Identification of gene expression signature in estrogen receptor positive breast carcinoma

Human-derived Tumor-On-Chip model to study the heterogeneity of breast cancer tissue

One of the leading causes that complicate the treatment of some malignancies, including breast cancer, is tumor heterogeneity. In addition to inter-heterogeneity and intra-heterogeneity of tumors that reflect the differences between cancer cell characteristics, heterogeneity in the tumor microenvironment plays a critical role in tumor progression and could be considered an overlooked and a proper target for the effective selection of therapeutic approaches. Due to the difficulty of completely capturing tumor heterogeneity in conventional detection methods, Tumor-on-Chip (TOC) devices with culturing patient-derived spheroids could be an appropriate alternative. In this research, human-derived spheroids from breast cancer individuals were cultured for 6 days in microfluidic devices. To compare TOC data with conventional detection methods, immunohistochemistry (IHC) and ITRAQ data were employed, and various protein expressions were validated using the transcriptomic databases. The behavior of the spheroids in the collagen matrix and the cell viability were monitored over 6 days of culture. IHC and immunocytochemistry (ICC) results revealed that inter and intra-heterogeneity of tumor spheroids are associated with HER2/ER expression. HER2 expression levels revealed a more important biomarker associated with invasion in the 3D culturing of spheroids. The expression levels of CD163 (as a marker for Ma2 macrophages) and CD44 (a marker for cancer stem cells (CSCs)) were also evaluated. Interestingly, the levels of M2a macrophages and CSCs were higher in triple-negative specimens and samples that showed higher migration and invasion. Cell density and extracellular matrix (ECM) stiffness were also important factors affecting the migration and invasion of the spheroids through the matrix. Among these, rigid ECM revealed a more crucial role than cell density. To sum up, these research findings demonstrated that human-derived spheroids from breast cancer specimens in microfluidic devices provide a dynamic condition for predicting tumor heterogeneity in patients, which can help move the field forward for better and more accurate therapeutic strategies.

Amino acids and their roles in tumor immunotherapy of breast cancer

Breast cancer is the most commonly diagnosed cancer among women. The primary treatment options include surgery, radiotherapy, chemotherapy, targeted therapy and hormone therapy. The effectiveness of breast cancer therapy varies depending on the stage and aggressiveness of the cancer, as well as individual factors. Advances in early detection and improved treatments have significantly increased survival rates for breast cancer patients. Nevertheless, specific subtypes of breast cancer, particularly triple-negative breast cancer, still lack effective treatment strategies. Thus, novel and effective therapeutic targets for breast cancer need to be explored. As substrates of protein synthesis, amino acids are important sources of energy and nutrition, only secondly to glucose. The rich supply of amino acids enables the tumor to maintain its proliferative competence through participation in energy generation, nucleoside synthesis and maintenance of cellular redox balance. Amino acids also play an important role in immune-suppressive microenvironment formation. Thus, the biological effects of amino acids may change unexpectedly in tumor-specific or oncogene-dependent manners. In recent years, there has been significant progress in the study of amino acid metabolism, particularly in their potential application as therapeutic targets in breast cancer. In this review, we provide an update on amino acid metabolism and discuss the therapeutic implications of amino acids in breast cancer.

Comparative Analysis of Transcriptomic Changes including mRNA and microRNA Expression Induced by the Xenoestrogens Zearalenone and Bisphenol A in Human Ovarian Cells

