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Graham JH, Schlachetzki JCM, Yang X, Breuss MW. Genomic Mosaicism of the Brain: Origin, Impact, and Utility. Neurosci Bull 2024; 40:759-776. [PMID: 37898991 PMCID: PMC11178748 DOI: 10.1007/s12264-023-01124-8] [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: 05/04/2023] [Accepted: 07/16/2023] [Indexed: 10/31/2023] Open
Abstract
Genomic mosaicism describes the phenomenon where some but not all cells within a tissue harbor unique genetic mutations. Traditionally, research focused on the impact of genomic mosaicism on clinical phenotype-motivated by its involvement in cancers and overgrowth syndromes. More recently, we increasingly shifted towards the plethora of neutral mosaic variants that can act as recorders of cellular lineage and environmental exposures. Here, we summarize the current state of the field of genomic mosaicism research with a special emphasis on our current understanding of this phenomenon in brain development and homeostasis. Although the field of genomic mosaicism has a rich history, technological advances in the last decade have changed our approaches and greatly improved our knowledge. We will provide current definitions and an overview of contemporary detection approaches for genomic mosaicism. Finally, we will discuss the impact and utility of genomic mosaicism.
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Affiliation(s)
- Jared H Graham
- Department of Pediatrics, Section of Clinical Genetics and Metabolism, University of Colorado School of Medicine, Aurora, 80045-2581, CO, USA
| | - Johannes C M Schlachetzki
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, 92093-0021, San Diego, CA, USA
| | - Xiaoxu Yang
- Department of Neurosciences, University of California San Diego, La Jolla, 92093-0021, San Diego, CA, USA
- Rady Children's Institute for Genomic Medicine, San Diego, 92123, CA, USA
| | - Martin W Breuss
- Department of Pediatrics, Section of Clinical Genetics and Metabolism, University of Colorado School of Medicine, Aurora, 80045-2581, CO, USA.
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2
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van de Weijer LL, Ercolano E, Zhang T, Shah M, Banton MC, Na J, Adams CL, Hilton D, Kurian KM, Hanemann CO. A novel patient-derived meningioma spheroid model as a tool to study and treat epithelial-to-mesenchymal transition (EMT) in meningiomas. Acta Neuropathol Commun 2023; 11:198. [PMID: 38102708 PMCID: PMC10725030 DOI: 10.1186/s40478-023-01677-9] [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: 07/12/2023] [Accepted: 10/23/2023] [Indexed: 12/17/2023] Open
Abstract
Meningiomas are the most common intracranial brain tumours. These tumours are heterogeneous and encompass a wide spectrum of clinical aggressivity. Treatment options are limited to surgery and radiotherapy and have a risk of post-operative morbidities and radiation neurotoxicity, reflecting the need for new therapies. Three-dimensional (3D) patient-derived cell culture models have been shown to closely recapitulate in vivo tumour biology, including microenvironmental interactions and have emerged as a robust tool for drug development. Here, we established a novel easy-to-use 3D patient-derived meningioma spheroid model using a scaffold-free approach. Patient-derived meningioma spheroids were characterised and compared to patient tissues and traditional monolayer cultures by histology, genomics, and transcriptomics studies. Patient-derived meningioma spheroids closely recapitulated morphological and molecular features of matched patient tissues, including patient histology, genomic alterations, and components of the immune microenvironment, such as a CD68 + and CD163 + positive macrophage cell population. Comprehensive transcriptomic profiling revealed an increase in epithelial-to-mesenchymal transition (EMT) in meningioma spheroids compared to traditional monolayer cultures, confirming this model as a tool to elucidate EMT in meningioma. Therefore, as proof of concept study, we developed a treatment strategy to target EMT in meningioma. We found that combination therapy using the MER tyrosine kinase (MERTK) inhibitor UNC2025 and the histone deacetylase (HDAC) inhibitor Trichostatin A (TSA) effectively decreased meningioma spheroid viability and proliferation. Furthermore, we demonstrated this combination therapy significantly increased the expression of the epithelial marker E-cadherin and had a repressive effect on WHO grade 2-derived spheroid invasion, which is suggestive of a partial reversal of EMT in meningioma spheroids.
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Affiliation(s)
- Laurien L van de Weijer
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - Emanuela Ercolano
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - Ting Zhang
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - Maryam Shah
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - Matthew C Banton
- Faculty of Health: School of Biomedical Sciences, University of Plymouth, Plymouth, PL4 8AA, Devon, UK
| | - Juri Na
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - Claire L Adams
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK
| | - David Hilton
- Department of Cellular and Anatomical Pathology, University Hospitals Plymouth NHS Trust, Derriford, Plymouth, PL6 8DH, Devon, UK
| | - Kathreena M Kurian
- University of Bristol Medical School & North Bristol Trust, Southmead Hospital, Bristol, BS1 0NB, UK
| | - C Oliver Hanemann
- Faculty of Health: Medicine, Dentistry and Human Sciences, Derriford Research Facility, University of Plymouth, Plymouth, PL6 8BU, Devon, UK.
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3
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Gomez F, Fisk B, McMichael JF, Mosior M, Foltz JA, Skidmore ZL, Duncavage EJ, Miller CA, Abel H, Li YS, Russler-Germain DA, Krysiak K, Watkins MP, Ramirez CA, Schmidt A, Martins Rodrigues F, Trani L, Khanna A, Wagner JA, Fulton RS, Fronick CC, O'Laughlin MD, Schappe T, Cashen AF, Mehta-Shah N, Kahl BS, Walker J, Bartlett NL, Griffith M, Fehniger TA, Griffith OL. Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma. CANCER RESEARCH COMMUNICATIONS 2023; 3:2312-2330. [PMID: 37910143 PMCID: PMC10648575 DOI: 10.1158/2767-9764.crc-23-0140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/27/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL. SIGNIFICANCE Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.
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Affiliation(s)
- Felicia Gomez
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
| | - Bryan Fisk
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Joshua F. McMichael
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Matthew Mosior
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Jennifer A. Foltz
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Zachary L. Skidmore
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Eric J. Duncavage
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - Christopher A. Miller
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Haley Abel
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Yi-Shan Li
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - David A. Russler-Germain
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Kilannin Krysiak
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - Marcus P. Watkins
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Cody A. Ramirez
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Alina Schmidt
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Fernanda Martins Rodrigues
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Lee Trani
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Ajay Khanna
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Julia A. Wagner
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Robert S. Fulton
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Catrina C. Fronick
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Michelle D. O'Laughlin
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Timothy Schappe
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Amanda F. Cashen
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Neha Mehta-Shah
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Brad S. Kahl
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Jason Walker
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Nancy L. Bartlett
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Malachi Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri
| | - Todd A. Fehniger
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
| | - Obi L. Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri
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4
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Ocaña-Tienda B, Pérez-Beteta J, Jiménez-Sánchez J, Molina-García D, Ortiz de Mendivil A, Asenjo B, Albillo D, Pérez-Romasanta LA, Valiente M, Zhu L, García-Gómez P, González-Del Portillo E, Llorente M, Carballo N, Arana E, Pérez-García VM. Growth exponents reflect evolutionary processes and treatment response in brain metastases. NPJ Syst Biol Appl 2023; 9:35. [PMID: 37479705 PMCID: PMC10361973 DOI: 10.1038/s41540-023-00298-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023] Open
Abstract
Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.
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Affiliation(s)
| | | | | | | | | | - Beatriz Asenjo
- Hospital Regional Universitario de Málaga, Málaga, Spain
| | | | | | - Manuel Valiente
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Lucía Zhu
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Pedro García-Gómez
- Brain Metastasis Group, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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5
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Olson ND, Wagner J, Dwarshuis N, Miga KH, Sedlazeck FJ, Salit M, Zook JM. Variant calling and benchmarking in an era of complete human genome sequences. Nat Rev Genet 2023:10.1038/s41576-023-00590-0. [PMID: 37059810 DOI: 10.1038/s41576-023-00590-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2023] [Indexed: 04/16/2023]
Abstract
Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.
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Affiliation(s)
- Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nathan Dwarshuis
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Fritz J Sedlazeck
- Baylor College of Medicine, Human Genome Sequencing Center, Houston, TX, USA
| | | | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
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6
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Aluri J, Cooper MA. Somatic mosaicism in inborn errors of immunity: Current knowledge, challenges, and future perspectives. Semin Immunol 2023; 67:101761. [PMID: 37062181 DOI: 10.1016/j.smim.2023.101761] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 04/18/2023]
Abstract
Inborn errors of immunity (IEI) are a diverse group of monogenic disorders of the immune system due to germline variants in genes important for the immune response. Over the past decade there has been increasing recognition that acquired somatic variants present in a subset of cells can also lead to immune disorders or 'phenocopies' of IEI. Discovery of somatic mosaicism causing IEI has largely arisen from investigation of seemingly sporadic cases of IEI with predominant symptoms of autoinflammation and/or autoimmunity in which germline disease-causing variants are not detected. Disease-causing somatic mosaicism has been identified in genes that also cause germline IEI, such as FAS, and in genes without significant corresponding germline disease, such as UBA1 and TLR8. There are challenges in detecting low-level somatic variants, and it is likely that the extent of the somatic mosaicism causing IEI is largely uncharted. Here we review the field of somatic mosaicism leading to IEI and discuss challenges and methods for somatic variant detection, including diagnostic approaches for molecular diagnoses of patients.
