Obesity-associated changes in molecular biology of primary breast cancer.

Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients' body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring.

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer.

Background: Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs. Methods: In this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts. Results: We identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups. Conclusion: This is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.

Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls.

BACKGROUND: Metabolomics is a potential means for biofluid-based lung cancer detection. We conducted a non-targeted, data-driven assessment of plasma from early-stage non-small cell lung cancer (ES-NSCLC) cases versus cancer-free controls (CFC) to explore and identify the classes of metabolites for further targeted metabolomics biomarker development. METHODS: Plasma from 250 ES-NSCLC cases and 250 CFCs underwent ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive and negative electrospray ionization (ESI) modes. Molecular feature extraction, formula generation, and find-by-ion tools annotated metabolic entities. Analysis was restricted to endogenous metabolites present in ≥ 80% of samples. Unsupervised hierarchical cluster analysis identified clusters of metabolites. The metabolites with the strongest correlation with the principal component of each cluster were included in logistic regression modeling to assess discriminatory performance with and without adjustment for clinical covariates. RESULTS: A total of 1900 UHPLC-QTOF-MS assessments identified 1667 and 2032 endogenous metabolites in the ESI-positive and ESI-negative modes, respectively. After data filtration, 676 metabolites remained, and 12 clusters of metabolites were identified from each ESI mode. Multivariable logistic regression using the representative metabolite from each cluster revealed effective classification of cases from controls with overall diagnostic accuracy of 91% (ESI positive) and 94% (ESI negative). Metabolites of interest identified for further targeted analysis include the following: 1b, 3a, 12a-trihydroxy-5b-cholanoic acid, pyridoxamine 5'-phosphate, sphinganine 1-phosphate, gamma-CEHC, 20-carboxy-leukotriene B4, isodesmosine, and 18-hydroxycortisol. CONCLUSIONS: Plasma-based metabolomic detection of early-stage NSCLC appears feasible. Further metabolomics studies targeting phospholipid, steroid, and fatty acid metabolism are warranted to further develop noninvasive metabolomics-based detection of early-stage NSCLC.

Nuclear TIGAR mediates an epigenetic and metabolic autoregulatory loop via NRF2 in cancer therapeutic resistance.

Metabolic and epigenetic reprogramming play important roles in cancer therapeutic resistance. However, their interplays are poorly understood. We report here that elevated TIGAR (TP53-induced glycolysis and apoptosis regulator), an antioxidant and glucose metabolic regulator and a target of oncogenic histone methyltransferase NSD2 (nuclear receptor binding SET domain protein 2), is mainly localized in the nucleus of therapeutic resistant tumor cells where it stimulates NSD2 expression and elevates global H3K36me2 mark. Mechanistically, TIGAR directly interacts with the antioxidant master regulator NRF2 and facilitates chromatin recruitment of NRF2, H3K4me3 methylase MLL1 and elongating Pol-II to stimulate the expression of both new (NSD2) and established (NQO1/2, PRDX1 and GSTM4) targets of NRF2, independent of its enzymatic activity. Nuclear TIGAR confers cancer cell resistance to chemotherapy and hormonal therapy in vitro and in tumors through effective maintenance of redox homeostasis. In addition, nuclear accumulation of TIGAR is positively associated with NSD2 expression in clinical tumors and strongly correlated with poor survival. These findings define a nuclear TIGAR-mediated epigenetic autoregulatory loop in redox rebalance for tumor therapeutic resistance.

DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation.

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis-specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors.

Comparative metabolomics studies of blood collected in streck and heparin tubes from lung cancer patients.

Metabolomics analysis of blood from patients (n = 42) undergoing surgery for suspected lung cancer was performed in this study. Venous and arterial blood was collected in both Streck and Heparin tubes. A total of 96 metabolites were detected, affected by sex (n = 56), collection tube (n = 33), and blood location (n = 8). These metabolites belonged to a wide array of compound classes including lipids, acids, pharmaceutical agents, signalling molecules, vitamins, among others. Phospholipids and carboxylic acids accounted for 28% of all detected compounds. Out of the 33 compounds significantly affected by collection tube, 18 compounds were higher in the Streck tubes, including allantoin and ketoleucine, and 15 were higher in the Heparin tubes, including LysoPC(P-16:0), PS 40:6, and chenodeoxycholic acid glycine conjugate. Based on our results, it is recommended that replicate blood samples from each patient should be collected in different types of blood collection tubes for a broader range of the metabolome. Several metabolites were found at higher concentrations in cancer patients such as lactic acid in Squamous Cell Carcinoma, and lysoPCs in Adenocarcinoma and Acinar Cell Carcinoma, which may be used to detect early onset and/or to monitor the progress of the cancer patients.

