Stratification Of Breast Cancer Patients Research Articles

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.

Abstract P6-09-17: Evaluation of a risk score based on biologic factors to enhance prognostic stratification by the American Joint Committee on Cancer (AJCC) Staging System

Abstract Background: The 7th edition AJCC staging system provides prognostic information based on the anatomic extent of disease determined by the tumor size (T), lymph node status (N), and presence or absence of metastatic disease (M). Tumor biology, including grade, estrogen receptor (ER) status and HER2 status, which are known to have prognostic and predictive value, are not incorporated. This study was undertaken to evaluate a risk score that takes into account the tumor grade and other biomarkers that can be added to the current anatomic TNM staging system to improve stratification of patients with respect to disease specific survival (DSS) and overall survival (OS). Methods: A prospectively maintained database was used to identify 3,327 patients with non-metastatic invasive breast cancer who underwent surgery as a first intervention from January 2007 through December 2013. Clinicopathologic data were recorded including: age, grade, ER status, HER2 status, and pathologic stage. Pathologic stage was determined according to the 7th edition of the AJCC staging guidelines. ER status was recorded as the percentage of cells staining positive by immunohistochemistry (IHC). Prior to 2010, tumors were classified as ER positive if there was >10% staining. A cut-off of 1% was used for patients treated after 2010, consistent with the change in American Society of Clinical Oncology (ASCO) guidelines. HER2 status was defined as positive if 3+ on immunohistochemistry or gene amplification was shown on fluorescence in situ hybridization. A risk score was calculated by assigning one point for each of the following tumor characteristics: ER-negative status, HER2 negative status and grade 3. Univariate survival analyses according to AJCC stage (I, IIA, IIB, IIIA and IIIC) and risk score (0-3) were performed for DSS and OS using the Kaplan Meier method. Results: Median follow-up time was 5.0 years (range, 0.1 to 8.8). Five year DSS for the entire cohort was 97.9% (95% CI: 97.3%-98.4%). The distribution in risk score in the entire cohort was: risk score 0=81 (2.4%), 1=2289 (68.8%), 2=683 (20.5%), and 3= (8.3%). As shown in the table, for all AJCC stages, the 5-yr DSS and 5-yr OS varied according to risk score (p<.01). StageRisk Scoren5-yr DSS (%)95% CI5-yr OS (%)95% CII (IA and IB)036100 9780.4-99.6 1117399.498.7-99.796.795.4-97.6 227498.896.4-00.694.691.0-06.8 311996.691.1-98.793.887.5-97.0IIA031100 96.879.2-99.5 163499.497.5-99.897.194.7-98.4 223697.593.2-99.194.188.7-97.0 39891.081.8-95.788.278.5-93.8IIB011100 100 130996.992.6-98.894.689.6-97.2 210792.983.6-97.189.380.1-94.4 34091.575.6-97.291.575.6-97.2IIIA03100 100 113498.388.2-99.891.582.6-96.0 25092.277.2-97.590.375.7-96.3 3768.621.3-91.268.621.3-91.2IIIC00 13992.272.1-98.084.463.7-93.9 21680.851.4-93.480.851.4-93.4 31033.36.3-64.633.36.2-64.6 Conclusion: The current study demonstrates that incorporating the risk score with current AJCC staging significantly improves the ability to stratify breast cancer patients with respect to DSS and OS. We recommend that the risk score be incorporated into the forthcoming revision of the AJCC staging system. Citation Format: Mittendorf EA, Vila J, Yi M, Chavez-MacGregor M, Chen RL, Giordano SH, Hunt KK. Evaluation of a risk score based on biologic factors to enhance prognostic stratification by the American Joint Committee on Cancer (AJCC) Staging System [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 P6-09-17.

Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis.

INTRODUCTIONBreast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).MATERIALS AND METHODSWe developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.RESULTSThe comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.CONCLUSIONSThus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.

