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Cited by 57Cancer Immunotherapy and BiomarkersT-cell and B-cell ImmunologyImmunotherapy and Immune Responses

Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy

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Sidhom, John-William, Oliveira, Giacomo, Ross-MacDonald, Petra, Wind-Rotolo, Megan, Wu, Catherine J., Pardoll, Drew M., Baras, Alexander S.·2022·Science Advances
DOI 10.1126/sciadv.abq5089PMC PMC9481116
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Abstract

T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders.

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Authors
Sidhom, J.-W., Oliveira, G., Ross-MacDonald, P., Wind-Rotolo, M., Wu, C. J., Pardoll, D. M., & Baras, A. S.
Affiliations
  • Johns Hopkins University (US)
  • Harvard University (US)
  • Bristol Myers Squibb, Princeton, NJ, USA.
  • Broad Institute (US)
Corresponding author
Sidhom, John-William
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2022-09-16
Volume
8
Issue
37
Pages
eabq5089-eabq5089
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.abq5089
PMC
PMC9481116
OpenAlex
W4296162382
Cited by
57
References
9
Citations by year
2026: 7 · 2025: 16 · 2024: 15 · 2023: 15 · 2022: 4
FWCI
5.23
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Science Advances (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
National Cancer Institute (P30CA006973)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords
T cell receptor repertoire, immunotherapy response prediction, CDR3 sequences, tumor-specific T cells, antigen specificity, immune checkpoint inhibitors, cancer immunotherapy, T cell dysfunction

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