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Kobayashi Y, Niida A, Nagayama S, Saeki K, Haeno H, Takahashi KK, Hayashi S, Ozato Y, Saito H, Hasegawa T, Nakamura H, Tobo T, Kitagawa A, Sato K, Shimizu D, Hirata H, Hisamatsu Y, Toshima T, Yonemura Y, Masuda T, Mizuno S, Kawazu M, Kohsaka S, Ueno T, Mano H, Ishihara S, Uemura M, Mori M, Doki Y, Eguchi H, Oshima M, Suzuki Y, Shibata T, Mimori K. Subclonal accumulation of immune escape mechanisms in microsatellite instability-high colorectal cancers. Br J Cancer 2023; 129:1105-1118. [PMID: 37596408 PMCID: PMC10539316 DOI: 10.1038/s41416-023-02395-8] [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: 02/15/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Intratumor heterogeneity (ITH) in microsatellite instability-high (MSI-H) colorectal cancer (CRC) has been poorly studied. We aimed to clarify how the ITH of MSI-H CRCs is generated in cancer evolution and how immune selective pressure affects ITH. METHODS We reanalyzed public whole-exome sequencing data on 246 MSI-H CRCs. In addition, we performed a multi-region analysis from 6 MSI-H CRCs. To verify the process of subclonal immune escape accumulation, a novel computational model of cancer evolution under immune pressure was developed. RESULTS Our analysis presented the enrichment of functional genomic alterations in antigen-presentation machinery (APM). Associative analysis of neoantigens indicated the generation of immune escape mechanisms via HLA alterations. Multiregion analysis revealed the clonal acquisition of driver mutations and subclonal accumulation of APM defects in MSI-H CRCs. Examination of variant allele frequencies demonstrated that subclonal mutations tend to be subjected to selective sweep. Computational simulations of tumour progression with the interaction of immune cells successfully verified the subclonal accumulation of immune escape mutations and suggested the efficacy of early initiation of an immune checkpoint inhibitor (ICI) -based treatment. CONCLUSIONS Our results demonstrate the heterogeneous acquisition of immune escape mechanisms in MSI-H CRCs by Darwinian selection, providing novel insights into ICI-based treatment strategies.
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Affiliation(s)
- Yuta Kobayashi
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Atsushi Niida
- Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1, Sirokane-dai, Minato-Ku, Tokyo, 108-8639, Japan
| | - Satoshi Nagayama
- Gastroenterological Center, Department of Gastroenterological Surgery, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-Ku, Tokyo, 135-8550, Japan
- Department of Surgery, Uji-Tokushukai Medical Center, Kyoto, 611-0041, Japan
| | - Koichi Saeki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 227-8561, Japan
| | - Hiroshi Haeno
- Division of Integrated Research, Research Institute for Biomedical Sciences, Tokyo University of Science, 2669 Yamazaki, Noda City, Chiba, 278-0022, Japan
| | - Kazuki K Takahashi
- Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1, Sirokane-dai, Minato-Ku, Tokyo, 108-8639, Japan
| | - Shuto Hayashi
- Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Yuki Ozato
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Hideyuki Saito
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Takanori Hasegawa
- Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8639, Japan
| | - Hiromi Nakamura
- Division of Cancer Genomics, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Taro Tobo
- Department of Pathology, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Akihiro Kitagawa
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Kuniaki Sato
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Head and Neck Surgery, National Hospital Organization Kyushu Cancer Center, Fukuoka, 811-1395, Japan
| | - Dai Shimizu
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Gastroenterological Surgery (Surgery II), Nagoya University Graduate School of Medicine, Nagoya, 466-8550, Japan
| | - Hidenari Hirata
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | - Yuichi Hisamatsu
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Takeo Toshima
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Yusuke Yonemura
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Takaaki Masuda
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan
| | - Shinichi Mizuno
- Division of Cancer Research, Center for Advanced Medical Innovation, Kyushu University, Fukuoka, 812-8582, Japan
| | - Masahito Kawazu
- Division of Cellular Signaling, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shinji Kohsaka
- Division of Cellular Signaling, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Toshihide Ueno
- Division of Cellular Signaling, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Hiroyuki Mano
- Division of Cellular Signaling, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Mamoru Uemura
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Masaki Mori
- Faculty of Medicine, Tokai University, Isegahara, 259-1193, Japan
| | - Yuichiro Doki
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Hidetoshi Eguchi
- Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Yamadaoka 2-2, Suita, Osaka, 565-0871, Japan
| | - Masanobu Oshima
- Division of Genetics, Cancer Research Institute, Kanazawa University, Kadoma-Cho, Kanazawa, 920-1164, Japan
| | - Yutaka Suzuki
- Laboratory of Systems Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Tatsuhiro Shibata
- Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1, Sirokane-dai, Minato-Ku, Tokyo, 108-8639, Japan
- Division of Cancer Genomics, National Cancer Center Japan, Research Institute 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospital, 4546 Tsurumihara, Beppu, 874-0838, Japan.