BackgroundSomatic mutations can be used as potential biomarkers for subtyping and predicting outcomes for cancer patients. However, cancer patients often carry many somatic mutations, which do not always concentrate on specific genomic loci, suggesting that the mutations may affect common pathways or gene interaction networks instead of common genes. The challenge is thus to identify the functional relationships among the mutations using multi-modal data. We developed a novel approach for integrating patient somatic mutation, transcriptome and clinical data to mine underlying functional gene groups that can be used to stratify cancer patients into groups with different clinical outcomes. Specifically, we use distance correlation metric to mine the correlations between expression profiles of mutated genes from different patients.ResultsWith this approach, we were able to cluster patients based on the functional relationships between the affected genes using their expression profiles, and to visualize the results using multi-dimensional scaling. Interestingly, we identified a stable subgroup of breast cancer patients that are highly enriched with ER-negative and triple-negative subtypes, and the somatic mutation genes they harbor were capable of acting as potential biomarkers to predict patient survival in several different breast cancer datasets, especially in ER-negative cohorts which has lacked reliable biomarkers.ConclusionsOur method provides a novel and promising approach for integrating genotyping and gene expression data in patient stratification in complex diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2902-0) contains supplementary material, which is available to authorized users.

Frequent detection of PIK3CA mutations in single circulating tumor cells of patients suffering from HER2-negative metastatic breast cancer

Modern technologies enable detection and characterization of circulating tumor cells (CTC) in peripheral blood samples. Thus, CTC have attracted interest as markers for therapeutic response in breast cancer. First studies have incorporated CTC analyses to guide therapeutic interventions and stratification of breast cancer patients. Aim of this study was to analyze characteristic features of CTC as biomarker for predicting resistance to HER2-targeted therapies. Therefore, CTC from metastatic breast cancer patients with HER2-negative primary tumors screened for the prospective randomized phase III trial DETECT III were explored for their HER2 status and the presence of PIK3CA mutations. Detection and characterization of HER2 expression of CTC were conducted with the CellSearch® system. Fifteen of 179 CTC-positive patients (8.4%) contained ≥1 CTC with strong HER2 expression. Genomic DNA from individual CTC isolated by micromanipulation was propagated by whole genome amplification and analyzed for PIK3CA mutations in exons 9 and 20 by Sanger sequencing. One or more CTC/7.5 mL were detected in 179/290 patients (61.7%). In 109 patients (34.8%), ≥5 CTC/7.5 mL were found. We detected at least one CTC with the mutation p.E542K, p.E545K, p.H1047R, p.H1047L or p.M1043V in 12/33 patients (36.4%). Thirty six of 114 CTC (31.6%) harbored one of these mutations. CTC in individual patients exhibited heterogeneity concerning PIK3CA mutations and HER2 expression. In conclusion, clinically relevant genomic aberrations such as mutations in the hotspot regions of exon 9 and 20 of the PIK3CA gene can be detected in single CTC and might provide insights into mechanisms of resistance to HER2-targeted therapies.

589 - Stratification of breast cancer patients using Tip60 staining paired with evaluation of a designed Tip60 specific histone acetyltransferase inhibitor for targeted treatment

589 - Stratification of breast cancer patients using Tip60 staining paired with evaluation of a designed Tip60 specific histone acetyltransferase inhibitor for targeted treatment

Estrogen Receptor Expression Is Associated with DNA Repair Capacity in Breast Cancer.

Estrogen-receptor-positive (ER+) tumors employ complex signaling that engages in crosstalk with multiple pathways through genomic and non-genomic regulation. A greater understanding of these pathways is important for developing improved biomarkers that can better determine treatment choices, risk of recurrence and cancer progression. Deficiencies in DNA repair capacity (DRC) is a hallmark of breast cancer (BC); therefore, in this work we tested whether ER signaling influences DRC. We analyzed the association between ER positivity (% receptor activation) and DRC in 270 BC patients, then further stratified our analysis by HER2 receptor status. Our results show that among HER2 negative, the likelihood of having low DRC values among ER- women is 1.92 (95% CI: 1.03, 3.57) times the likelihood of having low DRC values among ER+ women, even adjusting for different potential confounders (p<0.05); however, a contrary pattern was observed among HER2 positives women. In conclusion, there is an association between DRC levels and ER status, and this association is modified by HER2 receptor status. Adding a DNA repair capacity test to hormone receptor testing may provide new information on defective DNA repair phenotypes, which could better stratify BC patients who have ER+ tumors. ER+/HER2- tumors are heterogeneous, incompletely defined, and clinically challenging to treat; the addition of a DRC test could better characterize and classify these patients as well as help clinicians select optimal therapies, which could improve outcomes and reduce recurrences.