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Affiliation(s)
- Jahnavi Aluri
- Department of Pediatrics, Division of Rheumatology/Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Megan A Cooper
- Department of Pediatrics, Division of Rheumatology/Immunology, Washington University in St. Louis, St. Louis, MO 63110, USA.
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7
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Lee ND, Kaveh K, Bozic I. Clonal interactions in cancer: integrating quantitative models with experimental and clinical data. Semin Cancer Biol 2023; 92:61-73. [PMID: 37023969 DOI: 10.1016/j.semcancer.2023.04.002] [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: 11/30/2022] [Revised: 02/16/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023]
Abstract
Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.
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Affiliation(s)
- Nathan D Lee
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Kamran Kaveh
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States of America; Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.
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8
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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9
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Muñoz-Barrera A, Rubio-Rodríguez LA, Díaz-de Usera A, Jáspez D, Lorenzo-Salazar JM, González-Montelongo R, García-Olivares V, Flores C. From Samples to Germline and Somatic Sequence Variation: A Focus on Next-Generation Sequencing in Melanoma Research. Life (Basel) 2022; 12:1939. [PMID: 36431075 PMCID: PMC9695713 DOI: 10.3390/life12111939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/12/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Next-generation sequencing (NGS) applications have flourished in the last decade, permitting the identification of cancer driver genes and profoundly expanding the possibilities of genomic studies of cancer, including melanoma. Here we aimed to present a technical review across many of the methodological approaches brought by the use of NGS applications with a focus on assessing germline and somatic sequence variation. We provide cautionary notes and discuss key technical details involved in library preparation, the most common problems with the samples, and guidance to circumvent them. We also provide an overview of the sequence-based methods for cancer genomics, exposing the pros and cons of targeted sequencing vs. exome or whole-genome sequencing (WGS), the fundamentals of the most common commercial platforms, and a comparison of throughputs and key applications. Details of the steps and the main software involved in the bioinformatics processing of the sequencing results, from preprocessing to variant prioritization and filtering, are also provided in the context of the full spectrum of genetic variation (SNVs, indels, CNVs, structural variation, and gene fusions). Finally, we put the emphasis on selected bioinformatic pipelines behind (a) short-read WGS identification of small germline and somatic variants, (b) detection of gene fusions from transcriptomes, and (c) de novo assembly of genomes from long-read WGS data. Overall, we provide comprehensive guidance across the main methodological procedures involved in obtaining sequencing results for the most common short- and long-read NGS platforms, highlighting key applications in melanoma research.
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Affiliation(s)
- Adrián Muñoz-Barrera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Luis A. Rubio-Rodríguez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Ana Díaz-de Usera
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - David Jáspez
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - José M. Lorenzo-Salazar
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Rafaela González-Montelongo
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Víctor García-Olivares
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
| | - Carlos Flores
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Fernando de Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain
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10
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Inferring parameters of cancer evolution in chronic lymphocytic leukemia. PLoS Comput Biol 2022; 18:e1010677. [DOI: 10.1371/journal.pcbi.1010677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 11/16/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer, where two longitudinal samples are available for sequencing. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. Chronic lymphocytic leukemia (CLL), which often does not require treatment for years after diagnosis, presents an optimal system to study the untreated, natural evolution of cancer cell populations. When we apply our methodology to reconstruct the individual evolutionary histories of CLL patients, we find that the parental leukemic clone typically appears within the first fifteen years of life.
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11
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FVC as an adaptive and accurate method for filtering variants from popular NGS analysis pipelines. Commun Biol 2022; 5:975. [PMID: 36114280 PMCID: PMC9481582 DOI: 10.1038/s42003-022-03397-7] [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: 07/09/2021] [Accepted: 04/22/2022] [Indexed: 11/08/2022] Open
Abstract
The quality control of variants from whole-genome sequencing data is vital in clinical diagnosis and human genetics research. However, current filtering methods (Frequency, Hard-Filter, VQSR, GARFIELD, and VEF) were developed to be utilized on particular variant callers and have certain limitations. Especially, the number of eliminated true variants far exceeds the number of removed false variants using these methods. Here, we present an adaptive method for quality control on genetic variants from different analysis pipelines, and validate it on the variants generated from four popular variant callers (GATK HaplotypeCaller, Mutect2, Varscan2, and DeepVariant). FVC consistently exhibited the best performance. It removed far more false variants than the current state-of-the-art filtering methods and recalled ~51-99% true variants filtered out by the other methods. Once trained, FVC can be conveniently integrated into a user-specific variant calling pipeline. FVC is a method for calling specific gene variants from whole genome data, for potential use in clinical diagnosis and human genetics research.
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12
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Ahmadinejad N, Troftgruben S, Wang J, Chandrashekar PB, Dinu V, Maley C, Liu L. Accurate Identification of Subclones in Tumor Genomes. Mol Biol Evol 2022; 39:6617617. [PMID: 35749590 PMCID: PMC9260306 DOI: 10.1093/molbev/msac136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).
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Affiliation(s)
- Navid Ahmadinejad
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Shayna Troftgruben
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA
| | - Junwen Wang
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Pramod B Chandrashekar
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Valentin Dinu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Carlo Maley
- Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
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13
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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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14
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Steele CD, Pillay N, Alexandrov LB. An overview of mutational and copy number signatures in human cancer. J Pathol 2022; 257:454-465. [PMID: 35420163 PMCID: PMC9324981 DOI: 10.1002/path.5912] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/17/2022]
Abstract
The genome of each cell in the human body is constantly under assault from a plethora of exogenous and endogenous processes that can damage DNA. If not successfully repaired, DNA damage generally becomes permanently imprinted in cells, and all their progenies, as somatic mutations. In most cases, the patterns of these somatic mutations contain the tell‐tale signs of the mutagenic processes that have imprinted and are termed mutational signatures. Recent pan‐cancer genomic analyses have elucidated the compendium of mutational signatures for all types of small mutational events, including (1) single base substitutions, (2) doublet base substitutions, and (3) small insertions/deletions. In contrast to small mutational events, where, in most cases, DNA damage is a prerequisite, aneuploidy, which refers to the abnormal number of chromosomes in a cell, usually develops from mistakes during DNA replication. Such mistakes include DNA replication stress, mitotic errors caused by faulty microtubule dynamics, or cohesion defects that contribute to chromosomal breakage and can lead to copy number (CN) alterations (CNAs) or even to structural rearrangements. These aberrations also leave behind genomic scars which can be inferred from sequencing as CN signatures and rearrangement signatures. The analyses of mutational signatures of small mutational events have been extensively reviewed, so we will not comprehensively re‐examine them here. Rather, our focus will be on summarising the existing knowledge for mutational signatures of CNAs. As studying CN signatures is an emerging field, we briefly summarise the utility that mutational signatures of small mutational events have provided in basic science, cancer treatment, and cancer prevention, and we emphasise the future role that CN signatures may play in each of these fields. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Christopher D Steele
- Research Department of Pathology, Cancer Institute, University College London, London, UK
| | - Nischalan Pillay
- Research Department of Pathology, Cancer Institute, University College London, London, UK.,Department of Cellular and Molecular Pathology, Royal National Orthopaedic Hospital NHS Trust, Stanmore, UK
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA, USA.,Department of Bioengineering, UC San Diego, La Jolla, CA, USA.,Moores Cancer Center, UC San Diego, La Jolla, CA, USA
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15
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Ouellette TW, Awadalla P. Inferring ongoing cancer evolution from single tumour biopsies using synthetic supervised learning. PLoS Comput Biol 2022; 18:e1010007. [PMID: 35482653 PMCID: PMC9049314 DOI: 10.1371/journal.pcbi.1010007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/09/2022] [Indexed: 11/18/2022] Open
Abstract
Variant allele frequencies (VAF) encode ongoing evolution and subclonal selection in growing tumours. However, existing methods that utilize VAF information for cancer evolutionary inference are compressive, slow, or incorrectly specify the underlying cancer evolutionary dynamics. Here, we provide a proof-of-principle synthetic supervised learning method, TumE, that integrates simulated models of cancer evolution with Bayesian neural networks, to infer ongoing selection in bulk-sequenced single tumour biopsies. Analyses in synthetic and patient tumours show that TumE significantly improves both accuracy and inference time per sample when detecting positive selection, deconvoluting selected subclonal populations, and estimating subclone frequency. Importantly, we show how transfer learning can leverage stored knowledge within TumE models for related evolutionary inference tasks-substantially reducing data and computational time for further model development and providing a library of recyclable deep learning models for the cancer evolution community. This extensible framework provides a foundation and future directions for harnessing progressive computational methods for the benefit of cancer genomics and, in turn, the cancer patient.
