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Gad Getz

Researcher at Broad Institute

Publications -  627
Citations -  309042

Gad Getz is an academic researcher from Broad Institute. The author has contributed to research in topics: Cancer & Biology. The author has an hindex of 189, co-authored 520 publications receiving 247560 citations. Previous affiliations of Gad Getz include University of Colorado Denver & University of California, San Diego.

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Molecular and genetic properties of tumors associated with local immune cytolytic activity.

TL;DR: The genetic findings provide evidence for immunoediting in tumors and uncover mechanisms of tumor-intrinsic resistance to cytolytic activity, suggesting immune-mediated elimination.
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Discovery and saturation analysis of cancer genes across 21 tumour types

TL;DR: It is found that large-scale genomic analysis can identify nearly all known cancer genes in these cancer types and 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis.
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Comprehensivemolecular characterization of clear cell renal cell carcinoma

Chad J. Creighton, +291 more
- 28 Aug 2013 - 
TL;DR: Remodelling cellular metabolism constitutes a recurrent pattern in ccRCC that correlates with tumour stage and severity and offers new views on the opportunities for disease treatment.

Integrated genomic characterization of endometrial carcinoma

Gad Getz, +271 more
TL;DR: The genomic features of endometrial carcinomas permit a reclassification that may affect post-surgical adjuvant treatment for women with aggressive tumours, and these features are classified into four categories: POLE ultramutated, microsatellite instability hypermutated, copy- number low, and copy-number high.
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GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.

TL;DR: By separating SCNA profiles into underlying arm-level and focal alterations, the estimation of background rates for each category is improved, and a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence is described.