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Nesic M, Rasmussen MH, Henriksen TV, Demuth C, Frydendahl A, Nordentoft I, Dyrskjøt L, Andersen CL. Beyond basics: Key mutation selection features for successful tumor-informed ctDNA detection. Int J Cancer 2024; 155:925-933. [PMID: 38623608 DOI: 10.1002/ijc.34964] [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: 01/11/2024] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
Tumor-informed mutation-based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features-such as cancer cell fraction (CCF), multiplicity, and error rate-would improve the success rate of tumor-informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole-exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor-informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1-46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High-CCF mutations were detected more frequently than low-CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72-173, p = 1.73e-12; Cohort I: OR 2.24, 95% CI 1.44-3.52, p = 1.66e-04; and Cohort N: OR 1.78, 95% CI 1.14-2.79, p = 7.86e-03). The detection-likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14-2.11, p = 3.85e-03; Cohort I: OR 1.78, 95% CI 1.23-2.56, p = 1.34e-03; and Cohort N: OR 1.94, 95% CI 1.63-2.31, p = 2.83e-14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection-likelihood, particularly evident when plasma cell-free DNA tumor fractions were below 0.1% (p = 2.1e-07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor-informed ctDNA analysis.
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Affiliation(s)
- Marijana Nesic
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Mads H Rasmussen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Tenna V Henriksen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Christina Demuth
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Amanda Frydendahl
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Iver Nordentoft
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Lars Dyrskjøt
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Claus L Andersen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
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2
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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3
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Lai J, Liu Y, Scharpf RB, Karchin R. Evaluation of simulation methods for tumor subclonal reconstruction. ARXIV 2024:arXiv:2402.09599v1. [PMID: 38410652 PMCID: PMC10896360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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4
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Rheinnecker M, Fröhlich M, Rübsam M, Paramasivam N, Heilig CE, Fröhling S, Schlenk RF, Hutter B, Hübschmann D. ZygosityPredictor. BIOINFORMATICS ADVANCES 2024; 4:vbae017. [PMID: 38560552 PMCID: PMC10980564 DOI: 10.1093/bioadv/vbae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/05/2024] [Accepted: 02/02/2024] [Indexed: 04/04/2024]
Abstract
Summary ZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. Availability and implementation ZygosityPredictor was implemented as an R-package and is available via Bioconductor at https://bioconductor.org/packages/ZygosityPredictor. Detailed documentation is provided in the vignette including application to an example genome.
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Affiliation(s)
- Marco Rheinnecker
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Martina Fröhlich
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Marc Rübsam
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Nagarajan Paramasivam
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Christoph E Heilig
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Division of Translational Medical Oncology, NCT Heidelberg and DKFZ, 69120 Heidelberg, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Division of Translational Medical Oncology, NCT Heidelberg and DKFZ, 69120 Heidelberg, Germany
| | - Richard F Schlenk
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Department of Medical Oncology, NCT Heidelberg, Heidelberg University Hospital, 69120 Heidelberg, Germany
- Department of Hematology, Oncology and Rheumatology, Heidelberg University Hospital, 69120 Heidelberg, Germany
- NCT Trial Center, NCT Heidelberg, Heidelberg University Hospital and DKFZ, 69120 Heidelberg, Germany
| | - Barbara Hutter
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Daniel Hübschmann
- Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM), Pattern Recognition and Digital Medicine Group, 69120 Heidelberg, Germany
- Innovation and Service Unit for Bioinformatics and Precision Medicine, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
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5
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Grigoriadis K, Huebner A, Bunkum A, Colliver E, Frankell AM, Hill MS, Thol K, Birkbak NJ, Swanton C, Zaccaria S, McGranahan N. CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction. Nat Protoc 2024; 19:159-183. [PMID: 38017136 DOI: 10.1038/s41596-023-00913-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 08/24/2023] [Indexed: 11/30/2023]
Abstract
Intratumor heterogeneity provides the fuel for the evolution and selection of subclonal tumor cell populations. However, accurate inference of tumor subclonal architecture and reconstruction of tumor evolutionary histories from bulk DNA sequencing data remains challenging. Frequently, sequencing and alignment artifacts are not fully filtered out from cancer somatic mutations, and errors in the identification of copy number alterations or complex evolutionary events (e.g., mutation losses) affect the estimated cellular prevalence of mutations. Together, such errors propagate into the analysis of mutation clustering and phylogenetic reconstruction. In this Protocol, we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multisample tumor sequencing, accounting for both copy number alterations and mutation errors. CONIPHER has been used to reconstruct subclonal architecture and tumor phylogeny from multisample tumors with high-depth whole-exome sequencing from the TRACERx421 dataset, as well as matched primary-metastatic cases. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumor samples and clones, while completing in under 1.5 h on average. CONIPHER enables automated phylogenetic analysis that can be effectively applied to large sequencing datasets generated with different technologies. CONIPHER can be run with a basic knowledge of bioinformatics and R and bash scripting languages.
