Breast Cancer Gene Expression Data Research Articles

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

ForestSubtype: a cancer subtype identifying approach based on high-dimensional genomic data and a parallel random forest

BackgroundCancer subtype classification is helpful for personalized cancer treatment. Although, some approaches have been developed to classifying caner subtype based on high dimensional gene expression data, it is difficult to obtain satisfactory classification results. Meanwhile, some cancers have been well studied and classified to some subtypes, which are adopt by most researchers. Hence, this priori knowledge is significant for further identifying new meaningful subtypes.ResultsIn this paper, we present a combined parallel random forest and autoencoder approach for cancer subtype identification based on high dimensional gene expression data, ForestSubtype. ForestSubtype first adopts the parallel RF and the priori knowledge of cancer subtype to train a module and extract significant candidate features. Second, ForestSubtype uses a random forest as the base module and ten parallel random forests to compute each feature weight and rank them separately. Then, the intersection of the features with the larger weights output by the ten parallel random forests is taken as our subsequent candidate features. Third, ForestSubtype uses an autoencoder to condenses the selected features into a two-dimensional data. Fourth, ForestSubtype utilizes k-means++ to obtain new cancer subtype identification results. In this paper, the breast cancer gene expression data obtained from The Cancer Genome Atlas are used for training and validation, and an independent breast cancer dataset from the Molecular Taxonomy of Breast Cancer International Consortium is used for testing. Additionally, we use two other cancer datasets for validating the generalizability of ForestSubtype. ForestSubtype outperforms the other two methods in terms of the distribution of clusters, internal and external metric results. The open-source code is available at https://github.com/lffyd/ForestSubtype.ConclusionsOur work shows that the combination of high-dimensional gene expression data and parallel random forests and autoencoder, guided by a priori knowledge, can identify new subtypes more effectively than existing methods of cancer subtype classification.

Tumor suppressor miR-218 directly targets epidermal growth factor receptor (EGFR) expression in triple-negative breast cancer, sensitizing cells to irradiation

PurposeMicroRNA-218 (miR-218) is a key regulator of numerous processes relevant to tumor progression. In the present study, we aimed to characterize the relationship between miR-218 and the Epidermal Growth Factor Receptor (EGFR) as well as to understand downstream effects in triple-negative breast cancer (TNBC).MethodsWe assessed miR-218 and EGFR expression in cell lines and publicly available primary breast cancer gene expression data. We then overexpressed miR-218 in two TNBC cell lines and investigated effects on EGFR and downstream mitogen-activated protein (MAP) kinase signaling. Luciferase reporter assay was used to characterize a direct binding interaction between miR-218 and EGFR mRNA. Digital holographic microscopy helped investigate cell migration and dry mass after miR-218 overexpression. Cell division and invasion were assessed microscopically, while radiation response after miR-218 overexpression alone or combined with additional EGFR knockdown was investigated via clonogenic assays.ResultsWe found an inverse correlation between EGFR expression and miR-218 levels in cell lines and primary breast cancer tissues. MiR-218 overexpression resulted in a downregulation of EGFR via direct binding of the mRNA. Activation of EGFR and downstream p44/42 MAPK signaling were reduced after pre-miR-218 transfection. Cell proliferation, motility and invasiveness were inhibited whereas cell death and mitotic catastrophe were upregulated in miR-218 overexpressing cells compared to controls. MiR-218 overexpressing and EGFR siRNA-treated cells were sensitized to irradiation, more than miR-218 overexpressing cells alone.ConclusionThis study characterizes the antagonistic relationship between miR-218 and EGFR. It also demonstrates downstream functional effects of miR-218 overexpression, leading to anti-tumorigenic cellular changes.

High-dimensional linear regression with hard thresholding regularization: Theory and algorithm

<p style='text-indent:20px;'>Variable selection and parameter estimation are fundamental and important problems in high dimensional data analysis. In this paper, we employ the hard thresholding regularization method [<xref ref-type="bibr" rid="b1">1</xref>] to handle these issues under the framework of high-dimensional and sparse linear regression model. Theoretically, we establish a sharp non-asymptotic estimation error for the global solution and further show that the support of the global solution coincides with the target support with high probability. Motivated by the KKT condition, we propose a primal dual active set algorithm (PDAS) to solve the minimization problem, and show that the proposed PDAS algorithm is essentially a generalized Newton method, which guarantees that the proposed PDAS algorithm will converge fast if a good initial value is provided. Furthermore, we propose a sequential version of the PDAS algorithm (SPDAS) with a warm-start strategy to choose the initial value adaptively. The most significant advantage of the proposed procedure is its fast calculation speed. Extensive numerical studies demonstrate that the proposed method performs well on variable selection and estimation accuracy. It has favorable exhibition over the existing methods in terms of computational speed. As an illustration, we apply the proposed method to a breast cancer gene expression data set.</p>

A two-way additive model with unknown group-specific interactions applied to gene expression data.

We propose a two-way additive model with group-specific interactions, where the group information is unknown. We treat the group membership as latent information and propose an EM algorithm for estimation. With a single observation matrix and under the situation of diverging row and column numbers, we rigorously establish the estimation consistency and asymptotic normality of our estimator. Extensive simulation studies are conducted to demonstrate the finite sample performance. We apply the model to the triple negative breast cancer (TNBC) gene expression data and provide a new way to classify patients into different subtypes. Our analysis detects the potential genes that may be associated withTNBC.

Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm

Aim: Currently, the obesity epidemic is one of the biggest problems for human health. Obesity is impacted on survival in patients with breast cancer. However, key biomarkers of obesity-related breast cancer risk are still not well known. Thus, using machine learning to identify the most appropriate features in obesity-associated breast cancer patients may improve the predictive accuracy and interpretability of regression models. Methods: In the present study, we identified 23 differentially expressed genes (DEGs) from the GSE24185 transcriptome dataset. Seed genes were identified from DEGs, the co-expression network genes and hub genes of the protein-protein interaction network. Pathway enrichment analysis was performed for DEGs. The Ridge penalty regression model was executed by using P-values of enriched pathways and seed gene pathway association score to obtain the most relevant molecular signatures. The model was performed using 10-fold cross-validation to fit the penalized models. Results: Angiotensin II receptor type 1 (AGTR1), cyclin D1 (CCND1), glutamate ionotropic receptor AMPA type subunit 2 (GRIA2), interleukin-6 cytokine family signal transducer (IL6ST), matrix metallopeptidase 9 (MMP9), and protein kinase CAMP-dependent type II regulatory subunit beta (PRKAR2B) were considered as candidate molecular signatures of obese patients with breast cancer. In addition, RAF-independent MAPK1/3 activation, collagen degradation, bladder cancer, drug metabolism-cytochrome P450, and signaling by Hedgehog pathways in cancer were primarily associated with obesity-associated breast cancer. Conclusion: These genes may be used for risk analysis of the disease progression of obese patients with breast cancer. Corresponding genes and pathways should be validated via experimental studies.

A new test for high‐dimensional regression coefficients in partially linear models

Partially linear regression models are semiparametric models that contain both linear and nonlinear components. They are extensively used in many scientific fields for their flexibility and convenient interpretability. In such analyses, testing the significance of the regression coefficients in the linear component is typically a key focus. Under the high‐dimensional setting, i.e., “large p, small n,” the conventional F‐test strategy does not apply because the coefficients need to be estimated through regularization techniques. In this article, we develop a new test using a U‐statistic of order two, relying on a pseudo‐estimate of the nonlinear component from the classical kernel method. Using the martingale central limit theorem, we prove the asymptotic normality of the proposed test statistic under some regularity conditions. We further demonstrate our proposed test's finite‐sample performance by simulation studies and by analyzing some breast cancer gene expression data.

Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks.

Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on large breast cancer gene expression data and on other cancer datasets. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed sufficiently good for the metastatic event prediction tasks compared to other networks. Such performance was good enough to validate the utility of our generated word embeddings in constructing biological networks. Word representations as produced by text mining algorithms like word2vec, therefore are able to capture biologically meaningful relations between entities. Our generated embeddings are publicly available at https://github.com/genexplain/Word2vec-based-Networks/blob/main/README.md.

Sign-based Shrinkage Based on an Asymmetric LASSO Penalty.

Penalized regression provides an automated approach to preform simultaneous variable selection and parameter estimation and is a popular method to analyze high-dimensional data. Since the conception of the LASSO in the mid-to-late 1990s, extensive research has been done to improve penalized regression. The LASSO, and several of its variations, performs penalization symmetrically around zero. Thus, variables with the same magnitude are shrunk the same regardless of the direction of effect. To the best of our knowledge, sign-based shrinkage, preferential shrinkage based on the sign of the coefficients, has yet to be explored under the LASSO framework. We propose a generalization to the LASSO, asymmetric LASSO, that performs sign-based shrinkage. Our method is motivated by placing an asymmetric Laplace prior on the regression coefficients, rather than a symmetric Laplace prior. This corresponds to an asymmetric ℓ 1 penalty under the penalized regression framework. In doing so, preferential shrinkage can be performed through an auxiliary tuning parameter that controls the degree of asymmetry. Our numerical studies indicate that the asymmetric LASSO performs better than the LASSO when effect sizes are sign skewed. Furthermore, in the presence of positively-skewed effects, the asymmetric LASSO is comparable to the non-negative LASSO without the need to place an a priori constraint on the effect estimates and outperforms the non-negative LASSO when negative effects are also present in the model. A real data example using the breast cancer gene expression data from The Cancer Genome Atlas is also provided, where the asymmetric LASSO identifies two potentially novel gene expressions that are associated with BRCA1 with a minor improvement in prediction performance over the LASSO and non-negative LASSO.

Using sufficient direction factor model to analyze latent activities associated with breast cancer survival.

High-dimensional gene expression data often exhibit intricate correlation patterns as the result of coordinated genetic regulation. In practice, however, it is difficult to directly measure these coordinated underlying activities. Analysis of breast cancer survival data with gene expressions motivates us to use a two-stage latent factor approach to estimate these unobserved coordinated biologicalprocesses. Compared to existing approaches, our proposed procedure has several unique characteristics. In the first stage, an important distinction is that our procedure incorporates prior biological knowledge about gene-pathway membership into the analysis and explicitly model the effects of genetic pathways on the latent factors. Second, to characterize the molecular heterogeneity of breast cancer, our approach provides estimates specific to each cancer subtype. Finally, our proposed framework incorporates sparsity condition due to the fact that genetic networks are oftensparse. In the second stage, we investigate the relationship between latent factor activity levels and survival time with censoring using a general dimension reduction model in the survival analysis context. Combining the factor model and sufficient direction model provides an efficient way of analyzing high-dimensional data and reveals some interesting relations in the breast cancer gene expressiondata.

Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer

Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and copy number alteration (CNA) data separately and jointly. We hypothesize that the deep features are associated with patients’ clinical characteristics and outcomes. Two unsupervised denoising autoencoders (DAs) were developed to extract deep features from TCGA (The Cancer Genome Atlas) breast cancer gene expression and CNA data separately and jointly. A heat map was used to view and cluster patients into subgroups based on these DL features. Fisher’s exact test and Pearson’ Chi-square test were applied to test the associations of patients’ groups and clinical information. Survival differences between the groups were evaluated by Kaplan–Meier (KM) curves. Associations between each of the features and patient’s overall survival were assessed using Cox’s proportional hazards (COX-PH) model and a risk score for each feature set from the different omics data sets was generated from the survival regression coefficients. The risk scores for each feature set were binarized into high- and low-risk patient groups to evaluate survival differences using KM curves. Furthermore, the risk scores were traced back to their gene level DAs weights so that the three gene lists for each of the genomic data points were generated to perform gene set enrichment analysis. Patients were clustered into two groups based on concatenated features from the gene expression and CNA data and these two groups showed different overall survival rates (p-value = 0.049) and different ER (Estrogen receptor) statuses (p-value = 0.002, OR (odds ratio) = 0.626). All the risk scores from the gene expression and CNA data and their concatenated one were significantly associated with breast cancer survival. The patients with the high-risk group were significantly associated with patients’ worse outcomes (p-values ≤ 0.0023). The concatenated risk score was enriched by the AMP-activated protein kinase (AMPK) signaling pathway, the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, heart morphogenesis, the regulation of cellular macromolecule biosynthetic process, with FDR (false discovery rate) less than 0.05. We confirmed DAs can effectively extract meaningful genomic features from genomic data and concatenating multiple data sources can improve the significance of the features associated with breast cancer patients’ clinical characteristics and outcomes.

