Fine-mapping of a novel premenopausal breast cancer susceptibility locus at Chr4q31.22 in Caucasian women and validation in African and Chinese women.

We previously identified a novel breast cancer susceptibility variant on chromosome 4q31.22 locus (rs1429142) conferring risk among women of European ancestry. Here, we report replication of findings, validation of the variant in diverse populations and fine-mapping of the associated locus in Caucasian population. The SNP rs1429142 (C/T, minor allele frequency 18%) showed association for the overall breast cancer risk in Stages 1-4 (n = 4,331 cases/4271 controls; p = 4.35 × 10-8 ; odds ratio, ORC-allele ,1.25), and an elevated risk among premenopausal women (n = 1,503 cases/4271 controls; p = 5.81 × 10-10 ; ORC-allele 1.40) in European populations. SNP rs1429142 was associated with premenopausal breast cancer risk in women of African (T/C; p-value 1.45 × 10-02 ; ORC-allele 1.2) but not from Chinese ancestry. Fine-mapping of the locus revealed several potential causal variants which are present within a single association signal, revealed from the conditional regression analysis. Functional annotation of the potential causal variants revealed three putative SNPs rs1366691, rs1429139 and rs7667633 with active enhancer functions inferred based on histone marks, DNase hypersensitive sites in breast cell line data. These putative variants were bound by transcription factors (C-FOS, STAT1/3 and POL2/3) with known roles in inflammatory pathways. Furthermore, Hi-C data revealed several short-range interactions in the fine-mapped locus harboring the putative variants. The fine mapped locus was predicted to be within a single topologically associated domain, potentially facilitating enhancer-promoter interactions possibly leading to the regulation of nearby genes.

The clonal and mutational evolution spectrum of primary triple-negative breast cancers.

Primary triple-negative breast cancers (TNBCs), a tumour type defined by lack of oestrogen receptor, progesterone receptor and ERBB2 gene amplification, represent approximately 16% of all breast cancers. Here we show in 104 TNBC cases that at the time of diagnosis these cancers exhibit a wide and continuous spectrum of genomic evolution, with some having only a handful of coding somatic aberrations in a few pathways, whereas others contain hundreds of coding somatic mutations. High-throughput RNA sequencing (RNA-seq) revealed that only approximately 36% of mutations are expressed. Using deep re-sequencing measurements of allelic abundance for 2,414 somatic mutations, we determine for the first time-to our knowledge-in an epithelial tumour subtype, the relative abundance of clonal frequencies among cases representative of the population. We show that TNBCs vary widely in their clonal frequencies at the time of diagnosis, with the basal subtype of TNBC showing more variation than non-basal TNBC. Although p53 (also known as TP53), PIK3CA and PTEN somatic mutations seem to be clonally dominant compared to other genes, in some tumours their clonal frequencies are incompatible with founder status. Mutations in cytoskeletal, cell shape and motility proteins occurred at lower clonal frequencies, suggesting that they occurred later during tumour progression. Taken together, our results show that understanding the biology and therapeutic responses of patients with TNBC will require the determination of individual tumour clonal genotypes.

Determination of the Relative Efficacy of Eicosapentaenoic Acid and Docosahexaenoic Acid for Anti-Cancer Effects in Human Breast Cancer Models.

Epidemiological studies have associated high fish oil consumption with decreased risk of breast cancer (BC). n-3 long chain polyunsaturated fatty acids (n-3 LCPUFA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) found in fish and fish oils exert anti-cancer effects. However, few studies have examined the relative efficacy of EPA and DHA alone and in mixtures on BC subtypes. This was the objective of the present review, as this research is a necessity for the translation of findings to human health and disease. The literature suggests that DHA has a greater anti-cancer effect in triple negative BC (TNBC). In estrogen positive (ER+) BC, DHA has a greater effect on cell viability, while both fatty acids have similar effects on apoptosis and proliferation. These effects are associated with preferential uptake of DHA into TNBC lipid rafts and EPA in ER+ BC. EPA:DHA mixtures have anti-cancer activity; however, the ratio of EPA:DHA does not predict the relative incorporation of these two fatty acids into membrane lipids as EPA appears to be preferentially incorporated. In summary, DHA and EPA should be considered separately in the context of BC prevention. The elucidation of optimal EPA:DHA ratios will be important for designing targeted n-3 LCPUFA treatments.