Xenoestrogens are natural or synthetic compounds that mimic the effect of endogenous estrogens and might cause cancer. We aimed to compare the global transcriptomic response to zearalenone (ZEA; mycotoxin) and bisphenol A (BPA; plastic additive) with the effect of physiological estradiol (E2) in the PEO1 human ovarian cell line by mRNA and microRNA sequencing. Estrogen exposure induced remarkable transcriptomic changes: 308, 288 and 63 genes were upregulated (log₂FC > 1); 292, 260 and 45 genes were downregulated (log₂FC < -1) in response to E2 (10 nM), ZEA (10 nM) and BPA (100 nM), respectively. Furthermore, the expression of 13, 11 and 10 miRNAs changed significantly (log₂FC > 1, or log₂FC < -1) after exposure to E2, ZEA and BPA, respectively. Functional enrichment analysis of the significantly differentially expressed genes and miRNAs revealed several pathways related to the regulation of cell proliferation and migration. The effect of E2 and ZEA was highly comparable: 407 genes were coregulated by these molecules. We could identify 83 genes that were regulated by all three treatments that might have a significant role in the estrogen response of ovarian cells. Furthermore, the downregulation of several miRNAs (miR-501-5p, let-7a-2-3p, miR-26a-2-3p, miR-197-5p and miR-582-3p) was confirmed by qPCR, which might support the proliferative effect of estrogens in ovarian cells.

The effect of a phytoestrogen intervention and impact of genetic factors on tumor proliferation markers among Swedish patients with prostate cancer: study protocol for the randomized controlled PRODICA trial

BACKGROUND

A high intake of phytoestrogens, found in soy, rye, and seeds, is associated with a reduced risk of a prostate cancer diagnosis. Previously, we found that the overall decreased risk of prostate cancer diagnosis in males with a high intake of phytoestrogens was strongly modified by a nucleotide sequence variant in the estrogen receptor-beta (ERβ) gene. However, we do not know if phytoestrogens can inhibit the growth of prostate cancer in males with established diseases. If there is an inhibition or a delay, there is reason to believe that different variants of the ERβ gene will modify the effect. Therefore, we designed an intervention study to investigate the effect of the addition of foods high in phytoestrogens and their interaction with the ERβ genotype on prostate tumor proliferation in patients with prostate cancer.

METHOD

The PRODICA trial is a randomized ongoing intervention study in patients with low- and intermediate-risk prostate cancer with a Gleason score < 8, prostate-specific antigen (PSA) < 20, and scheduled for radical prostatectomy. The study is conducted at Sahlgrenska University Hospital in Gothenburg, Sweden. The intervention consists of a daily intake of soybeans and flaxseeds (~ 200 mg of phytoestrogens) until the surgery, approximately 6 weeks. The aim is to recruit 200 participants. The primary outcome is the difference in the proliferation marker Ki-67 between the intervention and the control groups. The genotype of ERβ will be investigated as an effect-modifying factor. Secondary outcomes include, e.g., concentrations of PSA and steroid hormones in the blood.

DISCUSSION

The results of the PRODICA trial will contribute important information on the relevance of increasing the intake of phytoestrogens in patients with prostate cancer who want to make dietary changes to improve the prognosis of their cancer. If genetic factors turn out to influence the effect of the intervention diet, dietary advice can be given to patients who most likely benefit from it. Dietary interventions are cost-effective, non-invasive, and result in few mild side effects. Lastly, the project will provide basic pathophysiological insights which could be relevant to the development of treatment strategies for patients with prostate cancer.

TRIAL REGISTRATION

CLINICALTRIALS

gov NCT02759380. Registered on 3 May 2016.

Transcriptomic profiling of Indian breast cancer patients revealed subtype-specific mRNA and lncRNA signatures

