1
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Serio RN, Scheben A, Lu B, Gargiulo DV, Patruno L, Buckholtz CL, Chaffee RJ, Jibilian MC, Persaud SG, Staklinski SJ, Hassett R, Brault LM, Ramazzotti D, Barbieri CE, Siepel AC, Nowak DG. Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer. Cancer Discov 2024; 14:1990-2009. [PMID: 38969342 DOI: 10.1158/2159-8290.cd-23-1332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/23/2024] [Accepted: 07/03/2024] [Indexed: 07/07/2024]
Abstract
The patterns by which primary tumors spread to metastatic sites remain poorly understood. Here, we define patterns of metastatic seeding in prostate cancer using a novel injection-based mouse model-EvoCaP (Evolution in Cancer of the Prostate), featuring aggressive metastatic cancer to bone, liver, lungs, and lymph nodes. To define migration histories between primary and metastatic sites, we used our EvoTraceR pipeline to track distinct tumor clones containing recordable barcodes. We detected widespread intratumoral heterogeneity from the primary tumor in metastatic seeding, with few clonal populations instigating most migration. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within the tissue. Migration patterns in our model were congruent with human prostate cancer seeding topologies. Our findings support the view of metastatic prostate cancer as a systemic disease driven by waves of aggressive clones expanding their niche, infrequently overcoming constraints that otherwise keep them confined in the primary or metastatic site. Significance: Defining the kinetics of prostate cancer metastasis is critical for developing novel therapeutic strategies. This study uses CRISPR/Cas9-based barcoding technology to accurately define tumor clonal patterns and routes of migration in a novel somatically engineered mouse model (EvoCaP) that recapitulates human prostate cancer using an in-house developed analytical pipeline (EvoTraceR).
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Affiliation(s)
- Ryan N Serio
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Armin Scheben
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Billy Lu
- Department of Pharmacology, Weill Cornell Medicine, New York, New York
| | | | - Lucrezia Patruno
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | | | - Ryan J Chaffee
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | | | | | - Stephen J Staklinski
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Rebecca Hassett
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Lise M Brault
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
| | - Daniele Ramazzotti
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Christopher E Barbieri
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Department of Urology, Weill Cornell Medicine, New York, New York
| | - Adam C Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Dawid G Nowak
- Meyer Cancer Center, Weill Cornell Medicine, New York, New York
- Department of Pharmacology, Weill Cornell Medicine, New York, New York
- Division of Hematology and Medical Oncology, Department of Medicine, New York Presbyterian Hospital, Weill Cornell Medicine, New York, New York
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2
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Koyyalagunta D, Ganesh K, Morris Q. Inferring cancer type-specific patterns of metastatic spread. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602790. [PMID: 39282311 PMCID: PMC11398359 DOI: 10.1101/2024.07.09.602790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
The metastatic spread of a cancer can be reconstructed from DNA sequencing of primary and metastatic tumours, but doing so requires solving a challenging combinatorial optimization problem. This problem often has multiple solutions that cannot be distinguished based on current maximum parsimony principles alone. Current algorithms use ad hoc criteria to select among these solutions, and decide, a priori, what patterns of metastatic spread are more likely, which is itself a key question posed by studies of metastasis seeking to use these tools. Here we introduce Metient, a freely available open-source tool which proposes multiple possible hypotheses of metastatic spread in a cohort of patients and rescores these hypotheses using independent data on genetic distance of metastasizing clones and organotropism. Metient is more accurate and is up to 50x faster than current state-of-the-art. Given a cohort of patients, Metient can calibrate its parsimony criteria, thereby identifying shared patterns of metastatic dissemination in the cohort. Reanalyzing metastasis in 169 patients based on 490 tumors, Metient automatically identifies cancer type-specific trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Metient's reconstructions usually agree with semi-manual expert analysis, however, in many patients, Metient identifies more plausible migration histories than experts, and further finds that polyclonal seeding of metastases is more common than previously reported. By removing the need for hard constraints on what patterns of metastatic spread are most likely, Metient introduces a way to further our understanding of cancer type-specific metastatic spread.
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3
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Li L, Xie W, Zhan L, Wen S, Luo X, Xu S, Cai Y, Tang W, Wang Q, Li M, Xie Z, Deng L, Zhu H, Yu G. Resolving tumor evolution: a phylogenetic approach. JOURNAL OF THE NATIONAL CANCER CENTER 2024; 4:97-106. [PMID: 39282584 PMCID: PMC11390690 DOI: 10.1016/j.jncc.2024.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/20/2024] [Indexed: 09/19/2024] Open
Abstract
The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.