A Genome-Wide Association Study to Identify Potential Germline Copy Number Variants for Sporadic Breast Cancer Susceptibility.

Breast cancer (BC) predisposition in populations arises from both genetic and nongenetic risk factors. Structural variations such as copy number variations (CNVs) are heritable determinants for disease susceptibility. The primary objectives of this study are (1) to identify CNVs associated with sporadic BC using a genome-wide association study (GWAS) design; (2) to utilize 2 distinct CNV calling algorithms to identify concordant CNVs as a strategy to reduce false positive associations in the hypothesis-generating GWAS discovery phase, and (3) to identify potential candidate CNVs for follow-up replication studies. We used Affymetrix SNP Array 6.0 data profiled on Caucasian subjects (422 cases/348 controls) to call CNVs using algorithms implemented in Nexus Copy Number and Partek Genomics Suite software. Nexus algorithm identified CNVs associated with BC (731 autosomal CNVs with >5% frequency in the total sample and Q < 0.05). Thirteen CNVs were identified when Partek algorithm-called CNVs were overlapped with Nexus-identified CNVs; these CNVs showed concordances for frequency, effect size, and direction. Coding genes present within BC-associated CNVs were known to play a role in disease etiology and prognosis. Long noncoding RNAs identified within CNVs showed tissue-specific expression, indicating potential functional relevance of the findings. The identified candidate CNVs warrant independent replication.

Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.

BACKGROUND: Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC. RESULTS: In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case-control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified using in silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1,423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration. CONCLUSIONS: The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.

Molecular characterization of mucinous ovarian tumours supports a stratified treatment approach with HER2 targeting in 19% of carcinomas.

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.

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction.

BACKGROUND: Population stratification is a systematic difference in allele frequencies between subpopulations. This can lead to spurious association findings in the case-control genome wide association studies (GWASs) used to identify single nucleotide polymorphisms (SNPs) associated with disease-linked phenotypes. Methods such as self-declared ancestry, ancestry informative markers, genomic control, structured association, and principal component analysis are used to assess and correct population stratification but each has limitations. We provide an alternative technique to address population stratification. RESULTS: We propose a novel machine learning method, ETHNOPRED, which uses the genotype and ethnicity data from the HapMap project to learn ensembles of disjoint decision trees, capable of accurately predicting an individual's continental and sub-continental ancestry. To predict an individual's continental ancestry, ETHNOPRED produced an ensemble of 3 decision trees involving a total of 10 SNPs, with 10-fold cross validation accuracy of 100% using HapMap II dataset. We extended this model to involve 29 disjoint decision trees over 149 SNPs, and showed that this ensemble has an accuracy of ≥ 99.9%, even if some of those 149 SNP values were missing. On an independent dataset, predominantly of Caucasian origin, our continental classifier showed 96.8% accuracy and improved genomic control's λ from 1.22 to 1.11. We next used the HapMap III dataset to learn classifiers to distinguish European subpopulations (North-Western vs. Southern), East Asian subpopulations (Chinese vs. Japanese), African subpopulations (Eastern vs. Western), North American subpopulations (European vs. Chinese vs. African vs. Mexican vs. Indian), and Kenyan subpopulations (Luhya vs. Maasai). In these cases, ETHNOPRED produced ensembles of 3, 39, 21, 11, and 25 disjoint decision trees, respectively involving 31, 502, 526, 242 and 271 SNPs, with 10-fold cross validation accuracy of 86.5% ± 2.4%, 95.6% ± 3.9%, 95.6% ± 2.1%, 98.3% ± 2.0%, and 95.9% ± 1.5%. However, ETHNOPRED was unable to produce a classifier that can accurately distinguish Chinese in Beijing vs. Chinese in Denver. CONCLUSIONS: ETHNOPRED is a novel technique for producing classifiers that can identify an individual's continental and sub-continental heritage, based on a small number of SNPs. We show that its learned classifiers are simple, cost-efficient, accurate, transparent, flexible, fast, applicable to large scale GWASs, and robust to missing values.

