Purpose: Microsatellite instability (MSI) and high tumor mutation burden (TMB-High) are promising pan-tumor biomarkers used to select patients for treatment with immune checkpoint blockade; however, real-time sequencing of unresectable or metastatic solid tumors is often challenging. We report a noninvasive approach for detection of MSI and TMB-High in the circulation of patients.Experimental Design: We developed an approach that utilized a hybrid-capture-based 98-kb pan-cancer gene panel, including targeted microsatellite regions. A multifactorial error correction method and a novel peak-finding algorithm were established to identify rare MSI frameshift alleles in cellfree DNA (cfDNA).Results: Through analysis of cfDNA derived from a combination of healthy donors and patients with metastatic cancer, the error correction and peak-finding approaches produced a specificity of >99% (n ¼ 163) and sensitivities of 78% (n ¼ 23) and 67% (n ¼ 15), respectively, for MSI and TMB-High. For patients treated with PD-1 blockade, we demonstrated that MSI and TMB-High in pretreatment plasma predicted progression-free survival (hazard ratios: 0.21 and 0.23, P ¼ 0.001 and 0.003, respectively). In addition, we analyzed cfDNA from longitudinally collected plasma samples obtained during therapy to identify patients who achieved durable response to PD-1 blockade.Conclusions: These analyses demonstrate the feasibility of noninvasive pan-cancer screening and monitoring of patients who exhibit MSI or TMB-High and have a high likelihood of responding to immune checkpoint blockade.See related commentary by Wang and Ajani, p. 6887

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Characterization of circulating tumor DNA (ctDNA) has been integrated into clinical practice. While labs have standardized validation procedures to develop single locus tests, the efficacy of on-site plasma-based next-generation sequencing (NGS) assays still need to be proven. In this retrospective study, we profiled DNA from matched tissue and plasma samples from 75 cancer patients. We applied the NGS test PGDx elio™ plasma resolve-RUO (EPR), which detects clinically relevant alterations in 33 genes and microsatellite instability (MSI), to analyze plasma cell-free DNA (cfDNA). The concordance between alterations detected in both tissue and plasma samples was higher in patients with metastatic disease. EPR detected 77% of sequence alterations, amplifications, and fusions that were found in metastatic samples compared to 45% of those alterations found in the primary tumor samples (P = 0.00005). There was

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The lack of validated, distributed comprehensive genomic profiling assays for patients with cancer inhibits access to precision oncology treatment. To address this, we describe elio tissue complete, which has been FDA-cleared for examination of 505 cancer-related genes. Independent analyses of clinically and biologically relevant sequence changes across 170 clinical tumor samples using MSK-IMPACT, FoundationOne, and PCR-based methods reveals a positive percent agreement of >97%. We observe high concordance with whole-exome sequencing for evaluation of tumor mutational burden for 307 solid tumors (Pearson r = 0.95) and comparison of the elio tissue complete microsatellite instability detection approach with an independent PCR assay for 223 samples displays a positive percent agreement of 99%. Finally, evaluation of amplifications and translocations against DNA- and RNA-based approaches exhibits >98% negative percent agreement and positive percent agreement of 86% and 82%, respectively. These methods provide an approach for pan-solid tumor comprehensive genomic profiling with high analytical performance.