New clinical trial design borrowing information across patient subgroups based on fusion-penalized regression models.

In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome.

Optimal systemic treatment and real-world clinical application of ctDNA in patients with metastatic HER2-mutant lung cancer.

Introduction: No definitive answers currently exist regarding optimal first-line therapy for HER2-mutant NSCLC. Access to rapid tissue sequencing is a major barrier to precision drug development in the first-line setting. ctDNA analysis has the potential to overcome these obstacles and guide treatment.

Molecular and Clinicopathologic Impact of GNAS Variants Across Solid Tumors.

Purpose: The molecular drivers underlying mucinous tumor pathogenicity are poorly understood. mutations predict metastatic burden and treatment resistance in mucinous appendiceal adenocarcinoma. We investigated the pan-cancer clinicopathologic relevance of variants.

Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics.

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles.