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Hernandez KM, Bramlett KS, Agius P, Baden J, Cao R, Clement O, Corner AS, Craft J, Dean DA, Dry JR, Grigaityte K, Grossman RL, Hicks J, Higa N, Holzer TR, Jensen J, Johann DJ, Katz S, Kolatkar A, Keynton JL, Lee JSH, Maar D, Martini JF, Meyer CG, Roberts PC, Ryder M, Salvatore L, Schageman JJ, Somiari S, Stetson D, Stern M, Xu L, Leiman LC. Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study. J Mol Diagn 2023; 25:143-155. [PMID: 36828596 DOI: 10.1016/j.jmoldx.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/21/2022] [Accepted: 12/02/2022] [Indexed: 02/24/2023] Open
Abstract
The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.
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Affiliation(s)
- Kyle M Hernandez
- Department of Medicine, University of Chicago, Chicago, Illinois; Center for Translational Data Science, University of Chicago, Chicago, Illinois
| | | | | | | | - Ru Cao
- Thermo Fisher Scientific, Austin, Texas
| | | | - Adam S Corner
- Digital Biology Group, Bio-Rad Laboratories Inc., Pleasanton, California
| | | | | | | | | | - Robert L Grossman
- Department of Medicine, University of Chicago, Chicago, Illinois; Open Commons Consortium, Chicago, Illinois; Pfizer, San Diego, California
| | - James Hicks
- Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA
| | - Nikki Higa
- Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA
| | | | | | - Donald J Johann
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | | | - Anand Kolatkar
- Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA
| | | | - Jerry S H Lee
- Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA; Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, California
| | - Dianna Maar
- Digital Biology Group, Bio-Rad Laboratories Inc., Pleasanton, California
| | | | - Christopher G Meyer
- Center for Translational Data Science, University of Chicago, Chicago, Illinois
| | | | | | | | | | | | | | - Mark Stern
- Bristol Myers Squibb, Newton, New Jersey
| | - Liya Xu
- Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA
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Si H, Jure-Kunkel M, Pencheva N, Xu S, Higgs B, Sasser K, Hamadeh H, Agius P, Grigaityte K. 915 Molecular characterization of AXL in solid tumor malignancies using real-world data. J Immunother Cancer 2021. [DOI: 10.1136/jitc-2021-sitc2021.915] [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] Open
Abstract
BackgroundThe receptor tyrosine kinase AXL is aberrantly expressed in many cancer types and associated with epithelial-to-mesenchymal transition (EMT), poor prognosis, and therapy resistance. To better understand the expression of this gene across specific disease subtypes, correlated pathways, and how certain therapies potentially modulate AXL expression, we investigated real-world clinical and molecular data across five solid tumor types before and after chemotherapy or immune checkpoint inhibitor (CPI) therapy.MethodsWhole transcriptome and exome sequencing were derived from patient tumor specimens obtained either prior to treatment or following chemotherapy or CPI therapies. RNA reads were mapped using STAR and data was normalized using transcripts per million. DNA reads were mapped using Novoalign and variants were called using Freebayes and Pindel. Clinical data was curated from multiple sources, QC’d and deidentified according to standard protocols. Five diseases were included: non-small cell lung cancer (NSCLC, n=1181), ovarian cancer (OV, n=300), urothelial carcinoma (UC, n=140), pancreatic ductal adenocarcinoma (PDAC, n=942), and skin cutaneous melanoma (SKCM, n=157). PD-L1 positivity was defined as ≥1% tumor cells with PD-L1 immunohistochemical staining at any intensity.ResultsAXL mRNA levels were highest in PDAC followed by NSCLC, SKCM, UC and OV. Within OV, AXL expression levels were higher in tumors pre-treated with chemotherapy relative to untreated. For other tumor types, chemotherapy or CPI pre-treated tumors had AXL mRNA levels comparable to untreated tumors. Copy number amplifications of AXL were rare across all tumor types (<3%) and did not associate with mRNA expression. Distinct molecular subtypes in several cancers displayed relatively high AXL mRNA levels, including the mesenchymal subtype in OV and the stromal rich subtypes in PDAC. AXL levels also correlated with an EMT mRNA signature across all tumors (rho=0.67). Further, higher AXL expression was associated with PD-L1 positivity in NSCLC, UC and PDAC (p<0.01), but not OV where only a few tumors were PD-L1 positive.Oncogenic KRAS mutations were associated with higher AXL expression in NSCLC and PDAC (p<0.001) and lower AXL expression in OV (p=0.01). Loss of KDM6A, known to induce tumorigenesis in PDAC, was associated with higher AXL expression in PDAC (p<0.01). Loss-of-function mutations in ARID1A, previously implicated as CPI sensitizing, were associated with lower AXL mRNA levels in OV tumors (p<0.001).ConclusionsAnalyses of real-world mRNA datasets showed that AXL was upregulated in specific tumor and treatment settings as well as in patient populations with specific mutations and disease subtypes. Findings here should be validated with independent datasets.
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3
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Showalter K, Spiera R, Magro C, Agius P, Martyanov V, Franks JM, Sharma R, Geiger H, Wood TA, Zhang Y, Hale CR, Finik J, Whitfield ML, Orange DE, Gordon JK. Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement. Ann Rheum Dis 2021; 80:228-237. [PMID: 33028580 PMCID: PMC8600653 DOI: 10.1136/annrheumdis-2020-217840] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [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: 05/03/2020] [Revised: 08/27/2020] [Accepted: 08/30/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma). METHODS Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis). RESULTS aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts. CONCLUSION CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.
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Affiliation(s)
- Kimberly Showalter
- Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA
| | - Robert Spiera
- Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA
| | - Cynthia Magro
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA
| | | | - Viktor Martyanov
- Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Jennifer M Franks
- Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | | | | | - Tammara A Wood
- Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Yaxia Zhang
- Department of Pathology, Hospital for Special Surgery, New York, New York, USA
| | - Caryn R Hale
- Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, New York, USA
| | - Jackie Finik
- Department of Medicine, Hospital for Special Surgery, New York, New York, USA
| | - Michael L Whitfield
- Molecular and Systems Biology, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
- Biomedical Data Science, Dartmouth College Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Dana E Orange
- Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA
- Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, New York, USA
| | - Jessica K Gordon
- Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA
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Agius P, Geiger H, Robine N. SCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions. Bioinformatics 2019; 35:4843-4845. [PMID: 31197308 DOI: 10.1093/bioinformatics/btz452] [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: 02/19/2019] [Revised: 05/16/2019] [Accepted: 05/28/2019] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The association of splicing signatures with disease is a leading area of study for prognosis, diagnosis and therapy. We present a novel fast-performing annotation-dependent tool called SCANVIS for scoring and annotating splice junctions (SJs), with an efficient visualization tool that highlights SJ details such as frame-shifts and annotation support for individual samples or a sample cohort. RESULTS Using publicly available samples, we show that the tissue specificity inherent in splicing signatures is maintained with the Relative Read Support scoring method in SCANVIS, and we showcase some visualizations to demonstrate the usefulness of incorporating annotation details into sashimi plots. AVAILABILITY AND IMPLEMENTATION https://github.com/nygenome/SCANVIS and https://bioconductor.org/packages/SCANVIS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Phaedra Agius
- Computational Biology, New York Genome Center, New York, NY, USA
| | - Heather Geiger
- Computational Biology, New York Genome Center, New York, NY, USA
| | - Nicolas Robine
- Computational Biology, New York Genome Center, New York, NY, USA
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5
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Frank MO, Koyama T, Rhrissorrakrai K, Robine N, Utro F, Emde AK, Chen BJ, Arora K, Shah M, Geiger H, Felice V, Dikoglu E, Rahman S, Fang X, Vacic V, Bergmann EA, Moore Vogel JL, Reeves C, Khaira D, Calabro A, Kim D, Lamendola-Essel MF, Esteves C, Agius P, Stolte C, Boockvar J, Demopoulos A, Placantonakis DG, Golfinos JG, Brennan C, Bruce J, Lassman AB, Canoll P, Grommes C, Daras M, Diamond E, Omuro A, Pentsova E, Orange DE, Harvey SJ, Posner JB, Michelini VV, Jobanputra V, Zody MC, Kelly J, Parida L, Wrzeszczynski KO, Royyuru AK, Darnell RB. Correction to: Sequencing and curation strategies for identifying candidate glioblastoma treatments. BMC Med Genomics 2019; 12:114. [PMID: 31375115 PMCID: PMC6676607 DOI: 10.1186/s12920-019-0563-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Mayu O Frank
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Takahiko Koyama
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | | | - Nicolas Robine
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Filippo Utro
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Anne-Katrin Emde
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Bo-Juen Chen
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Google, 76 9th Avenue, New York, NY, 10011, USA
| | - Kanika Arora
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Minita Shah
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Heather Geiger
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Vanessa Felice
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Esra Dikoglu
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Sadia Rahman
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Xiaolan Fang
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Vladimir Vacic
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: 23&Me, 899 W Evelyn Ave, Mountain View, CA, 94041, USA
| | - Ewa A Bergmann
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51, D-79108, Freiburg, Germany
| | - Julia L Moore Vogel
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.,Present address: The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Catherine Reeves
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Depinder Khaira
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Anthony Calabro
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: The Tisch Cancer Institute, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Duyang Kim
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Michelle F Lamendola-Essel
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Cecilia Esteves
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Phaedra Agius
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Christian Stolte
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - John Boockvar
- Northwell Health, Lenox Hill Hospital, 100 E. 77th Street, New York, NY, 10075, USA
| | - Alexis Demopoulos
- Northwell Health, The Brain Tumor Center, 450 Lakeville Road, Lake Success, Lakeville, NY, 11042, USA
| | | | - John G Golfinos
- New York University, School of Medicine, 550 First Avenue, New York, NY, 10016, USA
| | - Cameron Brennan
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Jeffrey Bruce
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Andrew B Lassman
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Peter Canoll
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Christian Grommes
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Mariza Daras
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Eli Diamond
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Antonio Omuro
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.,Present address: Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Elena Pentsova
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Dana E Orange
- Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.,Hospital for Special Surgery, 535 E. 70th Street, New York, NY, 10021, USA
| | - Stephen J Harvey
- IBM Watson Health, NW Broken Sound Bkwy, Boca Raton, FL, 33487, USA
| | - Jerome B Posner
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | | | - Vaidehi Jobanputra
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Michael C Zody
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - John Kelly
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Laxmi Parida
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | | | - Ajay K Royyuru
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Robert B Darnell
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA. .,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA. .,Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
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6
