Gene Expression Profiling Of Breast Cancer Research Articles

Prognostic value of PLEKHA4 and its correlation with tumor-infiltrating immune cells in breast cancer: a comprehensive study based on bioinformatics and clinical analysis validation.

Pleckstrin homology containing family A, number 4 (PLEKHA4) plays a role in a number of biological processes in human cells, including cell polarization, growth, and proliferation. However, the relationship between PLEKHA4 expression and survival in breast cancer (BC) remains unclear. The aim of this study is to investigate the potential of PLEKHA4 as a prognostic indicator in BC. We obtained gene expression profiles of BC and normal tissues from the Tumor Immune Estimation Resource (TIMER), UALCAN web. Immunohistochemistry (IHC) staining was performed to investigate the protein expression and prognostic value of PLEKHA4 in BC patients. The prognostic value was analyzed using Kaplan-Meier curve analysis and Cox regression analysis in R software after downloading The Cancer Genome Atlas (TCGA) databases. The correlations between PLEKHA4 and tumor immune infiltrates were investigated via gene set variation analysis (GSVA). Signaling pathways related to PLEKHA4 expression were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Both bioinformatics and IHC results showed that PLEKHA4 was expressed at low levels in BC tissues compared with the adjacent tissues. Furthermore, the expression of PLEKHA4 was negatively correlated with ages (χ2=6.394, P=0.01), molecular subtype (χ2=15.606, P=0.001), lymph node metastasis (χ2=13.753, P=0.004), tumor-node-metastasis (TNM) stage (χ2=22.616, P<0.001). Kaplan-Meier curves implicated low expression of PLEKHA4 was associated with worse survival of BC patients [hazard ratio (HR) =0.46, P=0.01]. Cox regression models showed that low PLEKHA4 expression could be an independent risk factor for BC (HR =0.911, P=0.006). The results of gene set enrichment analysis (GSEA) showed that cell cycle, Notch signaling pathway, nuclear factor erythroid 2-related factor 2 (NRF2) signaling pathway, and Rho GTPases were highly enriched in the low PLEKHA4 expression group, as identified by GO and KEGG. Additionally, in BC, PLEKHA4 expression displayed a positive correlation with the infiltration of natural killer (NK) cells (P<0.001), CD8+ T cells (P<0.001), B cells (P<0.001), neutrophils (P<0.001), and dendritic cells (DCs) (P<0.001). The findings indicate that PLEKHA4 is an independent prognostic biomarker associated with key signaling pathways and immune infiltration in BC. Targeting PLEKHA4 may contribute to improving immunotherapy for BC.

Molecular profiling after short-term preoperative endocrine therapy to identify oestrogen receptor positive (ER+)/HER2+ early breast tumours with favourable prognosis.

560 Background: The impeded anti-proliferative response of Estrogen Receptor positive (ER+) HER2+ breast cancer (BC) to endocrine therapy (ET) has been considered negated by treatment with anti-HER2 therapies but residual disease after anti-HER2 therapy remains at risk of recurrence. Studying the molecular changes (MolC) of ER+HER2+ BC in response to ET will provide clinical insights to treatment options. Methods: POETIC was a phase III trial of post-menopausal patients with ER+ BC randomized 2:1 to 2-weeks of peri-operative aromatase inhibitors (POAI) vs control, followed by standard-of-care. Paired pre-treatment (B) and on-treatment (2wk) samples from 313 ER+HER2+ BC (213 POAI/100 controls) were gene expression profiled (BC 360 codeset; NanoString). Association of MolC with Early biological response to AI was assessed by residual Ki672wk (low ≤10%; high &gt;10%) by T-test, multiple testing corrected by Benjamini &amp; Hochberg (FDR); with time to recurrence (TTR) was estimated using multivariable Cox regression models adjusted for post-surgery clinicopathological variables and age as adjuvant treatment surrogate. Results: In POAI tumours, immunity-related signatures and mammary stemness were significantly upregulated in responders while proliferation, DNA-damage repair (DDR), TP53mutational status and ER-signaling were downregulated (FDR&lt;0.05). Controls had exclusive downregulation of PDL1 and upregulation of hypoxia (FDR &lt; 0.05). We previously identified 5 new molecular subgroups based on baseline gene expression (1- Immune high, ESR1 low; 2- ECM, ESR1 low, highest ERBB2; 3- DDR deficiency, 4- Endocrine signalling high and 5- Endocrine and PI3K/MAPK/RAS signalling high) which were associated with different response to AI and differential outcome. Endocrine-signalling, PI3K/MAPK/RAS signalling, tumour-immunity and chemokines were upregulated at a higher magnitude in AI sensitive subgroups (3 and 4) while the molecular subgroup with early resistance to AI and poorer outcome at baseline (2) did not show significant changes. Intrinsic subtype (IS) shifting was significantly more prevalent in treated (37%, 79/213) than in controls (14%, 14/100) (p &lt;0.001). In POAI most Luminal B (LumB) shifted to Luminal A (LumA; 79%, 59/75), driven by a reduction in proliferation. LumA2wk was associated with better outcome compared to LumB2wk (HR 0.2; CI95% 0.06-0.72, p=0.01). IS2wk provides additional information predicting TTR, than ISB (AIC value = 217.2 vs 221.6). Conclusions: We provide a comprehensive picture on how ER+HER2+ BC gene expression profile changes in response to ET. Most LumB BC shift to LumA, being LumA2wk IS significantly associated with better outcome. There is a clinical utility of peri-operative ET for ER+HER2+ BC, guiding who would have good prognosis for adjuvant ET and who may need additional therapy. Clinical trial information: NCT02338310 .

