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

Genetic variants in immune-related pathways and breast cancer risk in African American women in the AMBER consortium

Background: Constitutional immunity shaped by exposure to endemic infectious diseases and parasitic worms in Sub-Saharan Africa may play a role in the etiology of breast cancer among African American (AA) women. Methods: A total of 149,514 gene variants in 433 genes across 45 immune pathways were analyzed in the AMBER consortium among 3,663 breast cancer cases and 4,687 controls. Gene-based pathway analyses were conducted using the adaptive rank truncated product statistic for overall breast cancer risk, and risk by estrogen receptor (ER) status. Unconditional logistic regression analysis was used to estimate ORs and 95% confidence intervals (CIs) for single variants. Results: The top pathways were Interleukin binding (P = 0.01), Biocarta TNFR2 (P = 0.005), and positive regulation of cytokine production (P = 0.024) for overall, ER+, ER- cancers, respectively. The most significant gene was IL2RB (P = 0.001) for overall cancer, with rs228952 being the top variant identified (OR = 0.85; 95% CI, 0.79-0.92). Only BCL3 contained a significant variant for ER+ breast cancer. Variants in IL2RB, TLR6, IL8, PRKDC, and MAP3K1 were associated with ER- disease. The only genes showing heterogeneity between ER- and ER+ cancers were TRAF1, MAP3K1, and MAPK3 (P < 0.02). We also noted genes associated with autoimmune and atopic disorders. Conclusions: Findings from this study suggest that genetic variants in immune pathways are relevant to breast cancer susceptibility among AA women, both for ER+ and ER- breast cancers. Impact: Results from this study extend our understanding of how inherited genetic variation in immune pathways is relevant to breast cancer susceptibility

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

The performance of the jet trigger for the ATLAS detector during 2011 data taking

The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton–proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon–nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by the trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction

Search for strong gravity in multijet final states produced in pp collisions at √s=13 TeV using the ATLAS detector at the LHC

A search is conducted for new physics in multijet final states using 3.6 inverse femtobarns of data from proton-proton collisions at √s = 13TeV taken at the CERN Large Hadron Collider with the ATLAS detector. Events are selected containing at least three jets with scalar sum of jet transverse momenta (HT) greater than 1TeV. No excess is seen at large HT and limits are presented on new physics: models which produce final states containing at least three jets and having cross sections larger than 1.6 fb with HT > 5.8 TeV are excluded. Limits are also given in terms of new physics models of strong gravity that hypothesize additional space-time dimensions