An Emerging Role for Radiation Oncology in Precision Oncology

Precision medicine is rapidly evolving in the management of cancer, in an effort to deliver the right therapy to the right patient at the right time. The use of novel molecular or genetic signatures in decision making regarding local–regional management is in its infancy. In their recent work published in EBioMedicine, Cheng and colleagues developed an 18-gene classifier capable of stratifying breast cancer patients with 0–3 positive lymph nodes (N0–1) after mastectomy into low and high risk groups to better determine the need for post-mastectomy radiation therapy (PMRT)(Cheng et al., 2016). Through their work, they found that patients, who were defined by the 18-gene classifier as high risk, had significantly reduced rates of 5-year loco-regional recurrence, distant metastasis-free survival, and overall survival, regardless of lymph node status and breast cancer subtype based on estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu status. The importance of this study lies in the fact that the gene expression profile developed by this group to support the decision for PMRT or not, which is in many ways similar to the Oncotype Dx 21-gene panel, which helps support the decision for adjuvant chemotherapy. This example of gene expression profiling is exactly what is necessary to move the field of radiation oncology forward towards precision medicine (PM). PM should not only integrate genomic data, but also relevant clinical, pathologic, and environmental parameters significant to the patient (Servant et al., 2014). The ability to improve outcomes in breast cancer using radiation therapy should be focused on incorporating novel genomic- or biology-based biomarkers, in addition to existing clinical and pathologic features, in the treatment of patients. Radiation therapy is a cornerstone of modern multidisciplinary breast cancer treatment. In the recent past, strategies to improve the outcome of patients undergoing radiation therapy have centered on: 1. Advanced imaging of tumor morphology and function for radiotherapy planning (e.g. functional imaging); 2. Improving the therapeutic ratio through the precision of radiotherapy delivery (e.g. computational algorithms, IMRT, IGRT, SBRT); and 3. Incorporation of novel radiosensitizing and molecular targeting agents with radiation therapy to reduce mechanisms of radiation resistance (e.g. epithelial growth factor receptor inhibitors and RT in head and neck cancer). Despite these achievements, determining the benefit of radiation therapy based on the molecular profile of a tumor remains largely unresolved across the spectrum of cancers. It is critical for radiation oncologists to systematically evaluate tailored treatment strategies for various malignancies based on the individual genetic profile of cancer (Collins and Varmus, 2015). In reporting these results, the authors presented a shifting paradigm in breast radiation oncology, namely using gene expression profiles to identify patients likely to benefit from PMRT in lieu of standard clinical and pathologic features (Cheng et al., 2016). Weighing the potential improvement in overall survival with PMRT against the potential long-term toxicities associated with PMRT, namely potential cardiopulmonary toxicity and secondary malignancies, we are hopeful that in the future, gene expression profiles will help spare low-risk patients unlikely to benefit from PMRT and truly identify those patients at high risk of recurrence that would benefit from PMRT. Prospective validation of this gene expression profile in a larger cohort of patients will be necessary to determine its prognostic and predictive impact in patients undergoing mastectomy. In addition, the cost-effectiveness of gene expression profiles versus that of standardized guidelines for patients who would benefit from PMRT needs to be estimated. Studies involving molecular profiling need to be developed to help usher the field of radiation oncology into the era of precision medicine in the 21st century.

Sense-antisense gene-pairs in breast cancer and associated pathological pathways.

