Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data

BACKGROUND

Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification.

RESULTS

This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected.

CONCLUSIONS

The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.

Low expression of NR1H3 correlates with macrophage infiltration and indicates worse survival in breast cancer

Background: Nuclear receptor NR1H3 is a key regulator of macrophage function and lipid homeostasis. Here, we aimed to visualize the prognostic value and immunological characterization of NR1H3 in breast cancer. Methods: The expression pattern and prognostic value of NR1H3 were analyzed via multiple databases, including TIMER2, GEPIA2 and Kaplan-Meier Plotter. TISIDB, TIMER2 and immunohistochemical analysis were used to investigate the correlation between NR1H3 expression and immune infiltration. GO enrichment analysis, KEGG analysis, Reactome analysis, ConsensusPathDB and GeneMANIA were used to visualize the functional enrichment of NR1H3 and signaling pathways related to NR1H3. Results: We demonstrated that the expression of NR1H3 was significantly lower in breast cancer compared with adjacent normal tissues. Kaplan-Meier survival curves showed shorter overall survival in basal breast cancer patients with low NR1H3 expression, and poorer prognosis of relapse-free survival in breast cancer patients with low NR1H3 expression. NR1H3 was mainly expressed in immune cells, and its expression was closely related with infiltrating levels of tumor-infiltrating immune cells in breast cancer. Additionally, univariate and multivariate analysis indicated that the expression of NR1H3 and the level of macrophage infiltration were independent prognostic factors for breast cancer. Gene interaction network analysis showed the function of NR1H3 involved in regulating of innate immune response and macrophage activation. Moreover, NR1H3 may function as a predictor of chemoresponsiveness in breast cancer. Conclusion: These findings suggest that NR1H3 serves as a prognostic biomarker and contributes to the regulation of macrophage activation in breast cancer.

Oral Contraceptive Use and Assessment of Breast Cancer Risk among Premenopausal Women via Molecular Characteristics: Systematic Review with Meta-Analysis

Breast cancer is divided into four molecular subtypes. Each one has distinct clinical features. The aim of this study was to assess individual breast cancer subtype risk in premenopausal women taking oral contraceptives (OCs). Databases (MEDLINE; PubMed, EMBASE, and the Cochrane Library) were searched to January 2022 to identify case-control studies meeting the inclusion criteria. The influence of OCs intake on the risk of ER-positive breast cancer (ER+BC) was revealed to be non-significant with regard to reduction: OR = 0.9134, 95% CI: 0.8128 to 1.0265, p = 0.128. Assessment of ER-negative subtype breast cancer (ER-BC) risk indicated that OCs use significantly increased the risk: OR = 1.3079, 95% CI: 1.0003 to 1.7100, p = 0.050. Analysis for HER2-positive breast cancer (HER2+BC) risk showed that OCs use statistically non-significantly lowered the risk: OR = 0.8810, 95% CI: 0.5977 to 1.2984, p = 0.522. Meta-analysis with regard to Triplet-negative breast cancer (TNBC) risk showed non-statistically significant increased risk: OR = 1.553, 95% CI: 0.99 to 2.43, p = 0.055. The findings of the meta-analysis suggest that breast cancer risk in premenopausal women may vary with respect to molecular subtypes. Extensive scientific work is still necessary in order to understand the impact of OCs use on breast cancer risk in young women.

rMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.

Exploring new Horizons in overcoming P-glycoprotein-mediated multidrug-resistant breast cancer via nanoscale drug delivery platforms

The high probability (13%) of women developing breast cancer in their lifetimes in America is exacerbated by the emergence of multidrug resistance after exposure to first-line chemotherapeutic agents. Permeation glycoprotein (P-gp)-mediated drug efflux is widely recognized as the major driver of this resistance. Initial in vitro and in vivo investigations of the co-delivery of chemotherapeutic agents and P-gp inhibitors have yielded satisfactory results; however, these results have not translated to clinical settings. The systemic delivery of multiple agents causes adverse effects and drug-drug interactions, and diminishes patient compliance. Nanocarrier-based site-specific delivery has recently gained substantial attention among researchers for its promise in circumventing the pitfalls associated with conventional therapy. In this review article, we focus on nanocarrier-based co-delivery approaches encompassing a wide range of P-gp inhibitors along with chemotherapeutic agents. We discuss the contributions of active targeting and stimuli responsive systems in imparting site-specific cytotoxicity and reducing both the dose and adverse effects.

Partially linear monotone methods with automatic variable selection and monotonicity direction discovery

In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example is a prediction model using both high-dimensional gene expression data, and low-dimensional clinical data, or when combining continuous and categorical covariates. We study methods for fitting the partially linear monotone model, where some covariates are assumed to have a linear effect on the response, and some are assumed to have a monotone (potentially nonlinear) effect. Most existing methods in the literature for fitting such models are subject to the limitation that they have to be provided the monotonicity directions a priori for the different monotone effects. We here present methods for fitting partially linear monotone models which perform both automatic variable selection, and monotonicity direction discovery. The proposed methods perform comparably to, or better than, existing methods, in terms of estimation, prediction, and variable selection performance, in simulation experiments in both classical and high-dimensional data settings.

Next-Generation Sequencing: An Emerging Tool for Drug Designing

BACKGROUND

With the outbreak of high throughput next-generation sequencing (NGS), the biological research of drug discovery has been directed towards the oncology and infectious disease therapeutic areas, with extensive use in biopharmaceutical development and vaccine production.

METHOD

In this review, an effort was made to address the basic background of NGS technologies, potential applications of NGS in drug designing. Our purpose is also to provide a brief introduction of various Nextgeneration sequencing techniques.

DISCUSSIONS

The high-throughput methods execute Large-scale Unbiased Sequencing (LUS) which comprises of Massively Parallel Sequencing (MPS) or NGS technologies. The Next geneinvolved necessarily executes Largescale Unbiased Sequencing (LUS) which comprises of MPS or NGS technologies. These are related terms that describe a DNA sequencing technology which has revolutionized genomic research. Using NGS, an entire human genome can be sequenced within a single day.

CONCLUSION

Analysis of NGS data unravels important clues in the quest for the treatment of various lifethreatening diseases and other related scientific problems related to human welfare.

