Characterizing mutation-treatment effects using clinico-genomics data of 78,287 patients with 20 types of cancers.

Evaluating the effectiveness of cancer treatments in relation to specific tumor mutations is essential for improving patient outcomes and advancing the field of precision medicine. Here we represent a comprehensive analysis of 78,287 U.S.

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.

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].

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.