Assisted graphical model for gene expression data analysis

The analysis of gene expression data has been playing a pivotal role in recent biomedical research. For gene expression data, network analysis has been shown to be more informative and powerful than individual-gene and geneset-based analysis. Despite promising successes, with the high dimensionality of gene expression data and often low sample sizes, network construction with gene expression data is still often challenged. In recent studies, a prominent trend is to conduct multidimensional profiling, under which data are collected on gene expressions as well as their regulators (copy number variations, methylation, microRNAs, SNPs, etc). With the regulation relationship, regulators contain information on gene expressions and can potentially assist in estimating their characteristics. In this study, we develop an assisted graphical model (AGM) approach, which can effectively use information in regulators to improve the estimation of gene expression graphical structure. The proposed approach has an intuitive formulation and can adaptively accommodate different regulator scenarios. Its consistency properties are rigorously established. Extensive simulations and the analysis of a breast cancer gene expression data set demonstrate the practical effectiveness of the AGM.

Identifying condition specific key genes from basal-like breast cancer gene expression data

Mining patterns of co-expressed genes across the subset of conditions help to narrow down the search space for the analysis of gene expression data. Identifying conditions specific key genes from the large-scale gene expression data is a challenging task. The conditions specific key gene signifies functional behavior of a group of co-expressed genes across the subset of conditions and can be act as biomarkers of the diseases. In this paper, we have propose a novel approach for identification of conditions specific key genes from Basal-Like Breast Cancer (BLBC) disease using biclustering algorithm and Gene Co-expression Network (GCN). The proposed approach is a two-stage approach. In the first stage, significant biclusters have been extracted with the help of ‘runibic’ biclustering algorithm. The second stage identifies conditions specific key genes from the extracted significant biclusters with the help of GCN. By using difference matrix and gene correlation matrix, we have constructed biologically meaningful and statistically strong GCN. Also, presented the proposed approach with the help of a process diagram and demonstrated the procedure with an example of bicluster number 93 (Bic93). From the experimental results, we observed that 95% and 85% of the extracted biclusters are found to be biologically significant at the p-values less than 0.05 and 0.01 respectively. We have compared proposed approach with the Weighted Gene Co-expression Network Analysis (WGCNA) based approach. From the comparison, our approach has performed effectively and extracted biologically significant biclusters. Also, identified conditions specific key genes which cannot be extracted using the WGCNA based approach. Some of the important identified known key genes are PIK3CA, SHC3, ERBB2, SHC4, PTOV1, STAG1, ZNF215 etc. These key genes can be used as a diagnostic and prognostic biomarker for the BLBC disease after the rigorous analysis. The identified conditions specific key genes can be helpful to reduce the analysis time and increase the accuracy of further research such as biomarker identification, drug target discovery etc.

Optimizing gene set annotations combining GO structure and gene expression data

BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression data.ResultsWe propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of annotations.ConclusionsA novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.

Expression of ID4 protein in breast cancer cells induces reprogramming of tumour-associated macrophages

BackgroundAs crucial regulators of the immune response against pathogens, macrophages have been extensively shown also to be important players in several diseases, including cancer. Specifically, breast cancer macrophages tightly control the angiogenic switch and progression to malignancy. ID4, a member of the ID (inhibitors of differentiation) family of proteins, is associated with a stem-like phenotype and poor prognosis in basal-like breast cancer. Moreover, ID4 favours angiogenesis by enhancing the expression of pro-angiogenic cytokines interleukin-8, CXCL1 and vascular endothelial growth factor. In the present study, we investigated whether ID4 protein exerts its pro-angiogenic function while also modulating the activity of tumour-associated macrophages in breast cancer.MethodsWe performed IHC analysis of ID4 protein and macrophage marker CD68 in a triple-negative breast cancer series. Next, we used cell migration assays to evaluate the effect of ID4 expression modulation in breast cancer cells on the motility of co-cultured macrophages. The analysis of breast cancer gene expression data repositories allowed us to evaluate the ability of ID4 to predict survival in subsets of tumours showing high or low macrophage infiltration. By culturing macrophages in conditioned media obtained from breast cancer cells in which ID4 expression was modulated by overexpression or depletion, we identified changes in the expression of ID4-dependent angiogenesis-related transcripts and microRNAs (miRNAs, miRs) in macrophages by RT-qPCR.ResultsWe determined that ID4 and macrophage marker CD68 protein expression were significantly associated in a series of triple-negative breast tumours. Interestingly, ID4 messenger RNA (mRNA) levels robustly predicted survival, specifically in the subset of tumours showing high macrophage infiltration. In vitro and in vivo migration assays demonstrated that expression of ID4 in breast cancer cells stimulates macrophage motility. At the molecular level, ID4 protein expression in breast cancer cells controls, through paracrine signalling, the activation of an angiogenic programme in macrophages. This programme includes both the increase of angiogenesis-related mRNAs and the decrease of members of the anti-angiogenic miR-15b/107 group. Intriguingly, these miRNAs control the expression of the cytokine granulin, whose enhanced expression in macrophages confers increased angiogenic potential.ConclusionsThese results uncover a key role for ID4 in dictating the behaviour of tumour-associated macrophages in breast cancer.