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Affiliation(s)
- Tom W. Ouellette
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario, Canada
- Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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16
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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17
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Ozcan Z, San Lucas FA, Wong JW, Chang K, Stopsack KH, Fowler J, Jakubek YA, Scheet P. Chromosomal imbalances detected via RNA-sequencing in 28 cancers. Bioinformatics 2022; 38:1483-1490. [PMID: 34999743 PMCID: PMC8896613 DOI: 10.1093/bioinformatics/btab861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 11/05/2021] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA. RESULTS We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings. AVAILABILITY AND IMPLEMENTATION The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zuhal Ozcan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Francis A San Lucas
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Justin W Wong
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kyle Chang
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Konrad H Stopsack
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jerry Fowler
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yasminka A Jakubek
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Paul Scheet
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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18
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Petti AA, Khan SM, Xu Z, Helton N, Fronick CC, Fulton R, Ramakrishnan SM, Nonavinkere Srivatsan S, Heath SE, Westervelt P, Payton JE, Walter MJ, Link DC, DiPersio J, Miller C, Ley TJ. Genetic and Transcriptional Contributions to Relapse in Normal Karyotype Acute Myeloid Leukemia. Blood Cancer Discov 2022; 3:32-49. [PMID: 35019859 PMCID: PMC9924296 DOI: 10.1158/2643-3230.bcd-21-0050] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/12/2021] [Accepted: 08/17/2021] [Indexed: 01/21/2023] Open
Abstract
To better understand clonal and transcriptional adaptations after relapse in patients with acute myeloid leukemia (AML), we collected presentation and relapse samples from six normal karyotype AML cases. We performed enhanced whole-genome sequencing to characterize clonal evolution, and deep-coverage single-cell RNA sequencing on the same samples, which yielded 142,642 high-quality cells for analysis. Identifying expressed mutations in individual cells enabled us to discriminate between normal and AML cells, to identify coordinated changes in the genome and transcriptome, and to identify subclone-specific cell states. We quantified the coevolution of genetic and transcriptional heterogeneity during AML progression, and found that transcriptional changes were significantly correlated with genetic changes. However, transcriptional adaptation sometimes occurred independently, suggesting that clonal evolution does not represent all relevant biological changes. In three cases, we identified cells at diagnosis that likely seeded the relapse. Finally, these data revealed a conserved relapse-enriched leukemic cell state bearing markers of stemness, quiescence, and adhesion. SIGNIFICANCE: These data enabled us to identify a relapse-enriched leukemic cell state with distinct transcriptional properties. Detailed case-by-case analyses elucidated the complex ways in which the AML genome, transcriptome, and immune microenvironment interact to evade chemotherapy. These analyses provide a blueprint for evaluating these factors in larger cohorts.This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Allegra A. Petti
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri.,Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Saad M. Khan
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri.,Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Ziheng Xu
- Washington University School of Medicine, St. Louis, Missouri
| | - Nichole Helton
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Catrina C. Fronick
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Robert Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Sai M. Ramakrishnan
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | | | - Sharon E. Heath
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Peter Westervelt
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Jacqueline E. Payton
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew J. Walter
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Daniel C. Link
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - John DiPersio
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Christopher Miller
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Timothy J. Ley
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri.,Corresponding Author: Timothy J. Ley, Washington University School of Medicine, Campus Box 8007, 660 South Euclid Avenue, St. Louis, MO 63110-1092. Phone: 314-362-8831; Fax: 314-362-9333; E-mail:
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19
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Yamamoto T, Sato Y, Yasuda S, Shikamura M, Tamura T, Takenaka C, Takasu N, Nomura M, Dohi H, Takahashi M, Mandai M, Kanemura Y, Nakamura M, Okano H, Kawamata S. OUP accepted manuscript. Stem Cells Transl Med 2022; 11:527-538. [PMID: 35445254 PMCID: PMC9154342 DOI: 10.1093/stcltm/szac014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/13/2022] [Indexed: 11/15/2022] Open
Abstract
Cell therapy using induced pluripotent stem cell (iPSC) derivatives may result in abnormal tissue generation because the cells undergo numerous cycles of mitosis before clinical application, potentially increasing the accumulation of genetic abnormalities. Therefore, genetic tests may predict abnormal tissue formation after transplantation. Here, we administered iPSC derivatives with or without single-nucleotide variants (SNVs) and deletions in cancer-related genes with various genomic copy number variant (CNV) profiles into immunodeficient mice and examined the relationships between mutations and abnormal tissue formation after transplantation. No positive correlations were found between the presence of SNVs/deletions and the formation of abnormal tissues; the overall predictivity was 29%. However, a copy number higher than 3 was correlated, with an overall predictivity of 86%. Furthermore, we found CNV hotspots at 14q32.33 and 17q12 loci. Thus, CNV analysis may predict abnormal tissue formation after transplantation of iPSC derivatives and reduce the number of tumorigenicity tests.
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Affiliation(s)
- Takako Yamamoto
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Yoji Sato
- Division of Cell-Based Therapeutic Products, National Institute of Health Sciences, Kawasaki, Japan
| | - Satoshi Yasuda
- Division of Cell-Based Therapeutic Products, National Institute of Health Sciences, Kawasaki, Japan
| | - Masayuki Shikamura
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Takashi Tamura
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Chiemi Takenaka
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | | | | | | | | | | | - Yonehiro Kanemura
- Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Japan
| | - Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Shin Kawamata
- R&D Center for Cell Therapy, Foundation for Biomedical Research and Innovation, Kobe, Japan
- Riken BDR, Kobe, Japan
- Corresponding author: Shin Kawamata, Minatojima-minamimachi 1-5-4, Chuo-ku Kobe, 650-0047 Japan.
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20
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Measurable Residual Disease Assessment as a Surrogate Marker in New Drug Development in Acute Myeloid Leukemia. Cancer J 2022; 28:73-77. [PMID: 35072377 PMCID: PMC8849520 DOI: 10.1097/ppo.0000000000000572] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
ABSTRACT Response criteria for patients treated for acute myeloid leukemia (AML) based on cytomorphology are inadequate. Many patients achieving a complete remission by such criteria will later relapse. Patients with AML in such remissions who test negative using higher sensitivity measures of residual disease burden (measurable residual disease [MRD]) have on average lower relapse rates and better survival than those testing positive. This association has raised the possibility that these technological advances in measurement of tumor burden could be used to optimize the drug development and regulatory approval processes in AML. The heterogeneous genetic etiology, diverse immunophenotypic profiles, related precursor states and polyclonal architecture however combine to make the development of standardized and validated MRD assessments for AML challenging. Current and future methods to measure residual disease in AML, performance characteristics of testing currently in use, and potential uses for optimized AML MRD tests including as a surrogate endpoint are discussed.
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21
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Chattopadhyay S, Karlsson J, Valind A, Andersson N, Gisselsson D. Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST. Brief Bioinform 2021; 22:bbab292. [PMID: 34343239 PMCID: PMC8981300 DOI: 10.1093/bib/bbab292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/16/2021] [Accepted: 07/09/2021] [Indexed: 12/20/2022] Open
Abstract
Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.
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Affiliation(s)
- Subhayan Chattopadhyay
- Division of Clinical Genetics, Department of Laboratory
Medicine, Lund University, Lund, Sweden
| | - Jenny Karlsson
- Division of Clinical Genetics, Department of Laboratory
Medicine, Lund University, Lund, Sweden
| | - Anders Valind
- Division of Clinical Genetics, Department of Laboratory
Medicine, Lund University, Lund, Sweden
- Department of Pediatrics, Skåne University
Hospital, Lund, Sweden
| | - Natalie Andersson
- Division of Clinical Genetics, Department of Laboratory
Medicine, Lund University, Lund, Sweden
| | - David Gisselsson
- Division of Clinical Genetics, Department of Laboratory
Medicine, Lund University, Lund, Sweden
- Division of Oncology and Pathology, Department of Clinical
Sciences, Lund University, Lund, Sweden
- Clinical Genetics and Pathology, Laboratory Medicine,
Lund University Hospital, Lund, Sweden
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22
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Genomic and Transcriptomic Characterization of Relapsed SCLC Through Rapid Research Autopsy. JTO Clin Res Rep 2021; 2:100164. [PMID: 34590014 PMCID: PMC8474405 DOI: 10.1016/j.jtocrr.2021.100164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 03/05/2021] [Indexed: 01/22/2023] Open
Abstract
Introduction Relapsed SCLC is characterized by therapeutic resistance and high mortality rate. Despite decades of research, mechanisms responsible for therapeutic resistance have remained elusive owing to limited tissues available for molecular studies. Thus, an unmet need remains for molecular characterization of relapsed SCLC to facilitate development of effective therapies. Methods We performed whole-exome and transcriptome sequencing of metastatic tumor samples procured from research autopsies of five patients with relapsed SCLC. We implemented bioinformatics tools to infer subclonal phylogeny and identify recurrent genomic alterations. We implemented immune cell signature and single-sample gene set enrichment analyses on tumor and normal transcriptome data from autopsy and additional primary and relapsed SCLC data sets. Furthermore, we evaluated T cell-inflamed gene expression profiles in neuroendocrine (ASCL1, NEUROD1) and non-neuroendocrine (YAP1, POU2F3) SCLC subtypes. Results Exome sequencing revealed clonal heterogeneity (intertumor and intratumor) arising from branched evolution and identified resistance-associated truncal and subclonal alterations in relapsed SCLC. Transcriptome analyses further revealed a noninflamed phenotype in neuroendocrine SCLC subtypes (ASCL1, NEUROD1) associated with decreased expression of genes involved in adaptive antitumor immunity whereas non-neuroendocrine subtypes (YAP1, POU2F3) revealed a more inflamed phenotype. Conclusions Our results reveal substantial tumor heterogeneity and complex clonal evolution in relapsed SCLC. Furthermore, we report that neuroendocrine SCLC subtypes are immunologically cold, thus explaining decreased responsiveness to immune checkpoint blockade. These results suggest that the mechanisms of innate and acquired therapeutic resistances are subtype-specific in SCLC and highlight the need for continued investigation to bolster therapy selection and development for this cancer.
