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. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.

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. METHODS: We retrospectively analyzed patients with metastatic HER2-mutant NSCLC who underwent prospective clinical ctDNA sequencing and received systemic therapy at Memorial Sloan Kettering Cancer Center (MSK) from January 2016 to September 2022. HER2 mutations were identified by next-generation sequencing through MSK-IMPACT, MSK-ACCESS or Resolution ctDx LungTM assay. Primary endpoints were time to the next treatment (TTNT) and overall survival (OS). RESULTS: Sixty-three patients were included in the primary analysis. Chemoimmunotherapy (33/63, 52.4 %) was the predominant first-line treatment with a median TTNT of 5.1 months (95 %CI 4.1 - 6.1) whereas 55.0 % (22/40) of patients who received second-line T-DXd obtained a median TTNT of 9.2 m (95 % CI, 0-22.2). Plasma ctDNA was tested before first-line therapy in 40 patients with a median OS of 28.0 months (95 % CI 21-34), in whom 31 patients (78.0 %) had detectable ctDNA. HER2 mutations were detected on ctDNA with a median turnaround time of 13 days, occasionally co-occurred with EGFR and MET alterations and were tracked longitudinally correlating with treatment response. Patients with detectable baseline ctDNA had significantly shorter OS (hazard ratio (HR), 5.25; 95 % CI, 1.2-23.9; p = 0.019). CONCLUSION: Chemoimmunotherapy remains a major treatment option for metastatic HER2-mutant NSCLC. ctDNA can rapidly detect HER2 and co-mutations, and it has the potential to guide and monitor optimal first-line therapy. As a negative prognostic biomarker, detectable ctDNA at baseline would need to be taken into account for patient selection in future studies.

Molecular and Clinicopathologic Impact of GNAS Variants Across Solid Tumors.

PURPOSE: The molecular drivers underlying mucinous tumor pathogenicity are poorly understood. GNAS mutations predict metastatic burden and treatment resistance in mucinous appendiceal adenocarcinoma. We investigated the pan-cancer clinicopathologic relevance of GNAS variants. METHODS: We assessed 58,043 patients with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (IMPACT)-sequenced solid tumors to identify oncogenic variants, including GNAS, associated with mucinous tumor phenotype. We then performed comprehensive molecular analyses to compare GNAS-mutant (mut) and wild-type tumors across cancers. Gene expression patterns associated with GNAS-mut tumors were assessed in a The Cancer Genome Atlas cohort. Associations between GNAS variant status and peritoneal metastasis, first-line systemic therapy response, progression-free survival (PFS), and overall survival (OS) were determined using a propensity-matched subcohort of patients with metastatic disease. RESULTS: Mucinous tumors were enriched for oncogenic GNAS variants. GNAS was mutated in >1% of small bowel, cervical, colorectal, pancreatic, esophagogastric, hepatobiliary, and GI neuroendocrine cancers. Across these cancers, GNAS-mut tumors exhibited a generally conserved C-to-T mutation-high, aneuploidy-low molecular profile with co-occurring prevalent KRAS variants (65% of GNAS-mut tumors) and fewer TP53 alterations. GNAS-mut tumors exhibited recurrently comutated alternative tumor suppressors (RBM10, INPPL1) and upregulation of MAPK and cell surface modulators. GNAS-mut tumors demonstrate an increased prevalence of peritoneal metastases (odds ratio [OR], 1.7 [95% CI, 1.1 to 2.5]; P = .006), worse response to first-line systemic therapy (OR, 2.2 [95% CI, 1.3 to 3.8]; P = .003), and shorter PFS (median, 5.6 v 7.0 months; P = .047). In a multivariable analysis, GNAS mutated status was independently prognostic of worse OS (hazard ratio, 1.25 [95% CI, 1.01 to 1.56]; adjusted P = .04). CONCLUSION: Across the assessed cancers, GNAS-mut tumors exhibit a conserved molecular and clinical phenotype defined by mucinous tumor status, increased peritoneal metastasis, poor response to first-line systemic therapy, and worse survival.

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. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.