Associations of breast cancer related exposures and gene expression profiles in normal breast tissue-The Norwegian Women and Cancer normal breast tissue study.

Normal breast tissue is utilized in tissue-based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity). We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer-free, post-menopausal women, using Illumina bead chip arrays. Principal component analysis and K-means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays. Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences. Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure-related molecular events in normal breast tissue of cancer-free, post-menopausal women.

Characterization of triple negative breast cancer gene expression profiles in Mexican patients.

Triple negative breast cancer (TNBC) is an aggressive type of cancer that accounts for ~23% of breast tumors in Mexico. In an attempt to understand in an improved way the behavior of TNBC, throughout the years, gene expression in these tumors has been studied. Lehman et al identified 6 subtypes of gene expression in TNBC with distinct characteristics. In the present study, it was aimed to assess clinical, pathological and prognostic characteristics of TNBC in a Mexican-based cohort. A total of 55 patients diagnosed with TNBC at Mexico's National Institute of Cancer (INCan) were included. Tumor needle biopsy samples were obtained and subjected to microarray analysis. Patients were thus classified into one of the 6 TNBC molecular subtypes. The prognostic, clinical and pathological information of patients was obtained, and differences across molecular subtypes were sought. Out of the 55 included patients, the following subtypes were identified: 9 basal-like-1, 11 basal-like-2 (BSL2), 16 immunomodulatory (IM), 12 mesenchymal, 6 androgen receptor-like and 1 mesenchymal stem-like. Mean follow-up time was 47.1 months. The IM molecular subtype had the best overall survival (OS) (median OS was not reached). BSL2 had the worst OS (15 months). A complete pathologic response to neoadjuvant chemotherapy was obtained more often in the IM subtype (P=0.032). No significant associations were found between any of the clinical or pathological characteristics and the TNBC molecular subtypes. The results obtained from the present study should be considered when seeking to implement a clinical-molecular model for TNBC patient care, particularly in Hispanic-based populations, as they have been frequently underrepresented in clinical studies assessing TNBC molecular subtypes.

Microarray analysis of breast cancer gene expression profiling in response to 2-deoxyglucose, metformin, and glucose starvation

