WITHDRAWN Signature for response to PD-L1 inhibitor in metastatic Urothelial Cancer

Music City Center 

About 90% of human cancer deaths are due to metastasis. To date, immune checkpoint inhibitors (ICIs) are one of the frontier treatments that have improved the survival of metastatic cancer patients with few side effects. However, the objective response rate for ICIs is low, only ~30% in urothelial carcinoma (UC), highlighting the need to identify signatures for response prediction. Several state-of-the-art signatures have been revealed in first-tier journals, demonstrating the area's importance. As the number of genes (features; ~20,000) greatly exceeds the sample sizes of training sets (≤300), we first developed feature selection procedures to reduce features to a few hundred. Next, we trained several classifiers using Imvigor210 and the selected genes, comprising RNA-seq and clinical data of ~298 patients with mUC, via 5-fold cross-validation. In particular, our predictor based on logit regression (LogitDA) with the revealed signature achieved a prediction AUC of 0.75; our signature outperformed the known signatures (e.g., PD-L1, PD-1, the IFNG, tGE8, T exhaust, and T inflamed). Overall, our findings show that LogitDA and our signature predict immunotherapy response well in mUC.

biomarker

cancer

machine learning

regression

prediction 

Section on Statistics in Genomics and Genetics