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23
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Xiao W, Ren L, Chen Z, Fang LT, Zhao Y, Lack J, Guan M, Zhu B, Jaeger E, Kerrigan L, Blomquist TM, Hung T, Sultan M, Idler K, Lu C, Scherer A, Kusko R, Moos M, Xiao C, Sherry ST, Abaan OD, Chen W, Chen X, Nordlund J, Liljedahl U, Maestro R, Polano M, Drabek J, Vojta P, Kõks S, Reimann E, Madala BS, Mercer T, Miller C, Jacob H, Truong T, Moshrefi A, Natarajan A, Granat A, Schroth GP, Kalamegham R, Peters E, Petitjean V, Walton A, Shen TW, Talsania K, Vera CJ, Langenbach K, de Mars M, Hipp JA, Willey JC, Wang J, Shetty J, Kriga Y, Raziuddin A, Tran B, Zheng Y, Yu Y, Cam M, Jailwala P, Nguyen C, Meerzaman D, Chen Q, Yan C, Ernest B, Mehra U, Jensen RV, Jones W, Li JL, Papas BN, Pirooznia M, Chen YC, Seifuddin F, Li Z, Liu X, Resch W, Wang J, Wu L, Yavas G, Miles C, Ning B, Tong W, Mason CE, Donaldson E, Lababidi S, Staudt LM, Tezak Z, Hong H, Wang C, Shi L. Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing. Nat Biotechnol 2021; 39:1141-1150. [PMID: 34504346 PMCID: PMC8506910 DOI: 10.1038/s41587-021-00994-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 06/18/2021] [Indexed: 02/01/2023]
Abstract
Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.
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Affiliation(s)
- Wenming Xiao
- The Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA.
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Zhong Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Li Tai Fang
- Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc., Belmont, CA, USA
| | - Yongmei Zhao
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Justin Lack
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | - Bin Zhu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | | | | | - Thomas M Blomquist
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | | | - Marc Sultan
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Kenneth Idler
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Charles Lu
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Andreas Scherer
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
| | | | - Malcolm Moos
- The Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Stephen T Sherry
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Ogan D Abaan
- Illumina Inc., Foster City, CA, USA
- Seven Bridges Genomics Inc., Cambridge, MA, USA
| | - Wanqiu Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Xin Chen
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Jessica Nordlund
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ulrika Liljedahl
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Roberta Maestro
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Maurizio Polano
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Centro di Riferimento Oncologico di Aviano IRCCS, National Cancer Institute, Unit of Oncogenetics and Functional Oncogenomics, Aviano, Italy
| | - Jiri Drabek
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- IMTM, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Petr Vojta
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- IMTM, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic
| | - Sulev Kõks
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Perron Institute for Neurological and Translational Science, Nedlands, Perth, Western Australia, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, Perth, Western Australia, Australia
| | - Ene Reimann
- European Infrastructure for Translational Medicine, Amsterdam, the Netherlands
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bindu Swapna Madala
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia
| | - Timothy Mercer
- Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia
| | - Chris Miller
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Howard Jacob
- Computational Genomics, Genomics Research Center, AbbVie, North Chicago, IL, USA
| | | | | | | | | | | | | | | | - Virginie Petitjean
- Biomarker Development, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Ashley Walton
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Tsai-Wei Shen
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Keyur Talsania
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Cristobal Juan Vera
- Advanced Biomedical and Computational Sciences, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | | | - Jennifer A Hipp
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | - James C Willey
- Departments of Medicine and Pathology, University of Toledo Medical Center, Toledo, OH, USA
| | - Jing Wang
- National Institute of Metrology, Beijing, China
| | - Jyoti Shetty
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yuliya Kriga
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Arati Raziuddin
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Bao Tran
- Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Ying Yu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Margaret Cam
- CCR Collaborative Bioinformatics Resource, Office of Science and Technology Resources, Center for Cancer Research, Bethesda, MD, USA
| | - Parthav Jailwala
- CCR Collaborative Bioinformatics Resource, Office of Science and Technology Resources, Center for Cancer Research, Bethesda, MD, USA
| | - Cu Nguyen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Daoud Meerzaman
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Qingrong Chen
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | - Chunhua Yan
- Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA
| | | | | | - Roderick V Jensen
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | | | - Jian-Liang Li
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Brian N Papas
- Integrative Bioinformatics, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yun-Ching Chen
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fayaz Seifuddin
- Bioinformatics and Computational Biology Core, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhipan Li
- Sentieon Inc., Mountain View, CA, USA
| | - Xuelu Liu
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Wolfgang Resch
- Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | | | - Leihong Wu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Gokhan Yavas
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Corey Miles
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Baitang Ning
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Eric Donaldson
- The Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA
| | - Samir Lababidi
- Office of the Chief Scientist, Office of the Commissioner, US Food and Drug Information, Silver Spring, MD, USA
| | - Louis M Staudt
- Lymphoid Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zivana Tezak
- The Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Huixiao Hong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA
| | - Charles Wang
- Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA.
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China.
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24
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Baghaarabani L, Goliaei S, Foroughmand-Araabi MH, Shariatpanahi SP, Goliaei B. Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data. BMC Bioinformatics 2021; 22:416. [PMID: 34461827 PMCID: PMC8404257 DOI: 10.1186/s12859-021-04338-7] [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: 02/21/2021] [Accepted: 08/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. RESULT In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. CONCLUSIONS In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.
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Affiliation(s)
- Leila Baghaarabani
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Sama Goliaei
- Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
| | | | | | - Bahram Goliaei
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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25
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Clonal Evolution of Multiple Myeloma-Clinical and Diagnostic Implications. Diagnostics (Basel) 2021; 11:diagnostics11091534. [PMID: 34573876 PMCID: PMC8469181 DOI: 10.3390/diagnostics11091534] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/17/2021] [Accepted: 08/19/2021] [Indexed: 12/22/2022] Open
Abstract
Plasma cell dyscrasias are a heterogeneous group of diseases characterized by the expansion of bone marrow plasma cells. Malignant transformation of plasma cells depends on the continuity of events resulting in a sequence of well-defined disease stages, from monoclonal gammopathy of undetermined significance (MGUS) through smoldering myeloma (SMM) to symptomatic multiple myeloma (MM). Evolution of a pre-malignant cell into a malignant cell, as well as further tumor progression, dissemination, and relapse, require development of multiple driver lesions conferring selective advantage of the dominant clone and allowing subsequent evolution under selective pressure of microenvironment and treatment. This process of natural selection facilitates tumor plasticity leading to the formation of genetically complex and heterogenous tumors that are notoriously difficult to treat. Better understanding of the mechanisms underlying tumor evolution in MM and identification of lesions driving the evolution from the premalignant clone is therefore a key to development of effective treatment and long-term disease control. Here, we review recent advances in clonal evolution patterns and genomic landscape dynamics of MM, focusing on their clinical implications.
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26
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Khanna A, Larson DE, Srivatsan SN, Mosior M, Abbott TE, Kiwala S, Ley TJ, Duncavage EJ, Walter MJ, Walker JR, Griffith OL, Griffith M, Miller CA. Bam-readcount - rapid generation of basepair-resolution sequence metrics. ARXIV 2021:arXiv:2107.12817v1. [PMID: 34341766 PMCID: PMC8328062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers1,2. In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2.3-6.