BackgroundBreast cancer (BC) is the most frequently diagnosed cancer in women. Altering glucose metabolism and its effects on cancer progression and treatment resistance is an emerging interest in BC research. For instance, combining chemotherapy with glucose-lowering drugs (2-deoxyglucose (2-DG), metformin (MET)) or glucose starvation (GS) has shown better outcomes than with chemotherapy alone. However, the genes and molecular mechanisms that govern the action of these glucose deprivation conditions have not been fully elucidated. Here, we investigated the differentially expressed genes in MCF-7 and MDA-MB-231 BC cell lines upon treatment with glucose-lowering drugs (2-DG, MET) and GS using microarray analysis to study the difference in biological functions between the glucose challenges and their effect on the vulnerability of BC cells.MethodsMDA-MB-231 and MCF-7 cells were treated with 20 mM MET or 4 mM 2-DG for 48 h. GS was performed by gradually decreasing the glucose concentration in the culture medium to 0 g/L, in which the cells remained with fetal bovine serum for one week. Expression profiling was carried out using Affymetrix Human Clariom S microarrays. Differentially expressed genes were obtained from the Transcriptome Analysis Console and enriched using DAVID and R packages.ResultsOur results showed that MDA-MB-231 cells were more responsive to glucose deprivation than MCF-7 cells. Endoplasmic reticulum stress response and cell cycle inhibition were detected after all three glucose deprivations in MDA-MB-231 cells and only under the metformin and GS conditions in MCF-7 cells. Induction of apoptosis and inhibition of DNA replication were observed with all three treatments in MDA-MB-231 cells and metformin-treated MCF-7 cells. Upregulation of cellular response to reactive oxygen species and inhibition of DNA repair mechanisms resulted after metformin and GS administration in MDA-MB-231 cell lines and metformin-treated MCF-7 cells. Autophagy was induced after 2-DG treatment in MDA-MB-231 cells and after metformin in MCF-7 cells. Finally, inhibition of DNA methylation were observed only with GS in MDA-MB-231 cells.ConclusionThe procedure used to process cancer cells and analyze their expression data distinguishes our study from others. GS had the greatest effect on breast cancer cells compared to 2-DG and MET. Combining MET and GS could restrain both cell lines, making them more vulnerable to conventional chemotherapy.

Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer

Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.

Immune Cell Infiltration-Based Characterization of Triple-Negative Breast Cancer Predicts Prognosis and Chemotherapy Response Markers.

Breast cancer represents the number one cause of cancer-associated mortality globally. The most aggressive molecular subtype is triple negative breast cancer (TNBC), of which limited therapeutic options are available. It is well known that breast cancer prognosis and tumor sensitivity toward immunotherapy are dictated by the tumor microenvironment. Breast cancer gene expression profiles were extracted from the METABRIC dataset and two TNBC clusters displaying unique immune features were identified. Activated immune cells formed a large proportion of cells in the high infiltration cluster, which correlated to a good prognosis. Differentially expressed genes (DEGs) extracted between two heterogeneous subtypes were used to further explore the underlying immune mechanism and to identify prognostic biomarkers. Functional enrichment analysis revealed that the DEGs were predominately related to some processes involved in activation and regulation of innate immune signaling. Using network analysis, we identified two modules in which genes were selected for further prognostic investigation. Validation by independent datasets revealed that CXCL9 and CXCL13 were good prognostic biomarkers for TNBC. We also performed comparisons between the above two genes and immune markers (CYT, APM, TILs, and TIS), as well as cell checkpoint marker expressions, and found a statistically significant correlation between them in both METABRIC and TCGA datasets. The potential of CXCL9 and CXCL13 to predict chemotherapy sensitivity was also evaluated. We found that the CXCL9 and CXCL13 were good predictors for chemotherapy and their expressions were higher in chemotherapy-responsive patients in contrast to those who were not responsive. In brief, immune infiltrate characterization on TNBC revealed heterogeneous subtypes with unique immune features allowed for the identification of informative and reliable characteristics representative of the local immune tumor microenvironment and were potential candidates to guide the management of TNBC patients.

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles-The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort.

Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood collection system up to two years before diagnosis. The final study sample included 197 breast cancer cases and 197 age-matched controls. We defined a causal directed acyclic graph to guide a Bayesian data analysis to estimate the risk of metastasis associated with the expression of all genes and with relevant sets of genes. We ranked genes and gene sets according to the sign probability for excess risk. Among the screening detected cancers, 82% were without metastasis, compared to 53% of between-screening detected cancers. Among the highest ranking genes and gene sets associated with metastasis risk, we identified plasmacytiod dentritic cell function, the SLC22 family of transporters, and glutamine metabolism as potential links between the immune system and metastasis. We conclude that there may be potentially wide-reaching differences in blood gene expression profiles between metastatic and non-metastatic breast cancer cases up to two years before diagnosis, which warrants future study.