Breast cancer (BC) is one of the leading causes of cancer-associated death in women. Despite the progress in therapeutic regimen, resistance and recurrence of breast cancer have affected the overall survival of patients. The present signatures, such as PAM50 and Oncotype DX, do not segregate the Indian breast samples based on molecular subtypes. This study aims at finding signatures of long noncoding RNA (lncRNA) and mRNA in Indian breast cancer patients using RNA-seq. We have analyzed the survival based on the menopausal and hormone status of 380 Indian breast cancer patients, and of these, we have sequenced and analyzed matched tumor–normal transcriptome of 17 (pre- and postmenopausal) Indian breast cancer patients representing six different subtypes, namely, four patients in triple-positive, three patients in estrogen receptor–positive (ER+ve), three patients in estrogen and progesterone receptors–positive (ER+ve, PR+ve), two patients in human epidermal growth factor receptor (Her2+ve), three patients in triple-negative, and one patient in ER+ve and Her2+ve subtypes. We have identified a 25 mRNA–27 lncRNA gene set, which segregated the subtypes in our data. A pathway analysis of the differentially expressed genes revealed downregulated ECM interaction and upregulated immune regulation, cell cycle, DNA damage response and repair, and telomere elongation in premenopausal women. Postmenopausal women showed downregulated metabolism, innate immune system, upregulated translation, sumoylation, and AKT2 activation. A Kaplan–Meier survival analysis revealed that menopausal status, grade of the tumor, and hormonal status displayed statistically significant effects (p < 0.05) on the risk of mortality due to breast cancer. Her2+ve patients showed low overall survival. One of the unique lncRNA-mRNA pairs specific to the EP-subtype, SNHG12 and EPB41, showed interaction, which correlates with their expression level; SNHG12 is downregulated and EPB41 is upregulated in EP samples.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Overexpression of MLPH in Rectal Cancer Patients Correlates with a Poorer Response to Preoperative Chemoradiotherapy and Reduced Patient Survival

Data mining of a public transcriptomic rectal cancer dataset (GSE35452) from the Gene Expression Omnibus, National Center for Biotechnology Information identified the melanophilin (MLPH) gene as the most significant intracellular protein transport-related gene (GO:0006886) associated with a poor response to preoperative chemoradiation. An MLPH immunostain was performed on biopsy specimens from 172 rectal cancer patients receiving preoperative chemoradiation; samples were divided into high- and low-expression groups by H-scores. Subsequently, the correlations between MLPH expression and clinicopathologic features, tumor regression grade, disease-specific survival (DSS), local recurrence-free survival (LRFS), and metastasis-free survival (MeFS) were analyzed. MLPH expression was significantly associated with CEA level (p = 0.001), pre-treatment tumor status (p = 0.022), post-treatment tumor status (p < 0.001), post-treatment nodal status (p < 0.001), vascular invasion (p = 0.028), and tumor regression grade (p < 0.001). After uni- and multi-variable analysis of five-year survival, MLPH expression was still associated with lower DSS (hazard ratio (HR), 10.110; 95% confidence interval (CI), 2.178–46.920; p = 0.003) and MeFS (HR, 5.621; 95% CI, 1.762–17.931; p = 0.004). In conclusion, identifying MLPH expression could help to predict the response to chemoradiation and survival, and aid in personal therapeutic modification.

[Advancement of E2F1 in Common Tumors]

细胞周期相关转录因子E2F1（E2F transcription factor 1）是细胞周期相关转录因子E2F家族成员之一，主要参与包括细胞周期进展、DNA修复、DNA复制、细胞分化，增殖和凋亡等多种细胞过程。E2F1在全身多种肿瘤组织和细胞中呈高表达，起着促癌基因的作用，E2F1表达上调与肿瘤的发生、发展、转移及预后密切相关。因此，E2F1有望成为肿瘤治疗的新靶点。本文就E2F1在目前常见肿瘤中的最新研究进展做一综述。

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.