Prognostic Significance of Tumor Subtypes in Women With Breast Cancer According to Stage: A Population-based Study

OBJECTIVES

The contribution of tumor subtypes (TS) in each stage of breast cancer with the use of contemporary therapies is unclear. The aim of this study was to analyze differences in overall survival (OS) by TS according to stage compared with other factors.

MATERIALS AND METHODS

We evaluated women with breast cancer diagnosed between 2010 and 2013 with known estrogen receptor and progesterone receptor (together hormone receptor [HR]) status and human epidermal growth factor receptor 2 (HER2) status reported to the SEER program. Patient characteristics were compared between TS. Univariate and multivariate analyses were performed to determine the effect of each variable on OS. Breast cancer-specific survival was a secondary endpoint.

RESULTS

We included 166,054 patients. TS distribution was: 72.5% HR-positive/HER2-negative, 10.8% HR-positive/HER2-positive, 4.8% HR-negative/HER2-positive, and 12% triple-negative (TN). Patients with HR-positive/HER2-negative tumors were older, had a lower grade and presented with the earlier stage (all P<0.0001). OS was significantly different according to TS in each stage (Pinteraction<0.0001). HR-positive/HER2-negative had the best OS in stage I (3-year OS, 97.2%). In contrast, HR-positive/HER2-positive had the best 3-year OS in stage II (94.5%), stage III (87.8%), and stage IV (54.8%). There was a 40.1% difference in OS at 3 years in stage IV between TN and HR-positive/HER2-positive. Multivariate analysis adjusted for age, race, grade, histology, and marital status confirmed these results.

CONCLUSIONS

Although HR-positive/HER2-negative tumors had better clinicopathologic features, the HR-positive/HER2-positive group had the best OS in most stages. OS was significantly different by TS in each of the 4 stages and these results remained significant in the multivariate model.

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and genetic variations of the breast cancer cells and helps in understanding the characteristic behavior of the cancer cells. But their analysis is limited to clustering and there is requirement to analyze what else can be done with the data for identifying the tumor subcategory and the stages of subclasses. In this work, we have extended the work with similar data (consisting of 105 breast tumor cell lines) to solve other detection and characterization problems through computation and intelligent representation learning. Most of our work comprises of systematic data cleaning, analysis, and building prediction models with deep computational architectures and establish that the transformed data can help in better distinction of the respective categories. Our main contribution is the novel gene-subcategory interaction-based regularization (GSIAR) based data selection and analysis concept, alongside the prediction, proven to enhance the performance of the classification techniques. Graphical Abstract A graphical abstract of our model - Gene-subcategory interaction affinity-based regularization (GSIAR).

Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis

Gene co-expression network analysis (GCNA) can detect alterations in regulatory activities in case/control comparisons. We propose a framework to detect novel genes and networks for predicting breast cancer recurrence. Thirty-four prognosis candidate genes were selected based on a literature review. Four Gene Expression Omnibus Series (GSE) microarray datasets (n = 920) were used to create gene co-expression networks based on these candidates. We applied the framework to four comparison groups according to node (+/−) and recurrence (+/−). We identified a sub-network containing two candidate genes (LST1 and IGHM) and six novel genes (IGHA1, IGHD, IGHG1, IGHG3, IGLC2, and IGLJ3) related to B cell-specific immunoglobulin. These novel genes were correlated with recurrence under the control of node status and were found to function as tumor suppressors; higher mRNA expression indicated a lower risk of recurrence (hazard ratio, HR = 0.87, p = 0.001). We created an immune index score by performing principle component analysis and divided the genes into low and high groups. This discrete index significantly predicted relapse-free survival (RFS) (high: HR = 0.77, p = 0.019; low: control). Public tool KM Plotter and TCGA-BRCA gene expression data were used to validate. We confirmed these genes are correlated with RFS and distal metastasis-free survival (DMFS) in triple-negative breast cancer (TNBC) and general breast cancer.

SHON expression predicts response and relapse risk of breast cancer patients after anthracycline-based combination chemotherapy or tamoxifen treatment

Background

SHON nuclear expression (SHON-Nuc⁺) was previously reported to predict clinical outcomes to tamoxifen therapy in ERα⁺ breast cancer (BC). Herein we determined if SHON expression detected by specific monoclonal antibodies could provide a more accurate prediction and serve as a biomarker for anthracycline-based combination chemotherapy (ACT).

Methods

SHON expression was determined by immunohistochemistry in the Nottingham early-stage-BC cohort (n = 1,650) who, if eligible, received adjuvant tamoxifen; the Nottingham ERα⁻ early-stage-BC (n = 697) patients who received adjuvant ACT; and the Nottingham locally advanced-BC cohort who received pre-operative ACT with/without taxanes (Neo-ACT, n = 120) and if eligible, 5-year adjuvant tamoxifen treatment. Prognostic significance of SHON and its relationship with the clinical outcome of treatments were analysed.

Results

As previously reported, SHON-Nuc⁺ in high risk/ERα⁺ patients was significantly associated with a 48% death risk reduction after exclusive adjuvant tamoxifen treatment compared with SHON-Nuc⁻ [HR (95% CI) = 0.52 (0.34–0.78), p = 0.002]. Meanwhile, in ERα⁻ patients treated with adjuvant ACT, SHON cytoplasmic expression (SHON-Cyto⁺) was significantly associated with a 50% death risk reduction compared with SHON-Cyto⁻ [HR (95% CI) = 0.50 (0.34–0.73), p = 0.0003]. Moreover, in patients received Neo-ACT, SHON-Nuc⁻ or SHON-Cyto⁺ was associated with an increased pathological complete response (pCR) compared with SHON-Nuc⁺ [21 vs 4%; OR (95% CI) = 5.88 (1.28–27.03), p = 0.012], or SHON-Cyto⁻ [20.5 vs. 4.5%; OR (95% CI) = 5.43 (1.18–25.03), p = 0.017], respectively. After receiving Neo-ACT, patients with SHON-Nuc⁺ had a significantly lower distant relapse risk compared to those with SHON-Nuc⁻ [HR (95% CI) = 0.41 (0.19–0.87), p = 0.038], whereas SHON-Cyto⁺ patients had a significantly higher distant relapse risk compared to SHON-Cyto⁻ patients [HR (95% CI) = 4.63 (1.05–20.39), p = 0.043]. Furthermore, multivariate Cox regression analyses revealed that SHON-Cyto⁺ was independently associated with a higher risk of distant relapse after Neo-ACT and 5-year tamoxifen treatment [HR (95% CI) = 5.08 (1.13–44.52), p = 0.037]. The interaction term between ERα status and SHON-Nuc⁺ (p = 0.005), and between SHON-Nuc⁺ and tamoxifen therapy (p = 0.007), were both statistically significant.

