1
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Leder K, Sun R, Wang Z, Zhang X. Parameter estimation from single patient, single time-point sequencing data of recurrent tumors. J Math Biol 2024; 89:51. [PMID: 39382689 DOI: 10.1007/s00285-024-02149-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 08/09/2024] [Accepted: 09/22/2024] [Indexed: 10/10/2024]
Abstract
In this study, we develop consistent estimators for key parameters that govern the dynamics of tumor cell populations when subjected to pharmacological treatments. While these treatments often lead to an initial reduction in the abundance of drug-sensitive cells, a population of drug-resistant cells frequently emerges over time, resulting in cancer recurrence. Samples from recurrent tumors present as an invaluable data source that can offer crucial insights into the ability of cancer cells to adapt and withstand treatment interventions. To effectively utilize the data obtained from recurrent tumors, we derive several large number limit theorems, specifically focusing on the metrics that quantify the clonal diversity of cancer cell populations at the time of cancer recurrence. These theorems then serve as the foundation for constructing our estimators. A distinguishing feature of our approach is that our estimators only require a single time-point sequencing data from a single tumor, thereby enhancing the practicality of our approach and enabling the understanding of cancer recurrence at the individual level.
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Affiliation(s)
- Kevin Leder
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN, 55455, USA
| | - Ruping Sun
- Department of Laboratory Medicine & Pathology Masonic Cancer Center, University of Minnesota, Twin Cities, MN, 55455, USA
| | - Zicheng Wang
- School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China
| | - Xuanming Zhang
- Department of Industrial and Systems Engineering, University of Minnesota, Twin Cities, MN, 55455, USA.
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2
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Gorlov IP, Gorlova OY, Tsavachidis S, Amos CI. Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden. Sci Rep 2024; 14:12732. [PMID: 38831004 PMCID: PMC11148192 DOI: 10.1038/s41598-024-63468-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 05/29/2024] [Indexed: 06/05/2024] Open
Abstract
Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.
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Affiliation(s)
- Ivan P Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA.
| | - Olga Y Gorlova
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
| | - Spyridon Tsavachidis
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, One Baylor Plaza, Mailstop: BCM451, Houston, TX, 77030, USA
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3
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Warrell J, Salichos L, Gancz M, Gerstein MB. Latent evolutionary signatures: a general framework for analysing music and cultural evolution. J R Soc Interface 2024; 21:20230647. [PMID: 38503341 PMCID: PMC10950459 DOI: 10.1098/rsif.2023.0647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Cultural processes of change bear many resemblances to biological evolution. The underlying units of non-biological evolution have, however, remained elusive, especially in the domain of music. Here, we introduce a general framework to jointly identify underlying units and their associated evolutionary processes. We model musical styles and principles of organization in dimensions such as harmony and form as following an evolutionary process. Furthermore, we propose that such processes can be identified by extracting latent evolutionary signatures from musical corpora, analogously to identifying mutational signatures in genomics. These signatures provide a latent embedding for each song or musical piece. We develop a deep generative architecture for our model, which can be viewed as a type of variational autoencoder with an evolutionary prior constraining the latent space; specifically, the embeddings for each song are tied together via an energy-based prior, which encourages songs close in evolutionary space to share similar representations. As illustration, we analyse songs from the McGill Billboard dataset. We find frequent chord transitions and formal repetition schemes and identify latent evolutionary signatures related to these features. Finally, we show that the latent evolutionary representations learned by our model outperform non-evolutionary representations in such tasks as period and genre prediction.
