Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning - Huibo Zhang; Lulu Chen; Lan Li; Yang Liu; Barnali Das; Shuang Zhai; Juan Tan; Yan Jiang; Simona Turco; Yi Yao; Dmitrij Frishman (npj Precision Oncology)
Abstract The density of tumor-infiltrating lymphocytes (TILs) serves as a valuable indicator for predicting anti-tumor responses, but its broad impact across various types of cancers remains underexplored. We introduce TILScout, a pan-cancer deep-learning approach to compute patch-level TIL scores from whole slide images (WSIs). TILScout achieved accuracies of 0.9787 and 0.9628, and AUCs of 0.9988 and 0.9934 in classifying WSI patches into three categories—TIL-positive, TIL-negative, and other/necrotic—on validation and independent test sets, respectively, surpassing previous studies. The biological significance of TILScout-derived TIL scores across 28 cancers was validated through comprehensive functional and correlational analyses. A consistent decrease in TIL scores with an increase in cancer stage provides direct evidence that the lower TIL content may stimulate cancer progression. Additionally, TIL scores correlated with immune checkpoint gene expression and genomic variation in common cancer driver genes. Our comprehensive pan-cancer survey highlights the critical prognostic significance of TILs within the tumor microenvironment.