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Affiliation(s)
- Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shaodi Wen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Department of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital, Nanjing, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Deng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hongyuan Zhu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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4
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Miura S, Dolker T, Sanderford M, Kumar S. Improving cellular phylogenies through the integrated use of mutation order and optimality principles. Comput Struct Biotechnol J 2023; 21:3894-3903. [PMID: 37602230 PMCID: PMC10432911 DOI: 10.1016/j.csbj.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 07/10/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
The study of tumor evolution is being revolutionalized by single-cell sequencing technologies that survey the somatic variation of cancer cells. In these endeavors, reliable inference of the evolutionary relationship of single cells is a key step. However, single-cell sequences contain many errors and missing bases, which necessitate advancing standard molecular phylogenetics approaches for applications in analyzing these datasets. We have developed a computational approach that integratively applies standard phylogenetic optimality principles and patterns of co-occurrence of sequence variations to produce more expansive and accurate cellular phylogenies from single-cell sequence datasets. We found the new approach to also perform well for CRISPR/Cas9 genome editing datasets, suggesting that it can be useful for various applications. We apply the new approach to some empirical datasets to showcase its use for reconstructing recurrent mutations and mutational reversals as well as for phylodynamics analysis to infer metastatic cell migrations between tumors.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tenzin Dolker
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
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5
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Huzar J, Shenoy M, Sanderford MD, Kumar S, Miura S. Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data. FRONTIERS IN BIOINFORMATICS 2023; 3:1090730. [PMID: 37261293 PMCID: PMC10228696 DOI: 10.3389/fbinf.2023.1090730] [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: 11/05/2022] [Accepted: 03/28/2023] [Indexed: 06/02/2023] Open
Abstract
Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.
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Affiliation(s)
- Jared Huzar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Madelyn Shenoy
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Maxwell D Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States
- Department of Biology, Temple University, Philadelphia, PA, United States
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6
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Chroni A, Miura S, Hamilton L, Vu T, Gaffney SG, Aly V, Karim S, Sanderford M, Townsend JP, Kumar S. Clone Phylogenetics Reveals Metastatic Tumor Migrations, Maps, and Models. Cancers (Basel) 2022; 14:cancers14174326. [PMID: 36077861 PMCID: PMC9454754 DOI: 10.3390/cancers14174326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 12/03/2022] Open
Abstract
Simple Summary Metastasis is the spread of cancer cells across organs and is a major cause of cancer mortality. Analysis of tumor sequencing data provides a means toward the reconstruction of routes of metastatic cell migrations. Our reconstructions demonstrated that many metastases were likely seeded from pre-existing metastasis of primary tumors. Additionally, multiple clone exchanges between tumor sites were common. In conclusion, the pattern of cancer cell migrations is often complex and is highly variable among patients. Abstract Dispersal routes of metastatic cells are not medically detected or even visible. A molecular evolutionary analysis of tumor variation provides a way to retrospectively infer metastatic migration histories and answer questions such as whether the majority of metastases are seeded from clones within primary tumors or seeded from clones within pre-existing metastases, as well as whether the evolution of metastases is generally consistent with any proposed models. We seek answers to these fundamental questions through a systematic patient-centric retrospective analysis that maps the dynamic evolutionary history of tumor cell migrations in many cancers. We analyzed tumor genetic heterogeneity in 51 cancer patients and found that most metastatic migration histories were best described by a hybrid of models of metastatic tumor evolution. Synthesizing across metastatic migration histories, we found new tumor seedings arising from clones of pre-existing metastases as often as they arose from clones from primary tumors. There were also many clone exchanges between the source and recipient tumors. Therefore, a molecular phylogenetic analysis of tumor variation provides a retrospective glimpse into general patterns of metastatic migration histories in cancer patients.
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Affiliation(s)
- Antonia Chroni
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Lauren Hamilton
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | | | - Vivian Aly
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Sajjad Karim
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 22252, Saudi Arabia
| | - Maxwell Sanderford
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
| | - Jeffrey P. Townsend
- Department of Biostatistics, Yale University, New Haven, CT 06510, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06525, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA
- Department of Biology, Temple University, Philadelphia, PA 19122, USA
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 22252, Saudi Arabia
- Correspondence:
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7
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Gagarinova A, Hosseinnia A, Rahmatbakhsh M, Istace Z, Phanse S, Moutaoufik MT, Zilocchi M, Zhang Q, Aoki H, Jessulat M, Kim S, Aly KA, Babu M. Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli. Nat Commun 2022; 13:4085. [PMID: 35835781 PMCID: PMC9283627 DOI: 10.1038/s41467-022-31819-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 07/05/2022] [Indexed: 11/25/2022] Open
Abstract
Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits. The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.
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Affiliation(s)
- Alla Gagarinova
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Ali Hosseinnia
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | | | - Zoe Istace
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | | | - Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Qingzhou Zhang
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Matthew Jessulat
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Sunyoung Kim
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Khaled A Aly
- Department of Biochemistry, University of Regina, Regina, SK, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK, Canada.
