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  1. Article ; Online: A data-driven approach for constructing mutation categories for mutational signature analysis.

    Gal Gilad / Mark D M Leiserson / Roded Sharan

    PLoS Computational Biology, Vol 17, Iss 10, p e

    2021  Volume 1009542

    Abstract: Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution ... ...

    Abstract Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.
    Keywords Biology (General) ; QH301-705.5
    Subject code 612
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A mixture model for signature discovery from sparse mutation data

    Itay Sason / Yuexi Chen / Mark D.M. Leiserson / Roded Sharan

    Genome Medicine, Vol 13, Iss 1, Pp 1-

    2021  Volume 12

    Abstract: Abstract Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have ... ...

    Abstract Abstract Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .
    Keywords Mutational signatures ; Probabilistic modeling ; Gene panel sequencing ; Medicine ; R ; Genetics ; QH426-470
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types

    Tae Yoon Park / Mark D.M. Leiserson / Gunnar W. Klau / Benjamin J. Raphael

    Cell Genomics, Vol 2, Iss 2, Pp 100099- (2022)

    2022  

    Abstract: Summary: Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene ... ...

    Abstract Summary: Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.
    Keywords CRISPR screen ; essential genes ; genetic dependency ; mutual exclusivity ; oncogene addiction ; oncogenic pathway ; Genetics ; QH426-470 ; Internal medicine ; RC31-1245
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

    Itay Sason / Damian Wojtowicz / Welles Robinson / Mark D.M. Leiserson / Teresa M. Przytycka / Roded Sharan

    iScience, Vol 23, Iss 3, Pp - (2020)

    2020  

    Abstract: Summary: The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of ... ...

    Abstract Summary: The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer. : Quantitative Genetics; Bioinformatics; Cancer Subject Areas: Quantitative Genetics, Bioinformatics, Cancer
    Keywords Science ; Q
    Subject code 612
    Language English
    Publishing date 2020-03-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Author Correction

    Sanju Sinha / Karina Barbosa / Kuoyuan Cheng / Mark D. M. Leiserson / Prashant Jain / Anagha Deshpande / David M. Wilson / Bríd M. Ryan / Ji Luo / Ze’ev A. Ronai / Joo Sang Lee / Aniruddha J. Deshpande / Eytan Ruppin

    Nature Communications, Vol 13, Iss 1, Pp 1-

    A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

    2022  Volume 1

    Keywords Science ; Q
    Language English
    Publishing date 2022-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer

    Damian Wojtowicz / Itay Sason / Xiaoqing Huang / Yoo-Ah Kim / Mark D. M. Leiserson / Teresa M. Przytycka / Roded Sharan

    Genome Medicine, Vol 11, Iss 1, Pp 1-

    2019  Volume 12

    Abstract: Abstract Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their ... ...

    Abstract Abstract Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.
    Keywords Mutational process ; Hidden Markov model ; Mutation signature ; Breast cancer ; Medicine ; R ; Genetics ; QH426-470
    Language English
    Publishing date 2019-07-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

    Yoo-Ah Kim / Damian Wojtowicz / Rebecca Sarto Basso / Itay Sason / Welles Robinson / Dorit S. Hochbaum / Mark D. M. Leiserson / Roded Sharan / Fabio Vadin / Teresa M. Przytycka

    Genome Medicine, Vol 12, Iss 1, Pp 1-

    2020  Volume 12

    Abstract: Abstract Background Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from ... ...

    Abstract Abstract Background Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Methods To investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming. Results Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures—one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes. Conclusions This work investigated, for the first ...
    Keywords Mutational signature ; Continuous cancer phenotype ; Gene network ; Network-phenotype association ; Breast cancer ; APOBEC ; Medicine ; R ; Genetics ; QH426-470
    Subject code 616
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing

    Sanju Sinha / Karina Barbosa / Kuoyuan Cheng / Mark D. M. Leiserson / Prashant Jain / Anagha Deshpande / David M. Wilson / Bríd M. Ryan / Ji Luo / Ze’ev A. Ronai / Joo Sang Lee / Aniruddha J. Deshpande / Eytan Ruppin

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Volume 13

    Abstract: CRISPR-Cas9 gene editing can induce a p53 mediated damage response. Here the authors investigate the possibility of selection of pre-existing cancer driver mutations during CRISPR-Cas9 knockout based gene editing and identify KRAS mutants that may confer ...

    Abstract CRISPR-Cas9 gene editing can induce a p53 mediated damage response. Here the authors investigate the possibility of selection of pre-existing cancer driver mutations during CRISPR-Cas9 knockout based gene editing and identify KRAS mutants that may confer a selected advantage to edited cells.
    Keywords Science ; Q
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Simultaneous identification of multiple driver pathways in cancer.

    Mark D M Leiserson / Dima Blokh / Roded Sharan / Benjamin J Raphael

    PLoS Computational Biology, Vol 9, Iss 5, p e

    2013  Volume 1003054

    Abstract: Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple ... ...

    Abstract Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways - including Rb, p53, PI(3)K, and cell cycle pathways - and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software.
    Keywords Biology (General) ; QH301-705.5
    Subject code 570
    Language English
    Publishing date 2013-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: A multifactorial model of T cell expansion and durable clinical benefit in response to a PD-L1 inhibitor.

    Mark D M Leiserson / Vasilis Syrgkanis / Amy Gilson / Miroslav Dudik / Sharon Gillett / Jennifer Chayes / Christian Borgs / Dean F Bajorin / Jonathan E Rosenberg / Samuel Funt / Alexandra Snyder / Lester Mackey

    PLoS ONE, Vol 13, Iss 12, p e

    2018  Volume 0208422

    Abstract: Checkpoint inhibitor immunotherapies have had major success in treating patients with late-stage cancers, yet the minority of patients benefit. Mutation load and PD-L1 staining are leading biomarkers associated with response, but each is an imperfect ... ...

    Abstract Checkpoint inhibitor immunotherapies have had major success in treating patients with late-stage cancers, yet the minority of patients benefit. Mutation load and PD-L1 staining are leading biomarkers associated with response, but each is an imperfect predictor. A key challenge to predicting response is modeling the interaction between the tumor and immune system. We begin to address this challenge with a multifactorial model for response to anti-PD-L1 therapy. We train a model to predict immune response in patients after treatment based on 36 clinical, tumor, and circulating features collected prior to treatment. We analyze data from 21 bladder cancer patients using the elastic net high-dimensional regression procedure and, as training set error is a biased and overly optimistic measure of prediction error, we use leave-one-out cross-validation to obtain unbiased estimates of accuracy on held-out patients. In held-out patients, the model explains 79% of the variance in T cell clonal expansion. This predicted immune response is multifactorial, as the variance explained is at most 23% if clinical, tumor, or circulating features are excluded. Moreover, if patients are triaged according to predicted expansion, only 38% of non-durable clinical benefit (DCB) patients need be treated to ensure that 100% of DCB patients are treated. In contrast, using mutation load or PD-L1 staining alone, one must treat at least 77% of non-DCB patients to ensure that all DCB patients receive treatment. Thus, integrative models of immune response may improve our ability to anticipate clinical benefit of immunotherapy.
    Keywords Medicine ; R ; Science ; Q
    Subject code 610 ; 616
    Language English
    Publishing date 2018-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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