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Wang YW, Allen I, Funingana G, Tischkowitz M, Joko-Fru YW. Predictive biomarkers for the efficacy of PARP inhibitors in ovarian cancer: an updated systematic review. BJC REPORTS 2025; 3:14. [PMID: 40069561 PMCID: PMC11897386 DOI: 10.1038/s44276-025-00122-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 12/06/2024] [Accepted: 01/09/2025] [Indexed: 03/15/2025]
Abstract
BACKGROUND PARP inhibitors are effective in treating ovarian cancer, especially for BRCA1/2 pathogenic variant carriers and those with HRD (homologous recombination deficiency). Concerns over toxicity and costs have led to the search for predictive biomarkers. We present an updated systematic review, expanding on a previous ESMO review on PARP inhibitor biomarkers. METHODS Following ESMO's 2020 review protocol, we extended our search to March 31, 2023, including PubMed and clinical trial data. We also reviewed the reference lists of review articles. We conducted a meta-analysis using a random-effects model to evaluate hazard ratios and assess the predictive potential of biomarkers and the effectiveness of PARP inhibitors in survival. RESULTS We found 375 articles, 103 of which were included after screening (62 primary research, 41 reviews). HRD remained the primary biomarker (95%), particularly BRCA1/2 variants (77%). In the non-HRD category, six articles (10%) introduced innovative biomarkers, including ADP-ribosylation, HOXA9 promoter methylation, patient-derived organoids, KELIM, and SLFN11. DISCUSSION Prospective assessment of real-time homologous recombination repair via nuclear RAD51 levels shows promise but needs validation. Emerging biomarkers like ADP-ribosylation, HOXA9 promoter methylation, patient-derived organoids, KELIM, and SLFN11 offer potential but require large-scale validation.
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Affiliation(s)
- Ying-Wen Wang
- Division of Gynaecologic Oncology, Department of Obstetrics and Gynaecology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
| | - Isaac Allen
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | | | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Yvonne Walburga Joko-Fru
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
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Nittas V, Ormond KE, Vayena E, Blasimme A. Realizing the promise of machine learning in precision oncology: expert perspectives on opportunities and challenges. BMC Cancer 2025; 25:276. [PMID: 39962436 PMCID: PMC11834663 DOI: 10.1186/s12885-025-13621-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 01/31/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image classification tasks in areas such as digital pathology and diagnostic radiology. The application of ML approaches to the analysis of DNA data, including tumor-derived genomic profiles, microRNAs, and cancer epigenetic signatures, while relatively more recent, has demonstrated some utility in identifying driver variants and molecular signatures with possible prognostic and therapeutic applications. METHODS We conducted semi-structured interviews with academic and clinical experts to capture the status quo, challenges, opportunities, ethical implications, and future directions. RESULTS Our participants agreed that machine learning in precision oncology is in infant stages, with clinical integration still rare. Overall, participants equated ongoing developments with better clinical workflows and improved treatment decisions for more cancer patients. They underscored the ability of machine learning to tackle the dynamic nature of cancer, break down the complexity of molecular data, and support decision-making. Our participants emphasized obstacles related to molecular data access, clinical utility, and guidelines. The availability of reliable and well-curated data to train and validate machine learning algorithms and integrate multiple data sources were described as constraints yet necessary for future clinical implementation. Frequently mentioned ethical challenges included privacy risks, equity, explainability, trust, and incidental findings, with privacy being the most polarizing. While participants recognized the issue of hype surrounding machine learning in precision oncology, they agreed that, in an assistive role, it represents the future of precision oncology. CONCLUSIONS Given the unique nature of medical AI, our findings highlight the field's potential and remaining challenges. ML will continue to advance cancer research and provide opportunities for patient-centric, personalized, and efficient precision oncology. Yet, the field must move beyond hype and toward concrete efforts to overcome key obstacles, such as ensuring access to molecular data, establishing clinical utility, developing guidelines and regulations, and meaningfully addressing ethical challenges.
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Affiliation(s)
- Vasileios Nittas
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, Zurich, 8001, Switzerland
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Hottingerstrasse 10, Zurich, 8092, Switzerland
| | - Kelly E Ormond
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Hottingerstrasse 10, Zurich, 8092, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Hottingerstrasse 10, Zurich, 8092, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Hottingerstrasse 10, Zurich, 8092, Switzerland.
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Chakraborty S, Guan Z, Kostrzewa CE, Shen R, Begg CB. Identifying somatic fingerprints of cancers defined by germline and environmental risk factors. Genet Epidemiol 2024; 48:455-467. [PMID: 38686586 PMCID: PMC11522022 DOI: 10.1002/gepi.22565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/18/2024] [Accepted: 04/16/2024] [Indexed: 05/02/2024]
Abstract
Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.
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Affiliation(s)
| | - Zoe Guan
- Mass General Research Institute, Boston, Massachusetts, USA
| | | | - Ronglai Shen
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Colin B Begg
- Memorial Sloan Kettering Cancer Center, New York, New York, USA
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4
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Kefas J, Flynn M. Unlocking the potential of immunotherapy in platinum-resistant ovarian cancer: rationale, challenges, and novel strategies. CANCER DRUG RESISTANCE (ALHAMBRA, CALIF.) 2024; 7:39. [PMID: 39534871 PMCID: PMC11555186 DOI: 10.20517/cdr.2024.67] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 10/07/2024] [Accepted: 10/09/2024] [Indexed: 11/16/2024]
Abstract
Ovarian cancer is a significant global health challenge, with cytoreductive surgery and platinum-based chemotherapy serving as established primary treatments. Unfortunately, most patients relapse and ultimately become platinum-resistant, at which point there are limited effective treatment options. Given the success of immunotherapy in inducing durable treatment responses in several other cancers, its potential in platinum-resistant ovarian cancer (PROC) is currently being investigated. However, in unselected advanced ovarian cancer populations, researchers have reported low response rates to immune checkpoint inhibition, and thus far, no validated biomarkers are predictive of response. Understanding the intricate interplay between platinum resistance, immune recognition, and the tumour microenvironment (TME) is crucial. In this review, we examine the research challenges encountered thus far, the biological rationale for immunotherapy, the underlying mechanisms of immune resistance, and new strategies to overcome resistance.
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Affiliation(s)
| | - Michael Flynn
- Medical Oncology, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK
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5
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Weiner AC, Williams MJ, Shi H, Vázquez-García I, Salehi S, Rusk N, Aparicio S, Shah SP, McPherson A. Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data. Nat Commun 2024; 15:8512. [PMID: 39353885 PMCID: PMC11445576 DOI: 10.1038/s41467-024-52544-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 09/11/2024] [Indexed: 10/03/2024] Open
Abstract
Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.
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Affiliation(s)
- Adam C Weiner
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab Salehi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Thapa B, De Sarkar N, Giri S, Sharma K, Kim M, Kilari D. Integrating PARP Inhibitors in mCRPC Therapy: Current Strategies and Emerging Trends. Cancer Manag Res 2024; 16:1267-1283. [PMID: 39308935 PMCID: PMC11416116 DOI: 10.2147/cmar.s411023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 08/16/2024] [Indexed: 09/25/2024] Open
Abstract
Metastatic castrate-resistant prostate cancer (mCRPC) is associated with poor prognosis. DNA damage response (DDR) genes are commonly altered in mCRPC rendering them as promising therapeutic targets. Poly (ADP ribose) polymerase inhibitors (PARPi) demonstrated antitumor activity in mCRPC patients with DDR gene mutations through synthetic lethality. Multiple clinical trials with PARPi monotherapy exhibited encouraging clinical outcomes in selected patients with mCRPC. More recently, three Phase III randomized clinical trials (RCTs) combining PARPi with androgen receptor signaling inhibitors (ARSIs) demonstrated improved antitumor activity compared to ARSI monotherapy in mCRPC patients as the first-line therapy. Clinical benefit was more pronounced in patients harboring DDR alterations, specifically BRCA1/2. Interestingly, antitumor activity was also observed irrespective of DDR gene mutations, highlighting BRCAness phenotype with androgen receptor blockade resulting in synergistic activity between ARSIs and PARPi. In this review, we discuss the clinical efficacy and safety data of the combination of PARPi plus ARSI in all Phase 3 randomized controlled trials (RCTs), emphasizing strategies for patient selection and highlighting emerging trends based on clinical trial data.
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Affiliation(s)
- Bicky Thapa
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Navonil De Sarkar
- Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
- Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Subhajit Giri
- Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Komal Sharma
- Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
- Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mingee Kim
- School of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Deepak Kilari
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
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7
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Williams MJ, Vázquez-García I, Tam G, Wu M, Varice N, Havasov E, Shi H, Satas G, Lees HJ, Lee JJK, Myers MA, Zatzman M, Rusk N, Ali E, Shah RH, Berger MF, Mohibullah N, Lakhman Y, Chi DS, Abu-Rustum NR, Aghajanian C, McPherson A, Zamarin D, Loomis B, Weigelt B, Friedman CF, Shah SP. Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609031. [PMID: 39229105 PMCID: PMC11370573 DOI: 10.1101/2024.08.21.609031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.