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Affiliation(s)
- Ajay Khanna
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
| | - David E. Larson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
- Current Affiliation: Benson Hill, Inc. St. Louis, MO
| | | | - Matthew Mosior
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- Current Affiliation: Moffitt Cancer Center, Tampa, FL
| | - Travis E. Abbott
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
- Current Affiliation: Google, Inc. Mountain View, CA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
| | - Timothy J. Ley
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO
| | - Eric J. Duncavage
- Department of Pathology, Washington University School of Medicine, St. Louis, MO
| | - Matthew J. Walter
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO
| | - Jason R. Walker
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
| | - Obi L. Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Malachi Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO
- Department of Genetics, Washington University School of Medicine, St. Louis, MO
| | - Christopher A. Miller
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO
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27
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Ramaker RC, Hardigan AA, Gordon ER, Wright CA, Myers RM, Cooper SJ. Pooled CRISPR screening in pancreatic cancer cells implicates co-repressor complexes as a cause of multiple drug resistance via regulation of epithelial-to-mesenchymal transition. BMC Cancer 2021; 21:632. [PMID: 34049503 PMCID: PMC8164247 DOI: 10.1186/s12885-021-08388-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/17/2021] [Indexed: 01/05/2023] Open
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) patients suffer poor outcomes, including a five-year survival of below 10%. Poor outcomes result in part from therapeutic resistance that limits the impact of cytotoxic first-line therapy. Novel therapeutic approaches are needed, but currently no targeted therapies exist to treat PDAC. Methods To assess cellular resistance mechanisms common to four cytotoxic chemotherapies (gemcitabine, 5-fluorouracil, irinotecan, and oxaliplatin) used to treat PDAC patients, we performed four genome-wide CRISPR activation (CRISPRact) and CRISPR knock-out (CRISPRko) screens in two common PDAC cell lines (Panc-1 and BxPC3). We used pathway analysis to identify gene sets enriched among our hits and conducted RNA-sequencing and chromatin immunoprecipitation-sequencing (ChIP-seq) to characterize top hits from our screen. We used scratch assays to assess changes in cellular migration with HDAC1 overexpression. Results Our data revealed activation of ABCG2, a well-described efflux pump, as the most consistent mediator of resistance in each of our screens. CRISPR-mediated activation of genes involved in transcriptional co-repressor complexes also conferred resistance to multiple drugs. Expression of many of these genes, including HDAC1, is associated with reduced survival in PDAC patients. Up-regulation of HDAC1 in vitro increased promoter occupancy and expression of several genes involved in the epithelial-to-mesenchymal transition (EMT). These cells also displayed phenotypic changes in cellular migration consistent with activation of the EMT pathway. The expression changes resulting from HDAC1 activation were also observed with activation of several other co-repressor complex members. Finally, we developed a publicly available analysis tool, PancDS, which integrates gene expression profiles with our screen results to predict drug sensitivity in resected PDAC tumors and cell lines. Conclusion Our results provide a comprehensive resource for identifying cellular mechanisms of drug resistance in PDAC, mechanistically implicate HDAC1, and co-repressor complex members broadly, in multi-drug resistance, and provide an analytical tool for predicting treatment response in PDAC tumors and cell lines. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08388-1.
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Affiliation(s)
- Ryne C Ramaker
- University of Alabama-Birmingham, Birmingham, AL, 35294, USA.,HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Andrew A Hardigan
- University of Alabama-Birmingham, Birmingham, AL, 35294, USA.,HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Emily R Gordon
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Carter A Wright
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.,University of Alabama - Huntsville, Huntsville, AL, 35899, USA
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA
| | - Sara J Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, 35806, USA.
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28
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Liu M, Chen J, Wang X, Wang C, Zhang X, Xie Y, Zuo Z, Ren J, Zhao Q. MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations. Gigascience 2021; 10:6279596. [PMID: 34018555 PMCID: PMC8138830 DOI: 10.1093/gigascience/giab036] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/23/2021] [Accepted: 04/23/2021] [Indexed: 11/17/2022] Open
Abstract
Background Multi-region sequencing (MRS) has been widely used to analyze intra-tumor heterogeneity (ITH) and cancer evolution. However, comprehensive analysis of mutational data from MRS is still challenging, necessitating complicated integration of a plethora of computational and statistical approaches. Findings Here, we present MesKit, an R/Bioconductor package that can assist in characterizing genetic ITH and tracing the evolutionary history of tumors based on somatic alterations detected by MRS. MesKit provides a wide range of analysis and visualization modules, including ITH evaluation, metastatic route inference, and mutational signature identification. In addition, MesKit implements an auto-layout algorithm to generate phylogenetic trees based on somatic mutations. The application of MesKit for 2 reported MRS datasets of hepatocellular carcinoma and colorectal cancer identified known heterogeneous features and evolutionary patterns, together with potential driver events during cancer evolution. Conclusions In summary, MesKit is useful for interpreting ITH and tracing evolutionary trajectory based on MRS data. MesKit is implemented in R and available at https://bioconductor.org/packages/MesKit under the GPL v3 license.
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Affiliation(s)
- Mengni Liu
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China
| | - Jianyu Chen
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Xin Wang
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Chengwei Wang
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Xiaolong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China
| | - Yubin Xie
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China
| | - Zhixiang Zuo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China
| | - Jian Ren
- School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.,State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China
| | - Qi Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651 E Dongfeng Road, Guangzhou, Guangdong 510060, China
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29
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Rosenthal SH, Gerasimova A, Ma C, Li HR, Grupe A, Chong H, Acab A, Smolgovsky A, Owen R, Elzinga C, Chen R, Sugganth D, Freitas T, Graham J, Champion K, Bhattacharya A, Racke F, Lacbawan F. Analytical validation and performance characteristics of a 48-gene next-generation sequencing panel for detecting potentially actionable genomic alterations in myeloid neoplasms. PLoS One 2021; 16:e0243683. [PMID: 33909614 PMCID: PMC8081174 DOI: 10.1371/journal.pone.0243683] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/14/2021] [Indexed: 11/18/2022] Open
Abstract
Identification of genomic mutations by molecular testing plays an important role in diagnosis, prognosis, and treatment of myeloid neoplasms. Next-generation sequencing (NGS) is an efficient method for simultaneous detection of clinically significant genomic mutations with high sensitivity. Various NGS based in-house developed and commercial myeloid neoplasm panels have been integrated into routine clinical practice. However, some genes frequently mutated in myeloid malignancies are particularly difficult to sequence with NGS panels (e.g., CEBPA, CARL, and FLT3). We report development and validation of a 48-gene NGS panel that includes genes that are technically challenging for molecular profiling of myeloid neoplasms including acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasms (MPN). Target regions were captured by hybridization with complementary biotinylated DNA baits, and NGS was performed on an Illumina NextSeq500 instrument. A bioinformatics pipeline that was developed in-house was used to detect single nucleotide variations (SNVs), insertions/deletions (indels), and FLT3 internal tandem duplications (FLT3-ITD). An analytical validation study was performed on 184 unique specimens for variants with allele frequencies ≥5%. Variants identified by the 48-gene panel were compared to those identified by a 35-gene hematologic neoplasms panel using an additional 137 unique specimens. The developed assay was applied to a large cohort (n = 2,053) of patients with suspected myeloid neoplasms. Analytical validation yielded 99.6% sensitivity (95% CI: 98.9-99.9%) and 100% specificity (95% CI: 100%). Concordance of variants detected by the 2 tested panels was 100%. Among patients with suspected myeloid neoplasms (n = 2,053), 54.5% patients harbored at least one clinically significant mutation: 77% in AML patients, 48% in MDS, and 45% in MPN. Together, these findings demonstrate that the assay can identify mutations associated with diagnosis, prognosis, and treatment options of myeloid neoplasms even in technically challenging genes.
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Affiliation(s)
- Sun Hee Rosenthal
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Anna Gerasimova
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Charles Ma
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Hai-Rong Li
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Andrew Grupe
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Hansook Chong
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Allan Acab
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Alla Smolgovsky
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Renius Owen
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Christopher Elzinga
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Rebecca Chen
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Daniel Sugganth
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Tracey Freitas
- Department of Molecular Oncology, Med Fusion, Lewisville, TX, United States of America
| | - Jennifer Graham
- Department of Molecular Oncology, Med Fusion, Lewisville, TX, United States of America
| | - Kristen Champion
- Department of Molecular Oncology, Med Fusion, Lewisville, TX, United States of America
| | - Anindya Bhattacharya
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Frederick Racke
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
| | - Felicitas Lacbawan
- Department of Advanced Diagnostics, Quest Diagnostics, San Juan Capistrano, CA, United States of America
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Zhang C, El-Kebir M, Ochoa I. Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples. Nat Commun 2021; 12:2204. [PMID: 33850139 PMCID: PMC8044184 DOI: 10.1038/s41467-021-22466-9] [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: 06/07/2020] [Accepted: 03/05/2021] [Indexed: 11/17/2022] Open
Abstract
Intra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor's mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss' improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics.
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Affiliation(s)
- Chuanyi Zhang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Idoia Ochoa
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical Engineering, University of Navarra, Tecnun, San Sebastian, Spain.
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Next Generation Sequencing Technology in the Clinic and Its Challenges. Cancers (Basel) 2021; 13:cancers13081751. [PMID: 33916923 PMCID: PMC8067551 DOI: 10.3390/cancers13081751] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 03/30/2021] [Accepted: 04/05/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Precise identification and annotation of mutations are of utmost importance in clinical oncology. Insights of the DNA sequence can provide meaningful knowledge to unravel the underlying genetics of disease. Hence, tailoring of personalized medicine often relies on specific genomic alteration for treatment efficacy. The aim of this review is to highlight that sequencing harbors much more than just four nucleotides. Moreover, the gradual transition from first to second generation sequencing technologies has led to awareness for choosing the most appropriate bioinformatic analytic tools based on the aim, quality and demand for a specific purpose. Thus, the same raw data can lead to various results reflecting the intrinsic features of different datamining pipelines. Abstract Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS.