Breast cancer prediction using genome wide single nucleotide polymorphism data.

BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach.

Urine and plasma metabolites originate from endogenous metabolic pathways in different organs and exogenous sources (diet). Urine and plasma were obtained from advanced cancer patients and investigated to determine if variations in lean and fat mass, dietary intake, and energy metabolism relate to variation in metabolite profiles. Patients (n = 55) recorded their diets for 3 d and after an overnight fast they were evaluated by DXA and indirect calorimetry. Metabolites were measured by NMR and direct injection MS. Three algorithms were used [partial least squares discriminant-analysis, support vector machines (SVM), and least absolute shrinkage and selection operator] to relate patients' plasma/urine metabolic profile with their dietary/physiological assessments. Leave-one-out cross-validation and permutation testing were conducted to determine statistical validity. None of the algorithms, using 63 urine metabolites, could learn to predict variations in individual's resting energy expenditure, respiratory quotient, or their intake of total energy, fat, sugar, or carbohydrate. Urine metabolites predicted appendicular lean tissue (skeletal muscle) with excellent cross-validation accuracy (98% using SVM). Total lean tissue correlated highly with appendicular muscle (Pearson r = 0.98; P < 0.0001) and gave similar cross-validation accuracies. Fat mass was effectively predicted using the 63 urine metabolites or the 143 plasma metabolites, exclusively. In conclusion, in this population, lean and fat mass variation could be effectively predicted using urinary metabolites, suggesting a potential role for metabolomics in body composition research. Furthermore, variation in lean and fat mass potentially confounds metabolomic studies attempting to characterize diet or disease conditions. Future studies should account or correct for such variation.

Personalized breast cancer onset prediction from lifestyle and health history information.

We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta's Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual's Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach-(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model-produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes-suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime.

Potential novel candidate polymorphisms identified in genome-wide association study for breast cancer susceptibility.

Previous genome-wide association studies (GWAS) have shown several risk alleles to be associated with breast cancer. However, the variants identified so far contribute to only a small proportion of disease risk. The objective of our GWAS was to identify additional novel breast cancer susceptibility variants and to replicate these findings in an independent cohort. We performed a two-stage association study in a cohort of 3,064 women from Alberta, Canada. In Stage I, we interrogated 906,600 single nucleotide polymorphisms (SNPs) on Affymetrix SNP 6.0 arrays using 348 breast cancer cases and 348 controls. We used single-locus association tests to determine statistical significance for the observed differences in allele frequencies between cases and controls. In Stage II, we attempted to replicate 35 significant markers identified in Stage I in an independent study of 1,153 cases and 1,215 controls. Genotyping of Stage II samples was done using Sequenom Mass-ARRAY iPlex platform. Six loci from four different gene regions (chromosomes 4, 5, 16 and 19) showed statistically significant differences between cases and controls in both Stage I and Stage II testing, and also in joint analysis. The identified variants were from EDNRA, ROPN1L, C16orf61 and ZNF577 gene regions. The presented joint analyses from the two-stage study design were not significant after genome-wide correction. The SNPs identified in this study may serve as potential candidate loci for breast cancer risk in a further replication study in Stage III from Alberta population or independent validation in Caucasian cohorts elsewhere.

Identification of proteins and cellular pathways targeted by 2-nitroimidazole hypoxic cytotoxins.

