Tiered Somatic Variant Classification Adoption Has Increased Worldwide With Some Practice Differences Based on Location and Institutional Setting

Clinical Practice Guideline for Blood-based Circulating Tumor DNA Assays

Circulating tumor DNA (ctDNA) has emerged as a promising tool for various clinical applications, including early diagnosis, therapeutic target identification, treatment response monitoring, prognosis evaluation, and minimal residual disease detection. Consequently, ctDNA assays have been incorporated into clinical practice. In this review, we offer an in-depth exploration of the clinical implementation of ctDNA assays. Notably, we examined existing evidence related to pre-analytical procedures, analytical components in current technologies, and result interpretation and reporting processes. The primary objective of this guidelines is to provide recommendations for the clinical utilization of ctDNA assays.

Assessments of Somatic Variant Classification Using the Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists Guidelines: A Report from the Association for Molecular Pathology

To assess the clinical implementation of the 2017 Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists, identify content that may result in classification inconsistencies, and evaluate implementation barriers, an Association for Molecular Pathology Working Group conducted variant interpretation challenges and a guideline implementation survey. A total of 134 participants participated in the variant interpretation challenges, consisting of 11 variants in four cancer cases. Results demonstrate 86% (range, 54% to 94%) of the respondents correctly classified clinically significant variants, variants of uncertain significance, and benign/likely benign variants; however, only 59% (range, 39% to 84%) of responses agreed with the working group's consensus intended responses regarding both tiers and categories of clinical significance. In the implementation survey, 71% (157/220) of respondents have implemented the 2017 guidelines for variant classification and reporting either with or without modifications. Collectively, this study demonstrates that, although they may not yet be optimized, the 2017 guideline recommendations are being adopted for standardized somatic variant classification. The working group identified significant areas for future guideline improvement, including the need for a more granular and comprehensive classification system and education resources to meet the growing needs of both laboratory professionals and medical oncologists.

Artificial intelligence-assisted serial analysis of clinical cancer genomics data identifies changing treatment recommendations and therapeutic targets

PURPOSE

Given the pace of predictive biomarker and targeted therapy development, it is unknown if repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically leveraged. In this study, we sought to determine the predictive yield of serial re-analysis of clinical tumor sequencing data.

EXPERIMENTAL DESIGN

Using artificial intelligence (AI)-assisted variant annotation, we retrospectively re-analyzed sequencing data from 2,219 cancer patients from a single academic medical center at 3-month intervals totaling 9 months in 2020. The yield of serial re-analysis was assessed by the proportion of patients with improved strength of therapeutic recommendations.

RESULTS

1,775 patients (80%) had {greater than or equal to}1 potentially clinically actionable mutation at baseline, including 243 (11%) patients who had an alteration targeted by an FDA-approved drug for their cancer type. By month nine, the latter increased to 458 (21%) patients mainly due to a single pan-cancer agent directed against tumors with high tumor mutation burden. Within this timeframe, 67 new therapies became available and 45 were no longer available. Variant pathogenicity classifications also changed leading to changes in treatment recommendations for 124 patients (6%).

CONCLUSIONS

Serial re-annotation of tumor sequencing data improved the strength of treatment recommendations (based on level of evidence) in a mixed cancer cohort and showed substantial changes in available therapies and variant classifications. These results suggest a role for repeat analysis of tumor sequencing data in clinical practice, which can be streamlined with AI support.