Transcriptomic signatures of tumors undergoing T cell attack
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Background: Studying tumor cell–T cell interactions in the tumor microenvironment (TME) can elucidate tumor immune escape mechanisms and help predict responses to cancer immunotherapy. Methods: We selected 14 pairs of highly tumor-reactive tumor-infiltrating lymphocytes (TILs) and autologous short-term cultured cell lines, covering four distinct tumor types, and co-cultured TILs and tumors at sub-lethal ratios in vitro to mimic the interactions occurring in the TME. We extracted gene signatures associated with a tumor-directed T cell attack based on transcriptomic data of tumor cells. Results: An autologous T cell attack induced pronounced transcriptomic changes in the attacked tumor cells, partially independent of IFN-γ signaling. Transcriptomic changes were mostly independent of the tumor histological type and allowed identifying common gene expression changes, including a shared gene set of 55 transcripts influenced by T cell recognition (Tumors undergoing T cell attack, or TuTack, focused gene set). TuTack scores, calculated from tumor biopsies, predicted the clinical outcome after anti-PD-1/anti-PD-L1 therapy in multiple tumor histologies. Notably, the TuTack scores did not correlate to the tumor mutational burden, indicating that these two biomarkers measure distinct biological phenomena. Conclusions: The TuTack scores measure the effects on tumor cells of an anti-tumor immune response and represent a comprehensive method to identify immunologically responsive tumors. Our findings suggest that TuTack may allow patient selection in immunotherapy clinical trials and warrant its application in multimodal biomarker strategies.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Cancer Immunology, Immunotherapy |
| Vol/bind | 71 |
| Udgave nummer | 3 |
| Sider (fra-til) | 553-563 |
| Antal sider | 11 |
| ISSN | 0340-7004 |
| DOI | |
| Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
MD has received honoraria for lectures from Roche and Novartis (past two years). IMS has received honoraria for consultancies and lectures from Novartis, Roche, Merck and Bristol-Myers Squibb; a restricted research grant from Novartis; and financial support for attending symposia from Bristol-Myers Squibb, Merck, Novartis, Pfizer and Roche. A patent (inventors AG, AS and MD) disclosing methods to predict response to immunotherapy has been submitted. The rights of the patent applications will be transferred to Capital Region of Denmark, according to the Danish Law of Public Inventions at Public Research Institutions. All other authors declare that they have no conflict of interest.
Funding Information:
This work was supported by the Danish Cancer Society (grant number R148-A9862); the Lundbeck Foundation (grant number R233-2016-3728); the Capital Region of Denmark Research Foundation (grant number R146-A5693); Herlev and Gentofte Research Fund (grant to MD); Beckett-Fonden (grant to MD); the Danish National Board of Health, grant “Empowering Cancer Immunotherapy in Denmark” (grant to IMS); and The National Research, Development and Innovation Fund of Hungary (FIEK_16-1-2016-0005). Funding for the acquisition of samples and transfer to public repositories are stated in the respective reference.
Funding Information:
The authors thank the patients who donated the samples from which cell lines and cell cultures were established. Marcin Krzystranek, Ph.D., and Miklos Diossy, Ph.D. (Technical University of Denmark, Lyngby, Denmark) are acknowledged for providing scientific insights. The results shown here are partly based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/ , by other authors who deposited clinical datasets in public repositories such as the Sequence Read Archive (accession GSE78220 and GSE91061), the European Nucleotide Archive (PRJEB23709 and PRJEB25780), the database of Genotypes and Phenotype (phs000452.v3.p1, phs001493.v1.p1, and phs001919.v1.p1), the European Genome-phenome Archive (EGAS00001002928, EGAS00001002556, and EGAS00001004445), Braun et al.[26], and their respective funding agencies. Therefore, this work was also supported by the National Human Genome Research Institute (NHGRI) Large Scale Sequencing Program, Grant U54 HG003067 to the Broad Institute (PI, Lander); AACR KureIt grant; Antoni Ribas MD, PhD University of California, Los Angeles, Funding Source:R35 CA197633 National Institutes of Health, Bethesda, MD, USA. The research makes use of data generated by Genentech/gRED.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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