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Affiliation(s)
- Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY 10023, USA
- Biomedical Data Science Center, New York Institute of Technology, New York, NY 10023, USA
| | - Michael Gancz
- Department of Music, Yale University, New Haven, CT 06520, USA
| | - Mark B. Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
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4
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Pablo-Fontecha V, Hernández-Illán E, Reparaz A, Asensio E, Morata J, Tonda R, Lahoz S, Parra C, Lozano JJ, García-Heredia A, Martínez-Roca A, Beltran S, Balaguer F, Jover R, Castells A, Trullàs R, Podlesniy P, Camps J. Quantification of rare somatic single nucleotide variants by droplet digital PCR using SuperSelective primers. Sci Rep 2023; 13:18997. [PMID: 37923774 PMCID: PMC10624686 DOI: 10.1038/s41598-023-39874-0] [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: 06/01/2023] [Accepted: 08/01/2023] [Indexed: 11/06/2023] Open
Abstract
Somatic single-nucleotide variants (SNVs) occur every time a cell divides, appearing even in healthy tissues at low frequencies. These mutations may accumulate as neutral variants during aging, or eventually, promote the development of neoplasia. Here, we present the SP-ddPCR, a droplet digital PCR (ddPCR) based approach that utilizes customized SuperSelective primers aiming at quantifying the proportion of rare SNVs. For that purpose, we selected five potentially pathogenic variants identified by whole-exome sequencing (WES) occurring at low variant allele frequency (VAF) in at-risk colon healthy mucosa of patients diagnosed with colorectal cancer or advanced adenoma. Additionally, two APC SNVs detected in two cancer lesions were added to the study for WES-VAF validation. SuperSelective primers were designed to quantify SNVs at low VAFs both in silico and in clinical samples. In addition to the two APC SNVs in colonic lesions, SP-ddPCR confirmed the presence of three out of five selected SNVs in the normal colonic mucosa with allelic frequencies ≤ 5%. Moreover, SP-ddPCR showed the presence of two potentially pathogenic variants in the distal normal mucosa of patients with colorectal carcinoma. In summary, SP-ddPCR offers a rapid and feasible methodology to validate next-generation sequencing data and accurately quantify rare SNVs, thus providing a potential tool for diagnosis and stratification of at-risk patients based on their mutational profiling.
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Affiliation(s)
- Verónica Pablo-Fontecha
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 28029, Madrid, Spain
| | - Eva Hernández-Illán
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
| | - Andrea Reparaz
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 28029, Madrid, Spain
- Neurobiology Unit, Institut d'Investigacions Biomèdiques de Barcelona (IIBB-CSIC), Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Elena Asensio
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
| | - Jordi Morata
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028, Barcelona, Spain
| | - Raúl Tonda
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028, Barcelona, Spain
| | - Sara Lahoz
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029, Madrid, Spain
| | - Carolina Parra
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
| | - Juan José Lozano
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029, Madrid, Spain
| | - Anabel García-Heredia
- Servicio de Medicina Digestiva, Hospital General Universitario de Alicante, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 03010, Alicante, Spain
| | - Alejandro Martínez-Roca
- Servicio de Medicina Digestiva, Hospital General Universitario de Alicante, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 03010, Alicante, Spain
| | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), 08028, Barcelona, Spain
| | - Francesc Balaguer
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029, Madrid, Spain
| | - Rodrigo Jover
- Servicio de Medicina Digestiva, Hospital General Universitario de Alicante, Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), 03010, Alicante, Spain
| | - Antoni Castells
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029, Madrid, Spain
| | - Ramon Trullàs
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 28029, Madrid, Spain
- Neurobiology Unit, Institut d'Investigacions Biomèdiques de Barcelona (IIBB-CSIC), Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Petar Podlesniy
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), 28029, Madrid, Spain
- Neurobiology Unit, Institut d'Investigacions Biomèdiques de Barcelona (IIBB-CSIC), Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036, Barcelona, Spain
| | - Jordi Camps
- Translational Colorectal Cancer Genomics, Gastrointestinal and Pancreatic Oncology Team, Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Rosselló 149-153, 4th Floor, 08036, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), 28029, Madrid, Spain.
- Unitat de Biologia Cel·lular i Genètica Mèdica, Departament de Biologia Cel·lular, Fisiologia i Immunologia, Facultat de Medicina, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain.
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5
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Lu T, Park S, Han Y, Wang Y, Hubert SM, Futreal PA, Wistuba I, Heymach JV, Reuben A, Zhang J, Wang T. Netie: inferring the evolution of neoantigen-T cell interactions in tumors. Nat Methods 2022; 19:1480-1489. [PMID: 36303017 PMCID: PMC10083098 DOI: 10.1038/s41592-022-01644-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/09/2022] [Indexed: 11/08/2022]
Abstract
Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8+ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.
