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Archival Tissue-Based Detection of Microsatellite Instability and High Tumor Mutation Burden.

Summary of Next Generation Sequencing Statistics for Healthy Donor Samples, Contrived Samples, and Clinical Plasma Samples

Comparison of Tumor Mutation Burden and Microsatellite Status for Patients Evaluated for Response to Immune Checkpoint Blockade

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 cell-free 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 = 163) and sensitivities of 78% (n = 23) and 67% (n = 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 = 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

Plasma-Based Detection of Microsatellite Instability and High Tumor Mutation Burden in Patients with Sufficient Circulating Tumor DNA.

Comparison of Microsatellite Status Determined through FFPE Tissue Analyses

Comparison of Microsatellite Status Determined through Healthy Donor, Contrived, and Clinical Plasma Analyses

Schematic of the Digital Peak Finding Bioinformatics Approach to Detect Microsatellite Instability in Circulating Tumor DNA from Plasma.

Plasma-Based Progression Free Survival Analysis for Patients Treated with Immune Checkpoint Blockade.

125 Gene List for FFPE Tissue Analyses

58 Gene List for Plasma Analyses

Summary of Next Generation Sequencing Statistics for FFPE Tumor and Matched Normal Samples

Summary of Clinical Information for 16 Patients Evaluated for Response to Immune Checkpoint Blockade

Introduction: Measurement of tumor-derived DNA molecules in the plasma (ctDNA) has become a useful tool to determine the overall tumor burden in patients with cancer. The ctDNA burden may change over time, decreasing after treatment response and increasing with development of resistance to therapy. Monitoring the dynamics of ctDNA burden over the course of treatment with a rapid, non-invasive test enables physicians to make timely treatment decisions. Using the DNA evaluation of fragments for early interception (DELFI) approach, we have developed the DELFI Monitoring Score (DMS) to longitudinally assess ctDNA burden during therapy of cancer patients. Methods: We performed low coverage whole genome sequencing on 302 cfDNA libraries of 76 treatment-naive metastatic colorectal cancer (mCRC) patients with at least one blood draw prior to and after treatment initiation. Mutations in KRAS, NRAS, or BRAF were independently measured by digital droplet PCR (ddPCR) for all timepoints evaluated. We trained a Bayesian regression model with the mutant allele fraction (MAF) of KRAS, NRAS, or BRAF as response and fragmentation-related features as predictors as well as a random intercept. To avoid overfitting and assess generalizability, we used cross-validation, wherein each patient’s samples were held out of the dataset and a model was trained on the samples of all other patients to generate predictions for the held-out samples. Patients with DMS below and above a detectability threshold at the first blood draw post-treatment (between 4-12 weeks) were classified as molecular responders and non-responders, respectively. Progression-free survival outcomes, defined as time to progression by RECIST 1.1 or death, were evaluated using a Kaplan-Meier estimator in these two groups. Results: Molecular responders based on DMS assessment experienced longer progression-free survival than molecular non-responders (p < 0.01), similar to a MAF-based approach. Additionally, in the 43 patients for whom the MAF of the KRAS, NRAS, or BRAF variants was 0% at the first blood draw post-treatment, we observed further separation on progression-free survival by the DELFI Monitoring Score (p = 0.014). Conclusions: We developed a novel quantitative measure of ctDNA burden using cell-free DNA fragmentation patterns that is estimable from low coverage whole genome sequencing. The DMS appears to be useful for measuring ctDNA burden and enabling a non-invasive approach to treatment monitoring, as it distinguishes therapeutic responses in a mCRC cohort. Our ability to detect differences in progression-free survival among patients with undetectable ddPCR MAFs suggests that DMS may be more sensitive and predictive than conventional mutation-based approaches for treatment response monitoring. Citation Format: Keith Lumbard, Laurel A. Keefer, Iris van't erve, Jacob Carey, Bryan Chesnick, Denise Butler, Michael Rongione, Cornelis J. Punt, Nicholas C. Dracopoli, Remond J. Fijneman, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Alessandro Leal. DELFI as a real-time treatment response assessment for patients with cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2224.

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.