Conclusion

SHON-Nuce⁺ in tumours predicts response to tamoxifen in ERα⁺ BC while SHON-Cyto⁺ predicts response to ACT.

Epidemiological characteristics, clinical outcomes and management patterns of metastatic breast cancer patients in routine clinical care settings of Greece: Results from the EMERGE multicenter retrospective chart review study

BACKGROUND

The "EMERGE" study, aimed to capture real-life management patterns and outcomes in metastatic breast cancer (MBC) in Greece, also accounting for hormone (HR) and human epidermal growth factor receptor 2 (HER2) status.

METHODS

"EMERGE" was a multicenter, retrospective cohort study of adult MBC patients diagnosed between 01-Janaury-2010 and 30-June-2012, either de novo or having progressed from a non-metastatic state. Patient data, including treatment patterns and outcomes, were mainly abstracted through medical chart review.

RESULTS

386 patients were enrolled by 16 hospital-based oncologists between 12-March-2013 and 31-March-2015. The median look-back period was 29.1 months. At MBC diagnosis, 56.1% of the patients were HR⁺/HER2^-, 16.6% HR⁺/HER2⁺, 14.5% HR^-/HER2^-, and 12.8% HR^-/HER2⁺. In the first line setting, chemotherapy, targeted therapy and endocrine therapy were received by 76.7, 52.4, and 28.3% of the overall population, and by 66.5/36.2/42.0%, 80.4/80.4/28.6%, 88.4/90.7/0.0, and 95.6%/56.5/6.5% of the HR⁺/HER2^-, HR⁺/HER2⁺, HR^-/HER2⁺, HR^-/HER2^- subpopulations, respectively. In the overall population, the disease progression incidence rate was 0.57 [95% confidence interval (CI): 0.48-0.67] per person-year; median progression-free survival (PFS) was 22.4 (95% CI: 20.4-24.7) and overall survival (OS) was 45.0 (95% CI: 40.9-55.0) months. Median PFS was 24.6 (95% CI: 21.3-27.9) in HR⁺/HER2^-, 19.7 (95% CI: 12.9-25.9) in HR⁺/HER2⁺, 23.0 (95% CI: 16.6-29.7) in HR^-/HER2⁺ and 18.3 (95% CI: 10.0-24.7) months in HR^-/HER2^- subpopulations. A multivariable Cox proportional hazards model, adjusted among other factors for age and duration of diagnosis, HR and HER2 status, demonstrated that in the overall population PFS was better among those receiving first line endocrine therapy (hazard ratio: 0.70; 95%CI: 0.51-0.95; p = 0.024).

CONCLUSIONS

"EMERGE" demonstrates differences between HR/HER2 subtypes in clinical outcomes and divergence from evidence-based guideline recommendations for MBC management, especially as it pertains to the HR⁺/HER2^- patients in which chemotherapy was favored over endocrine therapy in the first line setting.

STUDY REGISTRATION

The study has been registered on the electronic Registry of Non-Interventional Studies (RNIS) posted on the website of the Hellenic Association of Pharmaceutical Companies (SFEE): https://www.dilon.sfee.gr/studiesp_d.php?meleti_id=NIS-OGR-XXX-2012/1.

SET Overexpression is Associated with Worse Recurrence-Free Survival in Patients with Primary Breast Cancer Receiving Adjuvant Tamoxifen Treatment

Adjuvant tamoxifen reduces the recurrence rate of estrogen receptor (ER)-positive breast cancer. Previous in vitro studies have suggested that tamoxifen can affect the cancerous inhibitor of protein phosphatase 2A (CIP2A)/protein phosphatase 2A (PP2A)/phosphorylation Akt (pAkt) signaling in ER-negative breast cancer cells. In addition to CIP2A, SET nuclear proto-oncogene (SET) oncoprotein is another intrinsic inhibitor of PP2A, participating in cancer progression. In the current study, we explored the clinical significance of SET, CIP2A, PP2A, and Akt in patients with ER-positive breast cancer receiving adjuvant tamoxifen. A total of 218 primary breast cancer patients receiving adjuvant tamoxifen with a median follow-up of 106 months were analyzed, of which 17 (7.8%) experienced recurrence or metastasis. In an immunohistochemical (IHC) stain, SET overexpression was independently associated with worse recurrence-free survival (RFS) (hazard ratio = 3.72, 95% confidence interval 1.26–10.94, p = 0.017). In silico analysis revealed mRNA expressions of SET, PPP2CA, and AKT1 significantly correlated with worse RFS. In vitro, SET overexpression reduced tamoxifen-induced antitumor effects and drove luciferase activity in an Estrogen receptor element (ERE)-dependent manner. In conclusion, SET is a prognostic biomarker in patients with primary ER-positive breast cancer receiving adjuvant tamoxifen and may contribute to the failure of the tamoxifen treatment by modulating the ER signaling. Our study warrants further investigation into the potential role of SET in ER-positive breast cancer.

