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Transcriptomic signatures of tumors undergoing T cell attack

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@article{825f82a239f8439e8994204ad60e60bc,
title = "Transcriptomic signatures of tumors undergoing T cell attack",
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. 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.",
keywords = "Adaptive immune resistance, Anti-PD-1, Anti-PD-L1, Immunotherapy biomarkers, Patient selection, Transcriptomics",
author = "Aishwarya Gokuldass and Aimilia Schina and Martin Lauss and Katja Harbst and Chamberlain, {Christopher Aled} and Arianna Draghi and Westergaard, {Marie Christine Wulff} and Morten Nielsen and Krisztian Papp and Zsofia Sztupinszki and Istvan Csabai and Svane, {Inge Marie} and Zoltan Szallasi and G{\"o}ran J{\"o}nsson and Marco Donia",
note = "{\textcopyright} 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.",
year = "2021",
month = jul,
day = "17",
doi = "10.1007/s00262-021-03015-1",
language = "English",
journal = "Cancer Immunology and Immunotherapy",
issn = "0340-7004",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - Transcriptomic signatures of tumors undergoing T cell attack

AU - Gokuldass, Aishwarya

AU - Schina, Aimilia

AU - Lauss, Martin

AU - Harbst, Katja

AU - Chamberlain, Christopher Aled

AU - Draghi, Arianna

AU - Westergaard, Marie Christine Wulff

AU - Nielsen, Morten

AU - Papp, Krisztian

AU - Sztupinszki, Zsofia

AU - Csabai, Istvan

AU - Svane, Inge Marie

AU - Szallasi, Zoltan

AU - Jönsson, Göran

AU - Donia, Marco

N1 - © 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

PY - 2021/7/17

Y1 - 2021/7/17

N2 - 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.

AB - 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.

KW - Adaptive immune resistance

KW - Anti-PD-1

KW - Anti-PD-L1

KW - Immunotherapy biomarkers

KW - Patient selection

KW - Transcriptomics

UR - http://www.scopus.com/inward/record.url?scp=85110860501&partnerID=8YFLogxK

U2 - 10.1007/s00262-021-03015-1

DO - 10.1007/s00262-021-03015-1

M3 - Journal article

C2 - 34272988

JO - Cancer Immunology and Immunotherapy

JF - Cancer Immunology and Immunotherapy

SN - 0340-7004

ER -

ID: 67603108