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Orange DE, Agius P, DiCarlo EF, Mirza SZ, Pannellini T, Szymonifka J, Jiang CS, Figgie MP, Frank MO, Robinson WH, Donlin LT, Rozo C, Gravallese EM, Bykerk VP, Goodman SM. Histologic and Transcriptional Evidence of Subclinical Synovial Inflammation in Patients With Rheumatoid Arthritis in Clinical Remission. Arthritis Rheumatol 2019; 71:1034-1041. [PMID: 30835943 DOI: 10.1002/art.40878] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 02/28/2019] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Patients with rheumatoid arthritis (RA) in clinical remission may have subclinical synovial inflammation. This study was undertaken to determine the proportion of patients with RA in remission or with low disease activity at the time of arthroplasty who had histologic or transcriptional evidence of synovitis, and to identify clinical features that distinguished patients as having subclinical synovitis. METHODS We compared Disease Activity Score in 28 joints (DAS28) to synovial histologic features in 135 patients with RA undergoing arthroplasty. We also compared DAS28 scores to RNA-Seq data in a subset of 35 patients. RESULTS Fourteen percent of patients met DAS28 criteria for clinical remission (DAS28 <2.6), and another 15% met criteria for low disease activity (DAS28 <3.2). Histologic analysis of synovium revealed synovitis in 27% and 31% of samples from patients in remission and patients with low disease activity, respectively. Patients with low disease activity and synovitis also exhibited increased C-reactive protein (CRP) (P = 0.0006) and increased anti-cyclic citrullinated peptide (anti-CCP) antibody levels (P = 0.03) compared to patients without synovitis. Compared to patients with a "low inflammatory synovium" subtype, 183 genes were differentially expressed in the synovium of patients with subclinical synovitis. The majority of these genes (86%) were also differentially expressed in the synovium of patients with clinically active disease (DAS28 ≥3.2). CONCLUSION Thirty-one percent of patients with low clinical disease activity exhibited histologic evidence of subclinical synovitis, which was associated with increased CRP and anti-CCP levels. Our findings suggest that synovial gene expression signatures of clinical synovitis are present in patients with subclinical synovitis.
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Affiliation(s)
- Dana E Orange
- Hospital for Special Surgery and Rockefeller University, New York, New York
| | | | | | | | | | | | | | | | | | - William H Robinson
- Stanford University, Stanford, California, and VA Palo Alto Health Care System, Palo Alto, California
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7
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Yang W, Lee KW, Srivastava RM, Kuo F, Krishna C, Chowell D, Makarov V, Hoen D, Dalin MG, Wexler L, Ghossein R, Katabi N, Nadeem Z, Cohen MA, Tian SK, Robine N, Arora K, Geiger H, Agius P, Bouvier N, Huberman K, Vanness K, Havel JJ, Sims JS, Samstein RM, Mandal R, Tepe J, Ganly I, Ho AL, Riaz N, Wong RJ, Shukla N, Chan TA, Morris LGT. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat Med 2019; 25:767-775. [PMID: 31011208 PMCID: PMC6558662 DOI: 10.1038/s41591-019-0434-2] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 03/22/2019] [Indexed: 12/27/2022]
Abstract
Anti-tumor immunity is driven by self versus non-self discrimination. Many immunotherapeutic approaches to cancer have taken advantage of tumor neoantigens derived from somatic mutations. Here, we demonstrate that gene fusions are a source of immunogenic neoantigens that can mediate responses to immunotherapy. We identified an exceptional responder with metastatic head and neck cancer who experienced a complete response to immune checkpoint inhibitor therapy, despite a low mutational load and minimal pre-treatment immune infiltration in the tumor. Using whole-genome sequencing and RNA sequencing, we identified a novel gene fusion and demonstrated that it produces a neoantigen that can specifically elicit a host cytotoxic T cell response. In a cohort of head and neck tumors with low mutation burden, minimal immune infiltration and prevalent gene fusions, we also identified gene fusion-derived neoantigens that generate cytotoxic T cell responses. Finally, analyzing additional datasets of fusion-positive cancers, including checkpoint-inhibitor-treated tumors, we found evidence of immune surveillance resulting in negative selective pressure against gene fusion-derived neoantigens. These findings highlight an important class of tumor-specific antigens and have implications for targeting gene fusion events in cancers that would otherwise be less poised for response to immunotherapy, including cancers with low mutational load and minimal immune infiltration.
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Affiliation(s)
- Wei Yang
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ken-Wing Lee
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Raghvendra M Srivastava
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fengshen Kuo
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chirag Krishna
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Diego Chowell
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vladimir Makarov
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Douglas Hoen
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Martin G Dalin
- Department of Pediatrics, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Leonard Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronald Ghossein
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nora Katabi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zaineb Nadeem
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc A Cohen
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - S Ken Tian
- New York Genome Center, New York, NY, USA
| | | | | | | | | | - Nancy Bouvier
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kety Huberman
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katelynd Vanness
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonathan J Havel
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jennifer S Sims
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert M Samstein
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rajarsi Mandal
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Justin Tepe
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ian Ganly
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alan L Ho
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem Riaz
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard J Wong
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neerav Shukla
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timothy A Chan
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Luc G T Morris
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Surgery (Head and Neck Service), Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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8
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Frank MO, Koyama T, Rhrissorrakrai K, Robine N, Utro F, Emde AK, Chen BJ, Arora K, Shah M, Geiger H, Felice V, Dikoglu E, Rahman S, Fang A, Vacic V, Bergmann EA, Vogel JLM, Reeves C, Khaira D, Calabro A, Kim D, Lamendola-Essel MF, Esteves C, Agius P, Stolte C, Boockvar J, Demopoulos A, Placantonakis DG, Golfinos JG, Brennan C, Bruce J, Lassman AB, Canoll P, Grommes C, Daras M, Diamond E, Omuro A, Pentsova E, Orange DE, Harvey SJ, Posner JB, Michelini VV, Jobanputra V, Zody MC, Kelly J, Parida L, Wrzeszczynski KO, Royyuru AK, Darnell RB. Sequencing and curation strategies for identifying candidate glioblastoma treatments. BMC Med Genomics 2019; 12:56. [PMID: 31023376 PMCID: PMC6485090 DOI: 10.1186/s12920-019-0500-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 03/28/2019] [Indexed: 12/29/2022] Open
Abstract
Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.