Relationships between Reproductive Risk Factors for Breast Cancer and Tumor Molecular Subtypes

Background: Due to wide clinical differences in the various pathological types of breast cancer and also close associations between disease prognosis and molecular subtypes, relationships of the latter with traditional risk factors have been suggested. Hence, the present study aimed to assess any associations. Methods: This bi-center cross-sectional study was performed on 800 consecutive women with known breast cancer referred to two Comprehensive Cancer Centers in Tehran between 2006 and 2016. Baseline information related to reproductive risk profiles as well as pathological tumor diagnosis and molecular subtypes determined using immunohistochemical analysis by immune-staining for ER, PR, and HER2 molecules were collected by reviewing hospital records. Results: Of 800 samples included for immunohistochemical analysis, 314 (39.3%) were diagnosed as of Luminal A subtype, 107 (13.4%) as Luminal B subtype, 153 (19.1%) as HER-2 over-expressing, and 226 (28.3%) as triple negative. Among all reproductive risk factors initially assessed, young age was associated with HER-2 over-expression, greater tumor size and a history of abortion with the luminal B subtype, lower age at pregnancy with the luminal A subtype, and lower gravidity and a shorter duration of breastfeeding with the triple negative subtype. Conclusion: Each molecular subtype of breast cancer in our population may be associated with specific reproductive risk factors.

Gene Expression Signatures for Head and Neck Cancer Patient Stratification: Are Results Ready for Clinical Application?

OPINION STATEMENT

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer by incidence worldwide and considering the recent EUROCARE-5 population-based study the 5-year survival rate of HNSCC patients in Europe ranges between 69% in localized cases and 34% in patients with regional involvement. The development of high-throughput gene expression assays in the last two decades has provided the invaluable opportunity to improve our knowledge on cancer biology and to identify predictive signatures in the most deeply analyzed malignancies, such as hematological and breast cancers. At variance, till 2010, the number of reliable reports referring gene expression data related to HSNCC biology and prediction was quite limited. A critical revision of the literature reporting gene expression data in HNSCC indicated that in the last 6 years, there were new important studies with a relevant increase in the sample size and a more accurate selection of cases, the publication of a growing number of studies applying a computational integration (meta-analysis) of different microarray datasets addressing similar clinical/biological questions, the increased use of molecular sub-classification of tumors according to their gene expression, and the release of the publicly available largest dataset in HNSCC by The Cancer Genome Atlas (TCGA) consortium. Overall, also for this disease, it become evident that the expression analysis of the entire transcriptome has been enabling to achieve the identification of promising molecular signatures for (i) disclosure of the biology behind carcinogenesis with special focus on the HPV-related one, (ii) prediction of tumor recurrence or metastasis development, (iii) identification of subgroups of tumors with different biology and associated prognosis, and (iv) prediction of outcome and/or response to therapy. The increasing awareness of the relevance of strict collaboration among clinicians and translational researchers would in a near future enable the application of a personalized HNSCCs patients' treatment in the clinical practice based also on gene expression signatures.

Profiling of gene expression regulated by 17β-estradiol and tamoxifen in estrogen receptor-positive and estrogen receptor-negative human breast cancer cell lines

One area of great importance in breast cancer (BC) research is the study of gene expression regulated by both estrogenic and antiestrogenic agents. Although many studies have been performed in this area, most of them have only addressed the effects of 17β-estradiol (E2) and tamoxifen (TAM) on MCF7 cells. This study aimed to determine the effect of low doses of E2 and TAM on the expression levels of 84 key genes, which are commonly involved in breast carcinogenesis, in four BC cell lines differentially expressing estrogen receptor (ER) α and HER2 (MCF7, T47D, BT474, and SKBR3). The results allowed us to determine the expression patterns modulated by E2 and TAM in ERα+ and ERα− cell lines, as well as to identify differences in expression patterns. Although the MCF7 cell line is the most frequently used model to determine gene expression profiles in response to E2 and TAM, the changes in gene expression patterns identified in ERα+ and ERα− cell lines could reflect distinctive properties of these cells. Our results could provide important markers to be validated in BC patient samples, and subsequently used for predicting the outcome in ERα+ and ERα− tumors after TAM or hormonal therapy. Considering that BC is a molecularly heterogeneous disease, it is important to understand how well, and which cell lines, best model that diversity.

Insulin-induced enhancement of MCF-7 breast cancer cell response to 5-fluorouracil and cyclophosphamide

The study was designed to evaluate the potential use of insulin for cancer-specific treatment. Insulin-induced sensitivity of MCF-7 breast cancer cells to chemotherapeutic agents 5-fluorouracil and cyclophosphamide was evaluated. To investigate and establish the possible mechanisms of this phenomenon, we assessed cell proliferation, induction of apoptosis, activation of apoptotic and autophagic pathways, expression of glucose transporters 1 and 3, formation of reactive oxygen species, and wound-healing assay. Additionally, we reviewed the literature regarding theuse of insulin in cancer-specific treatment. We found that insulin increases the cytotoxic effect of 5-fluorouracil and cyclophosphamide in vitro up to two-fold. The effect was linked to enhancement of apoptosis, activation of apoptotic and autophagic pathways, and overexpression of glucose transporters 1 and 3 as well as inhibition of cell proliferation and motility. We propose a model for insulin-induced sensitization process. Insulin acts as a sensitizer of cancer cells to cytotoxic therapy through various mechanisms opening a possibility for metronomic insulin-based treatments.

Pegylated liposomal formulation of doxorubicin overcomes drug resistance in a genetically engineered mouse model of breast cancer

Success of cancer treatment is often hampered by the emergence of multidrug resistance (MDR) mediated by P-glycoprotein (ABCB1/Pgp). Doxorubicin (DOX) is recognized by Pgp and therefore it can induce therapy resistance in breast cancer patients. In this study our aim was to evaluate the susceptibility of the pegylated liposomal formulation of doxorubicin (PLD/Doxil®/Caelyx®) to MDR. We show that cells selected to be resistant to DOX are cross-resistant to PLD and PLD is also ineffective in an allograft model of doxorubicin-resistant mouse B-cell leukemia. In contrast, PLD was far more efficient than DOX as reflected by a significant increase of both relapse-free and overall survival of Brca1^-/-;p53^-/- mammary tumor bearing mice. Increased survival could be explained by the delayed onset of drug resistance. Consistent with the higher Pgp levels needed to confer resistance, PLD administration was able to overcome doxorubicin insensitivity of the mouse mammary tumors. Our results indicate that the favorable pharmacokinetics achieved with PLD can effectively overcome Pgp-mediated resistance, suggesting that PLD therapy could be a promising strategy for the treatment of therapy-resistant breast cancer patients.

[Molecular Classification of Colorectal Cancers and Clinical Application]

The molecular genetics of colorectal cancers (CRCs) is among the best understood of common human cancers. It is difficult to predict the prognosis and/or to predict chemoresponding in CRC patients. At present, prognosis is based predominantly on the tumor stage and pathological examination of the disease. Molecular classification of CRCs, based on genomics and transcriptomics, proposed that CRCs can be classified into at least three-to-six subtypes, depending on the gene expression pattern, and groups of marker genes representing to each subtype have also been reported. Gene expression-based subtyping is now widely accepted as a relevant source of disease stratification. We reviewed the previous studies on CRC subtyping, international consortium dedicated to large-scale data sharing and analytics recently established four consensus molecular subtypes with distinguishing features. Predictive markers identified in these studies are under investigation and large-scale clinical evaluations of molecular markers are currently in progress.

Targeting Intracellular Calcium Signaling ([Ca2+]i) to Overcome Acquired Multidrug Resistance of Cancer Cells: A Mini-Overview

Cancer is a main public health problem all over the world. It affects millions of humans no matter their age, gender, education, or social status. Although chemotherapy is the main strategy for the treatment of cancer, a major problem limiting its success is the intrinsic or acquired drug resistance. Therefore, cancer drug resistance is a major impediment in medical oncology resulting in a failure of a successful cancer treatment. This mini-overview focuses on the interdependent relationship between intracellular calcium ([Ca²⁺]_i) signaling and multidrug resistance of cancer cells, acquired upon treatment of tumors with anticancer drugs. We propose that [Ca²⁺]_i signaling modulates gene expression of multidrug resistant (MDR) genes which in turn can be modulated by epigenetic factors which in turn leads to modified protein expression in drug resistant tumor cells. A precise knowledge of these mechanisms will help to develop new therapeutic strategies for drug resistant tumors and will improve current chemotherapy.

Dipalmitoylphosphatidic acid inhibits tumor growth in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis, accounting for approximately 12-24% of breast cancer cases. Accumulating evidence has indicated that there is no effective targeted therapy available for TNBC. Dipalmitoylphosphatidic acid (DPPA) is a bioactive phospholipid. However, the function of DPPA in the growth of TNBC has not yet been studied. In this study, we employed TNBC cells and a subcutaneous tumor model to elucidate the possible effect of DPPA on tumor growth in TNBC. We showed that DPPA significantly inhibited tumor growth in the mouse subcutaneous tumor model and suppressed cell proliferation and angiogenesis in TNBC tumor tissues. This inhibition was mediated partly by suppressing the expression of cyclin B1 (CCNB1), which directly promoted the accumulation of cells in the G2 phase and arrested cell cycle progression in human TNBC. In addition, the inhibition of tumor growth by DPPA may also be mediated by the suppression of tumor angiogenesis in TNBC. This work provides initial evidence that DPPA might be vital as an anti-tumor drug to treat TNBC.

Screening-relevant age threshold of 70 years and older is a stronger determinant for the choice of adjuvant treatment in breast cancer patients than tumor biology

Purpose

The 70-year threshold determines whether patients are eligible or not for the breast cancer screening program in Germany. It is not known whether this age threshold also influences the choice of adjuvant treatment and ultimate outcome.

Methods

3463 patients were analyzed from the clinical cancer registry Regensburg (Germany) with primary, non-metastatic invasive breast cancer diagnosed between 2000 and 2012. The distribution of tumor biological subtypes was evaluated in breast cancer patients both in those eligible for screening (ESG, 50–69 years) and those not eligible for screening (NESG, ≥70 years). Local and systemic therapies in different subtypes as well as overall survival (OS) were analyzed.

Results

2171 patients (62.7%) pertained to the ESG and 1292 patients (37.3%) referred to the NESG. The distribution of the common subtypes Luminal A, Luminal B, HER2-like, and Basal-like was comparable in both groups. Treatment varied considerably with less systemic therapies in all subtypes in patients in the NESG. Regarding local therapies, patients in the NESG also received less surgery and less radiotherapy. As to Luminal A patients, best OS was seen in patients receiving endocrine therapy (ET) (7-year OS of 95.6%) and CHT plus ET (7-year OS of 93.1%) in the ESG. In the NESG, best OS was seen in patients receiving CHT plus ET (7-year OS of 95.2%), whereas patients receiving only ET had a 7-year OS of 73.9%.

Conclusions

Despite similar tumor biology, elderly patients are undertreated regarding both systemic and local therapies compared to younger patients, leading to reduced OS.

Further evidence to support bimodality of oestrogen receptor expression in breast cancer

AIMS

Although oestrogen receptor (ER)-negative breast cancers (BCs) do not respond to hormone therapy, the response of ER-positive BCs is reported to be variable, which may suggest a dose-dependent effect. The aim of this study was to assess the pattern of ER expression in BCs at the protein (immunohistochemistry) and transcriptome (microarray-based gene expression) levels.

METHODS AND RESULTS

ER immunohistochemical (IHC) expression was assessed in a large series of BCs, including 3649 core biopsies and 1892 cases prepared as tissue microarrays (TMAs) stained with specific antibodies. ESR1 mRNA expression was assessed in the METABRIC study (1980 cases), by the use of the Linear Models for Microarray Data (limma) software, and the results were compared with protein levels. IHC data confirmed the bimodality of ER expression, with 92.2% and 89.2% of the cases showing completely negative (<1%) or highly positive (≥70%) expression on the cores and TMAs, respectively. Weakly positive cases (1-10%) and intermediately positive (11-69%) cases were infrequent (2.7% and 5.1%, and 1.6% and 9.2%, in cores and TMAs, respectively), and did not show survival difference from ER-negative tumours. When full-face sections of the corresponding excision specimens were immunostained, 47% of the ER-low/intermediate group were deemed to be ER-negative. Transcriptomic data not only showed a significant correlation between ESR1 mRNA and protein expression levels, but also confirmed the bimodality of ER expression at the mRNA level.

CONCLUSIONS

Our study provides further evidence that ER expression is bimodal, and that it is observed at both the mRNA and protein levels. The reported poor survival of BC patients with low ER expression in the early clinical trials may be related to the inclusion of ER-negative cases.

