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Transcriptomic signatures of tumors undergoing T cell attack

Gokuldass, Aishwarya ; Schina, Aimilia ; Lauss, Martin LU ; Harbst, Katja LU orcid ; Chamberlain, Christopher Aled ; Draghi, Arianna ; Westergaard, Marie Christine Wulff ; Nielsen, Morten ; Papp, Krisztian and Sztupinszki, Zsofia , et al. (2022) In Cancer Immunology, Immunotherapy 71(3). p.553-563
Abstract

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.... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Adaptive immune resistance, Anti-PD-1, Anti-PD-L1, Immunotherapy biomarkers, Patient selection, Transcriptomics
in
Cancer Immunology, Immunotherapy
volume
71
issue
3
pages
553 - 563
publisher
Springer
external identifiers
  • pmid:34272988
  • scopus:85110860501
ISSN
0340-7004
DOI
10.1007/s00262-021-03015-1
language
English
LU publication?
yes
id
d1ac468c-5931-4312-91dc-0af72732d04a
date added to LUP
2021-09-02 08:49:19
date last changed
2024-06-15 15:34:01
@article{d1ac468c-5931-4312-91dc-0af72732d04a,
  abstract     = {{<p>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.</p>}},
  author       = {{Gokuldass, Aishwarya and Schina, Aimilia and Lauss, Martin and Harbst, Katja and Chamberlain, Christopher Aled and Draghi, Arianna and Westergaard, Marie Christine Wulff and Nielsen, Morten and Papp, Krisztian and Sztupinszki, Zsofia and Csabai, Istvan and Svane, Inge Marie and Szallasi, Zoltan and Jönsson, Göran and Donia, Marco}},
  issn         = {{0340-7004}},
  keywords     = {{Adaptive immune resistance; Anti-PD-1; Anti-PD-L1; Immunotherapy biomarkers; Patient selection; Transcriptomics}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{553--563}},
  publisher    = {{Springer}},
  series       = {{Cancer Immunology, Immunotherapy}},
  title        = {{Transcriptomic signatures of tumors undergoing T cell attack}},
  url          = {{http://dx.doi.org/10.1007/s00262-021-03015-1}},
  doi          = {{10.1007/s00262-021-03015-1}},
  volume       = {{71}},
  year         = {{2022}},
}