Primary triple negative breast cancers (TNBC) represent approximately 16% of all breast cancers1 and are a tumour type defined by exclusion, for which comprehensive landscapes of somatic mutation have not been determined. Here we show in 104 early TNBC cases, that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some exhibiting only a handful of somatic aberrations in a few pathways, whereas others contain hundreds of somatic events and multiple pathways implicated. Integration with matched whole transcriptome sequence data revealed that only ~36% of mutations are expressed. By examining single nucleotide variant (SNV) allelic abundance derived from deep re-sequencing (median >20,000 fold) measurements in 2414 somatic mutations, we determine for the first time in an epithelial tumour, the relative abundance of clonal genotypes among cases in the population. We show that TNBC vary widely and continuously in their clonal frequencies at the time of diagnosis, with basal subtype TNBC2,3 exhibiting more variation than non-basal TNBC. Although p53 and PIK3CA/PTEN somatic mutations appear clonally dominant compared with other pathways, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal and cell shape/motility proteins occurred at lower clonal frequencies, suggesting they occurred later during tumour progression. Taken together our results show that future attempts to dissect the biology and therapeutic responses of TNBC will require the determination of individual tumour clonal genotypes.

The clinical efficacy of anticancer nucleoside drugs depends on a complex interplay of transporters mediating entry of nucleoside drugs into cells, efflux mechanisms that remove drugs from intracellular compartments and cellular metabolism to active metabolites. Nucleoside transporters (NTs) are important determinants for salvage of preformed nucleosides and mediated uptake of antimetabolite nucleoside drugs into target cells. The focus of this review is the two families of human nucleoside transporters (hENTs, hCNTs) and their role in transport of cytotoxic chemotherapeutic nucleoside drugs. Resistance to anticancer nucleoside drugs is a major clinical problem in which NTs have been implicated. Single nucleotide polymorphisms (SNPs) in drug transporters may contribute to interindividual variation in response to nucleoside drugs. In this review, we give an overview of the functional and molecular characteristics of human NTs and their potential role in resistance to nucleoside drugs and discuss the potential use of genetic polymorphism analyses for NTs to address drug resistance.

Systemic and CNS-delimited inflammation triggers skeletal muscle catabolism in a manner dependent on glucocorticoid signaling.

A particular challenge in biomedical text mining is to find ways of handling ‘comprehensive’ or ‘associative’ queries such as ‘Find all genes associated with breast cancer’. Given that many queries in genomics, proteomics or metabolomics involve these kind of comprehensive searches we believe that a web-based tool that could support these searches would be quite useful. In response to this need, we have developed the PolySearch web server. PolySearch supports >50 different classes of queries against nearly a dozen different types of text, scientific abstract or bioinformatic databases. The typical query supported by PolySearch is ‘Given X, find all Y's’ where X or Y can be diseases, tissues, cell compartments, gene/protein names, SNPs, mutations, drugs and metabolites. PolySearch also exploits a variety of techniques in text mining and information retrieval to identify, highlight and rank informative abstracts, paragraphs or sentences. PolySearch's performance has been assessed in tasks such as gene synonym identification, protein–protein interaction identification and disease gene identification using a variety of manually assembled ‘gold standard’ text corpuses. Its f-measure on these tasks is 88, 81 and 79%, respectively. These values are between 5 and 50% better than other published tools. The server is freely available at http://wishart.biology.ualberta.ca/polysearch