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Duncavage EJ, Schroeder MC, O'Laughlin M, Wilson R, MacMillan S, Bohannon A, Kruchowski S, Garza J, Du F, Hughes AEO, Robinson J, Hughes E, Heath SE, Baty JD, Neidich J, Christopher MJ, Jacoby MA, Uy GL, Fulton RS, Miller CA, Payton JE, Link DC, Walter MJ, Westervelt P, DiPersio JF, Ley TJ, Spencer DH. Genome Sequencing as an Alternative to Cytogenetic Analysis in Myeloid Cancers. N Engl J Med 2021; 384:924-935. [PMID: 33704937 PMCID: PMC8130455 DOI: 10.1056/nejmoa2024534] [Citation(s) in RCA: 145] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Genomic analysis is essential for risk stratification in patients with acute myeloid leukemia (AML) or myelodysplastic syndromes (MDS). Whole-genome sequencing is a potential replacement for conventional cytogenetic and sequencing approaches, but its accuracy, feasibility, and clinical utility have not been demonstrated. METHODS We used a streamlined whole-genome sequencing approach to obtain genomic profiles for 263 patients with myeloid cancers, including 235 patients who had undergone successful cytogenetic analysis. We adapted sample preparation, sequencing, and analysis to detect mutations for risk stratification using existing European Leukemia Network (ELN) guidelines and to minimize turnaround time. We analyzed the performance of whole-genome sequencing by comparing our results with findings from cytogenetic analysis and targeted sequencing. RESULTS Whole-genome sequencing detected all 40 recurrent translocations and 91 copy-number alterations that had been identified by cytogenetic analysis. In addition, we identified new clinically reportable genomic events in 40 of 235 patients (17.0%). Prospective sequencing of samples obtained from 117 consecutive patients was performed in a median of 5 days and provided new genetic information in 29 patients (24.8%), which changed the risk category for 19 patients (16.2%). Standard AML risk groups, as defined by sequencing results instead of cytogenetic analysis, correlated with clinical outcomes. Whole-genome sequencing was also used to stratify patients who had inconclusive results by cytogenetic analysis into risk groups in which clinical outcomes were measurably different. CONCLUSIONS In our study, we found that whole-genome sequencing provided rapid and accurate genomic profiling in patients with AML or MDS. Such sequencing also provided a greater diagnostic yield than conventional cytogenetic analysis and more efficient risk stratification on the basis of standard risk categories. (Funded by the Siteman Cancer Research Fund and others.).
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Affiliation(s)
- Eric J Duncavage
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Molly C Schroeder
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Michele O'Laughlin
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Roxanne Wilson
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Sandra MacMillan
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Andrew Bohannon
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Scott Kruchowski
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - John Garza
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Feiyu Du
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Andrew E O Hughes
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Josh Robinson
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Emma Hughes
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Sharon E Heath
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Jack D Baty
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Julie Neidich
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Matthew J Christopher
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Meagan A Jacoby
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Geoffrey L Uy
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Robert S Fulton
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Christopher A Miller
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Jacqueline E Payton
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Daniel C Link
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Matthew J Walter
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Peter Westervelt
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - John F DiPersio
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - Timothy J Ley
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
| | - David H Spencer
- From the Department of Pathology and Immunology (E.J.D., M.C.S., A.E.O.H., J.N., J.E.P., D.H.S.), McDonnell Genome Institute (M.O., R.W., S.M., A.B., S.K., J.G., F.D., R.S.F., D.H.S.), and the Divisions of Oncology (J.R., E.H., S.E.H., M.J.C., M.A.J., G.L.U., C.A.M., D.C.L., M.J.W., P.W., J.F.D., T.J.L., D.H.S.) and Biostatistics (J.D.B.), Department of Medicine, Washington University School of Medicine, St. Louis
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van Belzen IAEM, Schönhuth A, Kemmeren P, Hehir-Kwa JY. Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology. NPJ Precis Oncol 2021; 5:15. [PMID: 33654267 PMCID: PMC7925608 DOI: 10.1038/s41698-021-00155-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 01/12/2021] [Indexed: 01/31/2023] Open
Abstract
Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.
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Affiliation(s)
| | - Alexander Schönhuth
- Genome Data Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Patrick Kemmeren
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Jayne Y Hehir-Kwa
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
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34
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Masoero L, Camerlenghi F, Favaro S, Broderick T. More for less: predicting and maximizing genomic variant discovery via Bayesian nonparametrics. Biometrika 2021. [DOI: 10.1093/biomet/asab012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Summary
While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains nontrivial. Under a fixed budget, scientists face a natural trade-off between quantity and quality: spending resources to sequence a greater number of genomes or spending resources to sequence genomes with increased accuracy. Our goal is to find the optimal allocation of resources between quantity and quality. Optimizing resource allocation promises to reveal as many new variations in the genome as possible. We introduce a Bayesian nonparametric methodology to predict the number of new variants in a follow-up study based on a pilot study. When experimental conditions are kept constant between the pilot and follow-up, we find that our prediction is competitive with the best existing methods. Unlike current methods, though, our new method allows practitioners to change experimental conditions between the pilot and the follow-up. We demonstrate how this distinction allows our method to be used for more realistic predictions and for optimal allocation of a fixed budget between quality and quantity. We validate our method on cancer and human genomics data.
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35
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A simple method to estimate the in-house limit of detection for genetic mutations with low allele frequencies in whole-exome sequencing analysis by next-generation sequencing. BMC Genom Data 2021; 22:8. [PMID: 33602132 PMCID: PMC7893872 DOI: 10.1186/s12863-020-00956-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 12/16/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Next-generation sequencing (NGS) has profoundly changed the approach to genetic/genomic research. Particularly, the clinical utility of NGS in detecting mutations associated with disease risk has contributed to the development of effective therapeutic strategies. Recently, comprehensive analysis of somatic genetic mutations by NGS has also been used as a new approach for controlling the quality of cell substrates for manufacturing biopharmaceuticals. However, the quality evaluation of cell substrates by NGS largely depends on the limit of detection (LOD) for rare somatic mutations. The purpose of this study was to develop a simple method for evaluating the ability of whole-exome sequencing (WES) by NGS to detect mutations with low allele frequency. To estimate the LOD of WES for low-frequency somatic mutations, we repeatedly and independently performed WES of a reference genomic DNA using the same NGS platform and assay design. LOD was defined as the allele frequency with a relative standard deviation (RSD) value of 30% and was estimated by a moving average curve of the relation between RSD and allele frequency. RESULTS Allele frequencies of 20 mutations in the reference material that had been pre-validated by droplet digital PCR (ddPCR) were obtained from 5, 15, 30, or 40 G base pair (Gbp) sequencing data per run. There was a significant association between the allele frequencies measured by WES and those pre-validated by ddPCR, whose p-value decreased as the sequencing data size increased. By this method, the LOD of allele frequency in WES with the sequencing data of 15 Gbp or more was estimated to be between 5 and 10%. CONCLUSIONS For properly interpreting the WES data of somatic genetic mutations, it is necessary to have a cutoff threshold of low allele frequencies. The in-house LOD estimated by the simple method shown in this study provides a rationale for setting the cutoff.
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36
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Tarabichi M, Salcedo A, Deshwar AG, Leathlobhair MN, Wintersinger J, Wedge DC, Loo PV, Morris QD, Boutros PC. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 2021; 18:144-155. [PMID: 33398189 PMCID: PMC7867630 DOI: 10.1038/s41592-020-01013-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 11/09/2020] [Indexed: 01/28/2023]
Abstract
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
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Affiliation(s)
- Maxime Tarabichi
- The Francis Crick Institute, London, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Adriana Salcedo
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Amit G. Deshwar
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Canada
| | - Máire Ni Leathlobhair
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom
| | - Jeff Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - David C. Wedge
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford, United Kingdom
- Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | | | - Quaid D. Morris
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute, Toronto, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York
- Donnelly Centre, University of Toronto, Toronto, Canada
| | - Paul C. Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Vector Institute, Toronto, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles
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37
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Starks ER, Swanson L, Docking TR, Bosdet I, Munro S, Moore RA, Karsan A. Assessing Limit of Detection in Clinical Sequencing. J Mol Diagn 2021; 23:455-466. [PMID: 33486075 DOI: 10.1016/j.jmoldx.2020.12.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 12/05/2020] [Accepted: 12/30/2020] [Indexed: 10/22/2022] Open
Abstract
Clinical reporting of solid tumor sequencing requires reliable assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, a theoretical model was developed for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. Binomial models were found to be appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. This model was empirically validated with sequencing experiments by using a series of DNA input amounts and sequencing depths. Based on these findings, a workflow is proposed for determining the limiting factors to sensitivity in different assay designs, and the formulas for these scenarios are presented. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.