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Affiliation(s)
- Mayu O Frank
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Takahiko Koyama
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | | | - Nicolas Robine
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Filippo Utro
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Anne-Katrin Emde
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Bo-Juen Chen
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Google, 76 9th Avenue, New York, NY, 10011, USA
| | - Kanika Arora
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Minita Shah
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Heather Geiger
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Vanessa Felice
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Esra Dikoglu
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA
| | - Sadia Rahman
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Alice Fang
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Vladimir Vacic
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: 23&Me, 899 W Evelyn Ave, Mountain View, CA, 94041, USA
| | - Ewa A Bergmann
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Max Planck Institute of Immunobiology and Epigenetics, Stübeweg 51 D-79108, Freiburg, Germany
| | - Julia L Moore Vogel
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.,Present address: The Scripps Research Institute, 10550 N. Torrey Pines Road, La Jolla, CA, 92037, USA
| | - Catherine Reeves
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Depinder Khaira
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Anthony Calabro
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: The Tisch Cancer Institute, 1470 Madison Avenue, New York, NY, 10029, USA
| | - Duyang Kim
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Michelle F Lamendola-Essel
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Cecilia Esteves
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Present address: Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA
| | - Phaedra Agius
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - Christian Stolte
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - John Boockvar
- Northwell Health, Lenox Hill Hospital, 100 E. 77th Street, New York, NY, 10075, USA
| | - Alexis Demopoulos
- Northwell Health, The Brain Tumor Center, 450 Lakeville Road, Lake Success, Lakeville, NY, 11042, USA
| | | | - John G Golfinos
- New York University, School of Medicine, 550 First Avenue, New York, NY, 10016, USA
| | - Cameron Brennan
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Jeffrey Bruce
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Andrew B Lassman
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Peter Canoll
- Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Christian Grommes
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Mariza Daras
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Eli Diamond
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Antonio Omuro
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA.,Present address: Yale School of Medicine, 333 Cedar Street, New Haven, CT, 06510, USA
| | - Elena Pentsova
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Dana E Orange
- Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.,Hospital for Special Surgery, 535 E. 70th Street, New York, NY, 10021, USA
| | - Stephen J Harvey
- IBM Watson Health, NW Broken Sound Bkwy, Boca Raton, FL, 33487, USA
| | - Jerome B Posner
- Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | | | - Vaidehi Jobanputra
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA.,Columbia University Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Michael C Zody
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA
| | - John Kelly
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Laxmi Parida
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | | | - Ajay K Royyuru
- IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, USA
| | - Robert B Darnell
- New York Genome Center, 101 Avenue of the Americas, New York, NY, 10013, USA. .,Laboratory of Molecular Neuro-Oncology, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA. .,Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY, 10065, USA.
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9
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Conlon EG, Fagegaltier D, Agius P, Davis-Porada J, Gregory J, Hubbard I, Kang K, Kim D, Phatnani H, Shneider NA, Manley JL. Unexpected similarities between C9ORF72 and sporadic forms of ALS/FTD suggest a common disease mechanism. eLife 2018; 7:37754. [PMID: 30003873 PMCID: PMC6103746 DOI: 10.7554/elife.37754] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [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: 04/23/2018] [Accepted: 07/09/2018] [Indexed: 12/13/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) represent two ends of a disease spectrum with shared clinical, genetic and pathological features. These include near ubiquitous pathological inclusions of the RNA-binding protein (RBP) TDP-43, and often the presence of a GGGGCC expansion in the C9ORF72 (C9) gene. Previously, we reported that the sequestration of hnRNP H altered the splicing of target transcripts in C9ALS patients (Conlon et al., 2016). Here, we show that this signature also occurs in half of 50 postmortem sporadic, non-C9 ALS/FTD brains. Furthermore, and equally surprisingly, these ‘like-C9’ brains also contained correspondingly high amounts of insoluble TDP-43, as well as several other disease-related RBPs, and this correlates with widespread global splicing defects. Finally, we show that the like-C9 sporadic patients, like actual C9ALS patients, were much more likely to have developed FTD. We propose that these unexpected links between C9 and sporadic ALS/FTD define a common mechanism in this disease spectrum.
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Affiliation(s)
- Erin G Conlon
- Department of Biological Sciences, Columbia University, New York, United States
| | - Delphine Fagegaltier
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | | | - Julia Davis-Porada
- Department of Biological Sciences, Columbia University, New York, United States
| | - James Gregory
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | - Isabel Hubbard
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | - Kristy Kang
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | - Duyang Kim
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | | | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, United States
| | - Neil A Shneider
- Department of Neurology, Columbia University Medical Center, New York, United States
| | - James L Manley
- Department of Biological Sciences, Columbia University, New York, United States
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10
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Orange DE, Agius P, DiCarlo EF, Robine N, Geiger H, Szymonifka J, McNamara M, Cummings R, Andersen KM, Mirza S, Figgie M, Ivashkiv LB, Pernis AB, Jiang CS, Frank MO, Darnell RB, Lingampali N, Robinson WH, Gravallese E, Bykerk VP, Goodman SM, Donlin LT. Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data. Arthritis Rheumatol 2018; 70:690-701. [PMID: 29468833 PMCID: PMC6336443 DOI: 10.1002/art.40428] [Citation(s) in RCA: 118] [Impact Index Per Article: 19.7] [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: 09/15/2017] [Accepted: 01/23/2018] [Indexed: 12/13/2022]
Abstract
OBJECTIVE In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning. METHODS Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes. RESULTS Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C-reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others. CONCLUSION Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.