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Affiliation(s)
- Marc J. Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, 10027, USA
| | - Grittney Tam
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michelle Wu
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nancy Varice
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Eliyahu Havasov
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gryte Satas
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hannah J. Lees
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jake June-Koo Lee
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew A. Myers
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Zatzman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Ali
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ronak H Shah
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael F. Berger
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Neeman Mohibullah
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Dennis S. Chi
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Nadeem R. Abu-Rustum
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Carol Aghajanian
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Brian Loomis
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Claire F. Friedman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sohrab P. Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- The Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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McPherson A, Vázquez-García I, Myers MA, Zatzman M, Al-Rawi D, Weiner A, Freeman S, Mohibullah N, Satas G, Williams MJ, Ceglia N, Zhang AW, Li J, Lim JLP, Wu M, Choi S, Havasov E, Grewal D, Shi H, Kim M, Schwarz R, Kaufmann T, Dinh KN, Uhlitz F, Tran J, Wu Y, Patel R, Ramakrishnan S, Kim D, Clarke J, Green H, Ali E, DiBona M, Varice N, Kundra R, Broach V, Gardner GJ, Roche KL, Sonoda Y, Zivanovic O, Kim SH, Grisham RN, Liu YL, Viale A, Rusk N, Lakhman Y, Ellenson LH, Tavaré S, Aparicio S, Chi DS, Aghajanian C, Abu-Rustum NR, Friedman CF, Zamarin D, Weigelt B, Bakhoum SF, Shah SP. Ongoing genome doubling promotes evolvability and immune dysregulation in ovarian cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.11.602772. [PMID: 39071261 PMCID: PMC11275742 DOI: 10.1101/2024.07.11.602772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Whole-genome doubling (WGD) is a critical driver of tumor development and is linked to drug resistance and metastasis in solid malignancies. Here, we demonstrate that WGD is an ongoing mutational process in tumor evolution. Using single-cell whole-genome sequencing, we measured and modeled how WGD events are distributed across cellular populations within tumors and associated WGD dynamics with properties of genome diversification and phenotypic consequences of innate immunity. We studied WGD evolution in 65 high-grade serous ovarian cancer (HGSOC) tissue samples from 40 patients, yielding 29,481 tumor cell genomes. We found near-ubiquitous evidence of WGD as an ongoing mutational process promoting cell-cell diversity, high rates of chromosomal missegregation, and consequent micronucleation. Using a novel mutation-based WGD timing method, doubleTime , we delineated specific modes by which WGD can drive tumor evolution: (i) unitary evolutionary origin followed by significant diversification, (ii) independent WGD events on a pre-existing background of copy number diversity, and (iii) evolutionarily late clonal expansions of WGD populations. Additionally, through integrated single-cell RNA sequencing and high-resolution immunofluorescence microscopy, we found that inflammatory signaling and cGAS-STING pathway activation result from ongoing chromosomal instability and are restricted to tumors that remain predominantly diploid. This contrasted with predominantly WGD tumors, which exhibited significant quiescent and immunosuppressive phenotypic states. Together, these findings establish WGD as an evolutionarily 'active' mutational process that promotes evolvability and dysregulated immunity in late stage ovarian cancer.
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Shah AM, Lee KY, Hidayat A, Falchook A, Muhammad W. A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset. Int J Med Inform 2024; 184:105375. [PMID: 38367390 DOI: 10.1016/j.ijmedinf.2024.105375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 01/25/2024] [Accepted: 02/07/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups. METHODS For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018. RESULTS The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018. CONCLUSION Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.
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Affiliation(s)
- Adnan Muhammad Shah
- Chair of Marketing and Innovation, University of Hamburg, 20146, Germany; Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States; Department of Computer Engineering, Gachon University, Seoul 13120. Republic of Korea.
| | - Kang Yoon Lee
- Department of Computer Engineering, Gachon University, Seoul 13120. Republic of Korea.
| | - Abdullah Hidayat
- Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States.
| | - Aaron Falchook
- Department of Radiation Oncology, Memorial Hospital West, Memorial Cancer Institute (MCI), Pembroke Pines, FL, United States.
| | - Wazir Muhammad
- Department of Physics, Charles E. Schmidt College of Science, Florida Atlantic University, FL 33431-0991, United States.
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10
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Chakraborty S, Guan Z, Begg CB, Shen R. Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach. Biometrics 2024; 80:ujae030. [PMID: 38682463 PMCID: PMC11056772 DOI: 10.1093/biomtc/ujae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 05/01/2024]
Abstract
Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.
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Affiliation(s)
- Saptarshi Chakraborty
- Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY 14214, USA
| | - Zoe Guan
- Biostatistics Center, Mass General Research Institute, Boston, MA 02114, USA
| | - Colin B Begg
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
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11
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Bateman NW, Abulez T, Soltis AR, McPherson A, Choi S, Garsed DW, Pandey A, Tian C, Hood BL, Conrads KA, Teng PN, Oliver J, Gist G, Mitchell D, Litzi TJ, Tarney CM, Crothers BA, Mhawech-Fauceglia P, Dalgard CL, Wilkerson MD, Pierobon M, Petricoin EF, Yan C, Meerzaman D, Bodelon C, Wentzensen N, Lee JSH, Huntsman DG, Shah S, Shriver CD, Phippen NT, Darcy KM, Bowtell DDL, Conrads TP, Maxwell GL. Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities. NPJ Precis Oncol 2024; 8:68. [PMID: 38480868 PMCID: PMC10937683 DOI: 10.1038/s41698-024-00519-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/15/2024] [Indexed: 03/17/2024] Open
Abstract
We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.
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Affiliation(s)
- Nicholas W Bateman
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA.
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA.
| | - Tamara Abulez
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Anthony R Soltis
- The American Genome Center, Collaborative Health Initiative Research Program, Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Andrew McPherson
- Department of Computational Oncology, Memorial Sloan Kettering Cancer Center, Manhattan, NY, USA
| | - Seongmin Choi
- Department of Computational Oncology, Memorial Sloan Kettering Cancer Center, Manhattan, NY, USA
| | - Dale W Garsed
- Peter MacCallum Cancer Centre, Parkville, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ahwan Pandey
- Peter MacCallum Cancer Centre, Parkville, Melbourne, Victoria, Australia
| | - Chunqiao Tian
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Brian L Hood
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Kelly A Conrads
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Pang-Ning Teng
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Julie Oliver
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Glenn Gist
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Dave Mitchell
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Tracy J Litzi
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
| | - Christopher M Tarney
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Barbara A Crothers
- The Joint Pathology Center, Defense Health Agency, National Capital Region Medical Directorate, Silver Spring, MD, USA
| | - Paulette Mhawech-Fauceglia
- Department of Anatomic Pathology, Division of Gynecologic Pathology, University of Southern California, Los Angeles, CA, USA
| | - Clifton L Dalgard
- The American Genome Center, Collaborative Health Initiative Research Program, Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Matthew D Wilkerson
- The American Genome Center, Collaborative Health Initiative Research Program, Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Mariaelena Pierobon
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
| | - Emanuel F Petricoin
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
| | - Chunhua Yan
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Daoud Meerzaman
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Clara Bodelon
- Division of Cancer Epidemiology and Genetics National Cancer Institute, Rockville, MD, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics National Cancer Institute, Rockville, MD, USA
| | - Jerry S H Lee
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, USA
| | - David G Huntsman
- Department of Pathology and Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Sohrab Shah
- Department of Computational Oncology, Memorial Sloan Kettering Cancer Center, Manhattan, NY, USA
| | - Craig D Shriver
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Neil T Phippen
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Kathleen M Darcy
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc, Bethesda, MD, USA
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - David D L Bowtell
- Peter MacCallum Cancer Centre, Parkville, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Thomas P Conrads
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA.
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA.
| | - G Larry Maxwell
- Gynecologic Cancer Center of Excellence, Gynecologic Surgery and Obstetrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA.
- The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA.
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA.
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12
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Jin H, Gulhan DC, Geiger B, Ben-Isvy D, Geng D, Ljungström V, Park PJ. Accurate and sensitive mutational signature analysis with MuSiCal. Nat Genet 2024; 56:541-552. [PMID: 38361034 PMCID: PMC10937379 DOI: 10.1038/s41588-024-01659-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 01/08/2024] [Indexed: 02/17/2024]
Abstract
Mutational signature analysis is a recent computational approach for interpreting somatic mutations in the genome. Its application to cancer data has enhanced our understanding of mutational forces driving tumorigenesis and demonstrated its potential to inform prognosis and treatment decisions. However, methodological challenges remain for discovering new signatures and assigning proper weights to existing signatures, thereby hindering broader clinical applications. Here we present Mutational Signature Calculator (MuSiCal), a rigorous analytical framework with algorithms that solve major problems in the standard workflow. Our simulation studies demonstrate that MuSiCal outperforms state-of-the-art algorithms for both signature discovery and assignment. By reanalyzing more than 2,700 cancer genomes, we provide an improved catalog of signatures and their assignments, discover nine indel signatures absent in the current catalog, resolve long-standing issues with the ambiguous 'flat' signatures and give insights into signatures with unknown etiologies. We expect MuSiCal and the improved catalog to be a step towards establishing best practices for mutational signature analysis.
