Prosthesis preferences for those with upper limb loss: Discrete choice study of PULLTY® for use in regulatory decisions.

Introduction: The patient's voice in shared decision-making has progressed from physician's office to regulatory decision-making for medical devices with FDA's Patient Preference Initiative. A discrete-choice preference measure for upper limb prosthetic devices was developed to investigate patient's risk/benefit preference choices for regulatory decision making. Methods: Rapid ethnographic procedures were used to design a discrete-choice measure describing risk and benefits of osseointegration with myoelectric control and test in a pilot preference study in adults with upper limb loss. Primary outcome is utility of each choice based conjoint (CBC) attribute using mixed-effects regression. Utilities with and without video, and between genders were compared. Results: Strongest negative preference was for avoiding infection risk (B = -1.77, p < 0.001) and chance of daily pain (B = -1.22, p, 0.001). Strongest positive preference was for attaining complete independence when cooking dinner (B = 1.62, p < 0.001) and smooth grip patterns at all levels (B = 1.62, B = 1.28, B = 1.26, p < 0.001). Trade-offs showed a 1% increase in risk of serious/treatable infection resulted in a 1.77 decrease in relative preference. There were gender differences, and where video was used, preferences were stronger. Conclusions: Strongest preferences were for attributes of functionality and independence versus connectedness and sensation but showed willingness to make risk-benefit trade-offs. Findings provide valuable information for regulatory benefit-risk decisions for prosthetic device innovations. Trial Registration: This study is not a clinical trial reporting results of a health care intervention so is not registered.

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.

Validation and Implementation of a Custom Next-Generation Sequencing Clinical Assay for Hematologic Malignancies.

Targeted next-generation sequencing panels to identify genetic alterations in cancers are increasingly becoming an integral part of clinical practice. We report here the design, validation, and implementation of a comprehensive 95-gene next-generation sequencing panel targeted for hematologic malignancies that we named rapid heme panel. Rapid heme panel is amplicon based and covers hotspot regions of oncogenes and most of the coding regions of tumor suppressor genes. It is composed of 1330 amplicons and covers 175 kb of genomic sequence in total. Rapid heme panel's average coverage is 1500× with <5% of the amplicons with <50× coverage, and it reproducibly detects single nucleotide variants and small insertions/deletions at allele frequencies of ≥5%. Comparison with a capture-based next-generation sequencing assay showed that there is >95% concordance among a wide array of variants across a range of allele frequencies. Read count analyses that used rapid heme panel showed high concordance with karyotypic results when tumor content was >30%. The average turnaround time was 7 days over a 6-month span with an average volume of ≥40 specimens per week and a low sample fail rate (<1%), demonstrating its suitability for clinical application.

Pulmonary large cell carcinoma lacking squamous differentiation is clinicopathologically indistinguishable from solid-subtype adenocarcinoma.

CONTEXT: Pulmonary large cell carcinoma (LCC) includes tumors not readily diagnosed as adenocarcinoma (ADC) or squamous cell carcinoma on morphologic grounds, without regard to immunophenotype, according to the World Health Organization (WHO). This ambiguous designation may cause confusion over selection of mutation testing and directed therapies. Several groups have proposed the use of immunohistochemistry (IHC) to recategorize LCC as ADC or squamous cell carcinoma; however, it remains unclear if strictly defined LCCs are a clinicopathologically distinct lung tumor subset. OBJECTIVE: To compare the pathologic, molecular, and clinical features of 2 morphologically similar tumors: solid-subtype ADC and LCC. DESIGN: Tumors were included on the basis of solid growth pattern; tumors with squamous or neuroendocrine differentiation were excluded. Solid ADC (n = 42) and LCC (n = 57) were diagnosed by using WHO criteria (5 intracellular mucin droplets in ≥2 high-power fields for solid ADC) and tested for KRAS, EGFR, and ALK alterations. RESULTS: Both solid ADC and LCC groups were dominated by tumors with "undifferentiated"-type morphology and both had a high frequency of thyroid transcription factor 1 expression. KRAS was mutated in 38% of solid ADCs versus 43% of LCCs (P = .62). One ALK-rearranged and 1 EGFR-mutated tumor were detected in the solid ADC and LCC groups, respectively. There were no significant differences in clinical features or outcomes; the prevalence of smoking in both groups was greater than 95%. CONCLUSIONS: Other than a paucity of intracellular mucin, LCC lacking squamous or neuroendocrine differentiation is indistinguishable from solid-subtype ADC. We propose the reclassification of these tumors as mucin-poor solid adenocarcinomas.

Edgetic perturbation models of human inherited disorders.

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of approximately 50,000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

Large-scale RACE approach for proactive experimental definition of C. elegans ORFeome.

Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

Isoform discovery by targeted cloning, 'deep-well' pooling and parallel sequencing.

Describing the 'ORFeome' of an organism, including all major isoforms, is essential for a system-level understanding of any species; however, conventional cloning and sequencing approaches are prohibitively costly and labor-intensive. We describe a potentially genome-wide methodology for efficiently capturing new coding isoforms using reverse transcriptase (RT)-PCR recombinational cloning, 'deep-well' pooling and a next-generation sequencing platform. This ORFeome discovery pipeline will be applicable to any eukaryotic species with a sequenced genome.

Efficient targeted transcript discovery via array-based normalization of RACE libraries.

Rapid amplification of cDNA ends (RACE) is a widely used approach for transcript identification. Random clone selection from the RACE mixture, however, is an ineffective sampling strategy if the dynamic range of transcript abundances is large. To improve sampling efficiency of human transcripts, we hybridized the products of the RACE reaction onto tiling arrays and used the detected exons to delineate a series of reverse-transcriptase (RT)-PCRs, through which the original RACE transcript population was segregated into simpler transcript populations. We independently cloned the products and sequenced randomly selected clones. This approach, RACEarray, is superior to direct cloning and sequencing of RACE products because it specifically targets new transcripts and often results in overall normalization of transcript abundance. We show theoretically and experimentally that this strategy leads indeed to efficient sampling of new transcripts, and we investigated multiplexing the strategy by pooling RACE reactions from multiple interrogated loci before hybridization.