Gene set analysis of post-lactational mammary gland involution gene signatures in inflammatory and triple-negative breast cancer.

BackgroundEpidemiological studies have found that triple-negative breast cancer (TNBC) and TN inflammatory breast cancer (IBC) are associated with lower frequency and duration of breast-feeding compared to non-TNBC and non-TN IBC, respectively. Limited breast-feeding could reflect abrupt or premature involution and contribute to a “primed” stroma that is permissive to the migration of cancer cells typical of IBC. We hypothesized that gene expression related to abrupt mammary gland involution after forced weaning may be enriched in the tissues of IBC patients and, if so, provide a potential correlation between limited breast-feeding and the development of aggressive breast cancer.MethodsWe utilized the Short Time-series Expression Miner (STEM) program to cluster significant signatures from two independent studies that analyzed gene expression at multiple time-points of mouse mammary gland involution. Using 10 significant signatures, we performed gene ontology analysis and gene set enrichment analysis (GSEA) on training and validation sets from human breast cancer gene expression data to identify specific genes that are enriched in IBC compared to non-IBC and in TN compared to non-TN in IBC and non-IBC groups.ResultsExamining the combined data, we identified 10 involution gene clusters (Inv1-10) that share time-dependent regulation after forced weaning. Inv5 was the only cluster significantly enriched in IBC in the training and validation set (nominal p-values <0.05) and only by unadjusted p-values (FDR q-values 0.26 and 0.46 respectively). Eight genes in Inv5 are upregulated in both the training and validation sets in IBC. Combining the training and validation sets, both Inv5 and Inv6 have nominal p-values <0.05 and q-values 0.39 and 0.20, respectively. The time course for both clusters includes genes that change within 12 hours after forced weaning.ConclusionsResults from this in silico study suggest correlation between molecular events during abrupt involution and aggressive breast cancer. Specifically, candidate genes from Inv5 merit functional investigation regarding the role of limited breast-feeding in IBC development.

Robust boosting with truncated loss functions

Boosting is a powerful machine learning tool with attractive theoretical properties. In recent years, boosting algorithms have been extended to many statistical estimation problems. For data contaminated with outliers, however, development of boosting algorithms is very limited. In this paper, innovative robust boosting algorithms utilizing the majorization-minimization (MM) principle are developed for binary and multi-category classification problems. Based on truncated loss functions, the robust boosting algorithms share a unified framework for linear and nonlinear effects models. The proposed methods can reduce the heavy influence from a small number of outliers which could otherwise distort the results. In addition, adaptive boosting for the truncated loss functions are developed to construct more sparse predictive models. We present convergence guarantees for smooth surrogate loss functions with both iteration-varying and constant step-sizes. We conducted empirical studies using data from simulations, a pediatric database developed for the US Healthcare Cost and Utilization Project, and breast cancer gene expression data. Compared with non-robust boosting, robust boosting improves classification accuracy and variable selection.