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Affiliation(s)
- Hu Jin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Doga C Gulhan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Benedikt Geiger
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Daniel Ben-Isvy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - David Geng
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Viktor Ljungström
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter J Park
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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13
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Neijzen D, Lunter G. Unsupervised learning for medical data: A review of probabilistic factorization methods. Stat Med 2023; 42:5541-5554. [PMID: 37850249 DOI: 10.1002/sim.9924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/13/2023] [Indexed: 10/19/2023]
Abstract
We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low-rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data.
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Affiliation(s)
- Dorien Neijzen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Gerton Lunter
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Weatherall Institute of Molecular Medicine, Oxford University, Oxford, UK
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14
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Peng X, Lee J, Adamow M, Maher C, Postow MA, Callahan MK, Panageas KS, Shen R. A topic modeling approach reveals the dynamic T cell composition of peripheral blood during cancer immunotherapy. CELL REPORTS METHODS 2023; 3:100546. [PMID: 37671017 PMCID: PMC10475788 DOI: 10.1016/j.crmeth.2023.100546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/15/2023] [Accepted: 07/10/2023] [Indexed: 09/07/2023]
Abstract
We present TopicFlow, a computational framework for flow cytometry data analysis of patient blood samples for the identification of functional and dynamic topics in circulating T cell population. This framework applies a Latent Dirichlet Allocation (LDA) model, adapting the concept of topic modeling in text mining to flow cytometry. To demonstrate the utility of our method, we conducted an analysis of ∼17 million T cells collected from 138 peripheral blood samples in 51 patients with melanoma undergoing treatment with immune checkpoint inhibitors (ICIs). Our study highlights three latent dynamic topics identified by LDA: a T cell exhaustion topic that independently recapitulates the previously identified LAG-3+ immunotype associated with ICI resistance, a naive topic and its association with immune-related toxicity, and a T cell activation topic that emerges upon ICI treatment. Our approach can be broadly applied to mine high-parameter flow cytometry data for insights into mechanisms of treatment response and toxicity.
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Affiliation(s)
- Xiyu Peng
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jasme Lee
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Matthew Adamow
- Immune Monitoring Facility, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA
| | - Colleen Maher
- Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Michael A. Postow
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Margaret K. Callahan
- Parker Institute for Cancer Immunotherapy, San Francisco, CA 94129, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Katherine S. Panageas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ronglai Shen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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15
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Kahane I, Leiserson MDM, Sharan R. A mutation-level covariate model for mutational signatures. PLoS Comput Biol 2023; 19:e1011195. [PMID: 37276234 DOI: 10.1371/journal.pcbi.1011195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 05/17/2023] [Indexed: 06/07/2023] Open
Abstract
Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.
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Affiliation(s)
- Itay Kahane
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Mark D M Leiserson
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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16
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Peng X, Lee J, Adamow M, Maher C, Postow MA, Callahan MK, Panageas KS, Shen R. Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538095. [PMID: 37162890 PMCID: PMC10168231 DOI: 10.1101/2023.04.24.538095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the model's utility, we analyzed ∼17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naïve topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.
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Affiliation(s)
- Xiyu Peng
- Department of Epidemiology and Biostatistics, San Francisco, CA
| | - Jasme Lee
- Department of Epidemiology and Biostatistics, San Francisco, CA
| | - Matthew Adamow
- Immune Monitoring Facility, San Francisco, CA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA
| | - Colleen Maher
- Parker Institute for Cancer Immunotherapy, San Francisco, CA
- Department of Medicine, Memorial Sloan Kettering Cancer Center New York, NY
| | - Michael A Postow
- Department of Medicine, Memorial Sloan Kettering Cancer Center New York, NY
- Weill Cornell Medical College, New York, NY
| | - Margaret K Callahan
- Parker Institute for Cancer Immunotherapy, San Francisco, CA
- Department of Medicine, Memorial Sloan Kettering Cancer Center New York, NY
- Weill Cornell Medical College, New York, NY
| | | | - Ronglai Shen
- Department of Epidemiology and Biostatistics, San Francisco, CA
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17
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Ostroverkhova D, Przytycka TM, Panchenko AR. Cancer driver mutations: predictions and reality. Trends Mol Med 2023:S1471-4914(23)00067-9. [PMID: 37076339 DOI: 10.1016/j.molmed.2023.03.007] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/23/2023] [Indexed: 04/21/2023]
Abstract
Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.
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Affiliation(s)
- Daria Ostroverkhova
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Teresa M Przytycka
- National Library of Medicine, National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Anna R Panchenko
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada; Department of Biology and Molecular Sciences, Queen's University, Kingston, ON, Canada; School of Computing, Queen's University, Kingston, ON, Canada; Ontario Institute of Cancer Research, Toronto, ON, Canada.
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18
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Ritch EJ, Herberts C, Warner EW, Ng SWS, Kwan EM, Bacon JVW, Bernales CQ, Schönlau E, Fonseca NM, Giri VN, Maurice-Dror C, Vandekerkhove G, Jones SJM, Chi KN, Wyatt AW. A generalizable machine learning framework for classifying DNA repair defects using ctDNA exomes. NPJ Precis Oncol 2023; 7:27. [PMID: 36914848 PMCID: PMC10011564 DOI: 10.1038/s41698-023-00366-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
Specific classes of DNA damage repair (DDR) defect can drive sensitivity to emerging therapies for metastatic prostate cancer. However, biomarker approaches based on DDR gene sequencing do not accurately predict DDR deficiency or treatment benefit. Somatic alteration signatures may identify DDR deficiency but historically require whole-genome sequencing of tumour tissue. We assembled whole-exome sequencing data for 155 high ctDNA fraction plasma cell-free DNA and matched leukocyte DNA samples from patients with metastatic prostate or bladder cancer. Labels for DDR gene alterations were established using deep targeted sequencing. Per sample mutation and copy number features were used to train XGBoost ensemble models. Naive somatic features and trinucleotide signatures were associated with specific DDR gene alterations but insufficient to resolve each class. Conversely, XGBoost-derived models showed strong performance including an area under the curve of 0.99, 0.99 and 1.00 for identifying BRCA2, CDK12, and mismatch repair deficiency in metastatic prostate cancer. Our machine learning approach re-classified several samples exhibiting genomic features inconsistent with original labels, identified a metastatic bladder cancer sample with a homozygous BRCA2 copy loss, and outperformed an existing exome-based classifier for BRCA2 deficiency. We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.
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Affiliation(s)
- Elie J Ritch
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Cameron Herberts
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Evan W Warner
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Sarah W S Ng
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Edmond M Kwan
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Jack V W Bacon
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Cecily Q Bernales
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Elena Schönlau
- Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Nicolette M Fonseca
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Veda N Giri
- Yale School of Medicine and Yale Cancer Center, New Haven, CT, USA
| | | | - Gillian Vandekerkhove
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada
| | - Steven J M Jones
- Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Kim N Chi
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.,Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Alexander W Wyatt
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. .,Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.
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19
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Liu M, Wu Y, Jiang N, Boot A, Rozen S. mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery. NAR Genom Bioinform 2023; 5:lqad005. [PMID: 36694663 PMCID: PMC9869330 DOI: 10.1093/nargab/lqad005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
Mutational signatures are characteristic patterns of mutations caused by endogenous or exogenous mutational processes. These signatures can be discovered by analyzing mutations in large sets of samples-usually somatic mutations in tumor samples. Most programs for discovering mutational signatures are based on non-negative matrix factorization (NMF). Alternatively, signatures can be discovered using hierarchical Dirichlet process (HDP) mixture models, an approach that has been less explored. These models assign mutations to clusters and view each cluster as being generated from the signature of a particular mutational process. Here, we describe mSigHdp, an improved approach to using HDP mixture models to discover mutational signatures. We benchmarked mSigHdp and state-of-the-art NMF-based approaches on four realistic synthetic data sets. These data sets encompassed 18 cancer types. In total, they contained 3.5 × 107 single-base-substitution mutations representing 32 signatures and 6.1 × 106 small insertion and deletion mutations representing 13 signatures. For three of the four data sets, mSigHdp had the best positive predictive value for discovering mutational signatures, and for all four data sets, it had the best true positive rate. Its CPU usage was similar to that of the NMF-based approaches. Thus, mSigHdp is an important and practical addition to the set of tools available for discovering mutational signatures.