Overexpression and alternative splicing of NF-YA in breast cancer

NF-Y is a CCAAT-binding trimeric transcription factor, whose regulome, interactome and oncogenic potential point to direct involvement in cellular transformation. Yet little is known about the levels of NF-Y subunits in tumors. We focused on breast carcinomas, and analyzed RNA-Seq datasets of TCGA and 54 BRCA cell lines at gene and isoforms level. We partitioned all tumors in the four major subclasses. NF-YA, but not histone-fold subunits NF-YB/NF-YC, is globally overexpressed, correlating with the proliferative Ki67 marker and a common set of 840 genes, with cell-cycle, metabolism GO terms. Their promoters are enriched in NF-Y, GC-rich and E2F sites. Surprisingly, there is an isoform switch, with the “short” isoform -NF-YAs- becoming predominant in tumors. E2F genes are also overexpressed in BRCA, but no switch in isoforms is observed. In Basal-like Claudin^low cell lines and tumors, expression of NF-YAl -long- isoform is high, together with 11 typical EMT markers and low levels of basal Keratins. Analysis of Progression-Free-Intervals indicates that tumors with unbalance of NF-YA isoforms ratios have worst clinical outcomes. The data suggest that NF-YA overexpression increases CCAAT-dependent, pro-growth genes in BRCA. NF-YAs is associated with a proliferative signature, but high levels of NF-YAl signal loss of epithelial features, EMT and acquisition of a more aggressive behavior in a subset of Claudin^low Basal-like tumors.

Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors

Recent development in high throughput proteomics and genomics profiling enable one to study regulations of genome alterations on protein activities in a systematic manner. In this article, we propose a new statistical method, ProMAP, to systematically characterize the regulatory relationships between proteins and DNA copy number alterations (CNA) in breast and ovarian tumors based on proteogenomic data from the CPTAC-TCGA studies. Because of the dynamic nature of mass spectrometry instruments, proteomics data from labeled mass spectrometry experiments usually have non-ignorable batch effects. Moreover, mass spectrometry based proteomic data often possesses high percentages of missing values and non-ignorable missing-data patterns. Thus, we use a linear mixed effects model to account for the batch structure and explicitly incorporate the abundance-dependent-missing-data mechanism of proteomic data in ProMAP. In addition, we employ a multivariate regression framework to characterize the multiple-to-multiple regulatory relationships between CNA and proteins. Further, we use proper statistical regularization to facilitate the detection of master genetic regulators, which affect the activities of many proteins and often play important roles in genetic regulatory networks. Improved performance of ProMAP over existing methods were illustrated through extensive simulation studies and real data examples. Applying ProMAP to the CPTAC-TCGA breast and ovarian cancer data sets, we identified many genome regions, including a few novel ones, whose CNA were associated with protein and or phosphoprotein abundances. For example, in breast tumors, a small region in 8p11.21 was recognized as the second biggest hub in the CNA-phosphoprotein regulatory map, and further investigation of the regulatory targets suggests the potential role of 8p11.21 CNA in perturbing oxygen binding and transport activities in tumor cells. This and other findings from our analyses help to characterize the impacts of CNAs on protein activity landscapes and cast light on the genetic regulation mechanisms underlying these tumors.

Study of Gene Expression Profiles of Breast Cancers in Indian Women

Breast cancer is the most common cancer among women globally. In India, the incidence of breast cancer has increased significantly during the last two decades with a higher proportion of the disease at a young age compared to the west. To understand the molecular processes underlying breast cancer in Indian women, we analysed gene expression profiles of 29 tumours and 9 controls using microarray. In the present study, we obtained 2413 differentially expressed genes, consisting of overexpressed genes such as COL10A1, COL11A1, MMP1, MMP13, MMP11, GJB2, and CST1 and underexpressed genes such as PLIN1, FABP4, LIPE, AQP7, LEP, ADH1A, ADH1B, and CIDEC. The deregulated pathways include cell cycle, focal adhesion and metastasis, DNA replication, PPAR signaling, and lipid metabolism. Using PAM50 classifier, we demonstrated the existence of molecular subtypes in Indian women. In addition, qPCR validation of expression of metalloproteinase genes, MMP1, MMP3, MMP11, MMP13, MMP14, ADAMTS1, and ADAMTS5 showed concordance with that of the microarray data; wherein we found a significant association of ADAMTS5 down-regulation with older age (≥55 years) of patients. Together, this study reports gene expression profiles of breast tumours from the Indian subcontinent, throwing light on the pathways and genes associated with the breast tumourigenesis in Indian women.

