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A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Abstract：

:Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic noncoding mutations from personal cancer genomes. Therefore, although most somatic mutations are localized in the noncoding cancer genome, their effects on driving tumorigenesis and progression have not been systematically explored and noncoding somatic alleles have not been leveraged in current clinical practice to guide personalized screening, diagnosis, and treatment. Here, we present a deep learning framework to capture pathogenic noncoding mutations in personal cancer genomes, which perturb gene regulation by altering chromatin architecture. We deployed the system specifically for localized prostate cancer by integrating large-scale prostate cancer genomes and the prostate-specific epigenome. We exhaustively evaluated somatic mutations in each patient's genome and agnostically identified thousands of somatic alleles altering the prostate epigenome. Functional genomic analyses subsequently demonstrated that affected genes displayed differential expression in prostate tumor samples, were vulnerable to expression alterations, and were convergent onto androgen receptor-mediated signaling pathways. Accumulation of pathogenic regulatory mutations in these affected genes was predictive of clinical observations, suggesting potential clinical utility of this approach. Overall, the deep learning framework has significantly expanded our view of somatic mutations in the vast noncoding genome, uncovered novel genes in localized prostate cancer, and will foster the development of personalized screening and therapeutic strategies for prostate cancer. SIGNIFICANCE: This study's characterization of the noncoding genome in prostate cancer reveals mutational signatures predictive of clinical observations, which may serve as a powerful prognostic tool in this disease.

journal_name

Cancer Res

journal_title

Cancer research

authors

Wang C,Li J

doi

10.1158/0008-5472.CAN-20-1791

subject

Has Abstract

pub_date

2020-11-01 00:00:00

pages

4644-4654

issue

eissn

0008-5472

issn

1538-7445

pii

0008-5472.CAN-20-1791

journal_volume

pub_type

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A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.