Transcriptomic signatures of tumors undergoing T cell attack

Area of Science:

• Immunology
• Oncology
• Genomics

Background:

• Understanding tumor microenvironment (TME) interactions is crucial for deciphering immune escape and predicting cancer immunotherapy outcomes.
• Studying T cell-tumor cell dynamics within the TME offers insights into treatment efficacy.

Purpose of the Study:

• To identify gene signatures reflecting T cell-mediated tumor cell attack.
• To develop a predictive biomarker for immunotherapy response.

Main Methods:

• Co-culture of tumor-infiltrating lymphocytes (TILs) with autologous tumor cell lines to mimic TME interactions.
• Transcriptomic analysis of tumor cells to identify gene signatures of T cell attack.
• Development and validation of TuTack scores from tumor biopsies.

Main Results:

• Autologous T cell attack induced significant transcriptomic changes in tumor cells, partly independent of IFN-γ.
• A common gene set (55 transcripts), termed TuTack, was identified across tumor types.
• TuTack scores predicted clinical outcomes in anti-PD-1/anti-PD-L1 therapy across multiple histologies.
• TuTack scores were independent of tumor mutational burden, indicating distinct biological insights.

Conclusions:

• TuTack scores effectively measure anti-tumor immune response effects on tumor cells, identifying immunologically responsive tumors.
• TuTack serves as a comprehensive biomarker for patient selection in immunotherapy clinical trials.
• The findings support the integration of TuTack into multimodal biomarker strategies for enhanced cancer treatment.