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Affiliation(s)
- Tianshi Lu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Republic of Korea
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shawna Marie Hubert
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - P Andy Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio Wistuba
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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6
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Lee ND, Bozic I. Inferring parameters of cancer evolution in chronic lymphocytic leukemia. PLoS Comput Biol 2022; 18:e1010677. [PMID: 36331987 PMCID: PMC9668150 DOI: 10.1371/journal.pcbi.1010677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [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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Affiliation(s)
- Nathan D. Lee
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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7
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van den Bosch T, Vermeulen L, Miedema DM. Quantitative models for the inference of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
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8
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Aramini B, Masciale V, Arienti C, Dominici M, Stella F, Martinelli G, Fabbri F. Cancer Stem Cells (CSCs), Circulating Tumor Cells (CTCs) and Their Interplay with Cancer Associated Fibroblasts (CAFs): A New World of Targets and Treatments. Cancers (Basel) 2022; 14:cancers14102408. [PMID: 35626011 PMCID: PMC9139858 DOI: 10.3390/cancers14102408] [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: 04/08/2022] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The world of small molecules in solid tumors as cancer stem cells (CSCs), circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) continues to be under-debated, but not of minor interest in recent decades. One of the main problems in regard to cancer is the development of tumor recurrence, even in the early stages, in addition to drug resistance and, consequently, ineffective or an incomplete response against the tumor. The findings behind this resistance are probably justified by the presence of small molecules such as CSCs, CTCs and CAFs connected with the tumor microenvironment, which may influence the aggressiveness and the metastatic process. The mechanisms, connections, and molecular pathways behind them are still unknown. Our review would like to represent an important step forward to highlight the roles of these molecules and the possible connections among them. Abstract The importance of defining new molecules to fight cancer is of significant interest to the scientific community. In particular, it has been shown that cancer stem cells (CSCs) are a small subpopulation of cells within tumors with capabilities of self-renewal, differentiation, and tumorigenicity; on the other side, circulating tumor cells (CTCs) seem to split away from the primary tumor and appear in the circulatory system as singular units or clusters. It is becoming more and more important to discover new biomarkers related to these populations of cells in combination to define the network among them and the tumor microenvironment. In particular, cancer-associated fibroblasts (CAFs) are a key component of the tumor microenvironment with different functions, including matrix deposition and remodeling, extensive reciprocal signaling interactions with cancer cells and crosstalk with immunity. The settings of new markers and the definition of the molecular connections may present new avenues, not only for fighting cancer but also for the definition of more tailored therapies.
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Affiliation(s)
- Beatrice Aramini
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, 47121 Forlì, Italy;
- Correspondence:
| | - Valentina Masciale
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, 41122 Modena, Italy; (V.M.); (M.D.)
| | - Chiara Arienti
- Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy; (C.A.); (G.M.); (F.F.)
| | - Massimo Dominici
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, 41122 Modena, Italy; (V.M.); (M.D.)
| | - Franco Stella
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, 47121 Forlì, Italy;
| | - Giovanni Martinelli
- Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy; (C.A.); (G.M.); (F.F.)
| | - Francesco Fabbri
- Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, 47014 Meldola, Italy; (C.A.); (G.M.); (F.F.)
