Introduction: Inflammatory myofibroblastic tumor (IMT), a locally aggressive neoplasm capable of metastasis, may show an IgG4-rich lymphoplasmacytic infiltrate. Prior reports suggest that storiform-fibrosis and obliterative phlebitis aid in the distinction of IMT from IgG4-related diseases. Herein, we highlight the morphologic overlap between the two diseases, and emphasize the importance of a multiplex fusion assay in the distinction of IgG4-RD from IMT. Methods: We identified 7 IMTs with morphologic and immunohistochemical features of IgG4-RD; 3 patients were originally diagnosed with IgG4-RD. Demographic, clinical and morphologic data was recorded. We also re-evaluated 56 patients with IgG4-RD. We performed immunohistochemistry for IgG4, IgG, ALK and ROS1. In situ hybridization for IgG4 and IgG was performed in selected cases. A multiplex next-generation sequencing (NGS) based RNA assay for gene fusions was performed to detect all known IMT-related gene fusions. Results: All 7 IMTs showed a dense lymphoplasmacytic infiltrate and storiform-type fibrosis, with obliterative phlebitis noted in 3 cases. The neoplastic stromal cells constituted <5% of overall cellularity and stromal atypia was either absent or focal and mild. Elevated numbers of IgG4 positive cells and increased IgG4 to IgG ratio was identified in all cases. Four cases showed ALK related abnormalities; while two patients showed ROS1 and NTRK3 fusions. One tumor was negative for known IMT-related gene fusions. All 56 IgG4-RD cases were negative for ALK and ROS1 on immunohistochemistry; 6 cases were negative on the fusion assay. Conclusion: Highly-inflamed IMTs are indistinguishable from IgG4-RD both histologically and on immunohistochemistry for IgG4. We advocate scrutinizing patients with presumptive single organ IgG4-RD for IMT and the diagnostic algorithm should include ALK and ROS1 immunohistochemistry and, in selected cases, a NGS-based fusion assay that covers known IMTassociated gene fusions.
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 , and, 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 and 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. 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. .
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.
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 platformsdanchored multiplex PCR (AMP), amplicon [TruSeq Custom Amplicon (TSCA)], and hybrid-capturedwere 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.
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.
<p>Supplementary Figure S1-S6 and Supplementary Tables S1-S8</p>
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 deletioneinsertion 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 problemdVarGrouperdthat automatically recognizes individual variants that arise from a deletioneinsertion 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.
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