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Affiliation(s)
- Dana E. Orange
- Dana E. Orange, MD, MSc: Hospital for Special Surgery, The Rockefeller University, and New York Genome Center, New York, New York
| | - Phaedra Agius
- Phaedra Agius, PhD, Nicolas Robine, PhD, Heather Geiger, BA: New York Genome Center, New York, New York
| | - Edward F. DiCarlo
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Nicolas Robine
- Phaedra Agius, PhD, Nicolas Robine, PhD, Heather Geiger, BA: New York Genome Center, New York, New York
| | - Heather Geiger
- Phaedra Agius, PhD, Nicolas Robine, PhD, Heather Geiger, BA: New York Genome Center, New York, New York
| | - Jackie Szymonifka
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Michael McNamara
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Ryan Cummings
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Kathleen M. Andersen
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Serene Mirza
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Mark Figgie
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Lionel B. Ivashkiv
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Alessandra B. Pernis
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Caroline S. Jiang
- Caroline S. Jiang, PhD: The Rockefeller University Hospital, New York, New York
| | - Mayu O. Frank
- Mayu O. Frank, NP, PhD, Robert B. Darnell, MD, PhD: The Rockefeller University and New York Genome Center, New York, New York
| | - Robert B. Darnell
- Mayu O. Frank, NP, PhD, Robert B. Darnell, MD, PhD: The Rockefeller University and New York Genome Center, New York, New York
| | - Nithya Lingampali
- Nithya Lingampali, BS, William H. Robinson, MD, PhD: Stanford University School of Medicine, Stanford, California
| | - William H. Robinson
- Nithya Lingampali, BS, William H. Robinson, MD, PhD: Stanford University School of Medicine, Stanford, California
| | - Ellen Gravallese
- Ellen Gravallese, MD: University of Massachusetts Memorial Medical Center, Worcester
| | | | - Vivian P. Bykerk
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Susan M. Goodman
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
| | - Laura T. Donlin
- Edward F. DiCarlo, MD, Jackie Szymonifka, PhD, Michael McNamara, BS, Ryan Cummings, AB, Kathleen M. Andersen, BSc, Serene Mirza, BS, Mark Figgie, MD, Lionel B. Ivashkiv, MD, Alessandra B. Pernis, PhD, Vivian P. Bykerk, MD, Susan M. Goodman, MD, Laura T. Donlin, PhD: Hospital for Special Surgery, New York, New York
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11
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Mazzu YZ, Hu Y, Soni RK, Mojica KM, Qin LX, Agius P, Waxman ZM, Mihailovic A, Socci ND, Hendrickson RC, Tuschl T, Singer S. miR-193b-Regulated Signaling Networks Serve as Tumor Suppressors in Liposarcoma and Promote Adipogenesis in Adipose-Derived Stem Cells. Cancer Res 2017; 77:5728-5740. [PMID: 28882999 DOI: 10.1158/0008-5472.can-16-2253] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [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: 12/05/2016] [Revised: 06/13/2017] [Accepted: 09/01/2017] [Indexed: 11/16/2022]
Abstract
Well-differentiated and dedifferentiated liposarcomas (WDLS/DDLS) account for approximately 13% of all soft tissue sarcoma in adults and cause substantial morbidity or mortality in the majority of patients. In this study, we evaluated the functions of miRNA (miR-193b) in liposarcoma in vitro and in vivo Deep RNA sequencing on 93 WDLS, 145 DDLS, and 12 normal fat samples demonstrated that miR-193b was significantly underexpressed in DDLS compared with normal fat. Reintroduction of miR-193b induced apoptosis in liposarcoma cells and promoted adipogenesis in human adipose-derived stem cells (ASC). Integrative transcriptomic and proteomic analysis of miR-193b-target networks identified novel direct targets, including CRK-like proto-oncogene (CRKL) and focal adhesion kinase (FAK). miR-193b was found to regulate FAK-SRC-CRKL signaling through CRKL and FAK. miR-193b also stimulated reactive oxygen species signaling by targeting the antioxidant methionine sulfoxide reductase A to modulate liposarcoma cell survival and ASC differentiation state. Expression of miR-193b in liposarcoma cells was downregulated by promoter methylation, resulting at least in part from increased expression of the DNA methyltransferase DNMT1 in WDLS/DDLS. In vivo, miR-193b mimetics and FAK inhibitor (PF-562271) each inhibited liposarcoma xenograft growth. In summary, miR-193b not only functions as a tumor suppressor in liposarcoma but also promotes adipogenesis in ASC. Furthermore, this study reveals key tyrosine kinase and DNA methylation pathways in liposarcoma, some with immediate implications for therapeutic exploration. Cancer Res; 77(21); 5728-40. ©2017 AACR.