Fusion of ALK to the melanophilin gene MLPH in pediatric Spitz nevi

Spitzoid neoplasms typically affect young individuals and include Spitz nevus, atypical Spitz tumor, and Spitzoid melanoma. Spitz tumors can exhibit gene fusions involving the receptor tyrosine kinases NTRK1, NTRK3, ALK, ROS1, RET, or MET, or the serine-threonine kinase BRAF. Because most studies have been based on adult cases, we studied ALK fusions in Spitz nevi occurring in pediatric patients. Twenty-seven cases were screened for ALK expression by immunohistochemistry, and 6 positive cases were identified. These cases were studied further using the TruSight RNA Fusion Panel, and in 4 cases, exon 20 of the ALK gene was found to be fused to exon 14 of the MLPH (melanophilin) gene, a gene fusion that has only been reported in a Spitz nevus in an adult. The remaining 2 cases showed no fusion of ALK with any gene. The cases with the MLPH-ALK fusion showed a similar histology to that described for Spitz nevi with ALK fusions, with spindle-shaped and epithelioid melanocytes in fusiform nests with a plexiform growth pattern and infiltrative border. We created a breakapart fluorescence in situ hybridization assay for MLPH, and all 4 cases with the MLPH-ALK fusion were positive, whereas the other 23 cases in the study were negative. Thus, ALK and MLPH were fused only to each other in our series. Melanophilin is part of the melanosome trafficking apparatus together with MYO5a, TPM3, and RAB27a, all constitutively expressed in melanocytes. Kinase fusions involving MYO5A and TPM3 have been reported in Spitz tumors, and our series adds MLPH to this group.

Netrin Family: Role for Protein Isoforms in Cancer

Netrins form a family of secreted and membrane-associated proteins. Netrins are involved in processes for axonal guidance, morphogenesis, and angiogenesis by regulating cell migration and survival. These processes are of special interest in tumor biology. From the netrin genes various isoforms are translated and regulated by alternative splicing. We review here the diversity of isoforms of the netrin family members and their known and potential roles in cancer.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

Robust identification of target genes and outliers in triple-negative breast cancer data

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.

Amino Acid Transporters and Glutamine Metabolism in Breast Cancer

Amino acid transporters are membrane transport proteins, most of which are members of the solute carrier families. Amino acids are essential for the survival of all types of cells, including tumor cells, which have an increased demand for nutrients to facilitate proliferation and cancer progression. Breast cancer is the most common malignancy in women worldwide and is still associated with high mortality rates, despite improved treatment strategies. Recent studies have demonstrated that the amino acid metabolic pathway is altered in breast cancer and that amino acid transporters affect tumor growth and progression. In breast cancer, glutamine is one of the key nutrients, and glutamine metabolism is closely related to the amino acid transporters. In this review, we focus on amino acid transporters and their roles in breast cancer. We also highlight the different subsets of upregulated amino acid transporters in breast cancer and discuss their potential applications as treatment targets, cancer imaging tracers, and drug delivery components. Glutamine metabolism as well as its regulation and therapeutic implication in breast cancer are also discussed.

Environmental factors and risk of aggressive prostate cancer among a population of New Zealand men - a genotypic approach

Prostate cancer is one of the most significant health concerns for men worldwide. Numerous researchers carrying out molecular diagnostics have indicated that genetic interactions with biological and behavioral factors play an important role in the overall risk and prognosis of this disease. Single nucleotide polymorphisms (SNPs) are increasingly becoming strong biomarker candidates to identify susceptibility to prostate cancer. We carried out a gene × environment interaction analysis linked to aggressive and non-aggressive prostate cancer (PCa) with a number of SNPs. By using this method, we identified the susceptible alleles in a New Zealand population, and examined the interaction with environmental factors. We have identified a number of SNPs that have risk associations both with and without environmental interaction. The results indicate that certain SNPs are associated with disease vulnerability based on behavioral factors. The list of genes with SNPs identified as being associated with the risk of PCa in a New Zealand population is provided in the graphical abstract.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BACKGROUND

Molecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients.

