Challenges of Providing Concordant Interpretation of Somatic Variants in Non-Small Cell Lung Cancer: A Multicenter Study

Performance Characteristics of Oncomine Focus Assay for Theranostic Analysis of Solid Tumors, A (21-Months) Real-Life Study

Next generation sequencing analysis is crucial for therapeutic decision in various solid tumor contexts. The sequencing method must remain accurate and robust throughout the instrument lifespan allowing the biological validation of patients' results. This study aims to evaluate the long-term sequencing performances of the Oncomine Focus assay kit allowing theranostic DNA and RNA variants detection on the Ion S5XL instrument. We evaluated the sequencing performances of 73 consecutive chips over a 21-month period and detailed the sequencing data obtained from both quality controls and clinical samples. The metrics describing sequencing quality remained stable throughout the study. We showed that an average of 11 × 10⁶ (±0.3 × 10⁶) reads were obtained using a 520 chip leading to an average of 6.0 × 10⁵ (±2.6 × 10⁵) mapped reads per sample. Of 400 consecutive samples, 95.8 ± 16% of amplicons reached the depth threshold of 500X. Slight modifications of the bioinformatics workflow improved DNA analytical sensitivity and allowed the systematic detection of expected SNV, indel, CNV, and RNA alterations in quality controls samples. The low inter-run variability of DNA and RNA-even at low variant allelic fraction, amplification factor, or reads counts-indicated that our method was adapted to clinical practice. The analysis of 429 clinical DNA samples indicated that the modified bioinformatics workflow allowed detection of 353 DNA variants and 88 gene amplifications. RNA analysis of 55 clinical samples revealed 7 alterations. This is the first study showing the long-term robustness of the Oncomine Focus assay in clinical routine practice.

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.

An Independent Assessment of a Commercial Clinical Interpretation Software Indicates That Software Can Mitigate Variation in Human Assessment

Comprehensive next-generation sequencing (NGS) panels for cancer diagnostics create a bottleneck for interpretation. QIAGEN Clinical Insights Interpret One (QCI) is a clinical decision support software that supports molecular pathologists in the classification of oncology-related variants. This study compares variant assessments by QCI to assessments utilizing current laboratory methods. Eight laboratories were recruited by the external quality assessment organization GenQA. The laboratories submitted VCFs from sequencing studies performed on both hematological disorders and solid tumors for analysis by QCI and an independent laboratory. Results were compared and conflicts were resolved using a panel of experts. In total, 14/149 variants (9%) reported as Tier 1 or Tier 2 by either QCI or the submitting laboratory were found to be discordant after expert panel review. In contrast, 41/149 variants (28%) reflected discrepancy among human reviewers. The expert panel was unable to reach resolution on eight variants. QCI demonstrates high concordance in the classification of actionable mutations with independent laboratory methods and expert assessment. The rate of disagreement among laboratories and the expert panel was greater than the disagreement between QCI and expert assessment. Disagreement among experts highlights the subjectivity of classifying variants. The study demonstrates that QCI interpretation supports streamlining and standardization of NGS variant interpretation.

From Information Overload to Actionable Insights: Digital Solutions for Interpreting Cancer Variants from Genomic Testing

Given the increase in genomic testing in routine clinical use, there is a growing need for digital technology solutions to assist pathologists, oncologists, and researchers in translating variant calls into actionable knowledge to personalize patient management plans. In this article, we discuss the challenges facing molecular geneticists and medical oncologists in working with test results from next-generation sequencing for somatic oncology, and propose key considerations for implementing a decision support software to aid the interpretation of clinically important variants. In addition, we review results from an example decision support software, NAVIFY Mutation Profiler. NAVIFY Mutation Profiler is a cloud-based software that provides curation, annotation, interpretation, and reporting of somatic variants identified by next-generation sequencing. The software reports a tiered classification based on consensus recommendations from AMP, ASCO, CAP, and ACMG. Studies with NAVIFY Mutation Profiler demonstrated that the software provided timely updates and accurate curation, as well as interpretation of variant combinations, demonstrating that decision support tools can help advance implementation of precision oncology.

The Science and Art of Clinical Genetic Variant Classification and Its Impact on Test Accuracy

Clinical genetic variant classification science is a growing subspecialty of clinical genetics and genomics. The field's continued improvement is essential for the success of precision medicine in both germline (hereditary) and somatic (oncology) contexts. This review focuses on variant classification for DNA next-generation sequencing tests. We first summarize current limitations in variant discovery and definition, and then describe the current five- and four-tier classification systems outlined in dominant standards and guideline publications for germline and somatic tests, respectively. We then discuss measures of variant classification discordance and the field's bias for positive results, as well as considerations for panel size and population screening in the context of estimates of positive predictive value thatincorporate estimated variant classification imperfections. Finally, we share opinions on the current state of variant classification from some of the authors of the most widely used standards and guideline publications and from other domain experts.

