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Santiago Gonzalez

Researcher at European Bioinformatics Institute

Publications -  20
Citations -  4606

Santiago Gonzalez is an academic researcher from European Bioinformatics Institute. The author has contributed to research in topics: Somatic evolution in cancer & DNA repair. The author has an hindex of 13, co-authored 20 publications receiving 2645 citations. Previous affiliations of Santiago Gonzalez include Barcelona Supercomputing Center & Wellcome Trust Sanger Institute.

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Pan-cancer analysis of whole genomes

Peter J. Campbell, +1332 more
- 06 Feb 2020 - 
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
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Non-coding recurrent mutations in chronic lymphocytic leukaemia

Xose S. Puente, +56 more
- 22 Jul 2015 - 
TL;DR: An integrated portrait of the CLL genomic landscape is provided, new recurrent driver mutations of the disease are identified, and clinical interventions that may improve the management of this neoplasia are suggested.
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The evolutionary history of 2,658 cancers

Moritz Gerstung, +64 more
- 06 Feb 2020 - 
TL;DR: Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.
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Genomic Evolution of Breast Cancer Metastasis and Relapse

Lucy R. Yates, +42 more
- 14 Aug 2017 - 
TL;DR: Several lines of analysis indicate that clones seeding metastasis or relapse disseminate late from primary tumors, but continue to acquire mutations, mostly accessing the same mutational processes active in the primary tumor.
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

Yu Fu, +11 more
TL;DR: Deep transfer learning is used to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data, showing the remarkable potential of computer vision in characterizing the molecular basis of tumor Histopathology.