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Abstract Randomized controlled trials (RCTs) are the gold standard for evaluating drug safety and efficacy in controlled settings, but they have limited generalizability due to their focus on selective and homogeneous populations. Real-world-data (RWD), collected from larger and more varied patient populations, can address inherent limitations of RCTs and aid in clinical trial design and drug approval. However, with the challenges of RWD, unlocking its full potential for tumor target and biomarker identification and validation demands innovative analytical approaches. Here we present RWD analyses of ConcertAI® electronic health records (EHR) linked with Caris Lab data for 2, 659 lung cancer patients. Comprehensive genomics analyses incorporate WES and RNA-seq data, coupled with rich clinical EHR data. By integrating clinical and genomics data, we showcase the potential of harnessing RWD as follows: (1) we investigated genomic alterations across five histology groups (adenocarcinoma, squamous, large cell, small cell lung cancer SCLC, neuroendocrine NE lung cancer). Notably, TP53 and LRP1B mutations were prevalent across all groups. LRP1B mutations correlated with higher tumor mutation burden, indicative of a favorable response to immunotherapy. Biallelic TP53 and RB1 inactivation was predominant in SCLC and NE lung tumors. The five histological groups exhibit distinct expression profiles, reflecting tumor intrinsic characteristics. Most immune gene signature scores were significantly lower in SCLC and NE tumors, aligned with that they are cold tumors with immune resistance, except CD56-centered NK and neuroendocrine gene signatures; (2) we analyzed patient treatment patterns and persistence by line of therapy. Among the 206 NSCLC patients with clinically actionable EGFR mutations, 143 received targeted therapy during their treatment courses (69. 4%). Time to treatment discontinuation (TTD) was used as a surrogate clinical outcome endpoint. TTD was significantly longer when TKI was the first line treatment compared to later lines (p = 0. 01), while the trend is opposite for ICI where TTD is significantly shorter in the 1st line setting (p = 0. 025) ; (3) we highlight the unique potential of leveraging H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2551.
Wu et al. (Fri,) studied this question.