Genome-wide identification and analysis of prognostic features in human cancers

Smith, Joan C, Sheltzer, Jason M (March 2022) Genome-wide identification and analysis of prognostic features in human cancers. Cell Reports, 38 (13). p. 110569. ISSN 2211-1247

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URL: https://www.ncbi.nlm.nih.gov/pubmed/35354049
DOI: 10.1016/j.celrep.2022.110569

Abstract

Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development.

Item Type: Paper
Subjects: diseases & disorders > cancer
bioinformatics > genomics and proteomics
diseases & disorders > neoplasms
organism description > animal > mammal > primates > hominids > human
diseases & disorders > cancer > drugs and therapies > patient outcomes
CSHL Authors:
Communities: CSHL labs > Sheltzer lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 29 March 2022
Date Deposited: 14 Apr 2022 13:31
Last Modified: 14 Apr 2022 13:31
URI: https://repository.cshl.edu/id/eprint/40583

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