A systematic review of phase II trials exploring anti-PD-1/PD-L1 combinations in patients with solid tumors.

BACKGROUND: A high number of combinations of PD-1/PD-L1 inhibitors with other anti-cancer therapies are in clinical development. The usefulness of phase II trials in evaluating their efficacy and safety is unclear. MATERIALS AND METHODS: We performed a systematic search on PubMed and Cochrane Library for phase II trials of PD-1/PD-L1 inhibitors in combination with other anti-cancer therapies (systemic therapy and/or radiotherapy) published between January 1st 2018 and December 31st 2020. Study design, primary endpoint and main outcomes were registered for each paper. RESULTS: 119 articles reporting on 65 regimens were included in our analysis. Backbone agents were more frequently PD-1 inhibitors (pembrolizumab = 47, nivolumab = 41, camrelizumab = 3) followed by anti-PD-L1 (durvalumab = 19, atezolizumab = 6, avelumab = 3). Therapeutic partners were other immunotherapeutic agents (n = 46), targeted therapies (n = 40), chemotherapy (n = 22) or radiotherapy (n = 11). The majority of articles reported on single-arm trials (n = 87, 73%) and response rate was the most frequent primary endpoint (n = 69, 58%). Objective responses, registered in 109 (92%) articles, ranged between 0% and 91%. The incidence of grade 3 or higher treatment-related adverse events, clearly reported in 97 (82%) articles, spanned from 0 to 100%. Five combinations received regulatory approval by Food and Drug Administration or European Medicine Agency for 9 different indications, based on the results of a phase II trial (n = 3) or on a confirmatory phase III trial (n = 6). CONCLUSIONS: The landscape of phase II trials evaluating PD-1/PD-L1 inhibitors with other anticancer therapies is heterogeneous. Combinations of two immunotherapeutic agents have been the most investigated. Only a minority of indications (8%) granted regulatory approval.

A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy.

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with "outstanding" 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care.