Mucinous ovarian carcinomas (MCs) typically do not respond to current conventional therapy. We have previously demonstrated amplification of HER2 in 6 of 33 (18.2%) mucinous ovarian carcinomas (MCs) and presented anecdotal evidence of response with HER2-targeted treatment in a small series of women with recurrent HER2-amplified (HER2+) MC. Here, we explore HER2 amplification and KRAS mutation status in an independent cohort of 189 MCs and 199 mucinous borderline ovarian tumours (MBOTs) and their association to clinicopathological features. HER2 status was assessed by immunohistochemistry (IHC), FISH, and CISH, and interpreted per ASCO/CAP guidelines, with intratumoural heterogeneity assessment on full sections, where available. KRAS mutation testing was performed with Sanger sequencing. Stage and grade were associated with recurrence on both univariate and multivariate analysis (p < 0.001). Assessment of HER2 status revealed overexpression/amplification of HER2 in 29/154 (18.8%) MCs and 11/176 (6.2%) MBOTs. There was excellent agreement between IHC, FISH, and CISH assessment of HER2 status (perfect concordance of HER2 0 or 1+ IHC with non-amplified status, and 3+ IHC with amplified status). KRAS mutations were seen in 31/71 (43.6%) MCs and 26/33 (78.8%) MBOTs, and were near mutually exclusive of HER2 amplification. In the 189 MC cases, a total of 54 recurrences and 59 deaths (53 of progressive disease) were observed. Within MCs, either HER2 amplification/overexpression or KRAS mutation was associated with decreased likelihood of disease recurrence (p = 0.019) or death (p = 0.0041) when compared to cases with neither feature. Intratumoural heterogeneity was noted in 26% of HER2-overexpressing cases. These data support the stratification of MCs for the testing of new treatments, with HER2-targeted therapy as a viable option for HER2+ advanced or recurrent disease. Further research is required to delineate the molecular and clinical features of the ∼34% of MC cases with neither HER2 amplification nor KRAS mutations.

BackgroundMicroRNAs (miRs) are small non‐coding RNAs that regulate gene (mRNA) expression. Although the pathological role of miRs have been studied in muscle wasting conditions such as myotonic and muscular dystrophy, their roles in cancer cachexia (CC) are still emerging.ObjectivesThe objectives are (i) to profile human skeletal muscle expressed miRs; (ii) to identify differentially expressed (DE) miRs between cachectic and non‐cachectic cancer patients; (iii) to identify mRNA targets for the DE miRs to gain mechanistic insights; and (iv) to investigate if miRs show potential prognostic and predictive value.MethodsStudy subjects were classified based on the international consensus diagnostic criteria for CC. Forty‐two cancer patients were included, of which 22 were cachectic cases and 20 were non‐cachectic cancer controls. Total RNA isolated from muscle biopsies were subjected to next‐generation sequencing.ResultsA total of 777 miRs were profiled, and 82 miRs with read counts of ≥5 in 80% of samples were retained for analysis. We identified eight DE miRs (up‐regulated, fold change of ≥1.4 at P < 0.05). A total of 191 potential mRNA targets were identified for the DE miRs using previously described human skeletal muscle mRNA expression data (n = 90), and a majority of them were also confirmed in an independent mRNA transcriptome dataset. Ingenuity pathway analysis identified pathways related to myogenesis and inflammation. qRT‐PCR analysis of representative miRs showed similar direction of effect (P < 0.05), as observed in next‐generation sequencing. The identified miRs also showed prognostic and predictive value.ConclusionsIn all, we identified eight novel miRs associated with CC.

Hereditary predisposition and causative environmental exposures have long been recognized in human malignancies. In most instances, cancer cases occur sporadically, suggesting that environmental influences are critical in determining cancer risk. To test the influence of genetic polymorphisms on breast cancer risk, we have measured 98 single nucleotide polymorphisms (SNPs) distributed over 45 genes of potential relevance to breast cancer etiology in 174 patients and have compared these with matched normal controls. Using machine learning techniques such as support vector machines (SVMs), decision trees, and naïve Bayes, we identified a subset of three SNPs as key discriminators between breast cancer and controls. The SVMs performed maximally among predictive models, achieving 69% predictive power in distinguishing between the two groups, compared with a 50% baseline predictive power obtained from the data after repeated random permutation of class labels (individuals with cancer or controls). However, the simpler naïve Bayes model as well as the decision tree model performed quite similarly to the SVM. The three SNP sites most useful in this model were (a) the ؉4536T/C site of the aldosterone synthase gene CYP11B2 at amino acid residue 386 Val/Ala (T/C) (rs4541); (b) the ؉4328C/G site of the aryl hydrocarbon hydroxylase CYP1B1 at amino acid residue 293 Leu/Val (C/G) (rs5292); and (c) the ؉4449C/T site of the transcription factor BCL6 at amino acid 387 Asp/Asp (rs1056932). No single SNP site on its own could achieve more than 60% in predictive accuracy. We have shown that multiple SNP sites from different genes over distant parts of the genome are better at identifying breast cancer patients than any one SNP alone. As high-throughput technology for SNPs improves and as more SNPs are identified, it is likely that much higher predictive accuracy will be achieved and a useful clinical tool developed.

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