MATERIALS AND METHODS

We reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER⁺ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER⁺ and ER^- breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival.

RESULTS

Genes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies.

CONCLUSION

A biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis

MOTIVATION

Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies.

RESULTS

We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients.

AVAILABILITY AND IMPLEMENTATION

An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm).

CONTACT

ctseng@pitt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Putative Prostate Cancer Risk SNP in an Androgen Receptor-Binding Site of the Melanophilin Gene Illustrates Enrichment of Risk SNPs in Androgen Receptor Target Sites

Genome-wide association studies have identified genomic loci, whose single-nucleotide polymorphisms (SNPs) predispose to prostate cancer (PCa). However, the mechanisms of most of these variants are largely unknown. We integrated chromatin-immunoprecipitation-coupled sequencing and microarray expression profiling in TMPRSS2-ERG gene rearrangement positive DUCaP cells with the GWAS PCa risk SNPs catalog to identify disease susceptibility SNPs localized within functional androgen receptor-binding sites (ARBSs). Among the 48 GWAS index risk SNPs and 3,917 linked SNPs, 80 were found located in ARBSs. Of these, rs11891426:T>G in an intron of the melanophilin gene (MLPH) was within a novel putative auxiliary AR-binding motif, which is enriched in the neighborhood of canonical androgen-responsive elements. T→G exchange attenuated the transcriptional activity of the ARBS in an AR reporter gene assay. The expression of MLPH in primary prostate tumors was significantly lower in those with the G compared with the T allele and correlated significantly with AR protein. Higher melanophilin level in prostate tissue of patients with a favorable PCa risk profile points out a tumor-suppressive effect. These results unravel a hidden link between AR and a functional putative PCa risk SNP, whose allele alteration affects androgen regulation of its host gene MLPH.

High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors

The objective of the present study was to validate prognostic gene signature for estrogen receptor alpha-positive (ER03B1+) and lymph node (+) breast cancer for improved selection of patients for adjuvant therapy. In our previous study, we identified a group of seven genes (GATA3, NTN4, SLC7A8, ENPP1, MLPH, LAMB2, and PLAT) that show elevated messenger RNA (mRNA) expression levels in ERα (+) breast cancer patient samples. The prognostic values of these genes were evaluated using gene expression data from three public data sets of breast cancer patients (n = 395). Analysis of ERα (+) breast cancer cohort (n = 195) showed high expression of GATA3, NTN4, and MLPH genes significantly associated with longer relapse-free survival (RFS). Next cohort of ERα (+) and node (+) samples (n = 109) revealed high mRNA expression of GATA3, SLC7A8, and MLPH significantly associated with longer RFS. Multivariate analysis of combined three-gene signature for ERα (+) cohort, and ERα (+) and node (+) cohorts showed better hazard ratio than individual genes. The validated three-gene signature sets for ERα (+) cohort, and ERα (+) and node (+) cohort may have potential clinical utility since they demonstrated predictive and prognostic ability in three independent public data sets.

Redox-Mediated Suberoylanilide Hydroxamic Acid Sensitivity in Breast Cancer

AIMS

Vorinostat (suberoylanilide hydroxamic acid; SAHA) is a histone deacetylase inhibitor (HDACi) approved in the clinics for the treatment of T-cell lymphoma and with the potential to be effective also in breast cancer. We investigated the responsiveness to SAHA in human breast primary tumors and cancer cell lines.