MVIAeval: a web tool for comprehensively evaluating the performance of a new missing value imputation algorithm

BACKGROUND

Missing value imputation is important for microarray data analyses because microarray data with missing values would significantly degrade the performance of the downstream analyses. Although many microarray missing value imputation algorithms have been developed, an objective and comprehensive performance comparison framework is still lacking. To solve this problem, we previously proposed a framework which can perform a comprehensive performance comparison of different existing algorithms. Also the performance of a new algorithm can be evaluated by our performance comparison framework. However, constructing our framework is not an easy task for the interested researchers. To save researchers' time and efforts, here we present an easy-to-use web tool named MVIAeval (Missing Value Imputation Algorithm evaluator) which implements our performance comparison framework.

RESULTS

MVIAeval provides a user-friendly interface allowing users to upload the R code of their new algorithm and select (i) the test datasets among 20 benchmark microarray (time series and non-time series) datasets, (ii) the compared algorithms among 12 existing algorithms, (iii) the performance indices from three existing ones, (iv) the comprehensive performance scores from two possible choices, and (v) the number of simulation runs. The comprehensive performance comparison results are then generated and shown as both figures and tables.

CONCLUSIONS

MVIAeval is a useful tool for researchers to easily conduct a comprehensive and objective performance evaluation of their newly developed missing value imputation algorithm for microarray data or any data which can be represented as a matrix form (e.g. NGS data or proteomics data). Thus, MVIAeval will greatly expedite the progress in the research of missing value imputation algorithms.

First report on molecular breast cancer subtypes and their clinico-pathological characteristics in Eastern Morocco: series of 2260 cases

BACKGROUND

Breast cancer is the most frequent malignancy among women in Eastern Morocco. In this paper, we provide the first report on molecular breast cancer subtypes in this region. This is the largest population-based study on breast cancer among Moroccan women.

METHODS

We analyzed 2260 breast cancer cases diagnosed at the Hassan II Regional Oncology Center between October 2005 and December 2012. Clinico-pathological and therapeutic features were studied. Molecular subtypes were determined and their associations with the clinico-pathological characteristics of the tumors were examined.

RESULTS

The mean age at diagnosis was 48.7 years ±11.4. Invasive ductal carcinoma was the predominant histological type (77.1%), followed by lobular invasive carcinoma (15.3%). The mean size of breast tumors was 3.5 cm ± 1.96, and 84% of our patients are diagnosed with tumors of more than 2 cm. Histological grade II tumors were the most frequent (70.4%), followed by advanced histological grade (18%). Lymph node positive tumors were observed in 64.8% of cases and 29.3% of patients had distant metastasis. Most tumors were hormone receptor-positive (73%) and 28.6% were HER2 positive. 86.1% of patients with hormone receptor-positive breast cancer were given hormone therapy, while 68.9% of patients with HER2+ breast cancer received targeted therapy with Herceptin. Luminal A was the commonest molecular subtype, followed by Luminal B, Triple Negative and HER2. The highest prevalence of premenopausal patients was observed in Triple Negative subtype (72.2%), followed by HER2 (64.1%), Luminal B (62.2%), and Luminal A (55.1%). Luminal B subtype had a poorer prognosis than Luminal A. Compared with Triple Negative, HER2 subtype tend to spread more aggressively and is associated with poorer prognosis.

CONCLUSIONS

Unlike Western countries, breast cancer occurs at an earlier age and is diagnosed at a more advanced stage in Eastern Morocco. In this region, hormone receptor-positive tumors are predominant and so the majority of breast cancer patients should benefit from hormone therapy. HER2 subtype presents an aggressive tendency, suggesting the importance of anti-HER2 therapy. This study will contribute in developing appropriate screening and cancer management strategies in Eastern Morocco.

Runx Genes in Breast Cancer and the Mammary Lineage

A full understanding of RUNX gene function in different epithelial lineages has been thwarted by the lethal phenotypes observed when constitutively knocking out these mammalian genes. However temporal expression of the Runx genes throughout the different phases of mammary gland development is indicative of a functional role in this tissue. A few studies have emerged describing how these genes impact on the fate of mammary epithelial cells by regulating lineage differentiation and stem/progenitor cell potential, with implications for the transformed state. The importance of the RUNX/CBFβ core factor binding complex in breast cancer has very recently been highlighted with both RUNX1 and CBFβ appearing in a comprehensive gene list of predicted breast cancer driver mutations. Nonetheless, the evidence to date shows that the RUNX genes can have dualistic outputs with respect to promoting or constraining breast cancer phenotypes, and that this may be aligned to individual subtypes of the clinical disease. We take this opportunity to review the current literature on RUNX and CBFβ in the normal and neoplastic mammary lineage while appreciating that this is likely to be the tip of the iceberg in our knowledge.

Expression of tumor suppressor genes related to the cell cycle in endometrial cancer patients

PURPOSE

Endometrial cancer is the most common gynecological malignancy in developed countries. The role of tumor suppressor genes (TSG) in endometrioid endometrial adenocarcinoma (EEC) has an important impact on patient survival prognosis. Thus, it is important to identify TSG transcripts that differentiate endometrial adenocarcinoma into various pathomorphological grades. The aim of this study was to analyze the expression profile of tumor suppressor genes related to the cell cycle in patients with endometrial adenocarcinoma across histological differentiation and to identify transcripts which differentiate endometrium into various pathomorphological grades.

MATERIAL AND METHODS

Gene expression analysis was completed for 19 endometrial endometrioid adenocarcinomas and 5 normal specimens (obtained from women with diagnosed uterine fibroids, benign ovarian tumors and a prolapsed uterus with histopathologically confirmed endometrium in the proliferative phase) using Affymetrix HG-U133A oligonucleotide microarrays. The statistical analysis was performed using the GeneSpring13.0 software and PANTHER classification system.

RESULTS

Significant changes in gene expression were observed across histological differentiation. The WT-1, CYR 61, TSPYL5 genes were statistically and biologically significant in all cancer grades, and were considered to be primary for the G1 grade in endometrial cancer. The G2 cancer specific genes were BCL2L2 and HNRNPA0, whereas in G3 there was only BAK.