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8
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Abstract
Integration of ecological and evolutionary features has begun to understand the interplay of tumor heterogeneity, microenvironment, and metastatic potential. Developing a theoretical framework is intrinsic to deciphering tumors' tremendous spatial and longitudinal genetic variation patterns in patients. Here, we propose that tumors can be considered evolutionary island-like ecosystems, that is, isolated systems that undergo evolutionary and spatiotemporal dynamic processes that shape tumor microenvironments and drive the migration of cancer cells. We examine attributes of insular systems and causes of insularity, such as physical distance and connectivity. These properties modulate migration rates of cancer cells through processes causing spatial and temporal isolation of the organs and tissues functioning as a supply of cancer cells for new colonizations. We discuss hypotheses, predictions, and limitations of tumors as islands analogy. We present emerging evidence of tumor insularity in different cancer types and discuss their relevance to the islands model. We suggest that the engagement of tumor insularity into conceptual and mathematical models holds promise to illuminate cancer evolution, tumor heterogeneity, and metastatic potential of cells.
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Affiliation(s)
- Antonia Chroni
- Institute for Genomics and Evolutionary Medicine, Temple University, USA
- Department of Biology, Temple University, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, USA
- Department of Biology, Temple University, USA
- Center for Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia
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9
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Migrations of cancer cells through the lens of phylogenetic biogeography. Sci Rep 2021; 11:17184. [PMID: 34433859 PMCID: PMC8387374 DOI: 10.1038/s41598-021-96215-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/06/2021] [Indexed: 12/02/2022] Open
Abstract
Malignant cells leave their initial tumor of growth and disperse to other tissues to form metastases. Dispersals also occur in nature when individuals in a population migrate from their area of origin to colonize other habitats. In cancer, phylogenetic biogeography is concerned with the source and trajectory of cell movements. We examine the suitability of primary features of organismal biogeography, including genetic diversification, dispersal, extinction, vicariance, and founder effects, to describe and reconstruct clone migration events among tumors. We used computer-simulated data to compare fits of seven biogeographic models and evaluate models’ performance in clone migration reconstruction. Models considering founder effects and dispersals were often better fit for the clone phylogenetic patterns, especially for polyclonal seeding and reseeding of metastases. However, simpler biogeographic models produced more accurate estimates of cell migration histories. Analyses of empirical datasets of basal-like breast cancer had model fits consistent with the patterns seen in the analysis of computer-simulated datasets. Our analyses reveal the powers and pitfalls of biogeographic models for modeling and inferring clone migration histories using tumor genome variation data. We conclude that the principles of molecular evolution and organismal biogeography are useful in these endeavors but that the available models and methods need to be applied judiciously.
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10
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Dasari K, Somarelli JA, Kumar S, Townsend JP. The somatic molecular evolution of cancer: Mutation, selection, and epistasis. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:56-65. [PMID: 34364910 DOI: 10.1016/j.pbiomolbio.2021.08.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 12/17/2022]
Abstract
Cancer progression has been attributed to somatic changes in single-nucleotide variants, copy-number aberrations, loss of heterozygosity, chromosomal instability, epistatic interactions, and the tumor microenvironment. It is not entirely clear which of these changes are essential and which are ancillary to cancer. The dynamic nature of cancer evolution in a patient can be illuminated using several concepts and tools from classical evolutionary biology. Neutral mutation rates in cancer cells are calculable from genomic data such as synonymous mutations, and selective pressures are calculable from rates of fixation occurring beyond the expectation by neutral mutation and drift. However, these cancer effect sizes of mutations are complicated by epistatic interactions that can determine the likely sequence of gene mutations. In turn, longitudinal phylogenetic analyses of somatic cancer progression offer an opportunity to identify key moments in cancer evolution, relating the timing of driver mutations to corresponding landmarks in the clinical timeline. These analyses reveal temporal aspects of genetic and phenotypic change during tumorigenesis and across clinical timescales. Using a related framework, clonal deconvolution, physical locations of clones, and their phylogenetic relations can be used to infer tumor migration histories. Additionally, genetic interactions with the tumor microenvironment can be analyzed with longstanding approaches applied to organismal genotype-by-environment interactions. Fitness landscapes for cancer evolution relating to genotype, phenotype, and environment could enable more accurate, personalized therapeutic strategies. An understanding of the trajectories underlying the evolution of neoplasms, primary, and metastatic tumors promises fundamental advances toward accurate and personalized predictions of therapeutic response.
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Affiliation(s)
| | | | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, and Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jeffrey P Townsend
- Yale College, New Haven, CT, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Yale Cancer Center, Yale University, New Haven, CT, USA; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
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