
| | - F Pollmann
- Department of Physics, Technical University of Munich, 85748 Garching, Germany.,Munich Center for Quantum Science and Technology (MCQST), Schellingstraße 4, 80799 München, Germany
| | - P Roushan
- Google Quantum AI, Mountain View, CA, USA
| |
Collapse
|
45
|
Sud S, Hall J, Tan X, Roberts O, Green R, Park S, Poellmann M, Bu J, Hong S, Wang A, Casey D. Prospective Characterization of Circulating Tumor Cell Kinetics in Patients With Oligometastatic Disease Receiving Definitive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
46
|
Zou M, Wu D, Zhu H, Huang X, Zhao X, Zhao J, Fu W, Li R, Li B, Wan P, Hong S, Li Y, Xiao H, Yang Z. Multiparametric quantitative MRI for the evaluation of dysthyroid optic neuropathy. Eur Radiol 2021; 32:1931-1938. [PMID: 34642808 DOI: 10.1007/s00330-021-08300-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/13/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To evaluate the ability of quantitative MRI parameters for predicting dysthyroid optic neuropathy (DON). METHODS We retrospectively collected and analyzed the clinical features and 3.0 T MRI data of 59 patients with Graves orbitopathy (GO), with (n = 26) and without DON (n = 33). We compared MRI quantitative parameters, including the modified muscle index (mMI), proptosis, volume of intra-orbital fat, mean apparent diffusion coefficient value, and T2 value of the optic nerve among patients with and without DON. A logistic regression analysis was performed to identify the risk factors associated with DON. Moreover, we performed a receiver operating characteristic curve analysis and decision curve analysis to evaluate the diagnostic performance of the identified parameters for DON. RESULTS We studied 118 orbits (43 and 75 with and without DON, respectively). The mMI and mean T2 value of the optic nerve were significantly greater in orbits with DON (p < 0.001). A greater mMI at 21 mm (odds ratio (OR), 1.039; 95% confidence interval (CI): 1.019, 1.058) and higher mean T2 value of the optic nerve (OR, 1.035; 95% CI: 1.017, 1.054) were associated with a higher risk of DON. A model combining the mMI at 21 mm and mean T2 values for the optic nerve effectively predicted DON in patients with GO, with a sensitivity and specificity of 95.3% and 76%, respectively. CONCLUSION A quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve can be an effective imaging marker for identifying DON. KEY POINTS • Patients with GO and DON had greater mMI than those without DON. • Optic nerves in patients with DON demonstrated an increased T2 value. • The quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve is the most effective method for diagnosing DON.
Collapse
Affiliation(s)
- Mengsha Zou
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Dide Wu
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Hongzhang Zhu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Xiahua Huang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Xiaojuan Zhao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Jing Zhao
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Wenhao Fu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Ruocheng Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Bin Li
- Department of Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Pengxia Wan
- Department of Ophthalmology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China.
| | - Zhiyun Yang
- Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2nd, Guangzhou, 510080, China.
| |
Collapse
|
47
|
Zhou M, Wu D, Yu F, Hong S, Ye J, Wang C, Li Y, Du M, Xiao H, Wan P. Corneal Endothelium: A Promising Quantitative Index for Graves Ophthalmopathy Activity Evaluation. Am J Ophthalmol 2021; 230:216-223. [PMID: 34102155 DOI: 10.1016/j.ajo.2021.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 04/04/2021] [Accepted: 05/24/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE To investigate the corneal endothelium damage in Graves ophthalmopathy (GO) and its role as a promising quantitative index to evaluate GO activity. DESIGN Cross-sectional study. METHODS This study included 128 eyes of 64 patients with GO. All subjects underwent ophthalmologic examinations, including proptosis, tear break-up time (BUT), corneal fluorescein staining, and Schirmer test. Corneal endothelium was measured by noncontact specular microscope and ocular biometric parameters were measured by IOLMaster 700. Each eye was assigned a specific clinical activity score (CAS), then grouped as active (CAS ≥3 points) or inactive (CAS <3 points). Ocular parameters between the 2 groups were compared using generalized estimating equations accounting for inter-eye correlation, and receiver operating characteristic (ROC) curves were also obtained. Main outcome measures were parameters of corneal endothelium. RESULTS Among the included eyes, 81 eyes had inactive GO and 47 eyes had active GO. Corneal endothelial cell morphology was altered in active GO compared with inactive GO. The coefficient variation of cell area (CV) was significantly higher in active GO compared with inactive GO (37.0 [34.4-41.2]% vs 33.9 [30.9-36.8]%, P = .001), and positively correlated with CAS (r = 0.322, P < .001). Moreover, CV showed a diagnostic capacity to differentiate the active eyes from inactive eyes. The area under the ROC curve was 0.705. CONCLUSIONS Active GO had morphologic changes in corneal endothelium compared with inactive GO. CV is a sensitive indicator to reflect corneal endothelial function, and has the potential to be adopted as a noninvasive, objective, and quantitative index for evaluating the activity status of GO patients.