Tumour hypoxia negatively impacts therapy outcomes and continues to be a major unsolved clinical problem. Nitroimidazoles are hypoxia selective compounds that become entrapped in hypoxic cells by forming drug-protein adducts. They are widely used as hypoxia diagnostics and have also shown promise as hypoxia-directed therapeutics. However, little is known about the protein targets of nitroimidazoles and the resulting effects of their modification on cancer cells. Here, we report the synthesis and applications of azidoazomycin arabinofuranoside (N3-AZA), a novel click-chemistry compatible 2-nitroimidazole, designed to facilitate (a) the LC-MS/MS-based proteomic analysis of 2-nitroimidazole targeted proteins in FaDu head and neck cancer cells, and (b) rapid and efficient labelling of hypoxic cells and tissues. Bioinformatic analysis revealed that many of the 62 target proteins we identified participate in key canonical pathways including glycolysis and HIF1A signaling that play critical roles in the cellular response to hypoxia. Critical cellular proteins such as the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the detoxification enzyme glutathione S-transferase P (GSTP1) appeared as top hits, and N3-AZA adduct formation significantly reduced their enzymatic activities only under hypoxia. Therefore, GAPDH, GSTP1 and other proteins reported here may represent candidate targets to further enhance the potential for nitroimidazole-based cancer therapeutics.

Uridine Glucuronosyltransferase 2B7 Polymorphism-Based Pharmacogenetic Dosing of Epirubicin in FEC Chemotherapy for Early-Stage Breast Cancer.

BACKGROUND: Epirubicin is metabolized by uridine glucuronosyltransferase 2B7 (UGT2B7). Patients homozygous for the minor allele (CC) in the UGT2B7 -161 promoter polymorphism have lower clearance and significantly higher rates of leukopenia compared to wild-type homozygote (TT) or heterozygote (CT) patients. This study was designed to determine if TT and CT genotype patients could tolerate a higher epirubicin dose compared to CC genotype patients. PATIENTS AND METHODS: We studied women with histologically confirmed non-metastatic, invasive breast cancer who were scheduled to receive at least three cycles of FE100C in the (neo)adjuvant setting. Patients received standard-dose FE100C during the first 21-day cycle. Based on genotype, the epirubicin dose was escalated in the second and third cycles to 115 and 130 mg/m2 or to 120 and 140 mg/m2 for CT and TT genotype patients, respectively. The main outcome measurements were myelosuppression and dose-limiting toxicity. These were analyzed for relationships with the three genotypes. RESULTS: Forty-five patients were enrolled (10 CC, 21 CT, and 14 TT genotypes) and received 100 mg/m2 of epirubicin in the first cycle. Twelve and 10 TT patients were dose escalated at the second and third cycles, respectively; 16 CT patients were dose escalated at the second and third cycles. Leukopenia, but not febrile neutropenia, was genotype and dose dependent and increased in patients with CT and TT genotypes as their dose was increased. However, the third-cycle leukopenia rates were comparable to patients with the CC genotype receiving standard-dose epirubicin. CONCLUSION: Pharmacogenetically guided epirubicin dosing is well tolerated and allowed dose escalation without increased toxicity.

Evidence for copurification of micronuclei in sucrose density gradient-enriched plasma membranes from cell lines.

Sucrose density gradient-enriched membrane preparations and membrane fraction enrichment through affinity purification techniques are commonly used in proteomic analysis. However, published proteomic profiles characterized by the above methods show the presence of nuclear proteins in addition to membrane proteins. While shuttling of nuclear proteins across cellular compartments and their transient residency at membrane interfaces could explain some of these observations, the presence of nuclear proteins in proteomic profiles generated with crude and enriched membranes could be the result of nonspecific contamination of nuclear debris during cell fractionation procedures. We hypothesized that micronuclei arising from the genomic instability inherent to cancer cells may copurify with plasma membrane fractions on sucrose gradients. Using sucrose gradient-enriched plasma membranes from breast cancer cell lines derived from the MCF-7 cell line, we provide experimental evidence to indicate that micronuclei are present in fresh preparations of plasma membranes. The origin of these micronuclei was traced to budding of nuclei in intact cells. Furthermore, mass spectrometric analysis confirmed the presence of nuclear proteins as well as membrane and associated signaling proteins in sucrose gradient-enriched preparations.