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Affiliation(s)
| | | | - Nanhai Jiang
- Programme in Cancer & Stem Cell Biology, Duke–NUS Medical School, 169857 Singapore,Centre for Computational Biology, Duke–NUS Medical School, 169857 Singapore
| | - Arnoud Boot
- Programme in Cancer & Stem Cell Biology, Duke–NUS Medical School, 169857 Singapore,Centre for Computational Biology, Duke–NUS Medical School, 169857 Singapore
| | - Steven G Rozen
- To whom correspondence should be addressed. Tel: +65 65164945;
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20
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Vázquez-García I, Uhlitz F, Ceglia N, Lim JLP, Wu M, Mohibullah N, Niyazov J, Ruiz AEB, Boehm KM, Bojilova V, Fong CJ, Funnell T, Grewal D, Havasov E, Leung S, Pasha A, Patel DM, Pourmaleki M, Rusk N, Shi H, Vanguri R, Williams MJ, Zhang AW, Broach V, Chi DS, Da Cruz Paula A, Gardner GJ, Kim SH, Lennon M, Long Roche K, Sonoda Y, Zivanovic O, Kundra R, Viale A, Derakhshan FN, Geneslaw L, Issa Bhaloo S, Maroldi A, Nunez R, Pareja F, Stylianou A, Vahdatinia M, Bykov Y, Grisham RN, Liu YL, Lakhman Y, Nikolovski I, Kelly D, Gao J, Schietinger A, Hollmann TJ, Bakhoum SF, Soslow RA, Ellenson LH, Abu-Rustum NR, Aghajanian C, Friedman CF, McPherson A, Weigelt B, Zamarin D, Shah SP. Ovarian cancer mutational processes drive site-specific immune evasion. Nature 2022; 612:778-786. [PMID: 36517593 PMCID: PMC9771812 DOI: 10.1038/s41586-022-05496-1] [Citation(s) in RCA: 129] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 10/28/2022] [Indexed: 12/15/2022]
Abstract
High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1-4 patterned by distinct mutational processes5,6, tumour heterogeneity7-9 and intraperitoneal spread7,8,10. Immunotherapies have had limited efficacy in HGSOC11-13, highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC. Homologous recombination-deficient HRD-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumours harboured inflammatory signalling and ongoing immunoediting, reflected in loss of HLA diversity and tumour infiltration with highly differentiated dysfunctional CD8+ T cells. By contrast, foldback-inversion-bearing tumours exhibited elevated immunosuppressive TGFβ signalling and immune exclusion, with predominantly naive/stem-like and memory T cells. Phenotypic state associations were specific to anatomical sites, highlighting compositional, topological and functional differences between adnexal tumours and distal peritoneal foci. Our findings implicate anatomical sites and mutational processes as determinants of evolutionary phenotypic divergence and immune resistance mechanisms in HGSOC. Our study provides a multi-omic cellular phenotype data substrate from which to develop and interpret future personalized immunotherapeutic approaches and early detection research.
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Affiliation(s)
- Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Florian Uhlitz
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jamie L P Lim
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michelle Wu
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neeman Mohibullah
- Integrated Genomics Operation, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juliana Niyazov
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arvin Eric B Ruiz
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Viktoria Bojilova
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christopher J Fong
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tyler Funnell
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Diljot Grewal
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eliyahu Havasov
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samantha Leung
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arfath Pasha
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Druv M Patel
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Maryam Pourmaleki
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hongyu Shi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allen W Zhang
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Vance Broach
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dennis S Chi
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arnaud Da Cruz Paula
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ginger J Gardner
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sarah H Kim
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Matthew Lennon
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kara Long Roche
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukio Sonoda
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Oliver Zivanovic
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ritika Kundra
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Agnes Viale
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fatemeh N Derakhshan
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Luke Geneslaw
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shirin Issa Bhaloo
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ana Maroldi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rahelly Nunez
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Fresia Pareja
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anthe Stylianou
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mahsa Vahdatinia
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yonina Bykov
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rachel N Grisham
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical Center, New York, NY, USA
| | - Ying L Liu
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical Center, New York, NY, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ines Nikolovski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Kelly
- Department of Information Systems, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jianjiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrea Schietinger
- Weill Cornell Medical Center, New York, NY, USA
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Travis J Hollmann
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Bristol Myers Squibb, Princeton, NJ, USA
| | - Samuel F Bakhoum
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert A Soslow
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lora H Ellenson
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nadeem R Abu-Rustum
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical Center, New York, NY, USA
| | - Carol Aghajanian
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Claire F Friedman
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Britta Weigelt
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Weill Cornell Medical Center, New York, NY, USA.
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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21
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Garsed DW, Pandey A, Fereday S, Kennedy CJ, Takahashi K, Alsop K, Hamilton PT, Hendley J, Chiew YE, Traficante N, Provan P, Ariyaratne D, Au-Yeung G, Bateman NW, Bowes L, Brand A, Christie EL, Cunningham JM, Friedlander M, Grout B, Harnett P, Hung J, McCauley B, McNally O, Piskorz AM, Saner FAM, Vierkant RA, Wang C, Winham SJ, Pharoah PDP, Brenton JD, Conrads TP, Maxwell GL, Ramus SJ, Pearce CL, Pike MC, Nelson BH, Goode EL, DeFazio A, Bowtell DDL. The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer. Nat Genet 2022; 54:1853-1864. [PMID: 36456881 PMCID: PMC10478425 DOI: 10.1038/s41588-022-01230-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 10/17/2022] [Indexed: 12/03/2022]
Abstract
Fewer than half of all patients with advanced-stage high-grade serous ovarian cancers (HGSCs) survive more than five years after diagnosis, but those who have an exceptionally long survival could provide insights into tumor biology and therapeutic approaches. We analyzed 60 patients with advanced-stage HGSC who survived more than 10 years after diagnosis using whole-genome sequencing, transcriptome and methylome profiling of their primary tumor samples, comparing this data to 66 short- or moderate-term survivors. Tumors of long-term survivors were more likely to have multiple alterations in genes associated with DNA repair and more frequent somatic variants resulting in an increased predicted neoantigen load. Patients clustered into survival groups based on genomic and immune cell signatures, including three subsets of patients with BRCA1 alterations with distinctly different outcomes. Specific combinations of germline and somatic gene alterations, tumor cell phenotypes and differential immune responses appear to contribute to long-term survival in HGSC.
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Affiliation(s)
- Dale W Garsed
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Ahwan Pandey
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Sian Fereday
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Catherine J Kennedy
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Kazuaki Takahashi
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan
| | - Kathryn Alsop
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Phineas T Hamilton
- The Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
| | - Joy Hendley
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Yoke-Eng Chiew
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Nadia Traficante
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Pamela Provan
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | | | - George Au-Yeung
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Nicholas W Bateman
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA
| | - Leanne Bowes
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Royal Women's Hospital, Parkville, Victoria, Australia
| | - Alison Brand
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Elizabeth L Christie
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Michael Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | | | - Paul Harnett
- The University of Sydney, Sydney, New South Wales, Australia
- Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, New South Wales, Australia
| | - Jillian Hung
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
| | - Bryan McCauley
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Orla McNally
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Royal Women's Hospital, Parkville, Victoria, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, Victoria, Australia
| | - Anna M Piskorz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Flurina A M Saner
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynecology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Robert A Vierkant
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Stacey J Winham
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Thomas P Conrads
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA
| | - George L Maxwell
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA
| | - Susan J Ramus
- School of Clinical Medicine, Faculty of Medicine and Health, University of NSW, Sydney, New South Wales, Australia
- Adult Cancer Program, Lowy Cancer Research Centre, University of NSW, Sydney, New South Wales, Australia
| | - Celeste Leigh Pearce
- Department of Epidemiology and Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Brad H Nelson
- The Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
- Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
| | - Ellen L Goode
- Division of Epidemology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anna DeFazio
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - David D L Bowtell
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
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22