Collapse
|
48
|
Liu J, Zhou H, Ma W, Zhang Y, Zhou T, Yang Y, Huang J, Zhao Y, Hong S, Zhan J, Zhao H, Huang Y, Fang W, Zhang L. MA03.05 DNA Damage Response (DDR) Gene Mutations and Correlation With Immunotherapy Response in NSCLC Patients. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
49
|
Pagliuca S, Gurnari C, Hong S, Kongkiatkamon S, Awada H, Terkawi L, Zawit M, Visconte V, Hamilton B, Carraway H, Majhail N, Maciejewski J. Topic: AS04-MDS Biology and Pathogenesis/AS04h-Immune deregulation. Leuk Res 2021. [DOI: 10.1016/j.leukres.2021.106678.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
50
|
Xue J, Li S, Shi P, Chen M, Yu S, Hong S, Li Y, Liu R, Xiao H. The ETS Inhibitor YK-4-279 Suppresses Thyroid Cancer Progression Independent of TERT Promoter Mutations. Front Oncol 2021; 11:649323. [PMID: 34221969 PMCID: PMC8242932 DOI: 10.3389/fonc.2021.649323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/01/2021] [Indexed: 12/24/2022] Open
Abstract
Hotspot mutations in the core promoter region of the telomerase reverse transcriptase (TERT) gene have been well established to associate with aggressive clinical characteristics, radioiodine refractory, tumor recurrence, and mortality in thyroid cancer. Several E-twenty-six (ETS) transcription factors were reported to selectively bound to the mutant TERT promoter and activated TERT expression. In this study we aimed to investigate whether TERT promoter mutations confer sensitivity to ETS inhibitor YK-4-279 in thyroid cancer cells and whether this inhibitor could be served as a potential therapeutic agent for thyroid cancer. In vitro assays showed that YK-4-279 treatment sharply suppressed cell viability, colony formation, migration, and invasion, as well as induced cell cycle arrest and apoptosis in a panel of thyroid cancer cells. The cell viability after YK-4-279 treatment was similar between cell lines harboring mutant and wild-type TERT promoters. Furthermore, YK-4-279 treatment reduced both luciferase activity and mRNA expression of TERT independent of TERT promoter mutation status. Data from RNA-seq further revealed that YK-4-279 significantly affected biological processes including DNA replication and cell cycle. Reduced DNA helicase activity and decreased expression of several helicase genes were observed after YK-4-279 treatment. Moreover, YK-4-279 significantly inhibited tumor growth and induced apoptosis in a xenograft mice model. Thus, ETS inhibitor YK-4-279 suppressed TERT expression and conferred anti-tumor activity in a TERT promoter mutation-independent manner, and it could be a potential agent for the treatment of advanced thyroid cancers.
Collapse
Affiliation(s)
- Junyu Xue
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shiyong Li
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Peijie Shi
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mengke Chen
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shuang Yu
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shubin Hong
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yanbing Li
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Rengyun Liu
- Institute of Precision Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haipeng Xiao
- Department of Endocrinology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|