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Affiliation(s)
- Ying Z Mazzu
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yulan Hu
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Rajesh K Soni
- Microchemistry and Proteomics Core, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kelly M Mojica
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Li-Xuan Qin
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Phaedra Agius
- Bioinformatics Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Zachary M Waxman
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Nicholas D Socci
- Bioinformatics Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ronald C Hendrickson
- Microchemistry and Proteomics Core, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Thomas Tuschl
- Laboratory of RNA Molecular Biology, The Rockefeller University, New York, New York
| | - Samuel Singer
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
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12
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Okada T, Lee AY, Qin LX, Agaram N, Mimae T, Shen Y, O'Connor R, López-Lago MA, Craig A, Miller ML, Agius P, Molinelli E, Socci ND, Crago AM, Shima F, Sander C, Singer S. Integrin-α10 Dependency Identifies RAC and RICTOR as Therapeutic Targets in High-Grade Myxofibrosarcoma. Cancer Discov 2016; 6:1148-1165. [PMID: 27577794 PMCID: PMC5050162 DOI: 10.1158/2159-8290.cd-15-1481] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [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: 12/17/2015] [Accepted: 08/25/2016] [Indexed: 12/31/2022]
Abstract
Myxofibrosarcoma is a common mesenchymal malignancy with complex genomics and heterogeneous clinical outcomes. Through gene-expression profiling of 64 primary high-grade myxofibrosarcomas, we defined an expression signature associated with clinical outcome. The gene most significantly associated with disease-specific death and distant metastasis was ITGA10 (integrin-α10). Functional studies revealed that myxofibrosarcoma cells strongly depended on integrin-α10, whereas normal mesenchymal cells did not. Integrin-α10 transmitted its tumor-specific signal via TRIO and RICTOR, two oncoproteins that are frequently co-overexpressed through gene amplification on chromosome 5p. TRIO and RICTOR activated RAC/PAK and AKT/mTOR to promote sarcoma cell survival. Inhibition of these proteins with EHop-016 (RAC inhibitor) and INK128 (mTOR inhibitor) had antitumor effects in tumor-derived cell lines and mouse xenografts, and combining the drugs enhanced the effects. Our results demonstrate the importance of integrin-α10/TRIO/RICTOR signaling for driving myxofibrosarcoma progression and provide the basis for promising targeted treatment strategies for patients with high-risk disease. SIGNIFICANCE Identifying the molecular pathogenesis for myxofibrosarcoma progression has proven challenging given the highly complex genomic alterations in this tumor type. We found that integrin-α10 promotes tumor cell survival through activation of TRIO-RAC-RICTOR-mTOR signaling, and that inhibitors of RAC and mTOR have antitumor effects in vivo, thus identifying a potential treatment strategy for patients with high-risk myxofibrosarcoma. Cancer Discov; 6(10); 1148-65. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 1069.
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Affiliation(s)
- Tomoyo Okada
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Ann Y Lee
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Li-Xuan Qin
- Department of Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Narasimhan Agaram
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Takahiro Mimae
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yawei Shen
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Rachael O'Connor
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Miguel A López-Lago
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Amanda Craig
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Martin L Miller
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Phaedra Agius
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Evan Molinelli
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nicholas D Socci
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Aimee M Crago
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York. Department of Surgery, Weill Cornell Medical College, New York, New York
| | - Fumi Shima
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Samuel Singer
- Sarcoma Biology Laboratory, Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York. Department of Surgery, Weill Cornell Medical College, New York, New York.
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Crago AM, Chmielecki J, Rosenberg M, O'Connor R, Byrne C, Wilder FG, Thorn K, Agius P, Kuk D, Socci ND, Qin LX, Meyerson M, Hameed M, Singer S. Near universal detection of alterations in CTNNB1 and Wnt pathway regulators in desmoid-type fibromatosis by whole-exome sequencing and genomic analysis. Genes Chromosomes Cancer 2015; 54:606-15. [PMID: 26171757 DOI: 10.1002/gcc.22272] [Citation(s) in RCA: 111] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 05/15/2015] [Accepted: 05/18/2015] [Indexed: 12/17/2022] Open
Abstract
CTNNB1 mutations or APC abnormalities have been observed in ∼85% of desmoids examined by Sanger sequencing and are associated with Wnt/β-catenin activation. We sought to identify molecular aberrations in "wild-type" tumors (those without CTNNB1 or APC alteration) and to determine their prognostic relevance. CTNNB1 was examined by Sanger sequencing in 117 desmoids; a mutation was observed in 101 (86%) and 16 were wild type. Wild-type status did not associate with tumor recurrence. Moreover, in unsupervised clustering based on U133A-derived gene expression profiles, wild-type and mutated tumors clustered together. Whole-exome sequencing of eight of the wild-type desmoids revealed that three had a CTNNB1 mutation that had been undetected by Sanger sequencing. The mutation was found in a mean 16% of reads (vs. 37% for mutations identified by Sanger). Of the other five wild-type tumors sequenced, two had APC loss, two had chromosome 6 loss, and one had mutation of BMI1. The finding of low-frequency CTNNB1 mutation or APC loss in wild-type desmoids was validated in the remaining eight wild-type desmoids; directed miSeq identified low-frequency CTNNB1 mutation in four and comparative genomic hybridization identified APC loss in one. These results demonstrate that mutations affecting CTNNB1 or APC occur more frequently in desmoids than previously recognized (111 of 117; 95%), and designation of wild-type genotype is largely determined by sensitivity of detection methods. Even true CTNNB1 wild-type tumors (determined by next-generation sequencing) may have genomic alterations associated with Wnt activation (chromosome 6 loss/BMI1 mutation), supporting Wnt/β-catenin activation as the common pathway governing desmoid initiation.
