e13508 Background: ASCO and NCCN guidelines strongly encourage participation in clinical trials though participation rates remain low. Although up to 60% of trials require biomarker information as a prerequisite to participation, guidelines are silent on biomarker testing to assess eligibility. To help inform policy development, we analyzed commercially available NGS panels to assess the capture rate of the specific gene variants required for targeted therapies in clinical trials. Methods: Publicly available gene variant information on 12 high volume NGS tissue and plasma commercial assays representative of panel size was accessed. The JAX-CKB database was queried to identify genomic prerequisites in active phase I-III clinical trials; mRNA and protein expression biomarkers were excluded. We used unpaired t-testing to compare the ability of large versus small panels to detect variants required for targeted treatments. (Somatic gene variant annotations and related content have been powered by The Jackson Laboratory Clinical Knowledgebase (JAX-CKBTM)). Results: Overall, 185 genes were linked to eligibility for targeted therapies in clinical trials (Table). Large panel assays (>80 genes) had a higher average capture rate compared to smaller panels, (84% vs. 46%, p value < 0.000002). Similarly, capture rates in select cancers were higher in larger panels: lung (85% vs 59%), breast (92% vs 54%), prostate (86% vs 34%), pancreatic (84% vs 54%), colorectal (88% vs 52%). All results were statistically significant (p < 0.05). Smaller panels were more likely to exclude large genes (e.g. BRCA), genome-wide signatures (e.g. TMB), fusions, and copy number variants. Conclusions: NGS-based panels with more than 80 genes had higher capture rates for biomarkers required for clinical trials of targeted therapies. Clinicians searching for all relevant treatment options for patients should include large genomic sequencing panels to maximize identification of potential clinical trial eligibility. Professional societies and policy makers interested in enhancing clinical trial enrollment rates should consider the panel size-related variability in capture rates of genetic variants when developing recommendations. [Table: see text]
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