Prognostic biomarkers related to breast cancer recurrence identified based on Logit model analysis

BackgroundThis study intended to determine important genes related to the prognosis and recurrence of breast cancer.MethodsGene expression data of breast cancer patients were downloaded from TCGA database. Breast cancer samples with recurrence and death were defined as poor disease-free survival (DFS) group, while samples without recurrence and survival beyond 5 years were defined as better DFS group. Another gene expression profile dataset (GSE45725) of breast cancer was downloaded as the validation data. Differentially expressed genes (DEGs) were screened between better and poor DFS groups, which were then performed function enrichment analysis. The DEGs that were enriched in the GO function and KEGG signaling pathway were selected for cox regression analysis and Logit regression (LR) model analysis. Finally, correlation analysis between LR model classification and survival prognosis was analyzed.ResultsBased on the breast cancer gene expression profile data in TCGA, 540 DEGs were screened between better DFS and poor DFS groups, including 177 downregulated and 363 upregulated DEGs. A total of 283 DEGs were involved in all GO functions and KEGG signaling pathways. Through LR model screening, 10 important feature DEGs were identified and validated, among which, ABCA3, CCL22, FOXJ1, IL1RN, KCNIP3, MAP2K6, and MRPL13, were significantly expressed in both groups in the two data sets. ABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 were good prognostic factors, while KCNIP3 and MRPL13 were poor prognostic factors.ConclusionABCA3, CCL22, FOXJ1, IL1RN, and MAP2K6 may serve as good prognostic factors, while KCNIP3 and MRPL13 may be poor prognostic factors for the prognosis of breast cancer.

Abstract 50: Differences in gene-expression profiles in breast cancer between African and European-ancestry women

Abstract Background: African American (AA) women suffer from higher incidence of some forms of breast cancer and higher overall mortality than European American (EA) women. Methods: To investigate the biological basis for the racial difference in breast cancer, we compared gene expression profiles in breast cancer tissues using RNA sequencing data derived from 260 AA and 155 EA women who are participants of the Southern Community Cohort Study (SCCS) and were diagnosed with breast cancer during the cohort follow-up. Genes differentially expressed between AA and EA breast cancer patients were identified using linear regression models thresholded at false discovery rate (FDR) &amp;lt; 0.01. Covariates adjusted in the analysis include age at diagnosis, PAM50 subtypes, TNM stages, probabilistic estimation of expression residuals (PEER) factors, and test batch. Penalized logistic regression via ridge regularization was used to avoid overfitting and overcome the potential problem of multicollinearity by shrinking the model coefficients in building the race-specific gene expression signature. Gene expression and clinical data of 180 AA and 838 EA breast cancer patients from the Cancer Genome Atlas (TCGA) were used for external validation of the gene signature. Penalized Cox regression via elastic net was used to identify a subset of the race-differentiated genes that are associated with breast cancer survival. Results: We identified 59 genes (54 protein-coding genes and 5 long intergenic non-coding RNAs) that were differentially expressed between EA and AA with an FDR adjusted p-value &amp;lt; 0.01. The top three identified genes were IL20A (OMIM 605620), MARCO (OMIM 604870), and BTN3A1 (OMIM 613593), all involved in immune-related pathways. All 59 genes were included in constructing the race-differentiated gene signature. A subset of 30 racial-differentiated genes were found significantly associated with overall breast cancer survival. We externally validated our 59-gene signature by fitting the model to TCGA data and evaluated the prediction performance of the gene signature in terms of both discrimination and calibration. The C-statistic was 0.81, indicating high discriminative ability in distinguishing AA and EA breast cancer patients. The Brier score, which measures disagreement between the observed outcome and a prediction, was 0.064, indicating high reliability and prediction accuracy. Conclusion: These findings provide insights into the biological differences in tumors and the survival disparity between AA and EA breast cancer patients. Citation Format: Jie Ping, Xingyi Guo, Fei Ye, Jirong Long, Loren Lipworth, Qiuyin Cai, William J. Blot, Xiao-ou Shu, Wei Zheng. Differences in gene-expression profiles in breast cancer between African and European-ancestry women [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 50.

Differences in gene-expression profiles in breast cancer between African and European-ancestry women.

Differences in gene-expression profiles in breast cancer between African and European-ancestry women.