CONCLUSION

In conclusion, the WT-1, CYR61 and TSPYL5 gene expressions are potentially correlated with patient survival in all endometrial cancer grades. The TSGs identified are considered to be important in EEC pathogenesis and further research is needed to confirm this.

Identification of novel biomarkers associated with poor patient outcomes in invasive breast carcinoma

Breast carcinoma (BC) corresponds to 23 % of all cancers in women, with 1.38 million new cases and 460,000 deaths worldwide annually. Despite the significant advances in the identification of molecular markers and different modalities of treatment for primary BC, the ability to predict its metastatic behavior is still limited. The purpose of this study was to identify novel molecular markers associated with distinct clinical outcomes in a Brazilian cohort of BC patients. We generated global gene expression profiles using tumor samples from 24 patients with invasive ductal BC who were followed for at least 5 years, including a group of 15 patients with favorable outcomes and another with nine patients who developed metastasis. We identified a set of 58 differentially expressed genes (p ≤ 0.01) between the two groups. The prognostic value of this metastasis signature was corroborated by its ability to stratify independent BC patient datasets according to disease-free survival and overall survival. The upregulation of B3GNT7, PPM1D, TNKS2, PHB, and GTSE1 in patients with poor outcomes was confirmed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) in an independent sample of patients with BC (47 with good outcomes and eight that presented metastasis). The expression of BCL2-associated agonist of cell death (BAD) protein was determined in 1276 BC tissue samples by immunohistochemistry and was consistent with the reduced BAD mRNA expression levels in metastatic cases, as observed in the oligoarray data. These findings point to novel prognostic markers that can distinguish breast carcinomas with metastatic potential from those with favorable outcomes.

Synergistic Effects Induced by Combined Treatments of Aqueous Extract of Propolis and Venom

Background and aims

Breast cancer is a heterogeneous disease and the leading cause of cancer mortality worldwide. Triple negative breast cancer (TNBC) is considered to be one of the most aggressive breast neoplasia due to failure of chemotherapy response. Thus, there is an urgent need of finding alternative therapies for TNBC. This study was designed to evaluate the synergistic effect induced by propolis and bee venom on luminal (MCF-7) and TNBC (Hs578T) cell lines.

Methods

In order to evaluate the synergistic effect of aqueous extract of propolis and bee venom, we treated in combination two breast cancer cell lines: MCF-7(luminal subtype) and Hs578T (TNBC subtype).

Results

Our results indicate that both cell lines exhibited similar sensitivity to the aqueous extract of propolis at a dilution of 0.072–0.09 mg/ml. The results concerning IC50 for bee venom on MCF-7 cells was 1 mg/ml, 20 times higher than 0.05 mg/ml in Hs578T cells. By combining the aqueous extract of propolis with bee venom, we obtained synergistic effects at a higher concentration, which was 5 and 2 times stronger than the two treatments alone.

Conclusion

Overall, the results from our study indicated that the combination of aqueous extract of propolis and bee venom treatments induced synergistic antiproliferative effects in a concentration-dependent manner in breast cancer cells. Thus we can hypothesize that the combination of honeybee propolis and venom might be involved in signaling pathways that could overcome cells resistance to therapy.

Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production

A major roadblock in the effective treatment of cancers is their heterogeneity, whereby multiple molecular landscapes are classified as a single disease. To explore the contribution of cellular metabolism to cancer heterogeneity, we analyse the Metabric dataset, a landmark genomic and transcriptomic study of 2,000 individual breast tumours, in the context of the human genome-scale metabolic network. We create personalized metabolic landscapes for each tumour by exploring sets of active reactions that satisfy constraints derived from human biochemistry and maximize congruency with the Metabric transcriptome data. Classification of the personalized landscapes derived from 997 tumour samples within the Metabric discovery dataset reveals a novel poor prognosis cluster, reproducible in the 995-sample validation dataset. We experimentally follow mechanistic hypotheses resulting from the computational study and establish that active serotonin production is a major metabolic feature of the poor prognosis group. These data support the reconsideration of concomitant serotonin-specific uptake inhibitors treatment during breast cancer chemotherapy.

Identification of a microRNA signature associated with risk of distant metastasis in nasopharyngeal carcinoma

Purpose

Despite significant improvement in locoregional control in the contemporary era of nasopharyngeal carcinoma (NPC) treatment, patients still suffer from a significant risk of distant metastasis (DM). Identifying those patients at risk of DM would aid in personalized treatment in the future. MicroRNAs (miRNAs) play many important roles in human cancers; hence, we proceeded to address the primary hypothesis that there is a miRNA expression signature capable of predicting DM for NPC patients.

Methods and results

The expression of 734 miRNAs was measured in 125 (Training) and 121 (Validation) clinically annotated NPC diagnostic biopsy samples. A 4-miRNA expression signature associated with risk of developing DM was identified by fitting a penalized Cox Proportion Hazard regression model to the Training data set (HR 8.25; p < 0.001), and subsequently validated in an independent Validation set (HR 3.2; p = 0.01). Pathway enrichment analysis indicated that the targets of miRNAs associated with DM appear to be converging on cell-cycle pathways.

Conclusions

This 4-miRNA signature adds to the prognostic value of the current “gold standard” of TNM staging. In-depth interrogation of these 4-miRNAs will provide important biological insights that could facilitate the discovery and development of novel molecularly targeted therapies to improve outcome for future NPC patients.

4-IHC classification of breast cancer subtypes in a large cohort of a clinical cancer registry: use in clinical routine for therapeutic decisions and its effect on survival

The aim of the present study was to evaluate to what extent the combination of standard histopathological parameters determines the biology of breast cancer and the effect on therapy and prognosis. The Clinical Cancer Registry Regensburg (Bavaria, Germany) included n = 4,480 female patients with primary, non-metastatic (M0) invasive breast cancer diagnosed between 2000 and 2012. Immuno-histochemical analyses, i.e., estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 (4-IHC), defined the tumor biological subtypes Luminal A, Luminal B, HER2-like, and Basal-like. Subtype-related differences in therapies and overall survival (OS) were analyzed using multivariable statistical methods. 4344 patients (97.0 %) could be classified into the four common tumor biological subtypes. The two most frequent entities were Luminal A (48.4 %), Luminal B (24.8 %), HER2-like (17.8 %), and Basal-like subtype (9.0 %). A multivariable Cox regression model showed that the best 7-year OS was seen in Luminal A patients and that OS of Luminal B and HER2-like patients was comparable (HR = 1.59, P < 0.001 versus HR = 1.51, P = 0.03). Lowest OS was seen in patients with Basal-like tumors (HR = 2.18, P < 0.001). In conclusion, the classification of tumor biological subtypes by the ER, PR, HER2, and Ki-67 biomarkers is practical in routine clinical work. Providing that quality assurance of these markers is ensured, this classification is useful for making therapy decisions in the routine clinical management of breast cancer patients.

