Comprehensive Genomic and Transcriptomic Profiling of Pediatric Malignancies Using the Tempus xT Next-Generation Sequencing Assay to Identify Clinically Meaningful Alterations

ASCO Annual Meeting 2021 Abstract
Authors Stephanie Toll, Aneta Kwiatkowska-Piwowarczyk, Jeff Schaeffer, Anna Ewa Schwarzbach, Stefanie Marie Thomas, Kristiyana Kaneva

Background: Our understanding of the genomic makeup of childhood cancers has accelerated over the past decade largely due to next-generation sequencing (NGS) utilized to identify genetic drivers, aid diagnostics and risk stratification, and detect therapeutic targets. Here, we present the genomic and transcriptomic landscape of pediatric malignancies tested with a broad NGS panel at a large commercial CLIA/CAP laboratory, Tempus Labs.

Methods: We used the Tempus LENS platform to analyze DNA- and RNA-seq data from a cohort of 150 de-identified records of patients with pediatric cancer aged 0 to 18 years who underwent NGS with the Tempus xT platform.

Results: The cohort included 139 solid tumors, 46 of which were central nervous system (CNS) tumors, and 11 hematologic malignancies. A total of 115 samples (77%) had at least one clinically meaningful pathogenic somatic variant detected, with TP53 variants being the most common (n=26; 17.3%). Gene fusions, most commonly EWSR1-FLI1 and KIAA1549-BRAF, were observed in 31 cases (20.7%). Matched tumor/normal testing revealed at least one incidental pathogenic germline variant in six patient records, with two cases harboring two distinct variants. Four cases had tumor mutational burdens (TMBs) greater than 10 mutations/megabase, including two that also exhibited high microsatellite instability (MSI).

Conclusions: The Tempus xT tumor/normal-matched platform detects clinically meaningful genomic alterations in pediatric cancers important for diagnosis, prognosis, therapeutic target identification, and incidental germline findings. We continue to accumulate and structure data to meet the need for a large, accessible pediatric cancer clinical and molecular dataset.