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Affiliation(s)
- Kristiana Grigoriadis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Abigail Bunkum
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Lab, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Kerstin Thol
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Nicolai J Birkbak
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
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6
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Warner EW, Van der Eecken K, Murtha AJ, Kwan EM, Herberts C, Sipola J, Ng SWS, Chen XE, Fonseca NM, Ritch E, Schönlau E, Bernales CQ, Donnellan G, Munzur AD, Parekh K, Beja K, Wong A, Verbeke S, Lumen N, Van Dorpe J, De Laere B, Annala M, Vandekerkhove G, Ost P, Wyatt AW. Multiregion sampling of de novo metastatic prostate cancer reveals complex polyclonality and augments clinical genotyping. NATURE CANCER 2024; 5:114-130. [PMID: 38177459 DOI: 10.1038/s43018-023-00692-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/15/2023] [Indexed: 01/06/2024]
Abstract
De novo metastatic prostate cancer is highly aggressive, but the paucity of routinely collected tissue has hindered genomic stratification and precision oncology. Here, we leveraged a rare study of surgical intervention in 43 de novo metastatic prostate cancers to assess somatic genotypes across 607 synchronous primary and metastatic tissue regions plus circulating tumor DNA. Intra-prostate heterogeneity was pervasive and impacted clinically relevant genes, resulting in discordant genotypes between select primary restricted regions and synchronous metastases. Additional complexity was driven by polyclonal metastatic seeding from phylogenetically related primary populations. When simulating clinical practice relying on a single tissue region, genomic heterogeneity plus variable tumor fraction across samples caused inaccurate genotyping of dominant disease; however, pooling extracted DNA from multiple biopsy cores before sequencing can rescue misassigned somatic genotypes. Our results define the relationship between synchronous treatment-sensitive primary and metastatic lesions in men with de novo metastatic prostate cancer and provide a framework for implementing genomics-guided patient management.
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Affiliation(s)
- Evan W Warner
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kim Van der Eecken
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Andrew J Murtha
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Edmond M Kwan
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Cameron Herberts
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Joonatan Sipola
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, Finland
| | - Sarah W S Ng
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Xinyi E Chen
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nicolette M Fonseca
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Elie Ritch
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Elena Schönlau
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cecily Q Bernales
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gráinne Donnellan
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Aslı D Munzur
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Karan Parekh
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kevin Beja
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Amanda Wong
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sofie Verbeke
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - Nicolaas Lumen
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Jo Van Dorpe
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - Bram De Laere
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Matti Annala
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Prostate Cancer Research Center, Faculty of Medicine and Health Technology, Tampere University and Tays Cancer Center, Tampere, Finland
| | - Gillian Vandekerkhove
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medical Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - Piet Ost
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Alexander W Wyatt
- Department of Urologic Sciences, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada.
- Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada.