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9
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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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10
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Ghosh A, Das C, Ghose S, Maitra A, Roy B, Majumder PP, Biswas NK. Integrative analysis of genomic and transcriptomic data of normal, tumour and co-occurring leukoplakia tissue triads drawn from patients with gingivobuccal oral cancer identifies signatures of tumour initiation and progression. J Pathol 2022; 257:593-606. [PMID: 35358331 PMCID: PMC9545831 DOI: 10.1002/path.5900] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 11/16/2022]
Abstract
A thickened, white patch — leukoplakia — in the oral cavity is usually benign, but sometimes (in ~9% of individuals) it progresses to malignant tumour. Because the genomic basis of this progression is poorly understood, we undertook this study and collected samples of four tissues — leukoplakia, tumour, adjacent normal, and blood — from each of 28 patients suffering from gingivobuccal oral cancer. We performed multiomics analysis of the 112 collected tissues (four tissues per patient from 28 patients) and integrated information on progressive changes in the mutational and transcriptional profiles of each patient to create this genomic narrative. Additionally, we generated and analysed whole‐exome sequence data from leukoplakia tissues collected from 11 individuals not suffering from oral cancer. Nonsynonymous somatic mutations in the CASP8 gene were identified as the likely events to initiate malignant transformation, since these were frequently shared between tumour and co‐occurring leukoplakia. CASP8 alterations were also shown to enhance expressions of genes that favour lateral spread of mutant cells. During malignant transformation, additional pathogenic mutations are acquired in key genes (TP53, NOTCH1, HRAS) (41% of patients); chromosomal‐instability (arm‐level deletions of 19p and q, focal‐deletion of DNA‐repair pathway genes and NOTCH1, amplification of EGFR) (77%), and increased APOBEC‐activity (23%) are also observed. These additional alterations were present singly (18% of patients) or in combination (68%). Some of these alterations likely impact immune‐dynamics of the evolving transformed tissue; progression to malignancy is associated with immune suppression through infiltration of regulatory T‐cells (56%), depletion of cytotoxic T‐cells (68%), and antigen‐presenting dendritic cells (72%), with a concomitant increase in inflammation (92%). Patients can be grouped into three clusters by the estimated time to development of cancer from precancer by acquiring additional mutations (range: 4–10 years). Our findings provide deep molecular insights into the evolutionary processes and trajectories of oral cancer initiation and progression. © 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)
- Arnab Ghosh
- National Institute of Biomedical Genomics, Kalyani, India
| | | | - Sandip Ghose
- Dr. R. Ahmed Dental College and Hospital, Kolkata, India
| | - Arindam Maitra
- National Institute of Biomedical Genomics, Kalyani, India
| | - Bidyut Roy
- Indian Statistical Institute, Kolkata, India
| | - Partha P Majumder
- National Institute of Biomedical Genomics, Kalyani, India.,Indian Statistical Institute, Kolkata, India
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11
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Stiehl T, Marciniak-Czochra A. Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples. Front Physiol 2021; 12:596194. [PMID: 34497529 PMCID: PMC8419336 DOI: 10.3389/fphys.2021.596194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.
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Affiliation(s)
- Thomas Stiehl
- Institute for Computational Biomedicine – Disease Modeling, RWTH Aachen University, Aachen, Germany
- Institute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, Germany
| | - Anna Marciniak-Czochra
- Institute of Applied Mathematics, Interdisciplinary Center for Scientific Computing and Bioquant Center, Heidelberg University, Heidelberg, Germany
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12
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Kazarian E, Marks A, Cui J, Darbinyan A, Tong E, Mueller S, Cha S, Aboian MS. Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas. Sci Rep 2021; 11:14377. [PMID: 34257334 PMCID: PMC8277861 DOI: 10.1038/s41598-021-92943-0] [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: 03/07/2021] [Accepted: 06/15/2021] [Indexed: 11/09/2022] Open
Abstract
We evaluate the topographic distribution of diffuse midline gliomas and hemispheric high-grade gliomas in children with respect to their normal gene expression patterns and pathologic driver mutation patterns. We identified 19 pediatric patients with diffuse midline or high-grade glioma with preoperative MRI from tumor board review. 7 of these had 500 gene panel mutation testing, 11 patients had 50 gene panel mutation testing and one 343 gene panel testing from a separate institution were included as validation set. Tumor imaging features and gene expression patterns were analyzed using Allen Brain Atlas. Twelve patients had diffuse midline gliomas and seven had hemispheric high-grade gliomas. Three diffuse midline gliomas had the K27M mutation in the tail of histone H3 protein. All patients undergoing 500 gene panel testing had additional mutations, the most common being in ACVR1, PPM1D, and p53. Hemispheric high-grade gliomas had either TP53 or IDH1 mutation and diffuse midline gliomas had H3 K27M-mutation. Gene expression analysis in normal brains demonstrated that genes mutated in diffuse midline gliomas had higher expression along midline structures as compared to the cerebral hemispheres. Our study suggests that topographic location of pediatric diffuse midline gliomas and hemispheric high-grade gliomas correlates with driver mutations of tumor to the endogenous gene expression in that location. This correlation suggests that cellular state that is required for increased gene expression predisposes that location to mutations and defines the driver mutations within tumors that arise from that region.