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2
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Koniar H, Miller C, Rahmim A, Schaffer P, Uribe C. A GATE simulation study for dosimetry in cancer cell and micrometastasis from the 225Ac decay chain. EJNMMI Phys 2023; 10:46. [PMID: 37525027 PMCID: PMC10390455 DOI: 10.1186/s40658-023-00564-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 07/24/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Radiopharmaceutical therapy (RPT) with alpha-emitting radionuclides has shown great promise in treating metastatic cancers. The successive emission of four alpha particles in the 225Ac decay chain leads to highly targeted and effective cancer cell death. Quantifying cellular dosimetry for 225Ac RPT is essential for predicting cell survival and therapeutic success. However, the leading assumption that all 225Ac progeny remain localized at their target sites likely overestimates the absorbed dose to cancer cells. To address limitations in existing semi-analytic approaches, this work evaluates S-values for 225Ac's progeny radionuclides with GATE Monte Carlo simulations. METHODS The cellular geometries considered were an individual cell (10 µm diameter with a nucleus of 8 µm diameter) and a cluster of cells (micrometastasis) with radionuclides localized in four subcellular regions: cell membrane, cytoplasm, nucleus, or whole cell. The absorbed dose to the cell nucleus was scored, and self- and cross-dose S-values were derived. We also evaluated the total absorbed dose with various degrees of radiopharmaceutical internalization and retention of the progeny radionuclides 221Fr (t1/2 = 4.80 m) and 213Bi (t1/2 = 45.6 m). RESULTS For the cumulative 225Ac decay chain, our self- and cross-dose nuclear S-values were both in good agreement with S-values published by MIRDcell, with per cent differences ranging from - 2.7 to - 8.7% for the various radionuclide source locations. Source location had greater effects on self-dose S-values than the intercellular cross-dose S-values. Cumulative 225Ac decay chain self-dose S-values increased from 0.167 to 0.364 GyBq-1 s-1 with radionuclide internalization from the cell surface into the cell. When progeny migration from the target site was modelled, the cumulative self-dose S-values to the cell nucleus decreased by up to 71% and 21% for 221Fr and 213Bi retention, respectively. CONCLUSIONS Our GATE Monte Carlo simulations resulted in cellular S-values in agreement with existing MIRD S-values for the alpha-emitting radionuclides in the 225Ac decay chain. To obtain accurate absorbed dose estimates in 225Ac studies, accurate understanding of daughter migration is critical for optimized injected activities. Future work will investigate other novel preclinical alpha-emitting radionuclides to evaluate therapeutic potency and explore realistic cellular geometries corresponding to targeted cancer cell lines.
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Affiliation(s)
- Helena Koniar
- Life Sciences Division, TRIUMF, Vancouver, BC, Canada.
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.
| | - Cassandra Miller
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Arman Rahmim
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Paul Schaffer
- Life Sciences Division, TRIUMF, Vancouver, BC, Canada
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Chemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Carlos Uribe
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Functional Imaging, BC Cancer, Vancouver, BC, Canada
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3
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Streck A, Kaufmann TL, Schwarz RF. SMITH: spatially constrained stochastic model for simulation of intra-tumour heterogeneity. Bioinformatics 2023; 39:7056642. [PMID: 36825830 PMCID: PMC10010604 DOI: 10.1093/bioinformatics/btad102] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/10/2023] [Accepted: 02/23/2023] [Indexed: 02/25/2023] Open
Abstract
MOTIVATION Simulations of cancer evolution are highly useful to study the effects of selection and mutation rates on cellular fitness. However, most methods are either lattice-based and cannot simulate realistically sized tumours, or they omit spatial constraints and lack the clonal dynamics of real-world tumours. RESULTS Stochastic model of intra-tumour heterogeneity (SMITH) is an efficient and explainable model of cancer evolution that combines a branching process with a new confinement mechanism limiting clonal growth based on the size of the individual clones as well as the overall tumour population. We demonstrate how confinement is sufficient to induce the rich clonal dynamics observed in spatial models and cancer samples across tumour types, while allowing for a clear geometric interpretation and simulation of 1 billion cells within a few minutes on a desktop PC. AVAILABILITY AND IMPLEMENTATION SMITH is implemented in C# and freely available at https://bitbucket.org/schwarzlab/smith. For visualizations, we provide the accompanying Python package PyFish at https://bitbucket.org/schwarzlab/pyfish. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Adam Streck
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.,Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Tom L Kaufmann
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Roland F Schwarz
- Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,BIFOLD-Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
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4
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Jayasinghe MK, Lee CY, Tran TTT, Tan R, Chew SM, Yeo BZJ, Loh WX, Pirisinu M, Le MTN. The Role of in silico Research in Developing Nanoparticle-Based Therapeutics. Front Digit Health 2022; 4:838590. [PMID: 35373184 PMCID: PMC8965754 DOI: 10.3389/fdgth.2022.838590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 02/16/2022] [Indexed: 12/12/2022] Open
Abstract
Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation—a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, in vivo biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. Via predictive modeling and deep learning, in silico approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the in vivo pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering.