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7
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Kinnaman MD, Zaccaria S, Makohon-Moore A, Arnold B, Levine MF, Gundem G, Arango Ossa JE, Glodzik D, Rodríguez-Sánchez MI, Bouvier N, Li S, Stockfisch E, Dunigan M, Cobbs C, Bhanot UK, You D, Mullen K, Melchor JP, Ortiz MV, O'Donohue TJ, Slotkin EK, Wexler LH, Dela Cruz FS, Hameed MR, Glade Bender JL, Tap WD, Meyers PA, Papaemmanuil E, Kung AL, Iacobuzio-Donahue CA. Subclonal Somatic Copy-Number Alterations Emerge and Dominate in Recurrent Osteosarcoma. Cancer Res 2023; 83:3796-3812. [PMID: 37812025 PMCID: PMC10646480 DOI: 10.1158/0008-5472.can-23-0385] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/14/2023] [Accepted: 09/01/2023] [Indexed: 10/10/2023]
Abstract
Multiple large-scale genomic profiling efforts have been undertaken in osteosarcoma to define the genomic drivers of tumorigenesis, therapeutic response, and disease recurrence. The spatial and temporal intratumor heterogeneity could also play a role in promoting tumor growth and treatment resistance. We conducted longitudinal whole-genome sequencing of 37 tumor samples from 8 patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. Subclonal copy-number alterations were identified in all patients except one. In 5 patients, subclones from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clones in 6 of 7 patients with multiple clones. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy-number clones. A chromosomal duplication timing analysis revealed that complex genomic rearrangements typically occurred prior to diagnosis, supporting a macroevolutionary model of evolution, where a large number of genomic aberrations are acquired over a short period of time followed by clonal selection, as opposed to ongoing evolution. A mutational signature analysis of recurrent tumors revealed that homologous repair deficiency (HRD)-related SBS3 increases at each time point in patients with recurrent disease, suggesting that HRD continues to be an active mutagenic process after diagnosis. Overall, by examining the clonal relationships between temporally and spatially separated samples from patients with relapsed/refractory osteosarcoma, this study sheds light on the intratumor heterogeneity and potential drivers of treatment resistance in this disease. SIGNIFICANCE The chemoresistant population in recurrent osteosarcoma is subclonal at diagnosis, emerges at the time of primary resection due to selective pressure from neoadjuvant chemotherapy, and is characterized by unique oncogenic amplifications.
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Affiliation(s)
- Michael D. Kinnaman
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Regeneron Pharmaceuticals, Inc., Tarrytown, New York
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, United Kingdom
| | - Alvin Makohon-Moore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Brian Arnold
- Department of Computer Science, Princeton University, Princeton, New Jersey
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey
| | - Max F. Levine
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gunes Gundem
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Juan E. Arango Ossa
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Dominik Glodzik
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Nancy Bouvier
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Shanita Li
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily Stockfisch
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marisa Dunigan
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Cassidy Cobbs
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Umesh K. Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
- Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katelyn Mullen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, New York
| | - Jerry P. Melchor
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael V. Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Tara J. O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily K. Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Leonard H. Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Filemon S. Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Meera R. Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Julia L. Glade Bender
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - William D. Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Paul A. Meyers
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elli Papaemmanuil
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrew L. Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Christine A. Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
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8
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Frankell AM, Dietzen M, Al Bakir M, Lim EL, Karasaki T, Ward S, Veeriah S, Colliver E, Huebner A, Bunkum A, Hill MS, Grigoriadis K, Moore DA, Black JRM, Liu WK, Thol K, Pich O, Watkins TBK, Naceur-Lombardelli C, Cook DE, Salgado R, Wilson GA, Bailey C, Angelova M, Bentham R, Martínez-Ruiz C, Abbosh C, Nicholson AG, Le Quesne J, Biswas D, Rosenthal R, Puttick C, Hessey S, Lee C, Prymas P, Toncheva A, Smith J, Xing W, Nicod J, Price G, Kerr KM, Naidu B, Middleton G, Blyth KG, Fennell DA, Forster MD, Lee SM, Falzon M, Hewish M, Shackcloth MJ, Lim E, Benafif S, Russell P, Boleti E, Krebs MG, Lester JF, Papadatos-Pastos D, Ahmad T, Thakrar RM, Lawrence D, Navani N, Janes SM, Dive C, Blackhall FH, Summers Y, Cave J, Marafioti T, Herrero J, Quezada SA, Peggs KS, Schwarz RF, Van Loo P, Miedema DM, Birkbak NJ, Hiley CT, Hackshaw A, Zaccaria S, Jamal-Hanjani M, McGranahan N, Swanton C. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 2023; 616:525-533. [PMID: 37046096 PMCID: PMC10115649 DOI: 10.1038/s41586-023-05783-5] [Citation(s) in RCA: 71] [Impact Index Per Article: 71.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/02/2023] [Indexed: 04/14/2023]
Abstract
Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource.