Gene expression profiling and clinical relevance unravel the role hypoxia and immune signaling genes and pathways in breast cancer

Hypoxia most often occurs in cancer and the occurrence of hypoxia helps the cells in adapting different responses than the normal such as the activation of of those signaling pathways which regulate proliferation, angiogenesis, and cell death. There are large number of genes which are known to be associated with diverse biological processes and their control and coordination and in different cancers, the hypoxia-response differs. In this study our goal is to understand the impact of alteration in expression of hypoxia and immune systems related genes and its survival in breast cancer and analyzed the hallmarks of molecular signatures. For this purpose we have collected the hypoxia-associated genes based on the literature related with diverse biological processes and functions. For all these genes, we have studied the survival analysis, breast cancer gene expression profiling, and relevant hypoxic genes alterations. Based on our study, we conclude that there are 17 critical pathways and 40 genes from hypoxic gene list appear to play the major roles in case of breast cancer and overall we observe that immune signaling pathways and its components are highly altered in case of breast cancer. Among the top raked hallmarks of molecular signatures are apoptosis, hypoxia, DNA repair, E2F targets, MYC targets, androgen and estrogen response, and TNFa signaling.

Differences in gene-expression profiles in breast cancer between African and European-ancestry women.

African American (AA) women have an excess breast cancer mortality than European American (EA) women. To investigate the contribution of tumor biology to this survival health disparity, we compared gene expression profiles in breast tumors using RNA sequencing data derived from 260 AA and 155 EA women who were prospectively enrolled in the Southern Community Cohort Study (SCCS) and developed breast cancer during follow-up. We identified 59 genes (54 protein-coding genes and 5 long intergenic non-coding RNAs) that were expressed differently between EA and AA at a stringent false discovery rate (FDR) < 0.01. A gene signature was derived with these 59 genes and externally validated using the publicly available Cancer Genome Atlas (TCGA) data from180 AA and 838 EA breast cancer patients. Applying C-statistics, we found that this 59-gene signature has a high discriminative ability in distinguishing AA and EA breast cancer patients in the TCGA dataset (C-index = 0.81). These findings may provide new insight into tumor biological differences and the causes of the survival disparity between AA and EA breast cancer patients.

Abstract P3-08-34: Development of distant recurrence risk model based on hormone receptor-positive breast cancer gene expression profiling in the Chinese population

Abstract Background For early-stage hormone receptor (HR)-positive breast cancer (BC), one of the most clinical challenges is how to identify the patients with good prognosis and avoid unnecessary adjuvant chemotherapy. Several multi-gene expression marker assays were developed to predict distant recurrence. However, most of them were derived from Caucasian patient gene expression landscape, and lack of large-scale validation in Asian cohorts. Accumulating evidence has proved the differences in key molecules and pathways in BC among different populations. In this study, we used RNA-seq analysis on primary breast tumors of 300 patients to identify differentially expressed genes between patients with or without distant metastases, and established a preliminary model to predict recurrence risk. Materials and Methods We reviewed the clinical and follow-up information of 1,602 Chinese patients with axillary node-negative, HR-positive early-stage BC from 2007 to 2012. Patients older than 60 years, and who had a family history of breast or ovarian cancer were excluded. The paraffin-embedded specimens from patients with recurrences and age-matched patients without recurrences were subject to whole transcriptome sequencing. The subjects were then randomly divided into two sets, a training (70%) and a validation (30%) set. Based on the data generated from highly degraded FFPE-extracted RNA, we set up a novel bioinformatics pipeline for gene-expression measurement. A prediction model was developed via lasso regression to further identify genes relating to breast cancer recurrence. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the model. Results Among the total 1,602 cases, 131 relapsed patients were identified, including eight with local recurrences, 92 with distant recurrences, and 31 with uncertain recurrences. Finally, RNA-seq data from 300 patients, including 97 with- (case) and 203 without- recurrences (control), were enrolled in the further analysis. Base on the differentially expressed genes, we developed a preliminary prognosis prediction model, including 117 genes. Patients in the validation set were divided into high-risk and low-risk group by the model. The area under the ROC curve (AUC) was 0.710, and the sensitivity and specificity were 68% and 75%, respectively. Consistent with previous studies, functional annotations and pathway analyses revealed that the majority of the genes (65/117, 55.6%) were components of major pathways involved in tumor metastasis, including cell proliferation (55/117, 47%), invasion (50/117, 42.7%), angiogenesis (25/117, 21.4%) and immune response (33/117, 28.2%). Top enriched specific pathways were Wnt signaling pathway and MAPK signaling pathway, including 29 and 22 genes, respectively. Conclusion Gene expression profiles has proved to be an effective tool to identify patients with high risk of recurrence of breast cancer in previous studies. This is the first model developed based on the data from the Chinese population, which may provide better prediction power for Asian patients. Larger validation and prospective studies are under way to modify and validate this model. Citation Format: Xin Wang, Li Ma, Xiangzhi Meng, Jiaqi Liu, Jingchao Chai, Jingyi Yang, Zhi Yang, Qixi Wu, Yueping Liu, Jianming Ying, Xiang Wang. Development of distant recurrence risk model based on hormone receptor-positive breast cancer gene expression profiling in the Chinese population [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-34.