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Affiliation(s)
- Aimee M Crago
- Sarcoma Biology Laboratory and Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Surgery, Weill Cornell Medical College, New York, NY
| | - Juliann Chmielecki
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.,Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Mara Rosenberg
- Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Rachael O'Connor
- Sarcoma Biology Laboratory and Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Caitlin Byrne
- Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Fatima G Wilder
- Sarcoma Biology Laboratory and Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Katherine Thorn
- Sarcoma Biology Laboratory and Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Phaedra Agius
- Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Deborah Kuk
- Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nicholas D Socci
- Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Li-Xuan Qin
- Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Matthew Meyerson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.,Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA.,Department of Pathology, Harvard Medical School, Boston, MA
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Samuel Singer
- Sarcoma Biology Laboratory and Sarcoma Disease Management Program, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.,Department of Surgery, Weill Cornell Medical College, New York, NY
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Abstract
Gene regulatory programs in distinct cell types are maintained in large part through the cell-type–specific binding of transcription factors (TFs). The determinants of TF binding include direct DNA sequence preferences, DNA sequence preferences of cofactors, and the local cell-dependent chromatin context. To explore the contribution of DNA sequence signal, histone modifications, and DNase accessibility to cell-type–specific binding, we analyzed 286 ChIP-seq experiments performed by the ENCODE Consortium. This analysis included experiments for 67 transcriptional regulators, 15 of which were profiled in both the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines. To model TF-bound regions, we trained support vector machines (SVMs) that use flexible k-mer patterns to capture DNA sequence signals more accurately than traditional motif approaches. In addition, we trained SVM spatial chromatin signatures to model local histone modifications and DNase accessibility, obtaining significantly more accurate TF occupancy predictions than simpler approaches. Consistent with previous studies, we find that DNase accessibility can explain cell-line–specific binding for many factors. However, we also find that of the 10 factors with prominent cell-type–specific binding patterns, four display distinct cell-type–specific DNA sequence preferences according to our models. Moreover, for two factors we identify cell-specific binding sites that are accessible in both cell types but bound only in one. For these sites, cell-type–specific sequence models, rather than DNase accessibility, are better able to explain differential binding. Our results suggest that using a single motif for each TF and filtering for chromatin accessible loci is not always sufficient to accurately account for cell-type–specific binding profiles.
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Affiliation(s)
- Aaron Arvey
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA
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Setty M, Helmy K, Khan AA, Silber J, Arvey A, Neezen F, Agius P, Huse JT, Holland EC, Leslie CS. Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma. Mol Syst Biol 2012; 8:605. [PMID: 22929615 PMCID: PMC3435504 DOI: 10.1038/msb.2012.37] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Accepted: 07/25/2012] [Indexed: 01/07/2023] Open
Abstract
Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.
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Affiliation(s)
- Manu Setty
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Karim Helmy
- Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Aly A Khan
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Joachim Silber
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Aaron Arvey
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Frank Neezen
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Phaedra Agius
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Jason T Huse
- Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Eric C Holland
- Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Christina S Leslie
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
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Chung WJ, Agius P, Westholm JO, Chen M, Okamura K, Robine N, Leslie CS, Lai EC. Computational and experimental identification of mirtrons in Drosophila melanogaster and Caenorhabditis elegans. Genome Res 2010; 21:286-300. [PMID: 21177960 DOI: 10.1101/gr.113050.110] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Mirtrons are intronic hairpin substrates of the dicing machinery that generate functional microRNAs. In this study, we describe experimental assays that defined the essential requirements for entry of introns into the mirtron pathway. These data informed a bioinformatic screen that effectively identified functional mirtrons from the Drosophila melanogaster transcriptome. These included 17 known and six confident novel mirtrons among the top 51 candidates, and additional candidates had limited read evidence in available small RNA data. Our computational model also proved effective on Caenorhabditis elegans, for which the identification of 14 cloned mirtrons among the top 22 candidates more than tripled the number of validated mirtrons in this species. A few low-scoring introns generated mirtron-like read patterns from atypical RNA structures, but their paucity suggests that relatively few such loci were not captured by our model. Unexpectedly, we uncovered examples of clustered mirtrons in both fly and worm genomes, including a <8-kb region in C. elegans harboring eight distinct mirtrons. Altogether, we demonstrate that discovery of functional mirtrons, unlike canonical miRNAs, is amenable to computational methods independent of evolutionary constraint.
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Affiliation(s)
- Wei-Jen Chung
- Department of Developmental Biology, Sloan-Kettering Institute, 1017 Rockefeller Research Laboratories, New York, New York 10065, USA
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Agius P, Arvey A, Chang W, Noble WS, Leslie C. High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions. PLoS Comput Biol 2010; 6:e1000916. [PMID: 20838582 PMCID: PMC2936517 DOI: 10.1371/journal.pcbi.1000916] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2010] [Accepted: 08/03/2010] [Indexed: 01/08/2023] Open
Abstract
Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.
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Affiliation(s)
- Phaedra Agius
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - Aaron Arvey
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - William Chang
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Christina Leslie
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America
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Betel D, Koppal A, Agius P, Sander C, Leslie C. Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol 2010; 11:R90. [PMID: 20799968 PMCID: PMC2945792 DOI: 10.1186/gb-2010-11-8-r90] [Citation(s) in RCA: 1219] [Impact Index Per Article: 87.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2010] [Accepted: 08/27/2010] [Indexed: 01/09/2023] Open
Abstract
mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.
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Affiliation(s)
- Doron Betel
- Computational Biology Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, 10065, NY, USA.
