Summary The Cancer Genome Atlas (TCGA) project has analyzed mRNA expression, miRNA expression, promoter methylation, and DNA copy number in 489 high-grade serous ovarian adenocarcinomas (HGS-OvCa) and the DNA sequences of exons from coding genes in 316 of these tumors. These results show that HGS-OvCa is characterized by TP53 mutations in almost all tumors (96%); low prevalence but statistically recurrent somatic mutations in 9 additional genes including NF1, BRCA1, BRCA2, RB1, and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three miRNA subtypes, four promoter methylation subtypes, a transcriptional signature associated with survival duration and shed new light on the impact on survival of tumors with BRCA1/2 and CCNE1 aberrations. Pathway analyses suggested that homologous recombination is defective in about half of tumors, and that Notch and FOXM1 signaling are involved in serous ovarian cancer pathophysiology.

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.genomics | therapeutics | predictor

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Peripheral blood MФ–cancer cell fusion hybrids identified in cancer patients correlate with disease stage and overall survival.

Adult male germ cell tumors (GCTs) comprise distinct groups: seminomas and nonseminomas, which include pluripotent embryonal carcinomas as well as other histologic subtypes exhibiting various stages of differentiation. Almost all GCTs show 12p gain, but the target genes have not been clearly defined. To identify 12p target genes, we examined Affymetrix (Santa Clara, CA) U133A+B microarray (f83% coverage of 12p genes) expression profiles of 17 seminomas, 84 nonseminoma GCTs, and 5 normal testis samples. Seventy-three genes on 12p were significantly overexpressed, including GLUT3 and REA (overexpressed in all GCTs) and CCND2 and FLJ22028 (overexpressed in all GCTs, except choriocarcinomas). We characterized a 200-kb gene cluster at 12p13.31 that exhibited coordinated overexpression in embryonal carcinomas and seminomas, which included the known stem cell genes NANOG, STELLA, and GDF3 and two previously uncharacterized genes. A search for other coordinately regulated genomic clusters of stem cell genes did not reveal any genomic regions similar to that at 12p13.31. Comparison of embryonal carcinoma with seminomas revealed relative overexpression of several stem cell-associated genes in embryonal carcinoma, including several core ''stemness'' genes (EBAF, TDGF1, and SOX2) and several downstream targets of WNT, NODAL, and FGF signaling (FGF4, NODAL, and ZFP42). Our results indicate that 12p gain is a functionally relevant change leading to activation of proliferation and reestablishment/maintenance of stem cell function through activation of key stem cell genes. Furthermore, the differential expression of core stem cell genes may explain the differences in pluripotency between embryonal carcinomas and seminomas. (Cancer Res 2006; 66(2): 820-7)

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

KRAS mutation is a hallmark of pancreatic ductal adenocarcinoma (PDA), but remains an intractable pharmacological target. Consequently, defining RAS effector pathway(s) required for PDA initiation and maintenance is critical to improve treatment of this disease. Here we demonstrate that expression of BRAFV600E, but not PIK3CAH1047R, in the mouse pancreas leads to pancreatic intraepithelial neoplasia (PanIN) lesions. Moreover, concomitant expression of BRAFV600E and TP53R270H result in lethal PDA. We tested pharmacologic inhibitors of Ras effectors against multiple human PDA cell lines. MEK inhibition was highly effective both in vivo and in vitro, and was synergistic with AKT inhibition in most cell lines tested. We demonstrate that RAF→MEK→ERK signaling is central to the initiation and maintenance of PDA and to rational combination strategies in this disease. These results emphasize the value of leveraging multiple complementary experimental systems to prioritize pathways for effective intervention strategies in PDA.

Models of bladder tumor progression have suggested that genetic alterations may determine both phenotype and clinical course.We have applied expression microarray analysis to a divergent set of bladder tumors to further elucidate the course of disease progression and to classify tumors into more homogeneous and clinically relevant subgroups. cDNA microarrays containing 10,368 human gene elements were used to characterize the global gene expression patterns in 80 bladder tumors, 9 bladder cancer cell lines, and 3 normal bladder samples. Robust statistical approaches accounting for the multiple testing problem were used to identify differentially expressed genes. Unsupervised hierarchical clustering successfully separated the samples into two subgroups containing superficial (pT a and pT 1 ) versus muscle-invasive (pT 2 -pT 4 ) tumors. Supervised classification had a 90.5% success rate separating superficial from muscle-invasive tumors based on a limited subset of genes. Tumors could also be classified into transitional versus squamous subtypes (89% success rate) and good versus bad prognosis (78% success rate). The performance of our stage classifiers was confirmed in silico using data from an independent tumor set. Validation of differential expression was done using immunohistochemistry on tissue microarrays for cathepsin E, cyclin A2, and parathyroid hormone^related protein. Genes driving the separation between tumor subsets may prove to be important biomarkers for bladder cancer development and progression and eventually candidates for therapeutic targeting.