Benzodiazepines use and breast cancer risk: A population-based study and gene expression profiling evidence

The aim of this study was to investigate whether long-term use of Benzodiazepines (BZDs) is associated with breast cancer risk through the combination of population-based observational and gene expression profiling evidence. We conducted a population-based case-control study by using 1998 to 2009year Taiwan National Health Insurance Research Database and investigated the association between BZDs use and breast cancer risk. We selected subjects age of >20years old and six eligible controls matched for age, sex and the index date (i.e., free of any cancer at the case diagnosis date) by using propensity scores. A bioinformatics analysis approach was also performed for the identification of oncogenesis effects of BZDs on breast cancer. We used breast cancer gene expression data from the Cancer Genome Atlas and perturbagen signatures of BZDs from the Library of Integrated Cellular Signatures database in order to identify the oncogenesis effects of BZDs on breast cancer. We found evidence of increased breast cancer risk for diazepam (OR, 1.16; 95%CI, 0.95–1.42; connectivity score [CS], 0.3016), zolpidem (OR, 1.11; 95%CI, 0.95–1.30; CS, 0.2738), but not for lorazepam (OR, 1.04; 95%CI, 0.89–1.23; CS, -0.2952) consistently in both methods. The finding for alparazolam was contradictory from the two methods. Diazepam and zolpidem trends showed association, although not statistically significant, with breast cancer risk in both epidemiological and bioinformatics analyses outcomes. The methodological value of our study is in introducing the way of combining epidemiological and bioinformatics approaches in order to answer a common scientific question. Combining the two approaches would be a substantial step towards uncovering, validation and further application of previously unknown scientific knowledge to the emerging field of precision medicine informatics.

Node-based differential network analysis in genomics

Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.

Abstract OT1-01-10: Multi-center phase IB trial of ACY-1215 (Ricolinostat) combined with nab-paclitaxel in unresectable or metastatic breast cancer

Abstract BACKGROUND: HDAC6, a cytoplasmic histone deacetylase, targets non-chromatin substrates including tubulin, HSP90, and cortactin, playing an important role in cell-cell interactions, motility, chaperone function, and protein degradation. ACY-1215 (Ricolinostat) is an orally active, selective HDAC6 inhibitor. Preclinical studies have demonstrated ACY-1215 to have activity in breast cancer, synergistic cytotoxicity with taxanes, possibly due to combined effects on microtubule stabilization, and protective effects against neurodegeneration and potentially peripheral neuropathy. Analysis of human primary breast cancer gene expression data has identified a set of 162 genes corresponding to a HDAC6 master regulator (MR) score that correlates with IC50 or sensitivity to ACY-1215, and may serve as a potential biomarker. TRIAL DESIGN: This is an open-label, multicenter Phase 1b trial (NCT01323751) of ACY-1215 combined with nab-paclitaxel in unresectable or metastatic breast cancer. Patients will receive ACY-1215 daily for 21 days of each 28-day cycle in combination with nab-paclitaxel 100 mg/m2 on days 1, 8, and 15 until progression of disease or unacceptable toxicity. KEY ELIGIBILITY CRITERIA: Patients aged ≥ 18 years with unresectable or metastatic breast adenocarcinoma, measurable or non-measurable disease, an ECOG performance status of 0 to 1, baseline toxicities and symptoms grade ≤1, and at least 2 weeks since prior treatment. STATISTICAL METHODS: Dose escalations will be performed according to the time to event continual reassessment method (TITE-CRM), starting at 120 mg to a maximum dose of 240 mg. The maximum tolerated dose (MTD) will be identified as the dose level associated with a target probability of dose limiting toxicity (DLT) of 0.25. The TITE-CRM will use an empirical dose-toxicity model, with a sample size of 24. The dose-toxicity model is calibrated to select a dose that yields between 17 and 33% DLT, which will be the recommended phase II dose (RP2D). OBJECTIVES: The primary objective of this trial is to determine the MTD of ACY-1215 in combination with weekly nab-paclitaxel. Secondary objectives include the evaluation of safety and tolerability, progression free survival, overall response rate (measured by RECIST 1.1), and clinical benefit rate. Exploratory analyses will assess pharmacokinetics and pharmacodynamics, correlation of biomarkers including the HDAC6 score with response, and the effect of ACY-1215 on taxane-induced peripheral neuropathy. ACCRUAL: Target Accrual is 24 patients. CONTACT INFORMATION: Kevin Kalinsky, MD, MS 212-305-1945 kk2693@cumc.columbia.edu. Citation Format: Onishi M, Chiuzan C, Fasano J, Crew K, Accordino M, Tiersten A, Shapiro C, Jarpe M, Qualye S, Trede N, Wheeler C, Hershman D, Silva J, Maurer M, Kalinksy K. Multi-center phase IB trial of ACY-1215 (Ricolinostat) combined with nab-paclitaxel in unresectable or metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr OT1-01-10.