Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design.

Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.

Dataset of phase I and II immunotherapy clinical trials used for a meta-analysis to assess the role of biomarkers in treatment outcomes in diverse cancers.

We performed a literature search in PubMed to identify phase I/II clinical trials with immunotherapy drugs approved by the Food and Drug Administration (labeled, off-label, and/or combined with investigational immune checkpoint inhibitors or other treatment modalities) from 2018 to 2020. We used the following key words: clinical trials, phase 1, Phase 2; and the following filters: cancer, humans; and selected the checkpoint inhibitors that had been approved by the FDA by March 2021, i.e., "pembrolizumab", "nivolumab", "atezolizumab", "durvalumab", "cemiplimab", "avelumab", and "ipilimumab. Clinical trials with their checkpoint inhibitors as in their labeled indications, off-label use or their combinations with investigational immune checkpoint inhibitors or other treatment modalities were included. Studies describing supportive care or locoregional treatments; cellular, viral, or vaccine therapy; studies in the adjuvant or neoadjuvant setting; and pediatric studies were excluded. Overall, 173 articles reporting on relevant studies were identified. Using these articles, we compiled a data file of study-specific covariates for each study. We recorded the immunotherapeutic agent, tumor type and biomarker, and clinical outcomes (objective response rate and median values [point estimate] and confidence intervals for progression-free survival and overall survival. Using these data, we carried out meta-analyses for the three outcomes and meta-regression on study-specific covariates. The same data could be used for any alternative implementation of meta-analysis and meta-regression, using more structured inference models reflecting different levels of dependence based on the available study-specific covariates.