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Open AccessJournal ArticleDOI

Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

Theo A. Knijnenburg, +37 more
- 03 Apr 2018 - 
- Vol. 23, Iss: 1, pp 239
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TLDR
These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy and a new machine-learning-based classifier developed from gene expression data allowed to identify alterations that phenocopy deleterious TP53 mutations.
About: 
This article is published in Cell Reports.The article was published on 2018-04-03 and is currently open access. It has received 706 citations till now. The article focuses on the topics: Loss of heterozygosity.

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The Immune Landscape of Cancer

Vesteinn Thorsson, +50 more
- 17 Apr 2018 - 
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.
Journal ArticleDOI

Oncogenic Signaling Pathways in The Cancer Genome Atlas

Francisco Sanchez-Vega, +53 more
- 05 Apr 2018 - 
TL;DR: This work charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity.
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Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

Katherine A Hoadley, +738 more
- 05 Apr 2018 - 
TL;DR: Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, Pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which may inform strategies for future therapeutic development.
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State-of-the-art strategies for targeting the DNA damage response in cancer

Patrick G. Pilie, +3 more
- 01 Feb 2019 - 
TL;DR: The authors review the progress made to date with PARP inhibitors, describe the expanding landscape of novel anticancer therapies targeting the DNA damage response and potential predictive biomarkers, mechanisms of resistance and combinatorial strategies are discussed.
Journal ArticleDOI

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

Kyle Ellrott, +14 more
- 28 Mar 2018 - 
TL;DR: The Multi-Center Mutation Calling in Multiple Cancers project, an effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses, is described.
References
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Journal ArticleDOI

Hallmarks of cancer: the next generation.

Douglas Hanahan, +1 more
- 04 Mar 2011 - 
TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
Journal Article

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, +15 more
- 01 Feb 2011 - 
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Journal ArticleDOI

VMD: Visual molecular dynamics

William Humphrey, +2 more
- 01 Feb 1996 - 
TL;DR: VMD is a molecular graphics program designed for the display and analysis of molecular assemblies, in particular biopolymers such as proteins and nucleic acids, which can simultaneously display any number of structures using a wide variety of rendering styles and coloring methods.
Journal ArticleDOI

The Protein Data Bank

Helen M. Berman, +7 more
- 01 Jan 2000 - 
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Posted Content

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, +18 more
- 02 Jan 2012 - 
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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Hallmarks of cancer: the next generation.

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