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      PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem.

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          Abstract

          Recent advances in single-cell sequencing (SCS) offer an unprecedented insight into tumor emergence and evolution. Principled approaches to tumor phylogeny reconstruction via SCS data are typically based on general computational methods for solving an integer linear program, or a constraint satisfaction program, which, although guaranteeing convergence to the most likely solution, are very slow. Others based on Monte Carlo Markov Chain or alternative heuristics not only offer no such guarantee, but also are not faster in practice. As a result, novel methods that can scale up to handle the size and noise characteristics of emerging SCS data are highly desirable to fully utilize this technology.

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          UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma

          Yochai Wolf, Osnat Bartok, Sushant Patkar … (2019)
          Summary Although clonal neo-antigen burden is associated with improved response to immune therapy, the functional basis for this remains unclear. Here we study this question in a novel controlled mouse melanoma model that enables us to explore the effects of intra-tumor heterogeneity (ITH) on tumor aggressiveness and immunity independent of tumor mutational burden. Induction of UVB-derived mutations yields highly aggressive tumors with decreased anti-tumor activity. However, single-cell-derived tumors with reduced ITH are swiftly rejected. Their rejection is accompanied by increased T cell reactivity and a less suppressive microenvironment. Using phylogenetic analyses and mixing experiments of single-cell clones, we dissect two characteristics of ITH: the number of clones forming the tumor and their clonal diversity. Our analysis of melanoma patient tumor data recapitulates our results in terms of overall survival and response to immune checkpoint therapy. These findings highlight the importance of clonal mutations in robust immune surveillance and the need to quantify patient ITH to determine the response to checkpoint blockade.
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            Toward understanding and exploiting tumor heterogeneity.

            Ash Alizadeh, Victoria Aranda, Alberto Bardelli … (2015)
            The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.
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              Tree inference for single-cell data

              Katharina Jahn, Jack Kuipers, Niko Beerenwinkel (2016)
              Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0936-x) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Bioinformatics
                Title: Bioinformatics (Oxford, England)
                Publisher: Oxford University Press (OUP)
                ISSN (Electronic): 1367-4811
                ISSN (Print): 1367-4803
                Publication date (Electronic): Jul 01 2020
                Volume: 36
                Issue: Supplement_1
                Affiliations
                [1 ] Department of Computer Science, Indiana University, Bloomington, IN 47408, USA.
                [2 ] Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
                [3 ] Program in Computational Biology, Bioinformatics and Genomics, University of Maryland, College Park, MD 20742, USA.
                [4 ] Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
                [5 ] Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute, London NW1 1AT, UK.
                [6 ] Cancer Research UK Lung Cancer Centre of Excellence London, University College London Cancer Institute, London WC1E 6DD, UK.
                [7 ] Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
                Article
                Publisher Item ID: 5870466
                DOI: 10.1093/bioinformatics/btaa464
                PMC ID: 7355310
                PubMed ID: 32657358
                SO-VID: 627d01bb-8bbe-4d9b-ac22-bc94a99f8fc1
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