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Funnell T, O’Flanagan CH, Williams MJ, McPherson A, McKinney S, Kabeer F, Lee H, Salehi S, Vázquez-García I, Shi H, Leventhal E, Masud T, Eirew P, Yap D, Zhang AW, Lim JLP, Wang B, Brimhall J, Biele J, Ting J, Au V, Van Vliet M, Liu YF, Beatty S, Lai D, Pham J, Grewal D, Abrams D, Havasov E, Leung S, Bojilova V, Moore RA, Rusk N, Uhlitz F, Ceglia N, Weiner AC, Zaikova E, Douglas JM, Zamarin D, Weigelt B, Kim SH, Da Cruz Paula A, Reis-Filho JS, Martin SD, Li Y, Xu H, de Algara TR, Lee SR, Llanos VC, Huntsman DG, McAlpine JN, IMAXT ConsortiumHannonGregory J.10BattistoniGeorgia10BressanDario10CannellIan G.10CasboltHannah10JausetCristina10KovačevićTatjana10MulveyClaire M.10NugentFiona10RibesMarta Paez10PearsonIsabella10QosajFatime10SawickaKirsty10WildSophia A.10WilliamsElena10LaksEmma3SmithAustin3LaiDaniel3RothAndrew3411BalasubramanianShankar101213LeeMaximilian101213BodenmillerBernd14BurgerMarcel14KuettLaura14TietscherSandra14WindhagerJonas14BoydenEdward S.15AlonShahar15CuiYi15EmenariAmauche15GoodwinDaniel R.15KaragiannisEmmanouil D.15SinhaAnubhav15WassieAsmamaw T.15CaldasCarlos16BrunaAlejandra16CallariMaurizio10GreenwoodWendy10LerdaGiulia10Eyal-LublingYaniv16RuedaOscar M.16SheaAbigail16HarrisOwen17BeckerRobby17GrimaldoFlaminia17HarrisSuvi17VoglSara Lisa17JoyceJohanna A.18WatsonSpencer S.18TavareSimon101920DinhKhanh N.1920FisherEyal10KunesRussell1920WaltonNicholas A.21Al Sa’dMohammed21ChornayNick21DariushAli21González-SolaresEduardo A.21González-FernándezCarlos21YoldaşAybüke Küpcü21MillerNeil21ZhuangXiaowei222324FanJean222324LeeHsuan222324SepúlvedaLeonardo A.222324XiaChenglong222324ZhengPu222324, Shah SP, Aparicio S, Cannell IG, Casbolt H, Jauset C, Kovačević T, Mulvey CM, Nugent F, Ribes MP, Pearson I, Qosaj F, Sawicka K, Wild SA, Williams E, Laks E, Smith A, Lai D, Roth A, Balasubramanian S, Lee M, Bodenmiller B, Burger M, Kuett L, Tietscher S, Windhager J, Boyden ES, Alon S, Cui Y, Emenari A, Goodwin DR, Karagiannis ED, Sinha A, Wassie AT, Caldas C, Bruna A, Callari M, Greenwood W, Lerda G, Eyal-Lubling Y, Rueda OM, Shea A, Harris O, Becker R, Grimaldo F, Harris S, Vogl SL, Joyce JA, Watson SS, et alFunnell T, O’Flanagan CH, Williams MJ, McPherson A, McKinney S, Kabeer F, Lee H, Salehi S, Vázquez-García I, Shi H, Leventhal E, Masud T, Eirew P, Yap D, Zhang AW, Lim JLP, Wang B, Brimhall J, Biele J, Ting J, Au V, Van Vliet M, Liu YF, Beatty S, Lai D, Pham J, Grewal D, Abrams D, Havasov E, Leung S, Bojilova V, Moore RA, Rusk N, Uhlitz F, Ceglia N, Weiner AC, Zaikova E, Douglas JM, Zamarin D, Weigelt B, Kim SH, Da Cruz Paula A, Reis-Filho JS, Martin SD, Li Y, Xu H, de Algara TR, Lee SR, Llanos VC, Huntsman DG, McAlpine JN, IMAXT ConsortiumHannonGregory J.10BattistoniGeorgia10BressanDario10CannellIan G.10CasboltHannah10JausetCristina10KovačevićTatjana10MulveyClaire M.10NugentFiona10RibesMarta Paez10PearsonIsabella10QosajFatime10SawickaKirsty10WildSophia A.10WilliamsElena10LaksEmma3SmithAustin3LaiDaniel3RothAndrew3411BalasubramanianShankar101213LeeMaximilian101213BodenmillerBernd14BurgerMarcel14KuettLaura14TietscherSandra14WindhagerJonas14BoydenEdward S.15AlonShahar15CuiYi15EmenariAmauche15GoodwinDaniel R.15KaragiannisEmmanouil D.15SinhaAnubhav15WassieAsmamaw T.15CaldasCarlos16BrunaAlejandra16CallariMaurizio10GreenwoodWendy10LerdaGiulia10Eyal-LublingYaniv16RuedaOscar M.16SheaAbigail16HarrisOwen17BeckerRobby17GrimaldoFlaminia17HarrisSuvi17VoglSara Lisa17JoyceJohanna A.18WatsonSpencer S.18TavareSimon101920DinhKhanh N.1920FisherEyal10KunesRussell1920WaltonNicholas A.21Al Sa’dMohammed21ChornayNick21DariushAli21González-SolaresEduardo A.21González-FernándezCarlos21YoldaşAybüke Küpcü21MillerNeil21ZhuangXiaowei222324FanJean222324LeeHsuan222324SepúlvedaLeonardo A.222324XiaChenglong222324ZhengPu222324, Shah SP, Aparicio S, Cannell IG, Casbolt H, Jauset C, Kovačević T, Mulvey CM, Nugent F, Ribes MP, Pearson I, Qosaj F, Sawicka K, Wild SA, Williams E, Laks E, Smith A, Lai D, Roth A, Balasubramanian S, Lee M, Bodenmiller B, Burger M, Kuett L, Tietscher S, Windhager J, Boyden ES, Alon S, Cui Y, Emenari A, Goodwin DR, Karagiannis ED, Sinha A, Wassie AT, Caldas C, Bruna A, Callari M, Greenwood W, Lerda G, Eyal-Lubling Y, Rueda OM, Shea A, Harris O, Becker R, Grimaldo F, Harris S, Vogl SL, Joyce JA, Watson SS, Tavare S, Dinh KN, Fisher E, Kunes R, Walton NA, Al Sa’d M, Chornay N, Dariush A, González-Solares EA, González-Fernández C, Yoldaş AK, Miller N, Zhuang X, Fan J, Lee H, Sepúlveda LA, Xia C, Zheng P, Shah SP, Aparicio S, IMAXT Consortium. Single-cell genomic variation induced by mutational processes in cancer. Nature 2022; 612:106-115. [PMID: 36289342 PMCID: PMC9712114 DOI: 10.1038/s41586-022-05249-0] [Show More Authors] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/17/2022] [Indexed: 12/15/2022]
Abstract
How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.
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Affiliation(s)
- Tyler Funnell
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ciara H. O’Flanagan
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Marc J. Williams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Andrew McPherson
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Steven McKinney
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Farhia Kabeer
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Hakwoo Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Sohrab Salehi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ignacio Vázquez-García
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Hongyu Shi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Emily Leventhal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Tehmina Masud
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Peter Eirew
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Damian Yap
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Allen W. Zhang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jamie L. P. Lim
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Beixi Wang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jazmine Brimhall
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Justina Biele
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jerome Ting
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Vinci Au
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Michael Van Vliet
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Yi Fei Liu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Sean Beatty
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Daniel Lai
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jenifer Pham
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Diljot Grewal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Douglas Abrams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Eliyahu Havasov
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samantha Leung
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Viktoria Bojilova
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Richard A. Moore
- grid.434706.20000 0004 0410 5424Michael Smith Genome Sciences Centre, Vancouver, British Columbia Canada
| | - Nicole Rusk
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Florian Uhlitz
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Nicholas Ceglia
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Adam C. Weiner
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Elena Zaikova
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - J. Maxwell Douglas
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Dmitriy Zamarin
- grid.51462.340000 0001 2171 9952GYN Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Britta Weigelt
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Sarah H. Kim
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Arnaud Da Cruz Paula
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Jorge S. Reis-Filho
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Spencer D. Martin
- grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Yangguang Li
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Hong Xu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Teresa Ruiz de Algara
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - So Ra Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Viviana Cerda Llanos
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - David G. Huntsman
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jessica N. McAlpine
- grid.17091.3e0000 0001 2288 9830Department of Gynecology and Obstetrics, University of British Columbia, Vancouver, British Columbia Canada
| | | | - Sohrab P. Shah
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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23
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Proteogenomic analysis of lung adenocarcinoma reveals tumor heterogeneity, survival determinants, and therapeutically relevant pathways. Cell Rep Med 2022; 3:100819. [PMID: 36384096 PMCID: PMC9729884 DOI: 10.1016/j.xcrm.2022.100819] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/09/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022]
Abstract
We present a deep proteogenomic profiling study of 87 lung adenocarcinoma (LUAD) tumors from the United States, integrating whole-genome sequencing, transcriptome sequencing, proteomics and phosphoproteomics by mass spectrometry, and reverse-phase protein arrays. We identify three subtypes from somatic genome signature analysis, including a transition-high subtype enriched with never smokers, a transversion-high subtype enriched with current smokers, and a structurally altered subtype enriched with former smokers, TP53 alterations, and genome-wide structural alterations. We show that within-tumor correlations of RNA and protein expression associate with tumor purity and immune cell profiles. We detect and independently validate expression signatures of RNA and protein that predict patient survival. Additionally, among co-measured genes, we found that protein expression is more often associated with patient survival than RNA. Finally, integrative analysis characterizes three expression subtypes with divergent mutations, proteomic regulatory networks, and therapeutic vulnerabilities. This proteogenomic characterization provides a foundation for molecularly informed medicine in LUAD.
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24
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Gasper W, Rossi F, Ligorio M, Ghersi D. Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data. PLoS Comput Biol 2022; 18:e1010576. [PMID: 36191033 PMCID: PMC9560611 DOI: 10.1371/journal.pcbi.1010576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 09/15/2022] [Indexed: 12/14/2022] Open
Abstract
Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.
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Affiliation(s)
- William Gasper
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
| | - Francesca Rossi
- Department of Surgery, University of Texas Southwestern, Dallas, Texas, United States of America
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern, Dallas, Texas, United States of America
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, United States of America
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25
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Eirew P, O'Flanagan C, Ting J, Salehi S, Brimhall J, Wang B, Biele J, Algara T, Lee SR, Hoang C, Yap D, McKinney S, Bates C, Kong E, Lai D, Beatty S, Andronescu M, Zaikova E, Funnell T, Ceglia N, Chia S, Gelmon K, Mar C, Shah S, Roth A, Bouchard-Côté A, Aparicio S. Accurate determination of CRISPR-mediated gene fitness in transplantable tumours. Nat Commun 2022; 13:4534. [PMID: 35927228 PMCID: PMC9352714 DOI: 10.1038/s41467-022-31830-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/01/2022] [Indexed: 11/09/2022] Open
Abstract
Assessing tumour gene fitness in physiologically-relevant model systems is challenging due to biological features of in vivo tumour regeneration, including extreme variations in single cell lineage progeny. Here we develop a reproducible, quantitative approach to pooled genetic perturbation in patient-derived xenografts (PDXs), by encoding single cell output from transplanted CRISPR-transduced cells in combination with a Bayesian hierarchical model. We apply this to 181 PDX transplants from 21 breast cancer patients. We show that uncertainty in fitness estimates depends critically on the number of transplant cell clones and the variability in clone sizes. We use a pathway-directed allelic series to characterize Notch signaling, and quantify TP53 / MDM2 drug-gene conditional fitness in outlier patients. We show that fitness outlier identification can be mirrored by pharmacological perturbation. Overall, we demonstrate that the gene fitness landscape in breast PDXs is dominated by inter-patient differences.