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Affiliation(s)
- Alexander M Frankell
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Maise Al Bakir
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emilia L Lim
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Takahiro Karasaki
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Abigail Bunkum
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Wing Kin Liu
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - Kerstin Thol
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | | | - Daniel E Cook
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Chris Bailey
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Mihaela Angelova
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Robert Bentham
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - John Le Quesne
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Pathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Dhruva Biswas
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Rachel Rosenthal
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Clare Puttick
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Claudia Lee
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Division of Medicine, University College London, London, UK
| | - Paulina Prymas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Jon Smith
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Wei Xing
- Scientific Computing, The Francis Crick Institute, London, UK
| | - Jerome Nicod
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Gillian Price
- Department of Medical Oncology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
- University of Aberdeen, Aberdeen, UK
| | - Keith M Kerr
- University of Aberdeen, Aberdeen, UK
- Department of Pathology, Aberdeen Royal Infirmary NHS Grampian, Aberdeen, UK
| | - Babu Naidu
- Birmingham Acute Care Research Group, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
| | - Gary Middleton
- University Hospital Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK
| | - Kevin G Blyth
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Queen Elizabeth University Hospital, Glasgow, UK
| | - Dean A Fennell
- University of Leicester, Leicester, UK
- University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Martin D Forster
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Siow Ming Lee
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Oncology, University College London Hospitals, London, UK
| | - Mary Falzon
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Madeleine Hewish
- Royal Surrey Hospital, Royal Surrey Hospitals NHS Foundation Trust, Guilford, UK
- University of Surrey, Guilford, UK
| | | | - Eric Lim
- Academic Division of Thoracic Surgery, Imperial College London, London, UK
- Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Sarah Benafif
- Department of Oncology, University College London Hospitals, London, UK
| | - Peter Russell
- Princess Alexandra Hospital, The Princess Alexandra Hospital NHS Trust, Harlow, UK
| | - Ekaterini Boleti
- Royal Free Hospital, Royal Free London NHS Foundation Trust, London, UK
| | - Matthew G Krebs
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Jason F Lester
- Singleton Hospital, Swansea Bay University Health Board, Swansea, UK
| | | | - Tanya Ahmad
- Department of Oncology, University College London Hospitals, London, UK
| | - Ricky M Thakrar
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - David Lawrence
- Department of Thoracic Surgery, University College London Hospital NHS Trust, London, UK
| | - Neal Navani
- Department of Thoracic Medicine, University College London Hospitals, London, UK
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Caroline Dive
- Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Manchester, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University of Manchester, Manchester, UK
| | - Fiona H Blackhall
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Yvonne Summers
- Division of Cancer Sciences, The University of Manchester and The Christie NHS Foundation Trust, Manchester, UK
| | - Judith Cave
- Department of Oncology, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Javier Herrero
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Sergio A Quezada
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Immune Regulation and Tumour Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Karl S Peggs
- Department of Haematology, University College London Hospitals, London, UK
- Cancer Immunology Unit, Research Department of Haematology, University College London Cancer Institute, London, UK
| | - Roland F Schwarz
- Institute for Computational Cancer Biology, Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
| | - Peter Van Loo
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Daniël M Miedema
- LEXOR, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Oncode Institute, Amsterdam, The Netherlands
| | - Nicolai J Birkbak
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Crispin T Hiley
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Allan Hackshaw
- Cancer Research UK and UCL Cancer Trials Centre, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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9
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Sandmann S, Richter S, Jiang X, Varghese J. Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5128. [PMID: 36982036 PMCID: PMC10049679 DOI: 10.3390/ijerph20065128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/04/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, 48149 Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
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10
