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.

A urinary test for liver protease activity detects NASH fibrosis and indicates early therapeutic treatment response.

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 Z 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. (J Mol Diagn 2016, 18: 697e706; http://dx

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Many real world problems have a structure where small problem instances are embedded within large problem instances, or where solution quality for large problem instances is loosely correlated to that of small problem instances. This structure can be exploited because smaller problem instances typically have smaller search spaces and are cheaper to evaluate. We present an evolutionary algorithm, INCREA, which is designed to incrementally solve a large, noisy, computationally expensive problem by deriving its initial population through recursively running itself on problem instances of smaller sizes. The INCREA algorithm also expands and shrinks its population each generation and cuts off work that doesn't appear to promise a fruitful result. For further efficiency, it addresses noisy solution quality efficiently by focusing on resolving it for small, potentially reusable solutions which have a much lower cost of evaluation. We compare INCREA to a general purpose evolutionary algorithm and find that in most cases INCREA arrives at the same solution in significantly less time.

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.

Modern high performance libraries, such as ATLAS and FFTW, and programming languages, such as PetaBricks, have shown that autotuning computer programs can lead to significant speedups.However, autotuning can be burdensome to the deployment of a program, since the tuning process can take a long time and should be rerun whenever the program, microarchitecture, execution environment, or tool chain changes. Failure to re-autotune programs often leads to widespread use of sub-optimal algorithms. With the growth of cloud computing, where computations can run in environments with unknown load and migrate between different (possibly unknown) microarchitectures, the need for online autotuning has become increasingly important.We present SiblingRivalry, a new model for alwayson online autotuning that allows parallel programs to continuously adapt and optimize themselves to their environment. In our system, requests are processed by dividing the available cores in half, and processing two identical requests in parallel on each half. Half of the cores are devoted to a known safe program configuration, while the other half are used for an experimental program configuration chosen by our self-adapting evolutionary algorithm.When the faster configuration completes, its results are returned, and the slower configuration is terminated. Over time, this constant experimentation allows programs to adapt to changing dynamic environments and often outperform the original algorithm that uses the entire system.

Abstract. We are using bandit-based adaptive operator selection while autotuning parallel computer programs. The autotuning, which uses evolutionary algorithm-based stochastic sampling, takes place over an extended duration and occurs in situ as programs execute. The environment or context during tuning is either largely static in one scenario or dynamic in another. We rely upon adaptive operator selection to dynamically generate worthy test configurations of the program. In this paper, we study how the choice of hyperparameters, which control the trade-off between exploration and exploitation, affects the effectiveness of adaptive operator selection which in turn affects the performance of the autotuner. We show that while the optimal assignment of hyperparameters varies greatly between different benchmarks, there exists a single assignment, for a context, of hyperparameters that performs well regardless of the program being tuned.