Pembrolizumab for advanced urothelial carcinoma: exploratory ctDNA biomarker analyses of the KEYNOTE-361 phase 3 trial.

Circulating tumor DNA (ctDNA) is emerging as a potential biomarker in early-stage urothelial cancer, but its utility in metastatic disease remains unknown. In the phase 3 KEYNOTE-361 study, pembrolizumab with and without chemotherapy was compared with chemotherapy alone in patients with metastatic urothelial cancer. The study did not meet prespecified efficacy thresholds for statistical significance.

Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework.

Objectives: Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients' privacy.

Materials And Methods: We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification).

Molecular determinants of clinical outcomes of pembrolizumab in recurrent ovarian cancer: Exploratory analysis of KEYNOTE-100.

Objective: This prespecified exploratory analysis evaluated the association of gene expression signatures, tumor mutational burden (TMB), and multiplex immunohistochemistry (mIHC) tumor microenvironment-associated cell phenotypes with clinical outcomes of pembrolizumab in advanced recurrent ovarian cancer (ROC) from the phase II KEYNOTE-100 study.

Methods: Pembrolizumab-treated patients with evaluable RNA-sequencing (n = 317), whole exome sequencing (n = 293), or select mIHC (n = 125) data were evaluated. The association between outcomes (objective response rate [ORR], progression-free survival [PFS], and overall survival [OS]) and gene expression signatures (T-cell-inflamed gene expression profile [TcellGEP] and 10 non-TcellGEP signatures), TMB, and prespecified mIHC cell phenotype densities as continuous variables was evaluated using logistic (ORR) and Cox proportional hazards regression (PFS; OS).

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

Background: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings.

An efficient and accurate distributed learning algorithm for modeling multi-site zero-inflated count outcomes.

Clinical research networks (CRNs), made up of multiple healthcare systems each with patient data from several care sites, are beneficial for studying rare outcomes and increasing generalizability of results. While CRNs encourage sharing aggregate data across healthcare systems, individual systems within CRNs often cannot share patient-level data due to privacy regulations, prohibiting multi-site regression which requires an analyst to access all individual patient data pooled together. Meta-analysis is commonly used to model data stored at multiple institutions within a CRN but can result in biased estimation, most notably in rare-event contexts.