Qilu Zhou - Tumor Microenvironment score in Predicting Immune Checkpoint Inhibitor Treatment Response

Immune checkpoint inhibitors (ICIs) have significantly changed cancer therapy, yet their response rates remain relatively low. Identifying methods for robust prediction is crucial. In this talk, I will present a meta-analysis that evaluates the efficacy of gene-based methods for deriving predictive tumor-microenvironment scores in cancer patients, focusing on their performances in predicting survival outcomes and response to ICI therapy across various cancer types. Our meta-analysis shows that no score is robustly applicable to all cancer types. Therefore, significant challenges remain due to the variability of tumor biology and host immune responses, and universally applicable method should be further explored.

To facilitate the choice of optimal score towards a specific cohort, we developed the Python package TMEImmune, which integrates four widely used prognostic scoring methods for ICI therapy. This package allows users to easily compare the performance of these methods across various cancer types, helping to identify the most predictive approach for each cancer. I will introduce the functionalities in TMEImmune and present the example codes and outputs.