2
|
Winter PS, Ramseier ML, Navia AW, Saksena S, Strouf H, Senhaji N, DenAdel A, Mirza M, An HH, Bilal L, Dennis P, Leahy CS, Shigemori K, Galves-Reyes J, Zhang Y, Powers F, Mulugeta N, Gupta AJ, Calistri N, Van Scoyk A, Jones K, Liu H, Stevenson KE, Ren S, Luskin MR, Couturier CP, Amini AP, Raghavan S, Kimmerling RJ, Stevens MM, Crawford L, Weinstock DM, Manalis SR, Shalek AK, Murakami MA. Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597767. [PMID: 38915726 PMCID: PMC11195125 DOI: 10.1101/2024.06.06.597767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples.
Collapse
Affiliation(s)
- Peter S. Winter
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michelle L. Ramseier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Andrew W. Navia
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Sachit Saksena
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, MIT, Cambridge, MA, USA
| | - Haley Strouf
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Nezha Senhaji
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alan DenAdel
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
| | - Mahnoor Mirza
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Hyun Hwan An
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Bilal
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Peter Dennis
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Catharine S. Leahy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kay Shigemori
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jennyfer Galves-Reyes
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Ye Zhang
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Foster Powers
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nolawit Mulugeta
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Nicholas Calistri
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Alex Van Scoyk
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kristen Jones
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Huiyun Liu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Siyang Ren
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Marlise R. Luskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Charles P. Couturier
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | | | - Srivatsan Raghavan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Mark M. Stevens
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
| | - Lorin Crawford
- Center for Computational Molecular Biology, Brown University, Providence, RI, USA
- Department of Biostatistics, Brown University, Providence, RI, USA
- Microsoft Research, Cambridge, MA, USA
| | - David M. Weinstock
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Current Address: Merck and Co., Rahway, NJ, USA
| | - Scott R. Manalis
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Alex K. Shalek
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA
- Institute for Medical Engineering & Science, MIT, Cambridge, MA, USA
- Department of Chemistry, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA
| | - Mark A. Murakami
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
5
|
Kimura S, Polonen P, Montefiori L, Park CS, Iacobucci I, Yeoh AE, Attarbaschi A, Moore AS, Brown A, Manabe A, Buldini B, Freeman BB, Chen C, Cheng C, Kean Hui C, Li CK, Pui CH, Qu C, Tomizawa D, Teachey DT, Varotto E, Paietta EM, Arnold ED, Locatelli F, Escherich G, Elisa Muhle H, Marquart HV, de Groot-Kruseman HA, Rowe JM, Stary J, Trka J, Choi JK, Meijerink JPP, Yang JJ, Takita J, Pawinska-Wasikowska K, Roberts KG, Han K, Caldwell KJ, Schmiegelow K, Crews KR, Eguchi M, Schrappe M, Zimmerman M, Takagi M, Maybury M, Svaton M, Reiterova M, Kicinski M, Prater MS, Kato M, Reyes N, Spinelli O, Thomas P, Mazilier P, Gao Q, Masetti R, Kotecha RS, Pieters R, Elitzur S, Luger SM, Mitchell S, Pruett-Miller SM, Shen S, Jeha S, Köhrer S, Kornblau SM, Skoczeń S, Miyamura T, Vincent TL, Imamura T, Conter V, Tang Y, Liu YC, Chang Y, Gu Z, Cheng Z, Yinmei Z, Inaba H, Mullighan CG. Biologic and clinical features of childhood gamma delta T-ALL: identification of STAG2/LMO2 γδ T-ALL as an extremely high risk leukemia in the very young. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.06.23298028. [PMID: 37986997 PMCID: PMC10659466 DOI: 10.1101/2023.11.06.23298028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
PURPOSE Gamma delta T-cell receptor-positive acute lymphoblastic leukemia (γδ T-ALL) is a high-risk but poorly characterized disease. METHODS We studied clinical features of 200 pediatric γδ T-ALL, and compared the prognosis of 93 cases to 1,067 protocol-matched non-γδ T-ALL. Genomic features were defined by transcriptome and genome sequencing. Experimental modeling was used to examine the mechanistic impacts of genomic alterations. Therapeutic vulnerabilities were identified by high throughput drug screening of cell lines and xenografts. RESULTS γδ T-ALL in children under three was extremely high-risk with 5-year event-free survival (33% v. 70% [age 3-<10] and 73% [age ≥10], P =9.5 x 10 -5 ) and 5-year overall survival (49% v. 78% [age 3-<10] and 81% [age ≥10], P =0.002), differences not observed in non-γδ T-ALL. γδ T-ALL in this age group was enriched for genomic alterations activating LMO2 activation and inactivating STAG2 inactivation ( STAG2/LMO2 ). Mechanistically, we show that inactivation of STAG2 profoundly perturbs chromatin organization by altering enhancer-promoter looping resulting in deregulation of gene expression associated with T-cell differentiation. Drug screening showed resistance to prednisolone, consistent with clinical slow treatment response, but identified a vulnerability in DNA repair pathways arising from STAG2 inactivation, which was efficaciously targeted by Poly(ADP-ribose) polymerase (PARP) inhibition, with synergism with HDAC inhibitors. Ex-vivo drug screening on PDX cells validated the efficacy of PARP inhibitors as well as other potential targets including nelarabine. CONCLUSION γδ T-ALL in children under the age of three is extremely high-risk and enriched for STAG2/LMO2 ALL. STAG2 loss perturbs chromatin conformation and differentiation, and STAG2/LMO2 ALL is sensitive to PARP inhibition. These data provide a diagnostic and therapeutic framework for pediatric γδ T-ALL. SUPPORT The authors are supported by the American and Lebanese Syrian Associated Charities of St Jude Children's Research Hospital, NCI grants R35 CA197695, P50 CA021765 (C.G.M.), the Henry Schueler 41&9 Foundation (C.G.M.), and a St. Baldrick's Foundation Robert J. Arceci Innovation Award (C.G.M.), Gabriella Miller Kids First X01HD100702 (D.T.T and C.G.M.) and R03CA256550 (D.T.T. and C.G.M.), F32 5F32CA254140 (L.M.), and a Garwood Postdoctoral Fellowship of the Hematological Malignancies Program of the St Jude Children's Research Hospital Comprehensive Cancer Center (S.K.). This project was supported by the National Cancer Institute of the National Institutes of Health under the following award numbers: U10CA180820, UG1CA189859, U24CA114766, U10CA180899, U10CA180866 and U24CA196173. DISCLAIMER The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies were not directly involved in the design of the study, gathering, analysis and interpretation of the data, writing of the manuscript, or decision to submit the manuscript for publication.
Collapse
|