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Affiliation(s)
- Migara Kavishka Jayasinghe
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chang Yu Lee
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Trinh T T Tran
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Vingroup Science and Technology Scholarship Program, Vin University, Hanoi, Vietnam
| | - Rachel Tan
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Sarah Min Chew
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Brendon Zhi Jie Yeo
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Life Sciences Undergraduate Program, Faculty of Science, National University of Singapore, Singapore, Singapore
| | - Wen Xiu Loh
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Marco Pirisinu
- Jotbody (HK) Pte Limited, Hong Kong, Hong Kong SAR, China
| | - Minh T N Le
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Immunology Program, Cancer Program and Nanomedicine Translational Program, Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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5
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Abstract
Our capacity to study individual cells has enabled a new level of resolution for understanding complex biological systems such as multicellular organisms or microbial communities. Not surprisingly, several methods have been developed in recent years with a formidable potential to investigate the somatic evolution of single cells in both healthy and pathological tissues. However, single-cell sequencing data can be quite noisy due to different technical biases, so inferences resulting from these new methods need to be carefully contrasted. Here, I introduce CellCoal, a software tool for the coalescent simulation of single-cell sequencing genotypes. CellCoal simulates the history of single-cell samples obtained from somatic cell populations with different demographic histories and produces single-nucleotide variants under a variety of mutation models, sequencing read counts, and genotype likelihoods, considering allelic imbalance, allelic dropout, amplification, and sequencing errors, typical of this type of data. CellCoal is a flexible tool that can be used to understand the implications of different somatic evolutionary processes at the single-cell level, and to benchmark dedicated bioinformatic tools for the analysis of single-cell sequencing data. CellCoal is available at https://github.com/dapogon/cellcoal.
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Affiliation(s)
- David Posada
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.,Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain.,Galicia Sur Health Research Institute, Vigo, Spain
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6
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Sun R, Nikolakopoulos AN. Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors. PLoS Comput Biol 2021; 17:e1008838. [PMID: 33730105 PMCID: PMC8007046 DOI: 10.1371/journal.pcbi.1008838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/29/2021] [Accepted: 02/26/2021] [Indexed: 12/24/2022] Open
Abstract
Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters.
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Affiliation(s)
- Ruping Sun
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Athanasios N. Nikolakopoulos
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, United States of America
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
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7
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Sego TJ, Glazier JA, Tovar A. Unification of aggregate growth models by emergence from cellular and intracellular mechanisms. ROYAL SOCIETY OPEN SCIENCE 2020; 7:192148. [PMID: 32968501 PMCID: PMC7481681 DOI: 10.1098/rsos.192148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 07/03/2020] [Indexed: 05/04/2023]
Abstract
Multicellular aggregate growth is regulated by nutrient availability and removal of metabolites, but the specifics of growth dynamics are dependent on cell type and environment. Classical models of growth are based on differential equations. While in some cases these classical models match experimental observations, they can only predict growth of a limited number of cell types and so can only be selectively applied. Currently, no classical model provides a general mathematical representation of growth for any cell type and environment. This discrepancy limits their range of applications, which a general modelling framework can enhance. In this work, a hybrid cellular Potts model is used to explain the discrepancy between classical models as emergent behaviours from the same mathematical system. Intracellular processes are described using probability distributions of local chemical conditions for proliferation and death and simulated. By fitting simulation results to a generalization of the classical models, their emergence is demonstrated. Parameter variations elucidate how aggregate growth may behave like one classical growth model or another. Three classical growth model fits were tested, and emergence of the Gompertz equation was demonstrated. Effects of shape changes are demonstrated, which are significant for final aggregate size and growth rate, and occur stochastically.
