Targeted Informatics for Optimal Detection, Characterization, and Quantification of FLT3 Internal Tandem Duplications Across Multiple Next-Generation Sequencing Platforms.

Assessment of internal tandem duplications in FLT3 (FLT3-ITDs) and their allelic ratio (AR) is recommended by clinical guidelines for diagnostic workup of acute myeloid leukemia and traditionally performed through capillary electrophoresis (CE). Although significant progress has been made integrating FLT3-ITD detection within contemporary next-generation sequencing (NGS) panels, AR estimation is not routinely part of clinical NGS practice because of inherent biases and challenges. In this study, data from multiple NGS platforms-anchored multiplex PCR (AMP), amplicon [TruSeq Custom Amplicon (TSCA)], and hybrid-capture-were analyzed through a custom algorithm, including platform-specific measures of AR. Sensitivity and specificity of NGS for FLT3-ITD status relative to CE were 100% (42/42) and 99.4% (1076/1083), respectively, by AMP on an unselected cohort and 98.1% (53/54) and 100% (48/48), respectively, by TSCA on a selected cohort. Primer analysis identified criteria for ITDs to escape detection by TSCA, estimated to occur in approximately 9% of unselected ITDs. Allelic fractions under AMP or TSCA were highly correlated to CE, with linear regression slopes near 1 for ITDs not duplicating primers, and systematically underestimated for ITDs duplicating a primer. Bias was alleviated in AMP through simple adjustments. This article provides an approach for targeted computational FLT3-ITD analysis for NGS data from multiple platforms; AMP was found capable of near perfect sensitivity and specificity with relatively accurate estimates of ARs.

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.

VarGrouper: A Bioinformatic Tool for Local Haplotyping of Deletion-Insertion Variants from Next-Generation Sequencing Data after Variant Calling.

Accurate genetic variant representation through nomenclature and annotation is essential for understanding functional consequence and properly noting the presence of variants across time, assays, and laboratories. Current variant calling algorithms detect single deletion-insertion variants as multiple indel and/or substitution variants from next-generation sequencing data. Consequently, these variants are separately annotated in bioinformatics pipelines, leading to inaccurate variant representation. We developed a bioinformatic solution to this problem-VarGrouper-that automatically recognizes individual variants that arise from a deletion-insertion variant and aggregates them into a single variant that can be properly annotated. This tool has been integrated into our routine clinical molecular diagnostics workflow for DNA sequencing of solid tumors. Over an 11-month period, VarGrouper variants were reported by all attending molecular pathologists involved in interpretation and represented 4.1% of all variants reported; 10.9% of cases with reportable variants contained at least one VarGrouper variant. VarGrouper improves the practice of molecular diagnostics by increasing the accuracy and consistency of variant annotation. VarGrouper is freely available for use by the molecular diagnostic community.

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.

Expressed Gene Fusions as Frequent Drivers of Poor Outcomes in Hormone Receptor-Positive Breast Cancer.

We sought to uncover genetic drivers of hormone receptor-positive (HR+) breast cancer, using a targeted next-generation sequencing approach for detecting expressed gene rearrangements without prior knowledge of the fusion partners. We identified intergenic fusions involving driver genes, including PIK3CA, AKT3, RAF1, and ESR1, in 14% (24/173) of unselected patients with advanced HR+ breast cancer. FISH confirmed the corresponding chromosomal rearrangements in both primary and metastatic tumors. Expression of novel kinase fusions in nontransformed cells deregulates phosphoprotein signaling, cell proliferation, and survival in three-dimensional culture, whereas expression in HR+ breast cancer models modulates estrogen-dependent growth and confers hormonal therapy resistance in vitro and in vivo Strikingly, shorter overall survival was observed in patients with rearrangement-positive versus rearrangement-negative tumors. Correspondingly, fusions were uncommon (<5%) among 300 patients presenting with primary HR+ breast cancer. Collectively, our findings identify expressed gene fusions as frequent and potentially actionable drivers in HR+ breast cancer.Significance: By using a powerful clinical molecular diagnostic assay, we identified expressed intergenic fusions as frequent contributors to treatment resistance and poor survival in advanced HR+ breast cancer. The prevalence and biological and prognostic significance of these alterations suggests that their detection may alter clinical management and bring to light new therapeutic opportunities. Cancer Discov; 8(3); 336-53. ©2017 AACR.See related commentary by Natrajan et al., p. 272See related article by Liu et al., p. 354This article is highlighted in the In This Issue feature, p. 253.

Design and implementation of an automated email notification system for results of tests pending at discharge.

Physicians are often unaware of the results of tests pending at discharge (TPADs). The authors designed and implemented an automated system to notify the responsible inpatient physician of the finalized results of TPADs using secure, network email. The system coordinates a series of electronic events triggered by the discharge time stamp and sends an email to the identified discharging attending physician once finalized results are available. A carbon copy is sent to the primary care physicians in order to facilitate communication and the subsequent transfer of responsibility. Logic was incorporated to suppress selected tests and to limit notification volume. The system was activated for patients with TPADs discharged by randomly selected inpatient-attending physicians during a 6-month pilot. They received approximately 1.6 email notifications per discharged patient with TPADs. Eighty-four per cent of inpatient-attending physicians receiving automated email notifications stated that they were satisfied with the system in a brief survey (59% survey response rate). Automated email notification is a useful strategy for managing results of TPADs.