Computational Prediction of the Pathogenic Status of Cancer-Specific Somatic Variants.

In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of cancer somatic SNVs located in coding and non-coding regions of the genome, respectively. The SVM classifiers achieved the area under the ROC curve of 0.94 and 0.89 regarding the classification of the pathogenic status of coding and non-coding cancer somatic SNVs, respectively. Our models outperform two well-known classification tools including FATHMM-FX and CScape in classifying both coding and non-coding cancer somatic variants. Furthermore, we applied our models to predict the pathogenic status of somatic variants identified in young breast cancer patients from METABRIC and TCGA-BRCA studies. The results indicated that using the classification threshold of 0.8 our "coding" model predicted 1853 positive SNVs (out of 6,910) from the TCGA-BRCA dataset, and 500 positive SNVs (out of 1882) from the METABRIC dataset. Interestingly, through comparative survival analysis of the positive predictions from our models, we identified a young-specific pathogenic somatic variant with potential for the prognosis of early onset of breast cancer in young women.

Paracrine Crosstalk between Fibroblasts and ER+ Breast Cancer Cells Creates an IL1ß-Enriched Niche that Promotes Tumor Growth.

Breast cancer-induced activated fibroblasts support tumor progression. However, the role of normal fibroblasts in tumor progression remains controversial. In this study, we used modified patient-derived organoid cultures and demonstrate that constitutively secreted cytokines from normal breast fibroblasts initiate a paracrine signaling mechanism with estrogen receptor-positive (ER+) breast cancer cells, which results in the creation of an interleukin (IL)-1ß-enriched microenvironment. We found that this paracrine signaling mechanism is shared between normal and activated fibroblasts. Interestingly, we observed that in reconstructed tumor microenvironment containing autologous ER+ breast cancer cells, activated fibroblasts, and immune cells, tamoxifen is more effective in reducing tumor cell proliferation when this paracrine signaling is blocked. Our findings then suggest that ER+ tumor cells could create a growth-promoting environment without activating stromal fibroblasts and that in breast-conserving surgeries, normal fibroblasts could be a significant modulator of tumor recurrence by enhancing the proliferation of residual breast cancer cells in the tumor-adjacent breast tissue.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

Claudin 1 Is Highly Upregulated by PKC in MCF7 Human Breast Cancer Cells and Correlates Positively with PKCε in Patient Biopsies.

Recent studies provide compelling evidence to suggest that the tight junction protein claudin 1, aberrantly expressed in several cancer types, plays an important role in cancer progression. Dysregulation of claudin 1 has been shown to induce epithelial mesenchymal transition (EMT). Furthermore, activation of the ERK signaling pathway by protein kinase C (PKC) was shown to be necessary for EMT induction. Whether PKC is involved in regulating breast cancer progression has not been addressed. The PKC activator 12-O-tetradecanoylphorbol 13-acetate (TPA) was used to investigate the effect of PKC activity on claudin 1 transcription and protein levels, subcellular distribution, and alterations in EMT markers in human breast cancer (HBC) cell lines. As well, tissue microarray analysis (TMA) of a large cohort of invasive HBC biopsies was conducted to investigate correlations between claudin 1 and PKC isomers. TPA upregulated claudin 1 levels in all HBC cell lines analyzed. In particular, a high induction of claudin 1 protein was observed in the MCF7 cell line. TPA treatment also led to an accumulation of claudin 1 in the cytoplasm. Additionally, we demonstrated that the upregulation of claudin 1 was through the ERK signaling pathway. In patient biopsies, we identified a significant positive correlation between claudin 1, PKCα, and PKCε in ER+ tumors. A similar correlation between claudin 1 and PKCε was identified in ER- tumors, and high PKCε was associated with shorter disease-free survival. Collectively, these studies demonstrate that claudin 1 and the ERK signaling pathway are important players in HBC progression.

Long Non-Coding RNA H19 Acts as an Estrogen Receptor Modulator that is Required for Endocrine Therapy Resistance in ER+ Breast Cancer Cells.