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Affiliation(s)
- Peter Eirew
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Jerome Ting
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Sohrab Salehi
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- AbCellera Biologics Inc., Vancouver, BC, Canada
| | - Beixi Wang
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Justina Biele
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- AbCellera Biologics Inc., Vancouver, BC, Canada
| | - Teresa Algara
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - So Ra Lee
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Corey Hoang
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- British Columbia Institute of Technology, Vancouver, BC, Canada
| | - Damian Yap
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Steven McKinney
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Cherie Bates
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Esther Kong
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Daniel Lai
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Sean Beatty
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | | | - Elena Zaikova
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Tyler Funnell
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen Chia
- Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Karen Gelmon
- Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada
| | - Colin Mar
- Department of Diagnostic Radiology, BC Cancer, Vancouver, BC, Canada
| | - Sohrab Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
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26
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Kumar R, Ningombam SS, Kumar R, Goel H, Gogia A, Khurana S, Deo SVS, Mathur S, Tanwar P. Comprehensive mutations analyses of FTO (fat mass and obesity-associated gene) and their effects on FTO’s substrate binding implicated in obesity. Front Nutr 2022; 9:852944. [PMID: 35923209 PMCID: PMC9339907 DOI: 10.3389/fnut.2022.852944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
An excessive amount of fat deposition in the body leads to obesity which is a complex disease and poses a generic threat to human health. It increases the risk of various other diseases like diabetes, cardiovascular disease, and multiple types of cancer. Genomic studies have shown that the expression of the fat mass obesity (FTO) gene was highly altered and identified as one of the key biomarkers for obesity. This study has been undertaken to investigate the mutational profile of the FTO gene and elucidates its effect on the protein structure and function. Harmful effects of various missense mutations were predicted using different independent tools and it was observed that all mutations were highly pathogenic. Molecular dynamics (MD) simulations were performed to study the structure and function of FTO protein upon different mutations and it was found that mutations decreased the structure stability and affected protein conformation. Furthermore, a protein residue network analysis suggested that the mutations affected the overall residues bonding and topology. Finally, molecular docking coupled with MD simulation suggested that mutations affected FTO substrate binding by changing the protein-ligand affinity. Hence, the results of this finding would help in an in-depth understanding of the molecular biology of the FTO gene and its variants and lead to the development of effective therapeutics against associated diseases and disorders.
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Affiliation(s)
- Rakesh Kumar
- Laboratory Oncology Unit, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Somorjit Singh Ningombam
- Laboratory Oncology Unit, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Rahul Kumar
- Laboratory Oncology Unit, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Harsh Goel
- Laboratory Oncology Unit, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Ajay Gogia
- Department of Medical Oncology, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Sachin Khurana
- Department of Medical Oncology, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - S. V. S. Deo
- Department of Surgical Oncology, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
| | - Sandeep Mathur
- Department of Pathology, All India Institute of Medical Sciences, New Delhi, India
| | - Pranay Tanwar
- Laboratory Oncology Unit, Dr.B.R.A. Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
- *Correspondence: Pranay Tanwar,
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27
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Boehm KM, Aherne EA, Ellenson L, Nikolovski I, Alghamdi M, Vázquez-García I, Zamarin D, Long Roche K, Liu Y, Patel D, Aukerman A, Pasha A, Rose D, Selenica P, Causa Andrieu PI, Fong C, Capanu M, Reis-Filho JS, Vanguri R, Veeraraghavan H, Gangai N, Sosa R, Leung S, McPherson A, Gao J, Lakhman Y, Shah SP. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. NATURE CANCER 2022; 3:723-733. [PMID: 35764743 PMCID: PMC9239907 DOI: 10.1038/s43018-022-00388-9] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 04/27/2022] [Indexed: 04/25/2023]
Abstract
Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY, USA
| | - Emily A Aherne
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lora Ellenson
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ines Nikolovski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohammed Alghamdi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignacio Vázquez-García
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Dmitriy Zamarin
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kara Long Roche
- Department of Surgical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ying Liu
- Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Druv Patel
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew Aukerman
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arfath Pasha
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Doori Rose
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pier Selenica
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Chris Fong
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marinela Capanu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jorge S Reis-Filho
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natalie Gangai
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramon Sosa
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Samantha Leung
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - JianJiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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28
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Abstract
Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.
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29
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Mas-Ponte D, McCullough M, Supek F. Spectrum of DNA mismatch repair failures viewed through the lens of cancer genomics and implications for therapy. Clin Sci (Lond) 2022; 136:383-404. [PMID: 35274136 PMCID: PMC8919091 DOI: 10.1042/cs20210682] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022]
Abstract
Genome sequencing can be used to detect DNA repair failures in tumors and learn about underlying mechanisms. Here, we synthesize findings from genomic studies that examined deficiencies of the DNA mismatch repair (MMR) pathway. The impairment of MMR results in genome-wide hypermutation and in the 'microsatellite instability' (MSI) phenotype-occurrence of indel mutations at short tandem repeat (microsatellite) loci. The MSI status of tumors was traditionally assessed by molecular testing of a selected set of MS loci or by measuring MMR protein expression levels. Today, genomic data can provide a more complete picture of the consequences on genomic instability. Multiple computational studies examined somatic mutation distributions that result from failed DNA repair pathways in tumors. These include analyzing the commonly studied trinucleotide mutational spectra of single-nucleotide variants (SNVs), as well as of other features such as indels, structural variants, mutation clusters and regional mutation rate redistribution. The identified mutation patterns can be used to rigorously measure prevalence of MMR failures across cancer types, and potentially to subcategorize the MMR deficiencies. Diverse data sources, genomic and pre-genomic, from human and from experimental models, suggest there are different ways in which MMR can fail, and/or that the cell-type or genetic background may result in different types of MMR mutational patterns. The spectrum of MMR failures may direct cancer evolution, generating particular sets of driver mutations. Moreover, MMR affects outcomes of therapy by DNA damaging drugs, antimetabolites, nonsense-mediated mRNA decay (NMD) inhibitors, and immunotherapy by promoting either resistance or sensitivity, depending on the type of therapy.
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Affiliation(s)
- David Mas-Ponte
- Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Baldiri Reixac 10, Barcelona 08028, Spain
| | - Marcel McCullough
- Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Baldiri Reixac 10, Barcelona 08028, Spain
| | - Fran Supek
- Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute for Science and Technology, Baldiri Reixac 10, Barcelona 08028, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Pg Lluís Companys, 23, Barcelona 08010, Spain
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30
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Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2022; 22:114-126. [PMID: 34663944 PMCID: PMC8810682 DOI: 10.1038/s41568-021-00408-3] [Citation(s) in RCA: 229] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/08/2021] [Indexed: 02/07/2023]
Abstract
Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.
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Affiliation(s)
- Kevin M Boehm
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pegah Khosravi
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rami Vanguri
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jianjiong Gao
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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31
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Wu Y, Chua EHZ, Ng AWT, Boot A, Rozen SG. Accuracy of mutational signature software on correlated signatures. Sci Rep 2022; 12:390. [PMID: 35013428 PMCID: PMC8748538 DOI: 10.1038/s41598-021-04207-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/17/2021] [Indexed: 11/09/2022] Open
Abstract
Mutational signatures are characteristic patterns of mutations generated by exogenous mutagens or by endogenous mutational processes. Mutational signatures are important for research into DNA damage and repair, aging, cancer biology, genetic toxicology, and epidemiology. Unsupervised learning can infer mutational signatures from the somatic mutations in large numbers of tumors, and separating correlated signatures is a notable challenge for this task. To investigate which methods can best meet this challenge, we assessed 18 computational methods for inferring mutational signatures on 20 synthetic data sets that incorporated varying degrees of correlated activity of two common mutational signatures. Performance varied widely, and four methods noticeably outperformed the others: hdp (based on hierarchical Dirichlet processes), SigProExtractor (based on multiple non-negative matrix factorizations over resampled data), TCSM (based on an approach used in document topic analysis), and mutSpec.NMF (also based on non-negative matrix factorization). The results underscored the complexities of mutational signature extraction, including the importance and difficulty of determining the correct number of signatures and the importance of hyperparameters. Our findings indicate directions for improvement of the software and show a need for care when interpreting results from any of these methods, including the need for assessing sensitivity of the results to input parameters.
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Affiliation(s)
- Yang Wu
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Ellora Hui Zhen Chua
- Department of Biological Sciences, National University of Singapore, Singapore, 117558, Singapore
| | - Alvin Wei Tian Ng
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Arnoud Boot
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Steven G Rozen
- Programme in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, 169857, Singapore.
