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Cited by 39Gene expression and cancer classificationCancer-related molecular mechanisms researchRNA Research and Splicing

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Identifying common transcriptome signatures of cancer by interpreting deep learning models

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Jha, Anupama, Quesnel-Vallières, Mathieu, Wang, David, Thomas-Tikhonenko, Andrei, Lynch, Kristen W, Barash, Yoseph·2022·Genome Biology
DOI 10.1186/s13059-022-02681-3PMC PMC9112525

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Abstract

Background: Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified. It is not clear, however, whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. Results: Here, we agnostically identify transcriptomic features that are commonly shared between cancer types using 13,461 RNA-seq samples from 19 normal tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression, or splice junction use, to distinguish between normal and tumor samples. All three models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints. Importantly, we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. Conclusions: Our results highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features on which core cancer pathways might converge across a large array of solid tumor types.

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  • Jha et al. proposed the EIG method, which identified significant splicing code features in the liver and was adapted for identifying common transcriptional signatures of cancers.

    “Jha et al. proposed the enhanced integrated gradients (EIG) method [169], which identified significant splicing code features in the liver and was adapted for identifying common transcriptional signatures of cancers [170].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [170]

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Metadata

Authors
Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y.
Affiliations
University of Pennsylvania (US)
Corresponding author
  • Jha, Anupama
  • Quesnel-Vallières, Mathieu
  • Barash, Yoseph
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2022-05-17
Volume
23
Issue
1
Pages
117-117
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-022-02681-3
PMC
PMC9112525
OpenAlex
W4280523186
Cited by
39
References
7
Citations by year
2026: 4 · 2025: 7 · 2024: 16 · 2023: 9 · 2022: 3
FWCI
2.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (LM013437, GM128096, CA232563)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
RNA-seq, feedforward neural networks, lncRNA expression, RNA processing genes, aberrant splicing, model interpretability, attribution analysis, evolutionary constraints, solid tumors
MeSH terms
Deep Learning, Humans, Neoplasms, Gene Expression Regulation, Neoplastic, Gene Expression Profiling, Genomics, Transcriptome

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