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Abstract
The use of free energy-based algorithms to compute RNA secondary structures produces, in general, large numbers of foldings. Recent research has addressed the problem of grouping structures into a small number of clusters and computing a representative folding for each cluster. At the heart of this problem is the need to compute a quantity that measures the difference between pairs of foldings. We introduce a new concept, the relaxed base-pair (RBP) score, designed to give a more biologically realistic measure of the difference between structures than the base-pair (BP) metric, which simply counts the number of base pairs in one structure but not the other. The degree of relaxation is determined by a single relaxation parameter, t. When t = 0, (no relaxation) our method is the same as the BP metric. At the other extreme, a very large value of t will give a distance of 0 for identical structures and 1 for structures that differ. Scores can be recomputed with different values of t, at virtually no extra computation cost, to yield satisfactory results. Our results indicate that relaxed measures give more stable and more meaningful clusters than the BP metric. We also use the RBP score to compute representative foldings for each cluster.
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Affiliation(s)
- Phaedra Agius
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA
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20
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Abstract
Cellular processes are often carried out by intricate systems of interacting genes and proteins. Some of these systems are rather well studied and described in pathway databases, while the roles and functions of the majority of genes are poorly understood. A large compendium of public microarray data is available that covers a variety of conditions, samples, and tissues and provides a rich source for genome-scale information. We focus our study on the analysis of 35 curated biological pathways in the context of gene co-expression over a large variety of biological conditions. By defining a global co-expression similarity rank for each gene and pathway, we perform exhaustive leave-one-out computations to describe existing pathway memberships using other members of the corresponding pathway as reference. We demonstrate that while successful in recovering biological base processes such as metabolism and translation, the global correlation measure fails to detect gene memberships in signaling pathways where co-expression is less evident. Our results also show that pathway membership detection is more effective when using only a subset of corresponding pathway members as reference, supporting the existence of more tightly co-expressed subsets of genes within pathways. Our study assesses the predictive power of global gene expression correlation measures in reconstructing biological systems of various functions and specificity. The developed computational network has immediate applications in detecting dubious pathway members and predicting novel member candidates.
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Affiliation(s)
- Priit Adler
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
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Ward DF, Salasznyk RM, Klees RF, Backiel J, Agius P, Bennett K, Boskey A, Plopper GE. Mechanical strain enhances extracellular matrix-induced gene focusing and promotes osteogenic differentiation of human mesenchymal stem cells through an extracellular-related kinase-dependent pathway. Stem Cells Dev 2007; 16:467-80. [PMID: 17610377 DOI: 10.1089/scd.2007.0034] [Citation(s) in RCA: 88] [Impact Index Per Article: 5.2] [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] [Indexed: 02/06/2023] Open
Abstract
Human mesenchymal stem cells (hMSCs) are a population of multipotent bone marrow cells capable of differentiating along multiple lineages, including bone. Our recently published proteomics studies suggest that focusing of gene expression is the basis of hMSC osteogenic transdifferentiation, and that extracellular matrix proteins play an important role in controlling this focusing. Here, we show that application of a 3-5% tensile strain to a collagen I substrate stimulates osteogenesis in the attached hMSCs through gene focusing via a MAP kinase signaling pathway. Mechanical strain increases expression levels of well-established osteogenic marker genes while simultaneously reducing expression levels of marker genes from three alternate lineages (chondrogenic, adipogenic, and neurogenic). Mechanical strain also increases matrix mineralization (a hallmark of osteogenic differentiation) and activation of extracellular signal-related kinase 1/2 (ERK). Addition of the MEK inhibitor PD98059 to reduce ERK activation decreases osteogenic gene expression and matrix mineralization while also blocking strain-induced down-regulation of nonosteogenic lineage marker genes. These results demonstrate that mechanical strain enhances collagen I-induced gene focusing and osteogenic differentiation in hMSCs through the ERK MAP kinase signal transduction pathway.
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Affiliation(s)
- Donald F Ward
- Department of Biology, Rensselaer Polytechnic Institute, Troy, NY 12180-3596, USA
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Agius P, Kreiswirth B, Naidich S, Bennett K. Typing Staphylococcus aureus using the spa gene and novel distance measures. IEEE/ACM Trans Comput Biol Bioinform 2007; 4:693-704. [PMID: 17975279 DOI: 10.1109/tcbb.2007.1053] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
We developed an approach for identifying groups or families of Staphylococcus aureus bacteria based on genotype data. With the emergence of drug resistant strains, S. aureus represents a significant human health threat. Identifying the family types efficiently and quickly is crucial in community settings. Here, we develop a hybrid sequence algorithm approach to type this bacterium using only its spa gene. Two of the sequence algorithms we used are well established, while the third, the Best Common Gap-Weighted Sequence (BCGS), is novel. We combined the sequence algorithms with a weighted match/mismatch algorithm for the spa sequence ends. Normalized similarity scores and distances between the sequences were derived and used within unsupervised clustering methods. The resulting spa groupings correlated strongly with the groups defined by the well-established Multi locus sequence typing (MLST) method. Spa typing is preferable to MLST typing which types seven genes instead of just one. Furthermore, our spa clustering methods can be fine-tuned to be more discriminative than MLST, identifying new strains that the MLST method may not. Finally, we performed a multidimensional scaling of our distance matrices to visualize the relationship between isolates. The proposed methodology provides a promising new approach to molecular epidemiology.
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Cosgrove JB, Agius P. Studies in multiple sclerosis. II. Comparison of the beta-gamma globulin ratio, gamma globulin elevation, and first-zone colloidal gold curve in the cerebrospinal fluid. Neurology 1966; 16:197-204. [PMID: 4159869 DOI: 10.1212/wnl.16.2_part_1.197] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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