We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Mutations affecting the BRCT domains of the breast cancer-associated tumor suppressor BRCA1 disrupt the recruitment of this protein to DNA double-strand breaks (DSBs). The molecular structures at DSBs recognized by BRCA1 are presently unknown. We report the interaction of the BRCA1 BRCT domain with RAP80, a ubiquitin-binding protein. RAP80 targets a complex containing the BRCA1-BARD1 (BRCA1-associated ring domain protein 1) E3 ligase and the deubiquitinating enzyme (DUB) BRCC36 to MDC1-gammaH2AX-dependent lysine(6)- and lysine(63)-linked ubiquitin polymers at DSBs. These events are required for cell cycle checkpoint and repair responses to ionizing radiation, implicating ubiquitin chain recognition and turnover in the BRCA1-mediated repair of DSBs.

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered.

Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

The survcomp package provides functions to assess and statistically compare the performance of survival/risk prediction models. It implements state-of-the-art statistics to (i) measure the performance of risk prediction models; (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework; and (iii) statistically compare the performance of competitive models.

MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data.

Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical tstatistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets.

The field of apoptosis is unusual in several respects. Firstly, its general importance has been widely recognised only in the past few years and its surprising significance is still being evaluated in a number of areas of biology. Secondly, although apoptosis is now accepted as a critical element in the repertoire of potential cellular responses, the picture of the intra‐cellular processes involved is probably still incomplete, not just in its details, but also in the basic outline of the process as a whole. It is therefore a very interesting and active area at present and is likely to progress rapidly in the next two or three years. This review emphasises recent work on the molecular mechanisms of apoptosis and, in particular, on the intracellular interactions which control this process. This latter area is of crucial importance since dysfunction of the normal control machinery is likely to have serious pathological consequences, probably including oncogenesis, autoimmunity and degenerative disease. The genetic analysis of programmed cell death during the development of the nematode Caenorhabditis elegans has proved very useful in identifying important events in the cell death programme. Recently defined genetic connections between C. elegans cell death and mammalian apoptosis have emphasized the value of this system as a model for cell death in mammalian cells, which, inevitably, is more complex. The signals inducing apoptosis are very varied‐and the same signals can induce differentiation and proliferation in other situations. However, some pathways appear to be of particular significance in the control of cell death; recent analysis of the apoptosis induced through the cell‐surface Fas receptor has been especially important for immunology. Two gene families are dealt with in particular detail because of their likely importance in apoptosis control. These are, first, the genes encoding the interleukin‐1β‐converting enzyme family of cysteine proteases and, second, those related to the proto‐oncogene bcl‐2. Both of these families are homologous to cell death genes in C. elegans. In mammalian cells the number of members of both families which have been identified is growing rapidly and considerable effort is being directed towards establishing the roles played by each member and the ways in which they interact to regulate apoptosis. Other genes with established roles in the regulation of proliferation and differentiation are also important in controlling apoptosis. Several of these are known proto‐oncogenes, e.g. c‐myc, or tumour suppressors, e.g. p.53, an observation which is consistent with the importance of defective apoptosis in the development of cancer. Viral manipulation of the apoptosis of host cells frequently involves interactions with these cellular proteins. Finally, the biochemistry of the closely controlled cellular self‐destruction which ensues when the apoptosis programme has been engaged is also very important. The biochemical changes involved in inducing phagocytosis of the apoptotic cell, for e...