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Kinnaman MD, Zaccaria S, Makohon-Moore A, Arnold B, Levine M, Gundem G, Ossa JEA, Glodzik D, Rodríguez-Sánchez MI, Bouvier N, Li S, Stockfisch E, Dunigan M, Cobbs C, Bhanot U, You D, Mullen K, Melchor J, Ortiz MV, O'Donohue T, Slotkin E, Wexler LH, Dela Cruz FS, Hameed M, Glade Bender JL, Tap WD, Meyers PA, Papaemmanuil E, Kung AL, Iacobuzio-Donahue CA. Subclonal somatic copy number alterations emerge and dominate in recurrent osteosarcoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522765. [PMID: 36711976 PMCID: PMC9881990 DOI: 10.1101/2023.01.05.522765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Multiple large-scale tumor genomic profiling efforts have been undertaken in osteosarcoma, however, little is known about the spatial and temporal intratumor heterogeneity and how it may drive treatment resistance. We performed whole-genome sequencing of 37 tumor samples from eight patients with relapsed or refractory osteosarcoma. Each patient had at least one sample from a primary site and a metastatic or relapse site. We identified subclonal copy number alterations in all but one patient. We observed that in five patients, a subclonal copy number clone from the primary tumor emerged and dominated at subsequent relapses. MYC gain/amplification was enriched in the treatment-resistant clone in 6 out of 7 patients with more than one clone. Amplifications in other potential driver genes, such as CCNE1, RAD21, VEGFA, and IGF1R, were also observed in the resistant copy number clones. Our study sheds light on intratumor heterogeneity and the potential drivers of treatment resistance in osteosarcoma.
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Affiliation(s)
- Michael D Kinnaman
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Alvin Makohon-Moore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Hackensack Meridian Health Center for Discovery and Innovation, Nutley, NJ, USA (current affiliation)
- Georgetown University Lombardi Comprehensive Cancer Center, Washington, DC, USA (current affiliation)
| | - Brian Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Max Levine
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Isabl, New York, NY, USA (current affiliation)
| | - Gunes Gundem
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Juan E Arango Ossa
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dominik Glodzik
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA (current affiliation)
| | - M Irene Rodríguez-Sánchez
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Wunderman Thompson Health, New York, NY, USA (current affiliation)
| | - Nancy Bouvier
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- IT and Digital Initiatives, Memorial Sloan Kettering Cancer Center, New York, NY, USA (current affiliation)
| | - Shanita Li
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Stockfisch
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Marisa Dunigan
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cassidy Cobbs
- Integrated Genomics Operation Core, Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Umesh Bhanot
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Precision Pathology Biobanking Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daoqi You
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katelyn Mullen
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY, USA
| | - Jerry Melchor
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael V Ortiz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tara O'Donohue
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Slotkin
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Leonard H Wexler
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Filemon S Dela Cruz
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera Hameed
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Julia L Glade Bender
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William D Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paul A Meyers
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elli Papaemmanuil
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andrew L Kung
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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11
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Lin PC, Tsai YS, Yeh YM, Shen MR. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care. Biomolecules 2022; 12:biom12081133. [PMID: 36009026 PMCID: PMC9405970 DOI: 10.3390/biom12081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.
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Affiliation(s)
- Peng-Chan Lin
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yi-Shan Tsai
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yu-Min Yeh
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Meng-Ru Shen
- Institute of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Pharmacology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Correspondence: ; Tel.: +886-6-235-3535
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12
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Sandmann S, Richter S, Jiang X, Varghese J. Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution. Cancer Genomics Proteomics 2022; 19:194-204. [PMID: 35181588 DOI: 10.21873/cgp.20314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges. MATERIALS AND METHODS By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool. RESULTS Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms. CONCLUSION Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany;
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
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Yan H, Tarabichi M, McGranahan N, Van Loo P. DeCiFering the subclonal composition of tumors. Cell Syst 2021; 12:955-957. [PMID: 34672959 DOI: 10.1016/j.cels.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accurately identifying the subclones that make up tumors is critical for understanding cancer biology. In an article in this issue of Cell Systems, Satas et al. examine mutations with an evolutionary perspective to decipher the composition of tumors.
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Affiliation(s)
- Haixi Yan
- The Francis Crick Institute, London, UK
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK; Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium
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