BACKGROUND/AIMS: Blocking estrogen signaling with endocrine therapies (Tamoxifen or Fulverstrant) is an effective treatment for Estrogen Receptor-α positive (ER+) breast cancer tumours. Unfortunately, development of endocrine therapy resistance (ETR) is a frequent event resulting in disease relapse and decreased overall patient survival. The long noncoding RNA, H19, was previously shown to play a significant role in estrogen-induced proliferation of both normal and malignant ER+ breast epithelial cells. We hypothesized that H19 expression is also important for the proliferation and survival of ETR cells. METHODS: Here we utilized established ETR cell models; the Tamoxifen (Tam)-resistant LCC2 and the Fulvestrant and Tam cross-resistant LCC9 cells. Gain and loss of H19 function were achieved through lentiviral transduction as well as pharmacological inhibitors of the Notch and c-Met receptor signaling pathways. The effects of altered H19 expression on cell viability and ETR were assessed using three-dimensional (3D) organoid cultures and 2D co-cultures with low passage tumour-associated fbroblasts (TAFs). RESULTS: Here we report that treating ETR cells with Tam or Fulvestrant increases H19 expression and that it's decreased expression overcomes resistance to Tam and Fulvestrant in these cells. Interestingly, H19 expression is regulated by Notch and HGF signaling in the ETR cells and pharmacological inhibitors of Notch and c-MET signaling together significantly reverse resistance to Tam and Fulvestrant in an H19-dependent manner in these cells. Lastly, we demonstrate that H19 regulates ERα expression at the transcript and protein levels in the ETR cells and that H19 protects ERα against Fulvestrant-mediated downregulation of ERα protein. We also observed that blocking Notch and the c-MET receptor signaling also overcomes Fulvestrant and Tam resistance in 3D organoid cultures by decreasing ERα and H19 expression in the ETR cells. CONCLUSION: In endocrine therapy resistant breast cancer cells Fulvestrant is ineffective in decreasing ERα levels. Our data suggest that in the ETR cells, H19 expression acts as an ER modulator and that its levels and subsequently ERα levels can be substantially decreased by blocking Notch and c-MET receptor signaling. Consequently, treating ETR cells with these pharmacological inhibitors helps overcome resistance to Fulvestrant and Tamoxifen.

A robust cell culture system for large scale feeder cell-free expansion of human breast epithelial progenitors.

BACKGROUND: Normal human breast epithelial cells are maintained by the proliferation and differentiation of different human breast epithelial progenitors (HBEPs). However, these progenitor subsets can only be obtained at low frequencies, limiting their further characterization. Recently, it was reported that HBEPs can be minimally expanded in Matrigel cocultures with stromal feeder cells. However, variability of generating healthy feeder cells significantly impacts the effective expansion of HBEPs. METHODS: Here, we report a robust feeder cell-free culture system for large-scale expansion of HBEPs in two-dimensional cultures. RESULTS: Using this cell culture system HBEPs can be exponentially expanded as bulk cultures. Moreover, purified HBEP subtypes can also be separately expanded using our cell culture system. The expanded HBEPs retain their undifferentiated phenotype and form distinct epithelial colonies in colony forming cell assays. CONCLUSIONS: The availability of a culture system enabling the large-scale expansion of HBEPs facilitates their application to screening platforms and other cell-based assays.

Androgen Receptor and Ki67 Expression and Survival Outcomes in Non-small Cell Lung Cancer.

Lung cancer is the most common cause of cancer-related deaths worldwide with non-small cell lung cancer (NSCLC) making up most of these cases. Males have poorer overall survival compared to women following a lung cancer diagnosis. Many studies have focused on the effects of estrogen to explain higher survival rates among women, but few have looked at the effects of androgens. We describe the expression of the androgen receptor (AR) and Ki67 in lung cancer specimens in the Manitoba Tumor Bank (MTB) and correlate these factors with patient outcome. Using the MTB, we performed immunohistochemistry on lung cancer tissue to determine expression of the AR and Ki67. These were then correlated with patient outcome. Of the 136 cases, 55% were female and 55% were adenocarcinoma. AR expression was not independently associated with outcome. Ki67 was associated with a significantly higher hazard ratio for death and recurrence (HR 2.19, 95% CI 1.30-3.70; HR 1.92, 95% CI 1.07-3.46, respectively). AR expression modified the effect of Ki67 on outcome, such that when both were expressed, there was no association with recurrence or survival (HR 2.39, 95% CI 1.31-4.36 for AR- Ki67+ vs HR 1.54, 95% CI 0.44-5.37 for AR+ Ki67+). Ki67 was associated with poorer outcomes alone. AR status alone was not associated with outcome. Although the mechanism remains unclear, AR status seems to negate the association of a high Ki67 and poor outcome.