PolySearch: a web-based text mining system for extracting relationships between human diseases, genes, mutations, drugs and metabolites.

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.

BIK drives an aggressive breast cancer phenotype through sublethal apoptosis and predicts poor prognosis of ER-positive breast cancer.

Apoptosis is fundamental to normal animal development and is the target for many anticancer therapies. Recent studies have explored the consequences of "failed apoptosis" where the apoptotic program is initiated but does not go to completion and does not cause cell death. Nevertheless, this failed apoptosis induces DNA double-strand breaks generating mutations that facilitate tumorigenesis. Whether failed apoptosis is relevant to clinical disease is unknown. BCL-2 interacting killer (BIK) is a stress-induced BH3-only protein that stimulates apoptosis in response to hormone and growth factor deprivation, hypoxia, and genomic stress. It was unclear whether BIK promotes or suppresses tumor survival within the context of breast cancer. We investigated this and show that BIK induces failed apoptosis with limited caspase activation and genomic damage in the absence of extensive cell death. Surviving cells acquire aggressive phenotypes characterized by enrichment of cancer stem-like cells, increased motility and increased clonogenic survival. Furthermore, by examining six independent cohorts of patients (total n = 969), we discovered that high BIK mRNA and protein levels predicted clinical relapse of Estrogen receptor (ER)-positive cancers, which account for almost 70% of all breast cancers diagnosed but had no predictive value for hormone receptor-negative (triple-negative) patients. Thus, this study identifies BIK as a biomarker for tumor recurrence of ER-positive patients and provides a potential mechanism whereby failed apoptosis contributes to cancer aggression.

Predicting node positivity in gastric cancer from gene expression profiles.

Lymphovascular invasion (LVI) in gastric cancer is readily demonstrated pre-operatively by mucosal biopsy during endoscopy, which can also provide samples for gene expression profiling. We have examined gene expression associated with lymphovascular invasion in a cohort of gastric cancer patients and have developed a 28-gene predictor of tumor aggressiveness for forecasting risk of node positivity (N+), which could potentially be useful pre-operatively. The resulting model's ranking of increasing tumor aggressiveness correlated positively with N+ status, reaching statistical significance, and was three times the correlation of LVI with N+ status.

Oncogenic activity of poly (ADP-ribose) glycohydrolase.

Poly (ADP-ribosylation), known as PARylation, is a post-translational modification catalyzed by poly (ADP-ribose) polymerases (PARP) and primarily removed by the enzyme poly (ADP-ribose) glycohydrolase (PARG). While the aberrant removal of post-translation modifications including phosphorylation and methylation has known tumorigenic effects, deregulation of PARylation has not been widely studied. Increased hydrolysis of PARylation chains facilitates cancer growth through enhancing estrogen receptor (ER)-driven proliferation, but oncogenic transformation has not been linked to increased PARG expression. In this study, we find that elevated PARG levels are associated with a poor prognosis in breast cancers, especially in HER2-positive and triple-negative subtypes. Using both in vitro and in vivo models, we demonstrate that heightened expression of catalytically active PARG facilitates cell transformation and invasion of normal mammary epithelial cells. Catalytically inactive PARG mutants did not recapitulate these phenotypes. Consistent with clinical data showing elevated PARG predicts poor outcomes in HER2+ patients, we observed that PARG acts in synergy with HER2 to promote neoplastic growth of immortalized mammary cells. In contrast, PARG depletion significantly impairs the growth and metastasis of triple-negative breast tumors. Mechanistically, we find that PARG interacts with SMAD2/3 and significantly decreases their PARylation in non-transformed cells, leading to enhanced expression of SMAD target genes. Further linking SMAD-mediated transcription to the oncogenicity of PARG, we show that PARG-mediated anchorage-independent growth and invasion are dependent, at least in part, on SMAD expression. Overall, our study underscores the oncogenic impact of aberrant protein PARylation and highlights the therapeutic potential of PARG inhibition in breast cancer.