More than 30% of human protein-coding genes form hereditary complex genome architectures composed of sense-antisense (SA) gene pairs (SAGPs) transcribing their RNAs from both strands of a given locus. Such architectures represent important novel components of genome complexity contributing to gene expression deregulation in cancer cells. Therefore, the architectures might be involved in cancer pathways and, in turn, be used for novel drug targets discovery. However, the global roles of SAGPs in cancer pathways has not been studied. Here we investigated SAGPs associated with breast cancer (BC)-related pathways using systems biology, prognostic survival and experimental methods. Gene expression analysis identified 73 BC-relevant SAGPs that are highly correlated in BC. Survival modelling and metadata analysis of the 1161 BC patients allowed us to develop a novel patient prognostic grouping method selecting the 12 survival-significant SAGPs. The qRT-PCR-validated 12-SAGP prognostic signature reproducibly stratified BC patients into low- and high-risk prognostic subgroups. The 1381 SAGP-defined differentially expressed genes common across three studied cohorts were identified. The functional enrichment analysis of these genes revealed the GABPA gene network, including BC-relevant SAGPs, specific gene sets involved in cell cycle, spliceosomal and proteasomal pathways. The co-regulatory function of GABPA in BC cells was supported using siRNA knockdown studies. Thus, we demonstrated SAGPs as the synergistically functional genome architectures interconnected with cancer-related pathways and associated with BC patient clinical outcomes. Taken together, SAGPs represent an important component of genome complexity which can be used to identify novel aspects of coordinated pathological gene networks in cancers.

The Manchester guidelines for contralateral risk-reducing mastectomy.

BackgroundRates of contralateral risk-reducing mastectomy (CRRM) are rising, despite a decreasing global incidence of contralateral breast cancer. Reasons for requesting this procedure are complex, and we have previously shown a variable practice amongst breast and plastic surgeons in England. We propose a protocol, based on a published systematic review, a national UK survey and the Manchester experience of CRRM.MethodsWe reviewed the literature for risk factors for contralateral breast cancer and have devised a 5-step process that includes history taking, calculating contralateral breast cancer risk, cooling off period/counselling, multi-disciplinary assessment and consent. Members of the multi-disciplinary team included the breast surgeon, plastic surgeon and geneticist, who formulated guidelines.ResultsA simple formula to calculate the life-time risk of contralateral breast cancer has been devised. This allows stratification of breast cancer patients into different risk-groups: low, above average, moderate and high risk. Recommendations vary according to different risk groups.ConclusionThese guidelines are a useful tool for clinicians counselling women requesting CRRM. Risk assessment is mandatory in this group of patients, and our formula allows evidence-based recommendations to be made.

Expression of Notch1 Correlates with Breast Cancer Progression and Prognosis

Various studies have evaluated the significance of Notch1 expression in breast cancer, but the results have ever been disputed. By using 21 studies involving 3867 patients, this meta-analysis revealed that the expression of Notch1 was significantly higher in breast cancer than in normal tissues (OR=7.21; 95%CI, 4.7-11.07) and that higher Notch1 expression was associated with transition from ductal carcinoma in situ (DCIS) to invasive cancer (OR=3.75; 95% CI, 1.8-7.78). Higher Notch1 activity was observed in the basal subtype of breast cancer (OR=2.53; 95% CI, 1.18-5.43). Moreover, patients with Notch1 overexpression exhibited significantly worse overall and recurrence-free survival. Our meta-analysis suggests that Notch inhibitors may be useful in blocking the early progression of DCIS and that the outcomes of clinical trials for Notch1-targeting therapeutics could be improved by the molecular stratification of breast cancer patients.

Diagnostic and prognostic microRNAs in the serum of breast cancer patients measured by droplet digital PCR

BackgroundBreast cancer circulating biomarkers include carcinoembryonic antigen and carbohydrate antigen 15–3, which are used for patient follow-up. Since sensitivity and specificity are low, novel and more useful biomarkers are needed. The presence of stable circulating microRNAs (miRNAs) in serum or plasma suggested a promising role for these tiny RNAs as cancer biomarkers. To acquire an absolute concentration of circulating miRNAs and reduce the impact of preanalytical and analytical variables, we used the droplet digital PCR (ddPCR) technique.ResultsWe investigated a panel of five miRNAs in the sera of two independent cohorts of breast cancer patients and disease-free controls. The study showed that miR-148b-3p and miR-652-3p levels were significantly lower in the serum of breast cancer patients than that in controls in both cohorts. For these two miRNAs, the stratification of breast cancer patients versus controls was confirmed by receiver operating characteristic curve analyses. In addition, we showed that higher levels of serum miR-10b-5p were associated with clinicobiological markers of poor prognosis.ConclusionsThe study revealed the usefulness of the ddPCR approach for the quantification of circulating miRNAs. The use of the ddPCR quantitative approach revealed very good agreement between two independent cohorts in terms of comparable absolute miRNA concentrations and consistent trends of dysregulation in breast cancer patients versus controls. Overall, this study supports the use of the quantitative ddPCR approach for monitoring the absolute levels of diagnostic and prognostic tumor-specific circulating miRNAs.Electronic supplementary materialThe online version of this article (doi:10.1186/s40364-015-0037-0) contains supplementary material, which is available to authorized users.

A novel prognostic index to improve patient stratification compared to the Nottingham grading system.

e22170 Background: The Nottingham breast tumor Grading System (NGS), renowned for its reproducibility and ease of use, stratifies breast cancer patients into prognostic subgroups and guides therape...

A novel metric to quantify cell-cycling kinetics and refine the Nottingham Grading System to improve breast cancer patient stratification.

e22149 Background: Ki67 Index (KI) and mitotic index (MI) are proliferation markers with established prognostic value in breast carcinomas. While KI is evaluated immunohistochemically and reported ...

Apoptosis inhibitor TRIAP1 is a novel effector of drug resistance

TP53-regulated inhibitor of apoptosis 1 (TRIAP1) is a novel apoptosis inhibitor that binds HSP70 in the cytoplasm and blocks the formation of the apoptosome and caspase-9 activation. TRIAP1 has been shown to be upregulated in many types of cancers; however, its role remains elusive. We determined the TRIAP1 mRNA levels in a panel of human tissues and found its expression to be ubiquitous. Normal breast, as well as non-tumorigenic breast cells, exhibited lower TRIAP1 mRNA levels than breast cancer cells or their drug-resistant derivatives. TRIAP1 is a small, evolutionarily conserved protein that is 76 amino acids long. We found that yeast cells, in which the TRIAP1 homologue was knocked out, had increased sensitivity to doxorubicin. Equally, RNA interference in breast cancer drug-resistant cells demonstrated that downregulation of TRIAP1 impaired cell growth in the presence of doxorubicin. As expected, caspase-9 activation was diminished after overexpression of TRIAP1 in drug-resistant cells. Importantly, stable transfections of a TRIAP1 expression plasmid in CAL51 cells led to a marked increase in the number of doxorubicin-resistant clones, that was abolished when cells expressed hairpins targeting TRIAP1. In addition, we showed that TRIAP1 expression was also triggered by estrogen deprivation in MCF-7 cells. Although both polyclonal and monoclonal antibodies generated for the present study failed to robustly detect TRIAP1, we demonstrated that TRIAP1 represents a novel marker for drug resistance in breast cancer cells and it may be used in the stratification of breast cancer patients once a suitable antibody has been developed. Equally, these studies open potential drug development strategies for blocking TRIAP1 activity and avoiding drug resistance.

TFF3 is a valuable predictive biomarker of endocrine response in metastatic breast cancer.

The stratification of breast cancer patients for endocrine therapies by oestrogen or progesterone receptor expression is effective but imperfect. The present study aims were to validate microarray studies that demonstrate TFF3 regulation by oestrogen and its association with oestrogen receptors in breast cancer, to evaluate TFF3 as a biomarker of endocrine response, and to investigate TFF3 function. Microarray data were validated by quantitative RT-PCR and northern and western transfer analyses. TFF3 was induced by oestrogen, and its induction was inhibited by antioestrogens, tamoxifen, 4-hydroxytamoxifen and fulvestrant in oestrogen-responsive breast cancer cells. The expression of TFF3 mRNA was associated with oestrogen receptor mRNA in breast tumours (Pearson's coefficient=0.762, P=0.000). Monoclonal antibodies raised against the TFF3 protein detected TFF3 by immunohistochemistry in oesophageal submucosal glands, intestinal goblet and neuroendocrine cells, Barrett's metaplasia and intestinal metaplasia. TFF3 protein expression was associated with oestrogen receptor, progesterone receptor and TFF1 expression in malignant breast cells. TFF3 is a specific and sensitive predictive biomarker of response to endocrine therapy, degree of response and duration of response in unstratified metastatic breast cancer patients (P=0.000, P=0.002 and P=0.002 respectively). Multivariate binary logistic regression analysis demonstrated that TFF3 is an independent biomarker of endocrine response and degree of response, and this was confirmed in a validation cohort. TFF3 stimulated migration and invasion of breast cancer cells. In conclusion, TFF3 expression is associated with response to endocrine therapy, and outperforms oestrogen receptor, progesterone receptor and TFF1 as an independent biomarker, possibly because it mediates the malign effects of oestrogen on invasion and metastasis.