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Affiliation(s)
- Elizabeth R Starks
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada.
| | - Lucas Swanson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - T Roderick Docking
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Ian Bosdet
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sarah Munro
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Richard A Moore
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Aly Karsan
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada; Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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38
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Chen Y, Yan W, Xie Z, Guo W, Lu D, Lv Z, Zhang X. Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer. Mol Clin Oncol 2021; 14:36. [PMID: 33414916 PMCID: PMC7783722 DOI: 10.3892/mco.2020.2198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 12/09/2020] [Indexed: 11/06/2022] Open
Abstract
Next generation sequencing (NGS) technology is an increasingly important clinical tool for therapeutic decision-making. However, interpretation of NGS data presents challenges at the point of care, due to limitations in understanding the clinical importance of gene variants and efficiently translating results into actionable information for the clinician. The present study compared two approaches for annotating and reporting actionable genes and gene mutations from tumor samples: The traditional approach of manual curation, annotation and reporting using an experienced molecular tumor bioinformationist; and a cloud-based cognitive technology, with the goal to detect gene mutations of potential significance in Chinese patients with lung cancer. Data from 285 gene-targeted exon sequencing previously conducted on 115 patient tissue samples between 2014 and 2016 and subsequently manually annotated and evaluated by the Guangdong Lung Cancer Institute (GLCI) research team were analyzed by the Watson for Genomics (WfG) cognitive genomics technology. A comparative analysis of the annotation results of the two methods was conducted to identify quantitative and qualitative differences in the mutations generated. The complete congruence rate of annotation results between WfG analysis and the GLCI bioinformatician was 43.48%. In 65 (56.52%) samples, WfG analysis identified and interpreted, on average, 1.54 more mutation sites in each sample than the manual GLCI review. These mutation sites were located on 27 genes, including EP300, ARID1A, STK11 and DNMT3A. Mutations in the EP300 gene were most prevalent, and present in 30.77% samples. The Tumor Mutation Burden (TMB) interpreted by WfG analysis (1.82) was significantly higher than the TMB (0.73) interpreted by GLCI review. Compared with manual curation by a bioinformatician, WfG analysis provided comprehensive insights and additional genetic alterations to inform clinical therapeutic strategies for patients with lung cancer. These findings suggest the valuable role of cognitive computing to increase efficiency in the comprehensive detection and interpretation of genetic alterations which may inform opportunities for targeted cancer therapies.
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Affiliation(s)
- Yu Chen
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Wenqing Yan
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Zhi Xie
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Weibang Guo
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Danxia Lu
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Zhiyi Lv
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
| | - Xuchao Zhang
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080, P.R. China.,Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China
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39
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Hashimoto S, Noguchi E, Bando H, Miyadera H, Morii W, Nakamura T, Hara H. Neoantigen prediction in human breast cancer using RNA sequencing data. Cancer Sci 2021; 112:465-475. [PMID: 33155341 PMCID: PMC7780012 DOI: 10.1111/cas.14720] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/08/2020] [Accepted: 11/02/2020] [Indexed: 12/30/2022] Open
Abstract
Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA-seq) data has been reported to be a low-accuracy but cost-effective tool, but the feasibility of RNA-seq data for neoantigen prediction has not been fully examined. In the present study, we used whole-exome sequencing (WES) and RNA-seq data of tumor and matched normal samples from six breast cancer patients to evaluate the utility of RNA-seq data instead of WES data in variant calling to detect neoantigen candidates. Somatic variants were called in three protocols using: (i) tumor and normal WES data (DNA method, Dm); (ii) tumor and normal RNA-seq data (RNA method, Rm); and (iii) combination of tumor RNA-seq and normal WES data (Combination method, Cm). We found that the Rm had both high false-positive and high false-negative rates because this method depended greatly on the expression status of normal transcripts. When we compared the results of Dm with those of Cm, only 14% of the neoantigen candidates detected in Dm were identified in Cm, but the majority of the missed candidates lacked coverage or variant allele reads in the tumor RNA. In contrast, about 70% of the neoepitope candidates with higher expression and rich mutant transcripts could be detected in Cm. Our results showed that Cm could be an efficient and a cost-effective approach to predict highly expressed neoantigens in tumor samples.
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Affiliation(s)
- Sachie Hashimoto
- Department of Breast and Endocrine SurgeryGraduate School of Comprehensive Human SciencesUniversity of TsukubaIbarakiJapan
| | - Emiko Noguchi
- Department of Medical GeneticsFaculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Hiroko Bando
- Department of Breast and Endocrine SurgeryFaculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Hiroko Miyadera
- Department of Medical GeneticsFaculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Wataru Morii
- Department of Medical GeneticsFaculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Takako Nakamura
- Department of Medical GeneticsFaculty of MedicineUniversity of TsukubaIbarakiJapan
| | - Hisato Hara
- Department of Breast and Endocrine SurgeryFaculty of MedicineUniversity of TsukubaIbarakiJapan
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40
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Lischer C, Vera-González J. The Road to Effective Cancer Immunotherapy—A Computational Perspective on Tumor Epitopes in Anti-Cancer Immunotherapy. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11605-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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41
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Das AB, Smith-Díaz CC, Vissers MCM. Emerging epigenetic therapeutics for myeloid leukemia: modulating demethylase activity with ascorbate. Haematologica 2021; 106:14-25. [PMID: 33099992 PMCID: PMC7776339 DOI: 10.3324/haematol.2020.259283] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/17/2020] [Indexed: 12/23/2022] Open
Abstract
The past decade has seen a proliferation of drugs that target epigenetic pathways. Many of these drugs were developed to treat acute myeloid leukemia, a condition in which dysregulation of the epigenetic landscape is well established. While these drugs have shown promise, critical issues persist. Specifically, patients with the same mutations respond quite differently to treatment. This is true even with highly specific drugs that are designed to target the underlying oncogenic driver mutations. Furthermore, patients who do respond may eventually develop resistance. There is now evidence that epigenetic heterogeneity contributes, in part, to these issues. Cancer cells also have a remarkable capacity to ‘rewire’ themselves at the epigenetic level in response to drug treatment, and thereby maintain expression of key oncogenes. This epigenetic plasticity is a promising new target for drug development. It is therefore important to consider combination therapy in cases in which both driver mutations and epigenetic plasticity are targeted. Using ascorbate as an example of an emerging epigenetic therapeutic, we review the evidence for its potential use in both of these modes. We provide an overview of 2-oxoglutarate dependent dioxygenases with DNA, histone and RNA demethylase activity, focusing on those which require ascorbate as a cofactor. We also evaluate their role in the development and maintenance of acute myeloid leukemia. Using this information, we highlight situations in which the use of ascorbate to restore 2-oxoglutarate dependent dioxygenase activity could prove beneficial, in contrast to contexts in which targeted inhibition of specific enzymes might be preferred. Finally, we discuss how these insights could be incorporated into the rational design of future clinical trials.
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Affiliation(s)
- Andrew B Das
- Department of Pathology and Biomedical Science, University of Otago, Christchurch.
| | - Carlos C Smith-Díaz
- Department of Pathology and Biomedical Science, University of Otago, Christchurch
| | - Margreet C M Vissers
- Department of Pathology and Biomedical Science, University of Otago, Christchurch
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42
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Zhang S, Gong Y, Li C, Yang W, Li L. Beyond regulations at DNA levels: A review of epigenetic therapeutics targeting cancer stem cells. Cell Prolif 2020; 54:e12963. [PMID: 33314500 PMCID: PMC7848960 DOI: 10.1111/cpr.12963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 02/05/2023] Open
Abstract
In the past few years, the paramount role of cancer stem cells (CSCs), in terms of cancer initiation, proliferation, metastasis, invasion and chemoresistance, has been revealed by accumulating studies. However, this level of cellular plasticity cannot be entirely explained by genetic mutations. Research on epigenetic modifications as a complementary explanation for the properties of CSCs has been increasing over the past several years. Notably, therapeutic strategies are currently being developed in an effort to reverse aberrant epigenetic alterations using specific chemical inhibitors. In this review, we summarize the current understanding of CSCs and their role in cancer progression, and provide an overview of epigenetic alterations seen in CSCs. Importantly, we focus on primary cancer therapies that target the epigenetic modification of CSCs by the use of specific chemical inhibitors, such as histone deacetylase (HDAC) inhibitors, DNA methyltransferase (DNMT) inhibitors and microRNA‐based (miRNA‐based) therapeutics.
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Affiliation(s)
- Shunhao Zhang
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Sichuan Province, Chengdu, China
| | - Yanji Gong
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Temporomandibular Joint, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China.,State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
| | - Chunjie Li
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
| | - Wenbin Yang
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Sichuan Province, Chengdu, China
| | - Longjiang Li
- State Key Laboratory of Oral Disease, National Clinical Research Center for Oral Disease, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan Province, China
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43
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Caravagna G, Heide T, Williams MJ, Zapata L, Nichol D, Chkhaidze K, Cross W, Cresswell GD, Werner B, Acar A, Chesler L, Barnes CP, Sanguinetti G, Graham TA, Sottoriva A. Subclonal reconstruction of tumors by using machine learning and population genetics. Nat Genet 2020; 52:898-907. [PMID: 32879509 PMCID: PMC7610388 DOI: 10.1038/s41588-020-0675-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 07/01/2020] [Indexed: 12/14/2022]
Abstract
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.
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Affiliation(s)
- Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Timon Heide
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Marc J Williams
- Evolution and Cancer Lab, Barts Cancer Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Luis Zapata
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Daniel Nichol
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Ketevan Chkhaidze
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - William Cross
- Evolution and Cancer Lab, Barts Cancer Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - George D Cresswell
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Ahmet Acar
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Louis Chesler
- Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology and UCL Genetics Institute, University College London, London, UK
| | - Guido Sanguinetti
- School of Informatics, University of Edinburgh, Edinburgh, UK
- International School for Advanced Studies, Trieste, Italy
| | - Trevor A Graham
- Evolution and Cancer Lab, Barts Cancer Institute, School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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44
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HPV-EM: an accurate HPV detection and genotyping EM algorithm. Sci Rep 2020; 10:14340. [PMID: 32868873 PMCID: PMC7459114 DOI: 10.1038/s41598-020-71300-7] [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: 05/14/2020] [Accepted: 08/07/2020] [Indexed: 11/09/2022] Open
Abstract
Accurate HPV genotyping is crucial in facilitating epidemiology studies, vaccine trials, and HPV-related cancer research. Contemporary HPV genotyping assays only detect < 25% of all known HPV genotypes and are not accurate for low-risk or mixed HPV genotypes. Current genomic HPV genotyping algorithms use a simple read-alignment and filtering strategy that has difficulty handling repeats and homology sequences. Therefore, we have developed an optimized expectation–maximization algorithm, designated HPV-EM, to address the ambiguities caused by repetitive sequencing reads. HPV-EM achieved 97–100% accuracy when benchmarked using cell line data and TCGA cervical cancer data. We also validated HPV-EM using DNA tiling data on an institutional cervical cancer cohort (96.5% accuracy). Using HPV-EM, we demonstrated HPV genotypic differences in recurrence and patient outcomes in cervical and head and neck cancers.