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Affiliation(s)
- Eve Kazarian
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Asher Marks
- Department of Pediatric Hematology & Oncology, Yale School of Medicine, New Haven, CT, USA
| | - Jin Cui
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Armine Darbinyan
- Department of Neuropathology, Yale School of Medicine, New Haven, CT, USA
| | - Elizabeth Tong
- Department of Radiology, , University of California, San Francisco, San Francisco, USA
| | - Sabine Mueller
- Division of Pediatric Hematology & Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, USA.,Department of Neurological Surgery, University of California, San Francisco, San Francisco, USA.,Department of Neurology, University of California, San Francisco, San Francisco, USA
| | - Soonmee Cha
- Department of Radiology, , University of California, San Francisco, San Francisco, USA
| | - Mariam S Aboian
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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13
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Salehi S, Kabeer F, Ceglia N, Andronescu M, Williams MJ, Campbell KR, Masud T, Wang B, Biele J, Brimhall J, Gee D, Lee H, Ting J, Zhang AW, Tran H, O'Flanagan C, Dorri F, Rusk N, de Algara TR, Lee SR, Cheng BYC, Eirew P, Kono T, Pham J, Grewal D, Lai D, Moore R, Mungall AJ, Marra MA, McPherson A, Bouchard-Côté A, Aparicio S, Shah SP. Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature 2021; 595:585-590. [PMID: 34163070 PMCID: PMC8396073 DOI: 10.1038/s41586-021-03648-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 05/17/2021] [Indexed: 02/02/2023]
Abstract
Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.
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Affiliation(s)
- Sohrab Salehi
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Farhia Kabeer
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nicholas Ceglia
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mirela Andronescu
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Marc J Williams
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kieran R Campbell
- Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital Joseph & Wolf Lebovic Health Complex, Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Tehmina Masud
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Beixi Wang
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Justina Biele
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Jazmine Brimhall
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - David Gee
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Hakwoo Lee
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Jerome Ting
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Allen W Zhang
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Hoa Tran
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Ciara O'Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Fatemeh Dorri
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nicole Rusk
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - So Ra Lee
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Brian Yu Chieh Cheng
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Peter Eirew
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Takako Kono
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Jenifer Pham
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Diljot Grewal
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Lai
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Richard Moore
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Andrew J Mungall
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Marco A Marra
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada
| | - Andrew McPherson
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandre Bouchard-Côté
- Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Sohrab P Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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14
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Yates JWT, Mistry H. Clone Wars: Quantitatively Understanding Cancer Drug Resistance. JCO Clin Cancer Inform 2020; 4:938-946. [PMID: 33112660 DOI: 10.1200/cci.20.00089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.
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Affiliation(s)
| | - Hitesh Mistry
- Division of Pharmacy and Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
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15
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Shnaider PV, Ivanova OM, Malyants IK, Anufrieva KS, Semenov IA, Pavlyukov MS, Lagarkova MA, Govorun VM, Shender VO. New Insights into Therapy-Induced Progression of Cancer. Int J Mol Sci 2020; 21:E7872. [PMID: 33114182 PMCID: PMC7660620 DOI: 10.3390/ijms21217872] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023] Open
Abstract
The malignant tumor is a complex heterogeneous set of cells functioning in a no less heterogeneous microenvironment. Like any dynamic system, cancerous tumors evolve and undergo changes in response to external influences, including therapy. Initially, most tumors are susceptible to treatment. However, remaining cancer cells may rapidly reestablish the tumor after a temporary remission. These new populations of malignant cells usually have increased resistance not only to the first-line agent, but also to the second- and third-line drugs, leading to a significant decrease in patient survival. Multiple studies describe the mechanism of acquired therapy resistance. In past decades, it became clear that, in addition to the simple selection of pre-existing resistant clones, therapy induces a highly complicated and tightly regulated molecular response that allows tumors to adapt to current and even subsequent therapeutic interventions. This review summarizes mechanisms of acquired resistance, such as secondary genetic alterations, impaired function of drug transporters, and autophagy. Moreover, we describe less obvious molecular aspects of therapy resistance in cancers, including epithelial-to-mesenchymal transition, cell cycle alterations, and the role of intercellular communication. Understanding these molecular mechanisms will be beneficial in finding novel therapeutic approaches for cancer therapy.
