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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. To compare the pathologic, molecular, and clinical features of 2 morphologically similar tumors: solid-subtype ADC and LCC. 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. 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%. 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.

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 ∼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. Synopsis Genotype‐to‐phenotype relationships in human genetic disease are often modeled as: ‘mutation in gene X leads to loss of gene product X, which leads to disease A’. However, single ‘gene‐loss’ models cannot explain the increasingly appreciated prevalence of complex genotype‐to‐phenotype relationships, particularly with instances of allelic or locus hetrogeneity (Goh et al , 2007 ). Genes and gene products function not in isolation but as components of complex networks of macromolecules (DNA, RNA, or proteins) and metabolites linked through biochemical or physical interactions, often represented in ‘interactome’ network models as ‘nodes’ and ‘edges’, respectively. Here we use network perturbation models to explain molecular dysfunctions underlying human disease in addition to the gene‐loss model. We hypothesize that different mutations leading to different molecular defects to proteins may cause distinct perturbations of cellular networks, giving rise to distinct phenotypic outcomes (Figure 1 ). For example, truncations close to the start of an open‐reading frame, or mutations that grossly destabilize a protein structure, can be modeled as removing a protein node from the network (‘node removal’). Alternatively, single amino‐acid substitutions that affect specific binding sites, or truncations that preserve certain domains of a protein, may give rise to partially functional gene products with specific changes in distinct molecular interaction(s) (edge‐specific or ‘edgetic’ perturbations) (Figure 1B ). Taking advantage of the large number of known disease‐causing allelic variations in human Mendelian disorders, we investigated how disease‐associated mutations may cause complete loss of gene products or, alternatively, may cause specific loss or gain of individual molecular interaction(s). We examined ∼50 000 Mendelian disease‐causing alleles, affecting over 1900 protein‐coding genes, altogether associated with more than 2000 human disorders available in the Human Gene Mutation Database (HGMD) (Stenson et al , 2003 ), that can be subdivided into two subsets: truncating’ alleles (truncations or frameshifts caused by stop codons, out‐of‐frame insertions or deletions, or defective splicing) versus ‘in‐frame’ alleles (missense mutations and in‐frame insertions or deletions). Over 50% (27 919/52 491) of Mendelian alleles in HGMD correspond to ‘in‐frame’ mutations. Our hypothesis is that, ‘in‐frame’ alleles may affect specific interactions of a given gene product while leaving most other interactions unperturbed. Although exceptions may apply, our hypothesis has several predictions. First, ‘truncating’ versus ‘in‐frame’ alleles may distribute differently among autosomal dominant and autosomal recessive disease, given that dominant mutations are more likely to be edgetic than recessive ones. Indeed, autosomal dominant and autosomal recessive traits annotated in the Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al , 2005 ) show a clear separation with respect to the associated ‘in‐frame’ versus ‘truncating’ mutations. Among genes affected solely by ‘in‐frame’ mutations, the proportion of dominant diseases is ∼10‐fold higher than that of recessive ones, supporting ‘in‐frame’ mutations causing distinct molecular defects as opposed to ‘truncating’ mutations. A proof‐of‐principle characterization of binary protein interaction defects of mutant alleles associated with five genetic disorders supports our hypothesis that ‘in‐frame’ alleles indeed produce mostly functional proteins, preserving many specific protein interactions. As grossly disruptive mutations versus mutations leading to loss or gain of specific interaction(s) probably distribute differently on protein structures, we examined available three‐dimensional structures of all disease proteins. Mutated residues in autosomal dominant disease are significantly more exposed to the surface of the structure than those in autosomal recessive disease, consistent with the idea that disease with distinct modes of inheritance probably involves distinct network perturbations. A second testable prediction of our edgetic perturbation model is that edgetic perturbation versus gene loss for a given gene product might in some cases cause different diseases. We examined 142 genes associated with two or more distinct diseases in which at least five distinct alleles have been reported for each disease. We found ∼30% of the cases for which distribution of ‘in‐frame’ versus ‘truncating’ mutations is significantly different between the two diseases linked to the same gene ( P <0.05). Hence, when affecting the same gene, node removal versus edgetic perturbation can confer strikingly different phenotypes. A third testable prediction is that different edgetic perturbations for a given gene product might cause phenotypically distinguishable diseases (Figure 6 ). We used predicted Pfam domains (Finn et al , 2006 ) as surrogates for functional interaction domains, assuming that ‘in‐frame’ mutations located in distinct Pfam domain‐encoding sequences probably alter distinct interactions. Among 169 genes associated with two or more diseases and encoding proteins containing at least two Pfam domains, nine proteins have at least two Pfam domains significantly enriched with ‘in‐frame’ mutations ( P <0.05). For each of the nine proteins, we found a striking pattern of near mutual exclusivity, whereby different Pfam domains seem to be specifically affected in distinct disorders (Figure 6B ). We conclude that edgetic alleles probably underlie many complex genotype‐to‐phenotype relationships in human disease, such as incomplete penetrance or variable expressivity, as well as allele‐specific phenotypic variations among patients. Edgetic perturbation of human inherited disorders might help explain how seemingly devastating alleles have appeared and persevered in human populations. We present alternative models to explain molecular dysfunctions underlying human inherited disorders based on interaction‐specific or “edgetic” perturbations rather than complete loss of gene products. We find that a substantial fraction of known genetic variants in human Mendelian disorders likely cause edgetic perturbations. We find frequent situations where edgetic perturbation models can explain how different mutations in a single gene can cause distinct disorders. Edgetic perturbation models should provide alternative explanations to complex genotype‐to‐phenotype relationships

High-throughput yeast two-hybrid screening is used to generate the largest C. elegans interactome resource available thus far. Using an empirical quality control framework presented in Venkatesan et al ., also online, the data set is evaluated for quality and is used to estimate the total size of the worm interactome. 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 ∼10,000 × ∼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 ∼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.

The complete set of coding sequences, including all splice isoforms, is not known for any metazoan organism. Combination of a normalized pooling scheme and a new assembly algorithm with 454 sequencing yields a methodological pipeline for isoform discovery. The validated pipeline may now be applied genome-wide. 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.

A framework based on numerous empirical data, including protein-protein interaction reference sets, provides parameters for assessing the quality and coverage of protein-protein interaction datasets and estimation of the size of the human interactome. Braun et al ., also in this issue, use the reference sets to help derive confidence scores for individual protein-protein interactions. 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 ∼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.

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.

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.

The dual pendula swing up problem is considered and solved using Smooth Nonlinear Trajectory Planning (SNTP) and linear quadratic Gaussian (LQG) feedback control. A physical dual pendula system is designed and built to demonstrate the viability of the dual pendula swing up via SNTP and LQG control. The dual pendula swing up is successfully demonstrated on the physical dual pendula system. SNTP determines an appropriate forcing of the cart, which the dual pendula are mounted to, such that the dual pendula swing up to the inverted equilibrium; that is, SNTP determines an appropriate control trajectory that results in a desired state trajectory. The LQG control is implemented in two separate controllers: a state trajectory tracking controller to reject disturbances in following the planned optimal swing up state trajectory and a stabilizing controller to balance the inverted dual pendula.

In a randomized trial, 1000 patients with severe aortic stenosis who were at low risk for death with surgery were assigned to undergo transcatheter aortic-valve replacement with a balloon-expandable valve or surgical aortic-valve replacement. At 1 year, the rate of death, stroke, or rehospitalization was significantly lower in the TAVR group.