RESULTS

We observed a differential response to drug treatment in both human breast primary tumors and cancer cell lines. Gene expression analysis of the breast cancer cell lines revealed that genes involved in cell adhesion and redox pathways, especially glutathione metabolism, were differentially expressed in the cell lines resistant to SAHA compared with the sensitive ones, indicating their possible association with drug resistance mechanisms. Notably, such an association was also observed in breast primary tumors. Indeed, addition of buthionine sulfoximine (BSO), a compound capable of depleting cellular glutathione, significantly enhanced the cytotoxicity of SAHA in both breast cancer cell lines and primary breast tumors.

INNOVATION

We identify and validate transcriptional differences in genes involved in redox pathways, which include potential predictive markers of sensitivity to SAHA.

CONCLUSION

In breast cancer, it could be relevant to evaluate the expression of antioxidant genes that may favor tumor resistance as a factor to consider for potential clinical application and treatment with epigenetic drugs (HDACis).

Molecular Features of Triple Negative Breast Cancer: Microarray Evidence and Further Integrated Analysis

Purpose

Breast cancer is a heterogeneous disease usually including four molecular subtypes such as luminal A, luminal B, HER2-enriched, and triple-negative breast cancer (TNBC). TNBC is more aggressive than other breast cancer subtypes. Despite major advances in ER-positive or HER2-amplified breast cancer, there is no targeted agent currently available for TNBC, so it is urgent to identify new potential therapeutic targets for TNBC.

Methods

We first used microarray analysis to compare gene expression profiling between TNBC and non-TNBC. Furthermore an integrated analysis was conducted based on our own and published data, leading to more robust, reproducible and accurate predictions. Additionally, we performed qRT-PCR in breast cancer cell lines to verify the findings in integrated analysis.

Results

After searching Gene Expression Omnibus database (GEO), two microarray studies were obtained according to the inclusion criteria. The integrated analysis was conducted, including 30 samples of TNBC and 77 samples of non-TNBC. 556 genes were found to be consistently differentially expressed (344 up-regulated genes and 212 down-regulated genes in TNBC). Functional annotation for these differentially expressed genes (DEGs) showed that the most significantly enriched Gene Ontology (GO) term for molecular functions was protein binding (GO: 0005515, P = 6.09E-21), while that for biological processes was signal transduction (GO: 0007165, P = 9.46E-08), and that for cellular component was cytoplasm (GO: 0005737, P = 2.09E-21). The most significant pathway was Pathways in cancer (P = 6.54E-05) based on Kyoto Encyclopedia of Genes and Genomes (KEGG). DUSP1 (Degree = 21), MYEOV2 (Degree = 15) and UQCRQ (Degree = 14) were identified as the significant hub proteins in the protein-protein interaction (PPI) network. Five genes were selected to perform qRT-PCR in seven breast cancer cell lines, and qRT-PCR results showed that the expression pattern of selected genes in TNBC lines and non-TNBC lines was nearly consistent with that in the integrated analysis.

Conclusion

This study may help to understand the pathogenesis of different breast cancer subtypes, contributing to the successful identification of therapeutic targets for TNBC.

Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

MODEL-BASED CLUSTERING WITH GENE RANKING USING PENALIZED MIXTURES OF HEAVY-TAILED DISTRIBUTIONS

Cluster analysis of biological samples using gene expression measurements is a common task which aids the discovery of heterogeneous biological sub-populations having distinct mRNA profiles. Several model-based clustering algorithms have been proposed in which the distribution of gene expression values within each sub-group is assumed to be Gaussian. In the presence of noise and extreme observations, a mixture of Gaussian densities may over-fit and overestimate the true number of clusters. Moreover, commonly used model-based clustering algorithms do not generally provide a mechanism to quantify the relative contribution of each gene to the final partitioning of the data. We propose a penalized mixture of Student's t distributions for model-based clustering and gene ranking. Together with a resampling procedure, the proposed approach provides a means for ranking genes according to their contributions to the clustering process. Experimental results show that the algorithm performs well comparably to traditional Gaussian mixtures in the presence of outliers and longer tailed distributions. The algorithm also identifies the true informative genes with high sensitivity, and achieves improved model selection. An illustrative application to breast cancer data is also presented which confirms established tumor sub-classes.

