2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Separating the Local and Malignant Dimensions of Cancer Adaptation

      article-commentary
      1 , 2 , 3 , 4 , 5
      Cancer Informatics
      SAGE Publications
      Cancer, genetics, evolution, somatic mutations, bioinformatics

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The repeatability observed across cancers arising in the same tissue can help understand the evolutionary process of tumour initiation. We recently developed a framework to quantify the local malignant adaptation of genetic clones in tissue-specific environments. In this Commentary, we argue that such a 1-dimensional model can be improved by separating its 2 components to obtain a dual scale: local adaptation, dictating proliferation rates in the local environment, and malignant adaptation, influencing the likelihood that a clone becomes cancerous and invasive. Such a change could strengthen our understanding of the population dynamics underlying cancer initiation and assess different evolutionary scenarios.

          Related collections

          Most cited references8

          • Record: found
          • Abstract: found
          • Article: not found

          The clonal evolution of tumor cell populations.

          P C Nowell (1976)
          It is proposed that most neoplasms arise from a single cell of origin, and tumor progression results from acquired genetic variability within the original clone allowing sequential selection of more aggressive sublines. Tumor cell populations are apparently more genetically unstable than normal cells, perhaps from activation of specific gene loci in the neoplasm, continued presence of carcinogen, or even nutritional deficiencies within the tumor. The acquired genetic insta0ility and associated selection process, most readily recognized cytogenetically, results in advanced human malignancies being highly individual karyotypically and biologically. Hence, each patient's cancer may require individual specific therapy, and even this may be thwarted by emergence of a genetically variant subline resistant to the treatment. More research should be directed toward understanding and controlling the evolutionary process in tumors before it reaches the late stage usually seen in clinical cancer.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer

            Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Somatic mutant clones colonize the human esophagus with age

              The extent to which cells in normal tissues accumulate mutations throughout life is poorly understood. Some mutant cells expand into clones that can be detected by genome sequencing. We mapped mutant clones in normal esophageal epithelium from nine donors (age range, 20 to 75 years). Somatic mutations accumulated with age and were caused mainly by intrinsic mutational processes. We found strong positive selection of clones carrying mutations in 14 cancer genes, with tens to hundreds of clones per square centimeter. In middle-aged and elderly donors, clones with cancer-associated mutations covered much of the epithelium, with NOTCH1 and TP53 mutations affecting 12 to 80% and 2 to 37% of cells, respectively. Unexpectedly, the prevalence of NOTCH1 mutations in normal esophagus was several times higher than in esophageal cancers. These findings have implications for our understanding of cancer and aging.
                Bookmark

                Author and article information

                Journal
                Cancer Inform
                Cancer Inform
                CIX
                spcix
                Cancer Informatics
                SAGE Publications (Sage UK: London, England )
                1176-9351
                05 September 2019
                2019
                : 18
                : 1176935119872954
                Affiliations
                [1 ]CREEC/UMR CNRS 5290-IRD 224-Université de Montpellier, Montpellier, France
                [2 ]Sorbonne Université, IRD, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes (UMMISCO), Bondy, France
                [3 ]Departamento de Etología, Fauna Silvestre y Animales de Laboratorio, Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México
                [4 ]MIVEGEC, IRD, CNRS, Université de Montpellier, Montpellier, France
                [5 ]Université Claude Bernard Lyon 1 (Univ Lyon), INSERM 1052, CNRS 5286, Centre Léon Bérard Cancer Research Center of Lyon, Lyon, France
                Author notes
                [*]Pierre Martinez, Université Claude Bernard Lyon 1 (Univ Lyon), INSERM 1052, CNRS 5286, Centre Léon Bérard Cancer Research Center of Lyon, 69373 Lyon, France. Email: pierre.martinez@ 123456lyon.unicancer.fr
                Author information
                https://orcid.org/0000-0001-7069-4413
                Article
                10.1177_1176935119872954
                10.1177/1176935119872954
                6728660
                59ea0927-8c61-4449-86af-0516c7bffcd4
                © The Author(s) 2019

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 31 July 2019
                : 8 August 2019
                Categories
                Commentary
                Custom metadata
                January-December 2019

                Oncology & Radiotherapy
                cancer,genetics,evolution,somatic mutations,bioinformatics
                Oncology & Radiotherapy
                cancer, genetics, evolution, somatic mutations, bioinformatics

                Comments

                Comment on this article