Genes and functions from breast cancer signatures.

BACKGROUND: Breast cancer is a heterogeneous disease and personalized medicine is the hope for the improvement of the clinical outcome. Multi-gene signatures for breast cancer stratification have been extensively studied in the past decades and more than 30 different signatures have been reported. A major concern is the minimal overlap of genes among the reported signatures. We investigated the breast cancer signature genes to address our hypothesis that the genes of different signature may share common functions, as well as to use these previously reported signature genes to build better prognostic models. METHODS: A total of 33 signatures and the corresponding gene lists were investigated. We first examined the gene frequency and the gene overlap in these signatures. Then the gene functions of each signature gene list were analysed and compared by the KEGG pathways and gene ontology (GO) terms. A classifier built using the common genes was tested using the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) data. The common genes were also tested for building the Yin Yang gene mean expression ratio (YMR) signature using public datasets (GSE1456 and GSE2034). RESULTS: Among a total of 2239 genes collected from the 33 breast cancer signatures, only 238 genes overlapped in at least two signatures; while from a total of 1979 function terms enriched in the 33 signature gene lists, 429 terms were common in at least two signatures. Most of the common function terms were involved in cell cycle processes. While there is almost no common overlapping genes between signatures developed for ER-positive (e.g. 21-gene signature) and those developed for ER-negative (e.g. basal signatures) tumours, they have common function terms such as cell death, regulation of cell proliferation. We used the 62 genes that were common in at least three signatures as a classifier and subtyped 1141 METABRIC cases including 144 normal samples into nine subgroups. These subgroups showed different clinical outcome. Among the 238 common genes, we selected those genes that are more highly expressed in normal breast tissue than in tumours as Yang genes and those more highly expressed in tumours than in normal as Yin genes and built a YMR model signature. This YMR showed significance in risk stratification in two datasets (GSE1456 and GSE2034). CONCLUSIONS: The lack of significant numbers of overlapping genes among most breast cancer signatures can be partially explained by our discovery that these signature genes represent groups with similar functions. The genes collected from these previously reported signatures are valuable resources for new model development. The subtype classifier and YMR signature built from the common genes showed promising results.

Estrogen receptor subtypes dictate the proliferative nature of the mammary gland.

Estrogen induces proliferation of breast epithelial cells and is responsible for breast development at puberty. This tightly regulated control is lost in estrogen-receptor-positive (ER+) breast cancers, which comprise over 70% of all breast cancers. Currently, breast cancer diagnosis and treatment considers only the α isoform of ER; however, there is a second ER, ERß. Whilst ERα mediates estrogen-driven proliferation of the normal breast in puberty and breast cancers, ERß has been shown to exert an anti-proliferative effect on the normal breast. It is not known how the expression of each ER (alone or in combination) correlates with the ability of estrogen to induce proliferation in the breast. We assessed the levels of each ER in normal mouse mammary glands subdivided into proliferative and non-proliferative regions. ERα was most abundant in the proliferative regions of younger mice, with ERß expressed most abundantly in old mice. We correlated this expression profile with function by showing that the ability of estrogen to induce proliferation was reduced in older mice. To show that the ER profile associated with breast cancer risk, we assessed ER expression in parous mice which are known to have a reduced risk of developing ERα breast cancer. ERα expression was significantly decreased yet co-localization analysis revealed ERß expression increased with parity. Parous mice had less unopposed nuclear ERα expression and increased levels of ERß. These changes suggest that the nuclear expression of ERs dictates the proliferative nature of the breast and may explain the decreased breast cancer risk with parity.

Recurrent copy number alterations in young women with breast cancer.

Breast cancer diagnosis in young women has emerged as an independent prognostic factor with higher recurrence risk and death than their older counterparts. We aim to find recurrent somatic copy number alteration (CNA) regions identified from breast cancer microarray data and associate the CNA status of the genes harbored in the regions to the survival of young women with breast cancer. By using the interval graph-based algorithm we developed, and the CNA data consisting of a Discovery set with 130 young women and a Validation set with 125 young women, we identified 38 validated recurrent CNAs containing 39 protein encoding genes. CNA gain regions encompassing genes CAPN2, CDC73 and ASB13 are the top 3 with the highest occurring frequencies in both the Discovery and Validation dataset, while gene SGCZ ranked top for the recurrent CNA loss regions. The mutation status of 9 of the 39 genes shows significant associations with breast cancer specific survival. Interestingly, the expression level of 2 of the 9 genes, ASB13 and SGCZ, shows significant association with survival outcome. Patients with CNA mutations in both of these genes had a worse survival outcome when compared to patients without the gene mutations. The mutated CNA status in gene ASB13 was associated with a higher gene expression, which predicted patient survival outcome. Together, identification of the CNA events with prognostic significance in young women with breast cancer may be used in genomic-guided treatment.

The exploration of contrasting pathways in Triple Negative Breast Cancer (TNBC).

BACKGROUND: Triple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype. METHODS: Gene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model. RESULTS: Among 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR < 0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPARα is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes. CONCLUSION: We first reported that the FOMX1 pathway is the most upregulated and the PPARα pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity.

Breast Cancers Activate Stromal Fibroblast-Induced Suppression of Progenitors in Adjacent Normal Tissue.

Human breast cancer cells are known to activate adjacent "normal-like" cells to enhance their own growth, but the cellular and molecular mechanisms involved are poorly understood. We now show by both phenotypic and functional measurements that normal human mammary progenitor cells are significantly under-represented in the mammary epithelium of patients' tumor-adjacent tissue (TAT). Interestingly, fibroblasts isolated from TAT samples showed a reduced ability to support normal EGF-stimulated mammary progenitor cell proliferation in vitro via their increased secretion of transforming growth factor ß. In contrast, TAT fibroblasts promoted the proliferation of human breast cancer cells when these were co-transplanted in immunodeficient mice. The discovery of a common stromal cell-mediated mechanism that has opposing growth-suppressive and promoting effects on normal and malignant human breast cells and also extends well beyond currently examined surgical margins has important implications for disease recurrence and its prevention.

A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer.

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16-gene signature capable of stratifying breast cancer patients into four risk levels with intention that low-risk patients would not undergo adjuvant systemic therapy, intermediate-low-risk patients will be treated with hormonal therapy only, and intermediate-high- and high-risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16-gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low-risk group (n = 51) of 205 estrogen receptor-positive and node negative (ER+/node-) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15-year disease-specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e-12) in stratifying ER+/node- subgroup than OncotypeDx (HR = 2.7, p = 1.3e-7), MammaPrint (HR = 2.5, p = 5.8e-7), rorS (HR = 2.4, p = 1.4e-6), and NPI (HR = 2.6, p = 1.2e-6). YMR signature may be developed as a clinical tool to select a subgroup of low-risk ER+/node- patients who do not require any adjuvant hormonal therapy (AHT).

Population-based analysis of breast cancer treatment by intrinsic sub-type in Manitoba, Canada.

BACKGROUND: Few descriptive epidemiological studies on the incidence, treatment and survival can accurately reflect a whole population. Manitoba, Canada has an accurate cancer registry, a drug information program network and a breast screening program since 1995. This combined with a stable population provides true population data that can accurately describe the region. METHODS: Using a retrospective cohort design all Breast Cancer cases were obtained from 2004-2010 (N=5399) and grouped by Intrinsic sub-type. Identifiable co-morbidities, prescribed endocrine therapy, staging, surgery, treatment and overall and disease-free survival by intrinsic sub-type were evaluated. RESULTS: Prevalence of Luminal A (41.7%), Luminal B HER2- (15.6%), Luminal B HER2+ (8.9%), Basal-like(10.8%), and HER2+ non-luminal (5.1%) were consistent with other descriptive studies in Canada and Spain. Over this time period the number of lumpectomies increased 1.7% per year (P=0.007). There was a steady increase of 3.4% per year in the use of aromatase inhibitors (P=0.005). Pre-menopausal patients had an increased proportion of HER2+ and Basal-like sub-types. The 7year overall/disease-free survival percentages for Luminal A, Luminal B HER2-, Luminal B HER2+, Basal-like, and HER2+ non-luminal were 88.7%/83.6, 78.2%/73.0, 81.5%/73.3%, 67.7%/63.2%, 70.4%/65.6% respectively. CONCLUSIONS: Reasons for variability in the prevalence of intrinsic sub-type by region is not fully understood. Manitoba is unique due the stability of the population, completeness of the registry and length of breast cancer screening program. Few true population-based studies grouped by intrinsic sub-type are available. IMPACT: Descriptive epidemiological studies guide future research by identifying factors that can affect treatment, recurrence, and survival.