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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

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

Cheng Wang¹, Jingjing Li²

¹The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, The Parker Institute for Cancer Immunotherapy, The Bakar Computational Health Sciences Institute, Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, California.

Cancer Research

|September 10, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning framework to identify noncoding mutations in prostate cancer genomes. This approach uncovers novel genes and potential prognostic tools for personalized cancer care.

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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

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Published on: October 18, 2013

Area of Science:

Genomics
Computational Biology
Oncology

Background:

Current methods for identifying noncoding mutations in cancer rely on large-scale sample aggregation, limiting individual mutation identification.
Most somatic mutations occur in the noncoding genome, yet their role in tumorigenesis and clinical application remains underexplored.

Purpose of the Study:

To develop and deploy a deep learning framework for detecting pathogenic noncoding mutations in personal cancer genomes.
To investigate the impact of these mutations on gene regulation and their potential clinical utility in localized prostate cancer.

Main Methods:

Developed a deep learning framework integrating large-scale prostate cancer genomes and prostate-specific epigenomic data.
Exhaustively evaluated somatic mutations to identify alleles affecting the prostate epigenome.
Performed functional genomic analyses to assess gene expression and pathway convergence.

Main Results:

Identified thousands of somatic alleles that alter the prostate epigenome.
Demonstrated that affected genes show differential expression in tumors and converge on androgen receptor signaling.
Found that the accumulation of pathogenic regulatory mutations predicts clinical observations.

Conclusions:

The deep learning framework expands the understanding of noncoding somatic mutations in prostate cancer.
Novel genes and mutational signatures predictive of clinical outcomes were uncovered.
This approach holds promise for developing personalized screening and therapeutic strategies for prostate cancer.