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Affiliation(s)
- Polina V. Shnaider
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia; (P.V.S.); (O.M.I.); (K.S.A.); (M.A.L.)
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
- Faculty of Biology, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Olga M. Ivanova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia; (P.V.S.); (O.M.I.); (K.S.A.); (M.A.L.)
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
| | - Irina K. Malyants
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
- Faculty of Chemical-Pharmaceutical Technologies and Biomedical Drugs, Mendeleev University of Chemical Technology of Russia, Moscow 125047, Russia
| | - Ksenia S. Anufrieva
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia; (P.V.S.); (O.M.I.); (K.S.A.); (M.A.L.)
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
- Moscow Institute of Physics and Technology (State University), Dolgoprudny 141701, Russia
| | - Ilya A. Semenov
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
| | - Marat S. Pavlyukov
- Laboratory of Membrane Bioenergetics, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia;
| | - Maria A. Lagarkova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia; (P.V.S.); (O.M.I.); (K.S.A.); (M.A.L.)
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
| | - Vadim M. Govorun
- Laboratory of Simple Systems, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia;
| | - Victoria O. Shender
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia; (P.V.S.); (O.M.I.); (K.S.A.); (M.A.L.)
- Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia; (I.K.M.); (I.A.S.)
- Laboratory of Molecular Oncology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
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16
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Bai H, Liu Z, Zhang T, Du J, Zhou C, He W, Chau JHC, Kwok RTK, Lam JWY, Tang BZ. Multifunctional Supramolecular Assemblies with Aggregation-Induced Emission (AIE) for Cell Line Identification, Cell Contamination Evaluation, and Cancer Cell Discrimination. ACS NANO 2020; 14:7552-7563. [PMID: 32484332 DOI: 10.1021/acsnano.0c03404] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It is undoubted the important role of cells in biology and medicine, but worldwide misidentified and cross-contaminated cell lines have caused much trouble in related fields. Herein, three kinds of supramolecular AIE (aggregation-induced emission) nanoassemblies were constructed by the host-guest interaction between tetraphenylethene (TPE) derivatives and cucurbit[8]uril (CB[8]). Based on the recognized mechanism of AIE, the TPE derivatives could achieve stronger fluorescence emission and higher fluorescence quantum yield after assembling with CB[8]. Moreover, the constructed supramolecular AIE complexes obtained well-confirmed nanostructures and exhibited different sizes and shapes. Correspondingly, they generated characteristic biological properties and fluorescence enhancement of cells. Inspired by the concept of Big Data Analysis, these fluorescence signals were further transformed into a unique fingerprint of cells via linear discriminant analysis. Immediately, we realized the veracious identification between a normal cell line, two cancer cell lines, and two metastasized cancer cell lines in a qualitative analysis. More importantly, it was well used to monitor the evaluation of cross-contaminated cells and the discrimination of cancer cells. As a proper bioapplication of ideal supramolecular nanomaterials, this system was easy to learn and apply, and the whole procedure was kept to 20 min, without cell disruption, centrifugation, or washing steps.
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Affiliation(s)
- Haotian Bai
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Zhiyang Liu
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- HKUST Shenzhen Research Institute, No. 9 Yuexing 1st Road, South Area, Hi-tech Park Nanshan, Shenzhen 518057, China
| | - Tianfu Zhang
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Jian Du
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Chengcheng Zhou
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Wei He
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Joe H C Chau
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Ryan T K Kwok
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- HKUST Shenzhen Research Institute, No. 9 Yuexing 1st Road, South Area, Hi-tech Park Nanshan, Shenzhen 518057, China
| | - Jacky W Y Lam
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- HKUST Shenzhen Research Institute, No. 9 Yuexing 1st Road, South Area, Hi-tech Park Nanshan, Shenzhen 518057, China
| | - Ben Zhong Tang
- Department of Chemical and Biological Engineering, Department of Chemistry, and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- HKUST Shenzhen Research Institute, No. 9 Yuexing 1st Road, South Area, Hi-tech Park Nanshan, Shenzhen 518057, China
- State Key Laboratory of Luminescent Materials and Devices, Center for Aggregation-Induced Emission (Guangzhou International Campus), South China University of Technology, Guangzhou 510640, China
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