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Affiliation(s)
- T. J. Sego
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James A. Glazier
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Andres Tovar
- Department of Mechanical and Energy Engineering, Indiana University–Purdue University Indianapolis, Indianapolis, IN, USA
- Author for correspondence: Andres Tovar e-mail:
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8
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Niida A, Hasegawa T, Innan H, Shibata T, Mimori K, Miyano S. A unified simulation model for understanding the diversity of cancer evolution. PeerJ 2020; 8:e8842. [PMID: 32296600 PMCID: PMC7150545 DOI: 10.7717/peerj.8842] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 03/02/2020] [Indexed: 01/24/2023] Open
Abstract
Because cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.
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Affiliation(s)
- Atsushi Niida
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Takanori Hasegawa
- Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Hideki Innan
- SOKENDAI, The Graduate University for Advanced Studies, Hayama, Japan
| | - Tatsuhiro Shibata
- Laboratory of Molecular Medicine, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Division of Cancer Genomics, National Cancer Center Research Institute, Tokyo, Japan
| | - Koshi Mimori
- Department of Surgery, Kyushu University Beppu Hospita, Beppu, Japan
| | - Satoru Miyano
- Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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9
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Miura S, Vu T, Deng J, Buturla T, Oladeinde O, Choi J, Kumar S. Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data. Sci Rep 2020; 10:3498. [PMID: 32103044 PMCID: PMC7044161 DOI: 10.1038/s41598-020-59006-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.
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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
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiamin Deng
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tiffany Buturla
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Olumide Oladeinde
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiyeong Choi
- 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. .,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
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10
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Niida A, Iwasaki WM, Innan H. Neutral Theory in Cancer Cell Population Genetics. Mol Biol Evol 2019; 35:1316-1321. [PMID: 29718454 DOI: 10.1093/molbev/msy091] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Kimura's neutral theory provides the whole theoretical basis of the behavior of mutations in a Wright-Fisher population. We here discuss how it can be applied to a cancer cell population, in which there is an increasing interest in genetic variation within a tumor. We explain a couple of fundamental differences between cancer cell populations and asexual organismal populations. Once these differences are taken into account, a number of powerful theoretical tools developed for a Wright-Fisher population could be readily contribute to our deeper understanding of the evolutionary dynamics of cancer cell population.
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Affiliation(s)
- Atsushi Niida
- The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Watal M Iwasaki
- Department of Evolutionary Studies of Biosystems, SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa, Japan
| | - Hideki Innan
- Department of Evolutionary Studies of Biosystems, SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa, Japan
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11
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Gallaher JA, Brown JS, Anderson ARA. The impact of proliferation-migration tradeoffs on phenotypic evolution in cancer. Sci Rep 2019; 9:2425. [PMID: 30787363 PMCID: PMC6382810 DOI: 10.1038/s41598-019-39636-x] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 01/28/2019] [Indexed: 12/13/2022] Open
Abstract
Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor's edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.
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Affiliation(s)
- Jill A Gallaher
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA.
| | - Joel S Brown
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA
| | - Alexander R A Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center, Tampa, FL, USA.
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12
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Olejarz J, Kaveh K, Veller C, Nowak MA. Selection for synchronized cell division in simple multicellular organisms. J Theor Biol 2018; 457:170-179. [PMID: 30172691 PMCID: PMC6169303 DOI: 10.1016/j.jtbi.2018.08.038] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 07/30/2018] [Accepted: 08/29/2018] [Indexed: 02/08/2023]
Abstract
The evolution of multicellularity was a major transition in the history of life on earth. Conditions under which multicellularity is favored have been studied theoretically and experimentally. But since the construction of a multicellular organism requires multiple rounds of cell division, a natural question is whether these cell divisions should be synchronous or not. We study a population model in which there compete simple multicellular organisms that grow by either synchronous or asynchronous cell divisions. We demonstrate that natural selection can act differently on synchronous and asynchronous cell division, and we offer intuition for why these phenotypes are generally not neutral variants of each other.
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Affiliation(s)
- Jason Olejarz
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA.
| | - Kamran Kaveh
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA.
| | - Carl Veller
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
| | - Martin A Nowak
- Program for Evolutionary Dynamics, Harvard University, Cambridge, MA 02138, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA; Department of Mathematics, Harvard University, Cambridge, MA 02138, USA.
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