Characterizing basal-like triple negative breast cancer using gene expression analysis: A data mining approach

Triple-negative breast cancer (TNBC) is characterized by the absence of expression of the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2). Therefore, TNBC is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Basal-like breast cancers (BLBCs) represent a subset of about 70% of TNBCs, more frequently affecting younger patients, being more prevalent in African-American women and significantly more aggressive than tumors of other molecular subtypes, with high rates of proliferation and extremely poor clinical outcomes. Proper classification of BLBCs using current pathological tools has been a major challenge. Although TNBCs have many BLBC characteristics, the relationship between clinically defined TNBC and the gene expression profile of BLBC is not fully examined. The purpose of this study is to process publicly-available TNBC gene expression datasets generated by Affymetrix gene chips and define a set of genes, or gene signature, that can classify TNBC samples between BLBC and Non-BLBC subtypes. We used over 3500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining methods along with dimensionality reduction and feature selection techniques were applied to the resultant dataset to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and to classify them more accurately according to important features extracted through microarray data analysis of BLBC and Non-BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.

Characterizing Basal‐like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach

Triple‐negative breast cancer (TNBC) constitutes approximately 20%–25% of all breast cancer cases with poor prognosis. It is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Recently, gene expression analysis has shown promise to characterize triple negative breast cancers (TNBC). Although TNBCs have many basal‐like breast cancer characteristics, the relationship between clinically defined triple‐negative breast cancer and the gene expression profile of basal‐like breast cancer (BLBC) is not fully examined. The purpose of this study is to assemble publicly‐available TNBC gene expression datasets generated on Affymetrix gene chips and define a set of genes, or gene signature, that can classify BLBC and Non‐BLBC subtypes under TNBC type. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining along with variable reduction and feature selection techniques were applied to the resultant data sets to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and classify them more accurately according to important features extracted from Microarray data analysis of BLBC and Non‐BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Pathologist's health-care value in the triage of Oncotype DX® testing: a value-based pathology study of tumour biology with outcomes.

Pathologists provide expert tissue assessment of breast cancer, yet their value to guide the appropriate use of breast cancer gene expression profile tests (GEPT) is underutilised. The specific aims of this study are to report morpho-immunohistological characteristics of breast tumours with Oncotype DX® (ODx) recurrence scores (RS) of 10 or fewer (ultra-low risk) and 25 or fewer (low risk) in order to determine if pathologists can identify prospectively patient tumours that do not require ODx testing. Oncotype DX® cases with RS < 10 from 2005 to 2010 comprised 441 of 2594 (17%) of clinical cases; this cohort had 5 years' follow-up and was treated with endocrine therapy alone. Tumours were analysed for tumour type, Nottingham grade, mitosis score (MS) semi-quantitative (H-score) hormone receptor content and Magee equation 3. Knowledge derived from this data set was used to develop algorithms in order to identify prospectively tumours with RS of 10 or fewer or 25 or fewer. Thirty-four per cent of tumours were low-grade special types, while the remainder were enriched with high hormone receptor content with MS of 1. These algorithmic selection criteria identified correctly all patient cases below the chemotherapy cut-point, i.e. RS < 25, indicating that these oncotype test orders were an unnecessary cost. This unique study demonstrates that (i) pathologists add great value to triage breast cancer for GEPT; and (ii) can identify prospectively low-grade tumour biology with high sensitivity and high specificity for those cases which do not require chemotherapy (RS < 25) using MS and hormone receptor content.