Clinical and biological characterization of skeletal muscle tissue biopsies of surgical cancer patients.

BACKGROUND: Researchers increasingly use intraoperative muscle biopsy to investigate mechanisms of skeletal muscle atrophy in patients with cancer. Muscles have been assessed for morphological, cellular, and biochemical features. The aim of this study was to conduct a state-of-the-science review of this literature and, secondly, to evaluate clinical and biological variation in biopsies of rectus abdominis (RA) muscle from a cohort of patients with malignancies. METHODS: Literature was searched for reports on muscle biopsies from patients with a cancer diagnosis. Quality of reports and risk of bias were assessed. Data abstracted included patient characteristics and diagnoses, sample size, tissue collection and biobanking procedures, and results. A cohort of cancer patients (n = 190, 88% gastrointestinal malignancies), who underwent open abdominal surgery as part of their clinical care, consented to RA biopsy from the site of incision. Computed tomography (CT) scans were used to quantify total abdominal muscle and RA cross-sectional areas and radiodensity. Biopsies were assessed for muscle fibre area (µm2 ), fibre types, myosin heavy chain isoforms, and expression of genes selected for their involvement in catabolic pathways of muscle. RESULTS: Muscle biopsy occurred in 59 studies (total N = 1585 participants). RA was biopsied intraoperatively in 40 studies (67%), followed by quadriceps (26%; percutaneous biopsy) and other muscles (7%). Cancer site and stage, % of male participants, and age were highly variable between studies. Details regarding patient medical history and biopsy procedures were frequently absent. Lack of description of the population(s) sampled and low sample size contributed to low quality and risk of bias. Weight-losing cases were compared with weight stable cancer or healthy controls without considering a measure of muscle mass in 21 out of 44 studies. In the cohort of patients providing biopsy for this study, 78% of patients had preoperative CT scans and a high proportion (64%) met published criteria for sarcopenia. Fibre type distribution in RA was type I (46% ± 13), hybrid type I/IIA (1% ± 1), type IIA (36% ± 10), hybrid type IIA/D (15% ± 14), and type IID (2% ± 5). Sexual dimorphism was prominent in RA CT cross-sectional area, mean fibre cross-sectional area, and in expression of genes associated with muscle growth, apoptosis, and inflammation (P < 0.05). Medical history revealed multiple co-morbid conditions and medications. CONCLUSIONS: Continued collaboration between researchers and cancer surgeons enables a more complete understanding of mechanisms of cancer-associated muscle atrophy. Standardization of biobanking practices, tissue manipulation, patient characterization, and classification will enhance the consistency, reliability, and comparability of future studies.

The Challenges and Opportunities in the Clinical Application of Noncoding RNAs: The Road Map for miRNAs and piRNAs in Cancer Diagnostics and Prognostics.

Discoveries on nonprotein-coding RNAs have induced a paradigm shift in our overall understanding of gene expression and regulation. We now understand that coding and noncoding RNA machinery work in concert to maintain overall homeostasis. Based on their length, noncoding RNAs are broadly classified into two groups-long (>200 nt) and small noncoding RNAs (<200 nt). These RNAs perform diverse functions-gene regulation, splicing, translation, and posttranscriptional modifications. MicroRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs) are two classes of small noncoding RNAs that are now classified as master regulators of gene expression. They have also demonstrated clinical significance as potential biomarkers and therapeutic targets for several diseases, including cancer. Despite these similarities, both these RNAs are generated through contrasting mechanisms, and one of the aims of this review is to cover the distance travelled since their discovery and compare and contrast the various facets of these RNAs. Although these RNAs show tremendous promise as biomarkers, translating the findings from bench to bedside is often met with roadblocks. The second aim of this review therefore is to highlight some of the challenges that hinder application of miRNA and piRNA as in guiding treatment decisions.