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45
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Affiliation(s)
- Susan E Bates
- From the Department of Medicine, Division of Hematology/Oncology, Columbia University Irving Medical Center and James J. Peters Veterans Affairs Medical Center, New York
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46
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Bustamante-Marin XM, Horani A, Stoyanova M, Charng WL, Bottier M, Sears PR, Yin WN, Daniels LA, Bowen H, Conrad DF, Knowles MR, Ostrowski LE, Zariwala MA, Dutcher SK. Mutation of CFAP57, a protein required for the asymmetric targeting of a subset of inner dynein arms in Chlamydomonas, causes primary ciliary dyskinesia. PLoS Genet 2020; 16:e1008691. [PMID: 32764743 PMCID: PMC7444499 DOI: 10.1371/journal.pgen.1008691] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 08/19/2020] [Accepted: 02/22/2020] [Indexed: 01/10/2023] Open
Abstract
Primary ciliary dyskinesia (PCD) is characterized by chronic airway disease, reduced fertility, and randomization of the left/right body axis. It is caused by defects of motile cilia and sperm flagella. We screened a cohort of affected individuals that lack an obvious axonemal defect for pathogenic variants using whole exome capture, next generation sequencing, and bioinformatic analysis assuming an autosomal recessive trait. We identified one subject with an apparently homozygous nonsense variant [(c.1762C>T), p.(Arg588*)] in the uncharacterized CFAP57 gene. Interestingly, the variant results in the skipping of exon 11 (58 amino acids), which may be due to disruption of an exonic splicing enhancer. In normal human nasal epithelial cells, CFAP57 localizes throughout the ciliary axoneme. Nasal cells from the PCD patient express a shorter, mutant version of CFAP57 and the protein is not incorporated into the axoneme. The missing 58 amino acids include portions of WD repeats that may be important for loading onto the intraflagellar transport (IFT) complexes for transport or docking onto the axoneme. A reduced beat frequency and an alteration in ciliary waveform was observed. Knockdown of CFAP57 in human tracheobronchial epithelial cells (hTECs) recapitulates these findings. Phylogenetic analysis showed that CFAP57 is highly conserved in organisms that assemble motile cilia. CFAP57 is allelic with the BOP2/IDA8/FAP57 gene identified previously in Chlamydomonas reinhardtii. Two independent, insertional fap57 Chlamydomonas mutant strains show reduced swimming velocity and altered waveforms. Tandem mass tag (TMT) mass spectroscopy shows that FAP57 is missing, and the "g" inner dyneins (DHC7 and DHC3) and the "d" inner dynein (DHC2) are reduced, but the FAP57 paralog FBB7 is increased. Together, our data identify a homozygous variant in CFAP57 that causes PCD that is likely due to a defect in the inner dynein arm assembly process.
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Affiliation(s)
- Ximena M. Bustamante-Marin
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Amjad Horani
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Mihaela Stoyanova
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Wu-Lin Charng
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Mathieu Bottier
- Department of Mechanical Engineering, Washington University, St. Louis, Missouri, United States of America
| | - Patrick R. Sears
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Wei-Ning Yin
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Leigh Anne Daniels
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Hailey Bowen
- Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Donald F. Conrad
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Division of Genetics, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, Oregon, United States of America
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Michael R. Knowles
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lawrence E. Ostrowski
- Department of Medicine, Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Maimoona A. Zariwala
- Department of Pathology and Laboratory Medicine and the Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Susan K. Dutcher
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
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Wang M, Luo W, Jones K, Bian X, Williams R, Higson H, Wu D, Hicks B, Yeager M, Zhu B. SomaticCombiner: improving the performance of somatic variant calling based on evaluation tests and a consensus approach. Sci Rep 2020; 10:12898. [PMID: 32732891 PMCID: PMC7393490 DOI: 10.1038/s41598-020-69772-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
It is challenging to identify somatic variants from high-throughput sequence reads due to tumor heterogeneity, sub-clonality, and sequencing artifacts. In this study, we evaluated the performance of eight primary somatic variant callers and multiple ensemble methods using both real and synthetic whole-genome sequencing, whole-exome sequencing, and deep targeted sequencing datasets with the NA12878 cell line. The test results showed that a simple consensus approach can significantly improve performance even with a limited number of callers and is more robust and stable than machine learning based ensemble approaches. To fully exploit the multi-callers, we also developed a software package, SomaticCombiner, that can combine multiple callers and integrates a new variant allelic frequency (VAF) adaptive majority voting approach, which can maintain sensitive detection for variants with low VAFs.
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Affiliation(s)
- Mingyi Wang
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA.
| | - Wen Luo
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Kristine Jones
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Xiaopeng Bian
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, 20850, USA
| | - Russell Williams
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Herbert Higson
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Dongjing Wu
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Belynda Hicks
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Meredith Yeager
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA
| | - Bin Zhu
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, 20877, USA.
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48
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Lu IN, Muller CP, He FQ. Applying next-generation sequencing to unravel the mutational landscape in viral quasispecies. Virus Res 2020; 283:197963. [PMID: 32278821 PMCID: PMC7144618 DOI: 10.1016/j.virusres.2020.197963] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 04/03/2020] [Accepted: 04/04/2020] [Indexed: 02/07/2023]
Abstract
Next-generation sequencing (NGS) has revolutionized the scale and depth of biomedical sciences. Because of its unique ability for the detection of sub-clonal variants within genetically diverse populations, NGS has been successfully applied to analyze and quantify the exceptionally-high diversity within viral quasispecies, and many low-frequency drug- or vaccine-resistant mutations of therapeutic importance have been discovered. Although many works have intensively discussed the latest NGS approaches and applications in general, none of them has focused on applying NGS in viral quasispecies studies, mostly due to the limited ability of current NGS technologies to accurately detect and quantify rare viral variants. Here, we summarize several error-correction strategies that have been developed to enhance the detection accuracy of minority variants. We also discuss critical considerations for preparing a sequencing library from viral RNAs and for analyzing NGS data to unravel the mutational landscape.
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Affiliation(s)
- I-Na Lu
- DKFZ-Division Translational Neurooncology at the WTZ, DKTK partner site, University Hospital Essen, D-45147 Essen, Germany; Department of Infectious Diseases, Aarhus University Hospital, DK-8200 Aarhus N, Denmark.
| | - Claude P Muller
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-Sur-Alzette, Luxembourg; Laboratoire National de Santé, L-3583 Dudelange, Luxembourg
| | - Feng Q He
- Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-Sur-Alzette, Luxembourg; Institute of Medical Microbiology, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
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49
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Applications of probability and statistics in cancer genomics. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0203-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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50
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Dang HX, Krasnick BA, White BS, Grossman JG, Strand MS, Zhang J, Cabanski CR, Miller CA, Fulton RS, Goedegebuure SP, Fronick CC, Griffith M, Larson DE, Goetz BD, Walker JR, Hawkins WG, Strasberg SM, Linehan DC, Lim KH, Lockhart AC, Mardis ER, Wilson RK, Ley TJ, Maher CA, Fields RC. The clonal evolution of metastatic colorectal cancer. SCIENCE ADVANCES 2020; 6:eaay9691. [PMID: 32577507 PMCID: PMC7286679 DOI: 10.1126/sciadv.aay9691] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 04/10/2020] [Indexed: 06/11/2023]
Abstract
Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine.
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Affiliation(s)
- Ha X. Dang
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Bradley A. Krasnick
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | | | - Julie G. Grossman
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | - Matthew S. Strand
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | - Jin Zhang
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Christopher A. Miller
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert S. Fulton
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - S. Peter Goedegebuure
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | - Catrina C. Fronick
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Malachi Griffith
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
- Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA
| | - David E. Larson
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian D. Goetz
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Jason R. Walker
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - William G. Hawkins
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | - Steven M. Strasberg
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
| | - David C. Linehan
- Department of Surgery and The Wilmot Cancer Institute, University of Rochester School of Medicine, Rochester, NY, USA
| | - Kian H. Lim
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - A. Craig Lockhart
- Division of Medical Oncology, Department of Medicine, The Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, Miami, FL, USA
| | - Elaine R. Mardis
- Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Richard K. Wilson
- Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - Timothy J. Ley
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher A. Maher
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Ryan C. Fields
- The Alvin J. Siteman Comprehensive Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Washington University in St. Louis, St. Louis, MO, USA
- Barnes-Jewish Hospital, St. Louis, MO, USA
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