Algorithms for Enumerating Circuits in Matroids

We present an incremental polynomial-time algorithm for enumerating all circuits of a matroid or, more generally, all minimal spanning sets for a flat. This result implies, in particular, that for a given infeasible system of linear equations, all its maximal feasible subsystems, as well as all minimal infeasible subsystems, can be enumerated in incremental polynomial time. We also show the NP-hardness of several related enumeration problems

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Interaction of rheumatoid factor and Entamoeba histolytica

The amoebae's cytotoxicity test and the amoebae's lysis test were used to show possible interactions between rheumatoid factor (RF) and Entamoeba histolytica. Amoebae's cytotoxic activity (ACA) was inhibited by affinity chromatography purified antiamoebae rabbit IgG (RIgG). Enhanced inhibition could be demonstrated with RIgG plus RF. But the same marked inhibition of ACA could be seen when replacing RF by heat inactivated normal human serum as a control. About 50% amoebae's lysis occurred when amoebae were brought together with native normal human serum (NNHS) as a source of complement. Amoebae's lysis increased to 60% when incubated with NHS plus human antiamoebae antibodies. No further augmentation could be obtained by the addition of RF. Using RIgG instead of human antibodies the lysis rate did not increase. Incubation of amoebae, NNHS, RIgG and RF even reduced amoebae's lysis. RF neither has an effect on ACA nor on complement mediated AL in vitro

International bullous diseases group: consensus on diagnostic criteria for epidermolysis bullosa acquisita

BACKGROUND: Epidermolysis bullosa acquisita (EBA) is a complex autoimmune bullous disease disease with variable clinical presentations and multiple possible diagnostic tests, making an international consensus on the diagnosis of EBA essential. ----- OBJECTIVES: To obtain an international consensus on the clinical and diagnostic criteria for EBA. ----- METHODS: The International Bullous Diseases Group (IBDG) met three times to discuss the clinical and diagnostic criteria for EBA. For the final voting exercise, 22 experts from 14 different countries voted on 50 different items. When > 30% disagreed with a proposal, a discussion was held and re-voting carried out. ----- RESULTS: In total, 48 of 50 proposals achieved consensus after discussion. This included nine diagnostic criteria, which are summarized in a flow chart. The IBDG was unable to determine one procedure that would be applicable worldwide. A limitation of the study is that differential diagnosis of bullous systemic lupus erythematosus has not been addressed. ----- CONCLUSIONS: This first international consensus conference established generally agreed-upon clinical and laboratory criteria defining the clinical classification of and diagnostic testing for EBA. Holding these voting exercises in person with the possibility of discussion prior to voting has advantages in reaching consensus over Delphi exercises with remote voting

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy