RT Journal Article
SR Electronic
T1 Identification of Relevant Genetic Alterations in Cancer using Topological Data Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.30.922310
DO 10.1101/2020.01.30.922310
A1 Raúl Rabadán
A1 Yamina Mohamedi
A1 Udi Rubin
A1 Tim Chu
A1 Oliver Elliott
A1 Luis Arnés
A1 Santiago Cal
A1 Álvaro J. Obaya
A1 Arnold J. Levine
A1 Pablo G. Cámara
YR 2020
UL http://biorxiv.org/content/early/2020/01/31/2020.01.30.922310.abstract
AB Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12−/− mice have a five-fold increase in the susceptibility to develop lung tumors, confirming the role of ADAMTS12 as a tumor suppressor gene. Our results demonstrate that data integration through topological techniques can increase our ability to identify previously unreported cancer-related alterations.