Contributions of the RhoA guanine nucleotide exchange factor Net1 to polyoma middle T antigen-mediated mammary gland tumorigenesis and metastasis

BackgroundThe RhoA activating protein Net1 contributes to breast cancer cell proliferation, motility, and invasion in vitro, yet little is known about its roles in mammary gland tumorigenesis and metastasis.MethodsNet1 knockout (KO) mice were bred to mice with mammary gland specific expression of the polyoma middle T antigen (PyMT) oncogene. Mammary gland tumorigenesis and lung metastasis were monitored. Individual tumors were assessed for proliferation, apoptosis, angiogenesis, RhoA activation, and activation of PyMT-dependent signaling pathways. Primary tumor cells from wild-type and Net1 KO mice were transplanted into the mammary glands of wild-type, nontumor-bearing mice, and tumor growth and metastasis were assessed. Gene expression in wild-type and Net1 KO tumors was analyzed by gene ontology enrichment and for relative activation of gene expression signatures indicative of signaling pathways important for breast cancer initiation and progression. A gene expression signature indicative of Net1 function was identified. Human breast cancer gene expression profiles were screened for the presence of a Net1 gene expression signature.ResultsWe show that Net1 makes fundamental contributions to mammary gland tumorigenesis and metastasis. Net1 deletion delays tumorigenesis and strongly suppresses metastasis in PyMT-expressing mice. Moreover, we observe that loss of Net1 reduces cancer cell proliferation, inhibits tumor angiogenesis, and promotes tumor cell apoptosis. Net1 is required for maximal RhoA activation within tumors and for primary tumor cell motility. Furthermore, the ability of PyMT to initiate oncogenic signaling to ERK1/2 and PI3K/Akt1 is inhibited by Net1 deletion. Primary tumor cell transplantation indicates that the reduction in tumor angiogenesis and lung metastasis observed upon Net1 deletion are tumor cell autonomous effects. Using a gene expression signature indicative of Net1 activity, we show that Net1 signaling is activated in 10% of human breast cancers, and that this correlates with elevated proliferation and PI3K pathway activity. We also demonstrate that human breast cancer patients with a high Net1 gene expression signature experience shorter distant metastasis-free survival.ConclusionsThese data indicate that Net1 is required for tumor progression in the PyMT mouse model and suggest that Net1 may contribute to breast cancer progression in humans.

Influence of provider factors and race on uptake of breast cancer gene expression profiling.

Gene expression profiling (GEP) has been rapidly adopted for early breast cancer and can aid in chemotherapy decision making. Study results regarding racial disparities in testing are conflicting, and may reflect different care settings. To the authors' knowledge, data regarding the influence of provider factors on testing are scarce. The authors used a statewide, multipayer, insurance claims database linked to cancer registry records to examine the impact of race and provider characteristics on GEP uptake in a cohort of patients newly diagnosed with breast cancer between 2005 and 2012. Incidence proportion models were used to examine the adjusted likelihood of testing. Models were stratified by lymph node status (N0 vs N1). Among 11,958 eligible patients, 23% of black and 26% of non-Hispanic white patients received GEP. Among patients with N0 disease, black individuals were 16% less likely to receive testing after adjustment for clinical factors and the provider's specialty and volume of patients with breast cancer (95% confidence interval, 0.77-0.93). Adjustment for provider characteristics did not attenuate the effect of race on testing. Patients of middle-volume providers were more likely to be tested compared with those with either high-volume or low-volume providers, whereas patients seeing a medical oncologist were more likely to be tested compared with those whose only providers were from surgical specialties. Provider volume and specialty were found to be significant predictors of GEP use, but did not explain racial disparities in testing. Further research concerning the key contributors to lagging test use among black women is needed to optimize the equitable use of GEPs and support personalized treatment decision making for all patients. Cancer 2018;124:1743-51. © 2018 American Cancer Society.

Influence of provider factors and race on uptake of breast cancer gene expression profiling

Influence of provider factors and race on uptake of breast cancer gene expression profiling