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.

Peptide-based urinary monitoring of fibrotic nonalcoholic steatohepatitis by mass-barcoded activity-based sensors.

Noninvasive detection of nonalcoholic steatohepatitis (NASH), the progressive form of nonalcoholic fatty liver disease, promises to improve patient screening, accelerate drug trials, and reduce health care costs. On the basis of protease dysregulation of the biological pathways of fibrotic NASH, we developed the Glympse Bio Test System (GBTS) for multiplexed quantification of liver protease activity. GBTS-NASH comprises a mixture of 19 mass-barcoded PEGylated peptides that is administered intravenously and senses liver protease activity by releasing mass-barcoded reporters into urine for analysis by mass spectrometry. To identify a protease signature of NASH, transcriptomic analysis of 355 human liver biopsies identified a 13-protease panel that discriminated clinically relevant NASH ≥F2 fibrosis from F0-F1 with high classification accuracy across two independent patient datasets. We screened 159 candidate substrates to identify a panel of 19 peptides that exhibited high activity for our 13-protease panel. In the choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) mouse model, binary classifiers trained on urine samples discriminated fibrotic NASH from simple steatosis and healthy controls across a range of nondisease conditions and indicated disease regression upon diet change [area under receiver operating characteristics (AUROCs) > 0.97]. Using a hepatoprotective triple combination treatment (FXR agonist, ACC and ASK1 inhibitors) in a rat model of NASH, urinary classification distinguished F0-F1 from ≥F2 animals and indicated therapeutic response as early as 1 week on treatment (AUROCs >0.91). Our results support GBTS-NASH to diagnose fibrotic NASH via an infusion of peptides, monitor changes in disease severity, and indicate early treatment response.

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.

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.

Health Care Infrastructure for Financially Sustainable Clinical Genomics.

Next-generation sequencing has evolved technically and economically into the method of choice for interrogating the genome in cancer and inherited disorders. The introduction of procedural code sets for whole-exome and genome sequencing is a milestone toward financially sustainable clinical implementation; however, achieving reimbursement is currently a major challenge. As part of a prospective quality-improvement initiative to implement the new code sets, we adopted Agile, a development methodology originally devised in software development. We implemented eight functionally distinct modules (request review, cost estimation, preauthorization, accessioning, prebilling, testing, reporting, and reimbursement consultation) and obtained feedback via an anonymous survey. We managed 50 clinical requests (January to June 2015). The fraction of pursued-to-requested cases (n = 15/50; utilization management fraction, 0.3) aimed for a high rate of preauthorizations. In 13 of 15 patients the insurance plan required preauthorization, which we obtained in 70% and ultimately achieved reimbursement in 50%. Interoperability enabled assessment of 12 different combinations of modules that underline the importance of an adaptive workflow and policy tailoring to achieve higher yields of reimbursement. The survey confirmed a positive attitude toward self-organizing teams. We acknowledge the individuals and their interactions and termed the infrastructure: human pipeline. Nontechnical barriers currently are limiting the scope and availability of clinical genomic sequencing. The presented human pipeline is one approach toward long-term financial sustainability of clinical genomics.