Assessment of a Highly Curated Somatic Oncology Database to Aid in the Interpretation of Clinically Important Variants in Next-Generation Sequencing Results

This study evaluated the accuracy of NAVIFY Mutation Profiler, a cloud-based CE-IVD software that aids in interpreting clinically relevant variants detected in somatic oncology next-generation sequencing tests. This tool reports tiered classifications based on different levels of clinical evidence from a highly curated, regularly updated database derived from medical guidelines, drug approvals, and peer-reviewed literature. A retrospective analysis was performed on next-generation sequencing results from 37 lung cancer cases treated with chemotherapy (n = 10), EGFR tyrosine kinase inhibitor (TKI) (n = 5), or ALK TKI (n = 22). Several aspects were assessed, including accuracy of interpretation compared with manual curation, validity of curation content updates over time, and agreement with public databases. For chemotherapy cases with no targetable biomarkers, NAVIFY Mutation Profiler did not identify any targeted therapies. In EGFR and ALK TKI cases, the software associated appropriate targeted therapies and accurately interpreted variant combinations containing drug-resistance variants. Of the nine unique ALK mutations conferring resistance to crizotinib, NAVIFY Mutation Profiler provided correct annotation for nine of nine mutations, whereas OncoKB and Catalogue of Somatic Mutations in Cancer indicated crizotinib resistance for eight of nine mutations. Of all 145 variants analyzed, there was substantial agreement (Cohen κ = 0.62) between NAVIFY Mutation Profiler and OncoKB for classifying actionable mutations. Furthermore, NAVIFY Mutation Profiler presented accurate targeted therapies across different regions (eg, European Union versus Canada) and remained up-to-date with evolving regional approvals and medical guidelines.

Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices

PURPOSE

The cancer research community is constantly evolving to better understand tumor biology, disease etiology, risk stratification, and pathways to novel treatments. Yet the clinical cancer genomics field has been hindered by redundant efforts to meaningfully collect and interpret disparate data types from multiple high-throughput modalities and integrate into clinical care processes. Bespoke data models, knowledgebases, and one-off customized resources for data analysis often lack adequate governance and quality control needed for these resources to be clinical grade. Many informatics efforts focused on genomic interpretation resources for neoplasms are underway to support data collection, deposition, curation, harmonization, integration, and analytics to support case review and treatment planning.

METHODS

In this review, we evaluate and summarize the landscape of available tools, resources, and evidence used in the evaluation of somatic and germline tumor variants within the context of molecular tumor boards.

RESULTS

Molecular tumor boards (MTBs) are collaborative efforts of multidisciplinary cancer experts equipped with genomic interpretation resources to aid in the delivery of accurate and timely clinical interpretations of complex genomic results for each patient, within an institution or hospital network. Virtual MTBs (VMTBs) provide an online forum for collaborative governance, provenance, and information sharing between experts outside a given hospital network with the potential to enhance MTB discussions. Knowledge sharing in VMTBs and communication with guideline-developing organizations can lead to progress evidenced by data harmonization across resources, crowd-sourced and expert-curated genomic assertions, and a more informed and explainable usage of artificial intelligence.

CONCLUSION

Advances in cancer genomics interpretation aid in better patient and disease classification, more streamlined identification of relevant literature, and a more thorough review of available treatments and predicted patient outcomes.

Assessment of significant procedures in multigene molecular detection for breast cancer in clinical laboratories: from variant detection to targeted therapy

BACKGROUND

In recent years, numerous novel targeted drugs against breast cancer have been developed because of the rapid progress in multigene molecular testing based on next-generation sequencing (NGS). However, it is a great challenge for clinicians to update the drug information timely, therefore necessitating that clinical laboratories provide adequate and comprehensive targeted drugs information to clinicians as a reference. The premise of providing this information is the accuracy of variant detection. Our study aimed to assess the entire process of variant detection, interpretation, and targeted therapy.

METHODS

Laboratories were instructed to use routine methods for variant detection. The results were evaluated based on a predefined score system, and differences in variant interpretation were analyzed. Targeted drug information provided by laboratories was also summarized, and its accuracy and sufficiency were assessed.

RESULTS

Overall, 90.1% (82/91) of the laboratories produced accurate results. 78.9% (15/19) of the errors were false positives or false negatives. Incorrect and insufficient drug information was mainly provided due to failure in timely database updating, inconsistencies with the detected mutations or given clinical information, and negligence during phase I clinical trials. To prioritize providing targeted drug information, laboratories collected data were based on different factors, including variant clinical significance, allele frequency, and variant positions in the signal pathway.

CONCLUSION

The variant detection capability was satisfactory, but the ability to provide accuracy and comprehensive targeted drug information should be urgently improved. Our study summarized a completed NGS-based multigene molecular detection pipeline, aiming to better inform precision treatment for breast cancer patients.