CDK4/6i-treated HR+/HER2- breast cancer tumors show higher ESR1 mutation prevalence and more altered genomic landscape.

As CDK4/6 inhibitor (CDK4/6i) approval changed treatment strategies for patients with hormone receptor-positive HER2-negative (HR+/HER2-) breast cancer (BC), understanding how exposure to CDK4/6i affects the tumor genomic landscape is critical for precision oncology. Using real-world data (RWD) with tumor genomic profiling from 5910 patients with metastatic HR+/HER2- BC, we investigated the evolution of alteration prevalence in commonly mutated genes across patient journeys. We found that ESR1 is more often altered in tumors exposed to at least 1 year of adjuvant endocrine therapy, contrasting with TP53 alterations. We observed a similar trend after first-line treatments in the advanced setting, but strikingly exposure to aromatase inhibitors (AI) combined with CDK4/6i led to significantly higher ESR1 alteration prevalence compared to AI alone, independent of treatment duration. Further, CDK4/6i exposure was associated with higher occurrence of concomitant alterations in multiple oncogenic pathways. Differences based on CDK4/6i exposure were confirmed in samples collected after 2L and validated in samples from the acelERA BC clinical trial. In conclusion, our work uncovers opportunities for further treatment personalization and stresses the need for effective combination treatments to address the altered tumor genomic landscape following AI+CDK4/6i exposure. Further, we demonstrated the potential of RWD for refining patient treatment strategy and guiding clinical trial design.

Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

Replication of Overall Survival, Progression-Free Survival, and Overall Response in Chemotherapy Arms of Non-Small Cell Lung Cancer Trials Using Real-World Data.

PURPOSE: The utility of real-world data (RWD) for use as external controls in drug development is informed by studies that replicate trial control arms for different endpoints. The purpose of this study was to replicate control arms from four non-small cell lung cancer (NSCLC) randomized controlled trials (RCT) to analyze overall survival (OS), progression-free survival (PFS), and overall response rate (ORR) using RWD. PATIENTS AND METHODS: This study used RWD from a nationwide de-identified database and a clinico-genomic database to replicate OS, PFS, and ORR endpoints in the chemotherapy control arms of four first-line NSCLC RCTs evaluating atezolizumab [IMpower150-wild-type (WT), IMpower130-WT, IMpower131, and IMpower132]. Additional objectives were to develop a definition of real-world PFS (rwPFS) and to evaluate the real-world response rate (rwRR) endpoint. RESULTS: Baseline demographic and clinical characteristics were balanced after application of propensity score weighting methods. For rwPFS and OS, RWD external controls were generally similar to their RCT control counterparts. Across all four trials, the hazard ratio (HR) point estimates comparing trial controls with external controls were closer to 1.0 for the PFS endpoint than for the OS endpoint. An exploratory assessment of rwRR in RWD revealed a slight but nonsignificant overestimation of RCT ORR, which was unconfounded by baseline characteristics. CONCLUSIONS: RWD can be used to reasonably replicate the OS and PFS of chemotherapy control arms of first-line NSCLC RCTs. Additional studies can provide greater insight into the utility of RWD in drug development.

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.