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore.
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32
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Mei Y, Liang D, Wang T, Yu D. Gaining insights into relevance across cancers based on mutation features of TP53 gene. Biochem Biophys Rep 2021; 28:101165. [PMID: 34786492 PMCID: PMC8579115 DOI: 10.1016/j.bbrep.2021.101165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 11/26/2022] Open
Abstract
The tumor suppressor gene TP53, one of the most frequently mutated genes, is recognized as the guardian of genome and can provide a significant barrier to neoplastic transformation and tumor progression. Traditional theory believes that TP53 mutations are equal among cancer types. However, to date, no study has explored the TP53 mutation profile from a holistic and systematic standpoint to discovery its relevance and feature with cancers. Mutation signature, an unbiased approach to identify the mutational processes, can be a potent indicator for exploring mutation-driven tumor occurrence and progression. In this research, several features such as hotspots, mutability and mutation signature of somatic TP53 mutations derived from 18 types of cancer tissues from cBioPortal were analyzed and manifested the organizational preference among cancers. Mutation signatures found in almost all cancer types were Signature 6 related to mismatch repair deficiency, and Signature 1 that reflects the natural decomposition of 5-methylcytosine into thymine associated with aging. Meanwhile, several signatures of TP53 mutations displayed tissue-selective. Mutations enriched in bladder, skin, lung cancer were associated with signatures of APOBEC activity (Signature 2 and 13), alkylating agents (Signature 11), and tobacco smoke (Signature 4), respectively. Moreover, Signature 4 and 29 associated with tobacco smoking or chewing found in lung, sarcoma, esophageal, and head and neck cancer may be related to their smoking history. In addition, several digestive cancers, including colorectal, stomach, pancreatic and esophageal cancers, showed the high correlation in context and mutation signature profiles. Our study suggests that the tissue-selective activity of mutational processes would reflect the tissue-specific enrichment of TP53 mutations and provides a new perspective to understand the relevance of diverse diseases based on the spectrum of TP53 mutations. TP53 mutations display the tissue-selective among cancers. Mutation signature of TP53 reflects the tissue-selective activity of mutational processes among cancers. Digestive cancers show the high correlation based on TP53 mutation signature.
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Affiliation(s)
- Yue Mei
- Center for Translational Medicine, Second Military Medical University, Shanghai, 200433, PR China
| | - Dong Liang
- Center for Translational Medicine, Second Military Medical University, Shanghai, 200433, PR China
| | - Tengjiao Wang
- Center for Translational Medicine, Second Military Medical University, Shanghai, 200433, PR China
| | - Dong Yu
- Center for Translational Medicine, Second Military Medical University, Shanghai, 200433, PR China
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33
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Chiang YC, Lin PH, Cheng WF. Homologous Recombination Deficiency Assays in Epithelial Ovarian Cancer: Current Status and Future Direction. Front Oncol 2021; 11:675972. [PMID: 34722237 PMCID: PMC8551835 DOI: 10.3389/fonc.2021.675972] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 09/17/2021] [Indexed: 01/02/2023] Open
Abstract
Epithelial ovarian cancer (EOC) patients are generally diagnosed at an advanced stage, usually relapse after initial treatments, which include debulking surgery and adjuvant platinum-based chemotherapy, and eventually have poor 5-year survival of less than 50%. In recent years, promising survival benefits from maintenance therapy with poly(ADP-ribose) polymerase (PARP) inhibitor (PARPi) has changed the management of EOC in newly diagnosed and recurrent disease. Identification of BRCA mutations and/or homologous recombination deficiency (HRD) is critical for selecting patients for PARPi treatment. However, the currently available HRD assays are not perfect predictors of the clinical response to PARPis in EOC patients. In this review, we introduce the concept of synthetic lethality, the rationale of using PARPi when HRD is present in tumor cells, the clinical trials of PARPi incorporating the HRD assays for EOC, the current HRD assays, and other HRD assays in development.
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Affiliation(s)
- Ying-Cheng Chiang
- Department of Obstetrics and Gynecology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Po-Han Lin
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan.,Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Wen-Fang Cheng
- Department of Obstetrics and Gynecology, College of Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Oncology, College of Medicine, National Taiwan University, Taipei, Taiwan
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34
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Sason I, Chen Y, Leiserson MDM, Sharan R. A mixture model for signature discovery from sparse mutation data. Genome Med 2021; 13:173. [PMID: 34724984 PMCID: PMC8559697 DOI: 10.1186/s13073-021-00988-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 10/12/2021] [Indexed: 12/15/2022] Open
Abstract
Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .
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Affiliation(s)
- Itay Sason
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Yuexi Chen
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 20742, MD, USA
| | - Mark D M Leiserson
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, 20742, MD, USA
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, 69978, Israel.
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35
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Kim YA, Leiserson MDM, Moorjani P, Sharan R, Wojtowicz D, Przytycka TM. Mutational Signatures: From Methods to Mechanisms. Annu Rev Biomed Data Sci 2021; 4:189-206. [PMID: 34465178 DOI: 10.1146/annurev-biodatasci-122320-120920] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.
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Affiliation(s)
- Yoo-Ah Kim
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
| | - Mark D M Leiserson
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Priya Moorjani
- Department of Molecular and Cell Biology and Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Damian Wojtowicz
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
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36
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Vöhringer H, Hoeck AV, Cuppen E, Gerstung M. Learning mutational signatures and their multidimensional genomic properties with TensorSignatures. Nat Commun 2021; 12:3628. [PMID: 34131135 PMCID: PMC8206343 DOI: 10.1038/s41467-021-23551-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 04/15/2021] [Indexed: 01/09/2023] Open
Abstract
We present TensorSignatures, an algorithm to learn mutational signatures jointly across different variant categories and their genomic localisation and properties. The analysis of 2778 primary and 3824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that all signatures operate dynamically in response to genomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis in 7 different cancer types. The algorithm also extracts distinct signatures of replication- and double strand break repair-driven mutagenesis by APOBEC3A and 3B with differential numbers and length of mutation clusters. Finally, TensorSignatures reproduces a signature of somatic hypermutation generating highly clustered variants at transcription start sites of active genes in lymphoid leukaemia, distinct from a general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types. In summary, TensorSignatures elucidates complex mutational footprints by characterising their underlying processes with respect to a multitude of genomic variables.
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Affiliation(s)
- Harald Vöhringer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Arne Van Hoeck
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Universiteitsweg 100, Utrecht, The Netherlands
- Hartwig Medical Foundation, Amsterdam, The Netherlands
| | - Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
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37
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Miller RE, Leary A, Scott CL, Serra V, Lord CJ, Bowtell D, Chang DK, Garsed DW, Jonkers J, Ledermann JA, Nik-Zainal S, Ray-Coquard I, Shah SP, Matias-Guiu X, Swisher EM, Yates LR. ESMO recommendations on predictive biomarker testing for homologous recombination deficiency and PARP inhibitor benefit in ovarian cancer. Ann Oncol 2020; 31:1606-1622. [PMID: 33004253 DOI: 10.1016/j.annonc.2020.08.2102] [Citation(s) in RCA: 287] [Impact Index Per Article: 57.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Homologous recombination repair deficiency (HRD) is a frequent feature of high-grade serous ovarian, fallopian tube and peritoneal carcinoma (HGSC) and is associated with sensitivity to PARP inhibitor (PARPi) therapy. HRD testing provides an opportunity to optimise PARPi use in HGSC but methodologies are diverse and clinical application remains controversial. MATERIALS AND METHODS To define best practice for HRD testing in HGSC the ESMO Translational Research and Precision Medicine Working Group launched a collaborative project that incorporated a systematic review approach. The main aims were to (i) define the term 'HRD test'; (ii) provide an overview of the biological rationale and the level of evidence supporting currently available HRD tests; (iii) provide recommendations on the clinical utility of HRD tests in clinical management of HGSC. RESULTS A broad range of repair genes, genomic scars, mutational signatures and functional assays are associated with a history of HRD. Currently, the clinical validity of HRD tests in ovarian cancer is best assessed, not in terms of biological HRD status per se, but in terms of PARPi benefit. Clinical trials evidence supports the use of BRCA mutation testing and two commercially available assays that also incorporate genomic instability for identifying subgroups of HGSCs that derive different magnitudes of benefit from PARPi therapy, albeit with some variation by clinical scenario. These tests can be used to inform treatment selection and scheduling but their use is limited by a failure to consistently identify a subgroup of patients who derive no benefit from PARPis in most studies. Existing tests lack negative predictive value and inadequately address the complex and dynamic nature of the HRD phenotype. CONCLUSIONS Currently available HRD tests are useful for predicting likely magnitude of benefit from PARPis but better biomarkers are urgently needed to better identify current homologous recombination proficiency status and stratify HGSC management.