Molecular classification and prediction in gastric cancer

Gastric cancer, a highly heterogeneous disease, is the second leading cause of cancer death and the fourth most common cancer globally, with East Asia accounting for more than half of cases annually. Alongside TNM staging, gastric cancer clinic has two well-recognized classification systems, the Lauren classification that subdivides gastric adenocarcinoma into intestinal and diffuse types and the alternative World Health Organization system that divides gastric cancer into papillary, tubular, mucinous (colloid), and poorly cohesive carcinomas. Both classification systems enable a better understanding of the histogenesis and the biology of gastric cancer yet have a limited clinical utility in guiding patient therapy due to the molecular heterogeneity of gastric cancer. Unprecedented whole-genome-scale data have been catalyzing and advancing the molecular subtyping approach. Here we cataloged and compared those published gene expression profiling signatures in gastric cancer. We summarized recent integrated genomic characterization of gastric cancer based on additional data of somatic mutation, chromosomal instability, EBV virus infection, and DNA methylation. We identified the consensus patterns across these signatures and identified the underlying molecular pathways and biological functions. The identification of molecular subtyping of gastric adenocarcinoma and the development of integrated genomics approaches for clinical applications such as prediction of clinical intervening emerge as an essential phase toward personalized medicine in treating gastric cancer.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

BACKGROUND

The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.

METHODS AND FINDINGS

The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.

CONCLUSIONS

The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

Microarrays as Research Tools and Diagnostic Devices

Molecular diagnostics comprises a main analytical division in clinical laboratory diagnostics. The analysis of RNA or DNA helps to diagnose infectious diseases and identify genetic determined disorders or even cancer. Starting from mono-parametric tests within the last years, technologies have evolved that allow for the detection of many parameters in parallel, e.g., by using multiplex nucleic acid amplification techniques, microarrays, or next-generation sequencing technologies. The introduction of closed-tube systems as well as lab-on-a-chip devices further resulted in a higher automation degree with a reduced contamination risk. These applications complement or even stepwise replace classical methods in clinical microbiology like virus cultures, resistance determination, microscopic and metabolic analyses, as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel biomarkers.

This article provides an overview of microarrays as diagnostics devices and research tools. Introduced in 1995 for transcription analysis, microarrays are used today to detect several different biomolecules like DNA, RNA, miRNA, and proteins among others. Mainly used in research, some microarrays also found their way to clinical diagnostics. Further, closed lab-on-a-chip devices that use DNA microarrays as detection tools are discussed, and additionally, an outlook toward applications of next-generation sequencing tools in diagnostics will be given.

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer--the INNODIAG study

BACKGROUND

Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts.

METHODS

We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification.

RESULTS

We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories.

CONCLUSION

We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response.

MiR-363 sensitizes cisplatin-induced apoptosis targeting in Mcl-1 in breast cancer

Myeloid cell leukemia-1 (Mcl-1) is an anti-apoptotic Bcl-2 family member that is often overexpressed in breast tumors, and has been reported to have an important role in regulating drug resistance in various types of cancer including breast cancer. However, the mechanisms underlying the aberrant expression of Mcl-1 are still unclear. In this study, we used bioinformatics, cellular, and molecular methods to predict and prove that miR-363 directly targeted Mcl-1 3'-UTR (3'-untranslated regions) and caused downregulation of Mcl-1 in breast cancer. Resistance to chemotherapy is a major barrier for the effective treatment for advanced breast cancer, but our study indicated that miR-363 reversed the resistance of the breast cancer cell line MDA-MB-231 to the chemotherapeutic agent cisplatin (CDDP). In addition, transfection of breast cancer cells with Mcl-1 expression plasmid abolished the sensitization effect of miR-363 to cisplatin-inducing cytotoxicity. In summary, our study showed that miR-363 was a negative regulator of Mcl-1 expression, and the combination of miR-363 and cisplatin may be a novel approach in the treatment for breast cancer.

The association of the expression of miR-122-5p and its target ADAM10 with human breast cancer

MicroRNAs can regulate many biological functions. miR-122-5p has a tumor suppressor function through different molecular pathways. Also, our second hit, ADAM10, targeted by miR-122-5p, is a major determinant of HER2 shedding causing that trastuzumab cannot bind to HER2 receptors. Therefore, our analysis upon ADAM10 expression and miR-122-5p was a good point to understand molecular mechanism of breast cancer. In our study, we investigated the expression profiles of miR-122-5p and its target ADAM10 in 71 breast cancer patients. Immunohistochemical analysis of ER, PR and HER2 gene products was used to categorize tumors in patients. Expression data and immunohistochemical findings were evaluated to comment on the relationship between miR-122-5p and ADAM10. ADAM10 expression was higher in tumor than that of normal tissue but miR-122-5p expression was lower in tumor than that of normal tissue. The expression pattern in HER2+ patients was reverse of the overall result. It can be explained like that miR-122-5p expression increases especially in HER2+ cancer cell to suppress ADAM10 shedding activity on HER2 receptor. However, increase in expression of tumor suppressor miR-122-5p is not enough to inhibit ADAM10. All in all, we can think miR-122-5p as potential regulator of ADAM10 and trastuzumab resistance. Since if we increase miR-122-5p activity together with trastuzumab administration, then HER2+ breast cancer cells may overcome trastuzumab resistance by inhibiting ADAM10 shedding activity on HER2 receptors and increase the efficiency of trastuzumab.

Breast cancer classification: linking molecular mechanisms to disease prognosis

Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ∼550 breast tumours from The Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios covering molecular characteristics, biomarker status, tumour stages and survival patterns. We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.