Clinical Validation of a Cell-Free DNA Gene Panel.

The use of liquid biopsies to identify driver mutations in patients with solid tumors holds great promise for performing targeted therapy selection, monitoring disease progression, and detecting treatment resistance mechanisms. We describe herein the development and clinical validation of a 28-gene cell-free DNA panel that targets the most common genetic alterations in solid tumors. Bioinformatic and variant filtering solutions were developed to improve test sensitivity and specificity. The panel and these tools were used to analyze commercially available controls, allowing establishment of a limit of detection allele fraction cutoff of 0.25%, with 100% (95% CI, 81.5%-100%) specificity and 89.8% (95% CI, 81.0%-94.9%) sensitivity. In addition, we analyzed a total of 163 blood samples from patients with metastatic cancer (n = 123) and demonstrated a >90% sensitivity for detecting previously identified expected mutations. Longitudinal monitoring of patients revealed a strong correlation of variant allele frequency changes and clinical outcome. Additional clinically relevant information included identification of resistance mutations in patients receiving targeted treatment and detection of complex patterns of mutational heterogeneity. Achieving lower limits of detection will require additional improvements to molecular barcoding; however, these data strongly support clinical implementation of cell-free DNA panels in advanced cancer patients.

Clinically Integrated Molecular Diagnostics in Adenoid Cystic Carcinoma.

BACKGROUND: Adenoid cystic carcinoma (ACC) is an aggressive salivary gland malignancy without effective systemic therapies. Delineation of molecular profiles in ACC has led to an increased number of biomarker-stratified clinical trials; however, the clinical utility and U.S.-centric financial sustainability of integrated next-generation sequencing (NGS) in routine practice has, to our knowledge, not been assessed. MATERIALS AND METHODS: In our practice, NGS genotyping was implemented at the discretion of the primary clinician. We combined NGS-based mutation and fusion detection, with MYB break-apart fluorescent in situ hybridization (FISH) and MYB immunohistochemistry. Utility was defined as the fraction of patients with tumors harboring alterations that are potentially amenable to targeted therapies. Financial sustainability was assessed using the fraction of global reimbursement. RESULTS: Among 181 consecutive ACC cases (2011-2018), prospective genotyping was performed in 11% (n = 20/181; n = 8 nonresectable). Testing identified 5/20 (25%) NOTCH1 aberrations, 6/20 (30%) MYB-NFIB fusions (all confirmed by FISH), and 2/20 (10%) MYBL1-NFIB fusions. Overall, these three alterations (MYB/MYBL1/NOTCH1) made up 65% of patients, and this subset had a more aggressive course with significantly shorter progression-free survival. In 75% (n = 6/8) of nonresectable patients, we detected potentially actionable alterations. Financial analysis of the global charges, including NGS codes, indicated 63% reimbursement, which is in line with national (U.S.-based) and international levels of reimbursement. CONCLUSION: Prospective routine clinical genotyping in ACC can identify clinically relevant subsets of patients and is approaching financial sustainability. Demonstrating clinical utility and financial sustainability in an orphan disease (ACC) requires a multiyear and multidimensional program. IMPLICATIONS FOR PRACTICE: Delineation of molecular profiles in adenoid cystic carcinoma (ACC) has been accomplished in the research setting; however, the ability to identify relevant patient subsets in clinical practice has not been assessed. This work presents an approach to perform integrated molecular genotyping of patients with ACC with nonresectable, recurrent, or systemic disease. It was determined that 75% of nonresectable patients harbor potentially actionable alterations and that 63% of charges are reimbursed. This report outlines that orphan diseases such as ACC require a multiyear, multidimensional program to demonstrate utility in clinical practice.

Artificial Intelligence Approach for Variant Reporting.

Purpose: Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods: We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results: For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion: Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.