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Affiliation(s)
- R E Miller
- Department of Medical Oncology, University College London, London, UK; Department of Medical Oncology, St Bartholomew's Hospital, London, UK
| | - A Leary
- Department of Medicine and INSERM U981, Gustave Roussy Cancer Center, Université Paris-Saclay, Paris, France
| | - C L Scott
- Peter MacCallum Cancer Centre, Melbourne, Australia; The University of Melbourne, Melbourne, Australia
| | - V Serra
- Experimental Therapeutics Group Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - C J Lord
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK; CRUK Gene Function Laboratory, The Institute of Cancer Research, London, UK
| | - D Bowtell
- Peter MacCallum Cancer Centre, Melbourne, Australia; The University of Melbourne, Melbourne, Australia
| | - D K Chang
- Glasgow Precision Oncology Laboratory, Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow, UK; West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, UK
| | - D W Garsed
- Peter MacCallum Cancer Centre, Melbourne, Australia; The University of Melbourne, Melbourne, Australia
| | - J Jonkers
- Division of Molecular Pathology, Oncode Institute, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J A Ledermann
- UCL Cancer Institute, University College London, London, UK
| | - S Nik-Zainal
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK; MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - I Ray-Coquard
- Centre Leon Berard, Lyon, France; University Claude Bernard Groupe University of Lyon, France
| | - S P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - X Matias-Guiu
- Departments of Pathology, Hospital U Arnau de Vilanova and Hospital U de Bellvitge, Universities of Lleida and Barcelona, Irblleida, Idibell, Ciberonc, Barcelona, Spain
| | - E M Swisher
- Department of Obstetrics and Gynecology, University of Washington, Seattle, USA
| | - L R Yates
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge; Guy's Cancer Centre, Guys and St Thomas' NHS Foundation Trust, London, UK.
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38
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Li BZ, Putnam CD, Kolodner RD. Mechanisms underlying genome instability mediated by formation of foldback inversions in Saccharomyces cerevisiae. eLife 2020; 9:58223. [PMID: 32762846 PMCID: PMC7467729 DOI: 10.7554/elife.58223] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/04/2020] [Indexed: 01/09/2023] Open
Abstract
Foldback inversions, also called inverted duplications, have been observed in human genetic diseases and cancers. Here, we used a Saccharomyces cerevisiae genetic system that generates gross chromosomal rearrangements (GCRs) mediated by foldback inversions combined with whole-genome sequencing to study their formation. Foldback inversions were mediated by formation of single-stranded DNA hairpins. Two types of hairpins were identified: small-loop hairpins that were suppressed by MRE11, SAE2, SLX1, and YKU80 and large-loop hairpins that were suppressed by YEN1, TEL1, SWR1, and MRC1. Analysis of CRISPR/Cas9-induced double strand breaks (DSBs) revealed that long-stem hairpin-forming sequences could form foldback inversions when proximal or distal to the DSB, whereas short-stem hairpin-forming sequences formed foldback inversions when proximal to the DSB. Finally, we found that foldback inversion GCRs were stabilized by secondary rearrangements, mostly mediated by different homologous recombination mechanisms including single-strand annealing; however, POL32-dependent break-induced replication did not appear to be involved forming secondary rearrangements.
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Affiliation(s)
- Bin-Zhong Li
- Ludwig Institute for Cancer Research, University of California School of Medicine, San Diego, San Diego, United States
| | - Christopher D Putnam
- Ludwig Institute for Cancer Research, University of California School of Medicine, San Diego, San Diego, United States.,Departments of Medicine, University of California School of Medicine, San Diego, San Diego, United States
| | - Richard David Kolodner
- Ludwig Institute for Cancer Research, University of California School of Medicine, San Diego, San Diego, United States.,Cellular and Molecular Medicine, University of California School of Medicine, San Diego, San Diego, United States.,Moores-UCSD Cancer Center, University of California School of Medicine, San Diego, San Diego, United States.,Institute of Genomic Medicine, University of California School of Medicine, San Diego, San Diego, United States
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39
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Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, Boot A, Covington KR, Gordenin DA, Bergstrom EN, Islam SMA, Lopez-Bigas N, Klimczak LJ, McPherson JR, Morganella S, Sabarinathan R, Wheeler DA, Mustonen V, Getz G, Rozen SG, Stratton MR. The repertoire of mutational signatures in human cancer. Nature 2020; 578:94-101. [PMID: 32025018 PMCID: PMC7054213 DOI: 10.1038/s41586-020-1943-3] [Citation(s) in RCA: 2166] [Impact Index Per Article: 433.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 11/18/2019] [Indexed: 01/27/2023]
Abstract
Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.
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Affiliation(s)
- Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nicholas J Haradhvala
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Mi Ni Huang
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Alvin Wei Tian Ng
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Yang Wu
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Arnoud Boot
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Kyle R Covington
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Dmitry A Gordenin
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
| | - Erik N Bergstrom
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - S M Ashiqul Islam
- Department of Cellular and Molecular Medicine, Department of Bioengineering, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Leszek J Klimczak
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences (NIEHS), Durham, NC, USA
| | - John R McPherson
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore
| | | | - Radhakrishnan Sabarinathan
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ville Mustonen
- Department of Computer Science, University of Helsinki, Helsinki, Finland
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Steven G Rozen
- Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore.
- Centre for Computational Biology, Duke-NUS Medical School, Singapore, Singapore.
- SingHealth, Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore, Singapore.
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40
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Nussinov R, Tsai CJ, Jang H. Why Are Some Driver Mutations Rare? Trends Pharmacol Sci 2019; 40:919-929. [PMID: 31699406 DOI: 10.1016/j.tips.2019.10.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/09/2019] [Accepted: 10/10/2019] [Indexed: 12/13/2022]
Abstract
Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumor-specific therapeutic vulnerabilities.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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41
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Himoto Y, Veeraraghavan H, Zheng J, Zamarin D, Snyder A, Capanu M, Nougaret S, Vargas HA, Shitano F, Callahan M, Wang W, Sala E, Lakhman Y. Computed Tomography-Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer. JCO Precis Oncol 2019; 3:1900038. [PMID: 32914033 DOI: 10.1200/po.19.00038] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2019] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To determine if radiomic measures of tumor heterogeneity derived from baseline contrast-enhanced computed tomography (CE-CT) are associated with durable clinical benefit and time to off-treatment in patients with recurrent ovarian cancer (OC) enrolled in prospective immunotherapeutic trials. MATERIALS AND METHODS This retrospective study included 75 patients with recurrent OC who were enrolled in prospective immunotherapeutic trials (n = 74) or treated off-label (n = 1) and had baseline CE-CT scans. Disease burden (total tumor volume, number of disease sites), radiomic measures of intertumor heterogeneity (cluster-site entropy, cluster-site dissimilarity), and intratumor heterogeneity of the largest lesion (Haralick texture features) were computed. Associations of clinical, conventional imaging, and radiomic measures with durable clinical benefit and time to off-treatment were examined. RESULTS In univariable analysis, fewer disease sites, lower intertumor heterogeneity (lower cluster-site entropy, lower cluster-site dissimilarity), and lower intratumor heterogeneity of the largest lesion (higher energy) were significantly associated with durable clinical benefit (P ≤ .031). More disease sites, presence of pleural disease and/or distant metastases, higher intertumor heterogeneity (higher cluster-site entropy, higher cluster-site dissimilarity), and higher intratumor heterogeneity of the largest lesion (higher Contrastlargest-lesion) were significantly associated with shorter time to off-treatment (P ≤ .034). In multivariable analysis, higher Energylargest-lesion (indicator of lower intratumor heterogeneity; P = .006; odds ratio, 1.41) and fewer disease sites (P = .003; odds ratio, 1.64) remained significant indicators of durable clinical benefit (multivariable model C-index, 0.821). Higher cluster-site dissimilarity (indicator of higher intertumor heterogeneity) was a modest but single independent indicator of shorter time to off-treatment (P = .004; hazard ratio, 1.19; C-index, 0.6). CONCLUSION Fewer disease sites and lower intra- and intertumor heterogeneity modeled from the baseline CE-CT may indicate better response of OC to immunotherapy.
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Affiliation(s)
- Yuki Himoto
- Memorial Sloan Kettering Cancer Center, New York, NY.,Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | | | - Junting Zheng
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Dmitriy Zamarin
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill Cornell Medical College, New York, NY
| | - Alexandra Snyder
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill Cornell Medical College, New York, NY.,Merck, Kenilworth, NJ
| | | | - Stephanie Nougaret
- Memorial Sloan Kettering Cancer Center, New York, NY.,Institut de Recherche en Cancérologie de Montpellier, Montpellier, France.,Institut National de la Santé et de la Recherche Médicale, U1194, Montpellier, France.,Université de Montpellier, Montpellier, France.,Institut Régional du Cancer de Montpellier, Montpellier, France
| | | | - Fuki Shitano
- Memorial Sloan Kettering Cancer Center, New York, NY.,Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Margaret Callahan
- Memorial Sloan Kettering Cancer Center, New York, NY.,Weill Cornell Medical College, New York, NY
| | - Wei Wang
- Memorial Sloan Kettering Cancer Center, New York, NY.,Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.,Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Evis Sala
- Memorial Sloan Kettering Cancer Center, New York, NY.,Cancer Research UK Cambridge Center, Cambridge, United Kingdom
| | - Yulia Lakhman
- Memorial Sloan Kettering Cancer Center, New York, NY
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