GSE129127
Patient-derived xenograft platform for metastatic melanoma: RNA sequencing of 95 melanoma PDX samples
Although high clinical response rates are seen for immune checkpoint blockade (ICB) of metastatic melanoma, both intrinsic and acquired ICB resistance remain formidable challenges. Combination  ICB shows improved clinical benefit, but is associated with severe adverse events and exceedingly high cost. Therefore, there is a dire need to stratify individual patients for their likelihood of responding to either anti-PD-1 or anti-CTLA-4 monotherapy, or the combination. Since it is conceivable that ICB responses are influenced by both tumor cell-intrinsic and stromal factors, we hypothesized that a predictive classifier ought to mirror both of these distinct features. We used a panel of melanoma patient-derived xenografts (PDX), in which human stromal cells upon transplantation are naturally replaced by their murine counterparts, to computationally subtract PDX RNA expression signals from those in patients' melanomas. We thus derived both “Stromal immune” (SIM) and tumor cell-specific “Tumor-autonomous inflammation” (TAF) signatures. Here we report  that the SIM signature predicts response to anti-CTLA-4 but not anti-PD-1  treatment, whereas the tumor TAF signature predicts response to anti-PD-1 but not anti-CTLA-4. Moreover, when used in conjunction, the signatures accurately predict response in two independent patient cohorts treated with the anti-CTLA-4 + anti-PD-1 combination. These signatures may be clinically exploited for personalized treatment advice based on the predicted benefit from either anti-CTLA-4 or anti-PD-1 monotherapy or their combination. Overall design: RNA sequencing of 95 melanoma PDX samples.