Prognostic significance and gene expression profiles of p53 mutations in microsatellite-stable stage III colorectal adenocarcinomas

Although the prognostic value of p53 abnormalities in Stage III microsatellite stable (MSS) colorectal cancers (CRCs) is known, the gene expression profiles specific to the p53 status in the MSS background are not known. Therefore, the current investigation has focused on identification and validation of the gene expression profiles associated with p53 mutant phenotypes in MSS Stage III CRCs. Genomic DNA extracted from 135 formalin-fixed paraffin-embedded tissues, was analyzed for microsatellite instability (MSI) and p53 mutations. Further, mRNA samples extracted from five p53-mutant and five p53-wild-type MSS-CRC snap-frozen tissues were profiled for differential gene expression by Affymetrix Human Genome U133 Plus 2.0 arrays. Differentially expressed genes were further validated by the high-throughput quantitative nuclease protection assay (qNPA), and confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) and by immunohistochemistry (IHC). Survival rates were estimated by Kaplan-Meier and Cox regression analyses. A higher incidence of p53 mutations was found in MSS (58%) than in MSI (30%) phenotypes. Both univariate (log-rank, P = 0.025) and multivariate (hazard ratio, 2.52; 95% confidence interval, 1.25-5.08) analyses have demonstrated that patients with MSS-p53 mutant phenotypes had poor CRC-specific survival when compared to MSS-p53 wild-type phenotypes. Gene expression analyses identified 84 differentially expressed genes. Of 49 down-regulated genes, LPAR6, PDLIM3, and PLAT, and, of 35 up-regulated genes, TRIM29, FUT3, IQGAP3, and SLC6A8 were confirmed by qNPA, qRT-PCR, and IHC platforms. p53 mutations are associated with poor survival of patients with Stage III MSS CRCs and p53-mutant and wild-type phenotypes have distinct gene expression profiles that might be helpful in identifying aggressive subsets.

Phenotypic and genotypic assessment of concomitant drug-induced toxic effects in liver, kidney and blood

Several studies have characterized drug-induced toxicity in liver and kidney. However, the majority of these studies have been performed with 'individual' organs in isolation. Separately, little is known about the role of whole blood as a surrogate tissue in drug-induced toxicity. Accordingly, we investigated the 'concurrent' response of liver, kidney and whole blood during a toxic assault. Rats were acutely treated with therapeutics (acetaminophen, rosiglitazone, fluconazole, isoniazid, cyclophosphamide, amphotericin B, gentamicin and cisplatin) reported for their liver and/or kidney toxicity. Changes in clinical chemistry parameters (e.g. AST, urea) and/or observed microscopic tissue damage confirmed induced hepatotoxicity and/or nephrotoxicity by all drugs. Drug-induced toxicity was not confined to an 'individual' organ. Not all drugs elicited significant alterations in phenotypic parameters of toxicity (e.g. ALT, creatinine). Accordingly, the transcriptional profile of the organs was studied using a toxicity panel of 30 genes derived from literature. Each of the test drugs generated specific gene expression patterns which were unique for all three organs. Hierarchical cluster analyses of purported hepatotoxicants and nephrotoxicants each led to characteristic 'fingerprints' (e.g. decrease in Cyp3a1 indicative of hepatotoxicity; increase in Spp1 and decrease in Gstp1 indicative of nephrotoxicity). In whole blood cells, a set of genes was derived which closely correlated with individual drug-induced concomitant changes in liver or kidney. Collectively, these data demonstrate drug-induced multi-organ toxicity. Furthermore, our findings underscore the importance of transcriptional profiling during inadequate phenotypic anchorage and suggest that whole blood may be judiciously used as a surrogate for drug-induced extra-hematological organ toxicity.