Interaction of CDCP1 with HER2 enhances HER2-driven tumorigenesis and promotes trastuzumab resistance in breast cancer.

Understanding the molecular pathways that contribute to the aggressive behavior of HER2-positive breast cancers may aid in the development of novel therapeutic interventions. Here, we show that CDCP1 and HER2 are frequently co-overexpressed in metastatic breast tumors and associated with poor patient prognosis. HER2 and CDCP1 co-overexpression leads to increased transformation ability, cell migration, and tumor formation in vivo, and enhanced HER2 activation and downstream signaling in different breast cancer cell lines. Mechanistically, we demonstrate that CDCP1 binds to HER2 through its intracellular domain, thereby increasing HER2 interaction with the non-receptor tyrosine kinase c-SRC (SRC), leading to trastuzumab resistance. Taken together, our findings establish that CDCP1 is a modulator of HER2 signaling and a biomarker for the stratification of breast cancer patients with poor prognosis. Our results also provide a rationale for therapeutic targeting of CDCP1 in HER2-positive breast cancer patients.

Glycan-related gene expression signatures in breast cancer subtypes; relation to survival

Alterations in glycan structures are early signs of malignancy and have recently been proposed to be in part a driving force behind malignant transformation. Here, we explore whether differences in expression of genes related to the process of glycosylation exist between breast carcinoma subtypes – and look for their association to clinical parameters.Five expression datasets of 454 invasive breast carcinomas, 31 ductal carcinomas in situ (DCIS), and 79 non-malignant breast tissue samples were analysed. Results were validated in 1960 breast carcinomas. 419 genes encoding glycosylation-related proteins were selected.The DCIS samples appeared expression-wise similar to carcinomas, showing altered gene expression related to glycosaminoglycans (GAGs) and N-glycans when compared to non-malignant samples. In-situ lesions with different aggressiveness potentials demonstrated changes in glycosaminoglycan sulfation and adhesion proteins. Subtype-specific expression patterns revealed down-regulation of genes encoding glycan-binding proteins in the luminal A and B subtypes. Clustering basal-like samples using a consensus list of genes differentially expressed across discovery datasets produced two clusters with significantly differing prognosis in the validation dataset. Finally, our analyses suggest that glycolipids may play an important role in carcinogenesis of breast tumors – as demonstrated by association of B3GNT5 and UGCG genes to patient survival.In conclusion, most glycan-specific changes occur early in the carcinogenic process. We have identified glycan-related alterations specific to breast cancer subtypes including a prognostic signature for two basal-like subgroups. Future research in this area may potentially lead to markers for better prognostication and treatment stratification of breast cancer patients.

Calculation of NPI Score: Prognosis of Breast Cancer

The Nottingham Prognostic Index (NPI) is a widely used prognostic tool for stratification of breast cancer patients. Aim of this study was to compare the Nottingham Prognostic Index (NPI) score with prognosis of breast cancer and clinicopathological prognostic parameters. In the present study, One hundred twenty five breast cancer patients ranging in age from <40 to ≥60 years were evaluated. Breast cancer patients were stratified into good, moderate and poor prognosis. 46.4% frequency of patients was found with moderate prognosis, 36.8%, 16.8% frequency of patients was found in poor and good prognosis group respectively. Majority of the young patients were in poor prognosis group. Breast cancer patients with grade III showed the poor prognosis as compared with grade I and II. Poor prognosis was in positive correlation with tumor size and lymph node status. In conclusion, further combined prognostic index validation strategies are required which should be simple, accurate and have independent value as assessed by multivariate analysis.