Conservation of Breast Cancer Molecular Subtypes and Transcriptional Patterns of Tumor Progression Across Distinct Ethnic Populations

Clustering of HR + /HER2- breast cancer in an Asian cohort is driven by immune phenotypes

Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2- breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2- breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2- samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Advanced breast cancer diagnosis: Multiplex RT-qPCR for precise typing and angiogenesis profiling

Breast cancer (BC) remains the foremost cause of cancer-related mortality, with an estimated 2.3 million new cases anticipated globally. The timely diagnosis of BC is pivotal for effective treatment. Currently, BC diagnosis predominantly relies on Immunohistochemistry (IHC), a method known for its sluggishness, expense, and dependence on proficient pathologists for confident cancer typing. In this study, we introduce a novel approach to enhance the accuracy, speed, and cost-effectiveness of BC diagnosis. We employ multiplex Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) with touch-down methods, which consistently yield significantly lower Cycle Threshold (CT) values. The study evaluates gene expression profiles of HER2, PGR, ESR, and Ki67 genes across 61 samples representing four BC subtypes, using RPL13A as the endogenous control gene. The results demonstrate that our method offers remarkable precision, nearly equivalent to IHC, in detecting gene expressions vital for BC diagnosis and subtyping. Moreover, we explore the gene expression of Hif1A, ANG, and VEGFR genes involved in angiogenesis, shedding light on the metastatic potential of the tested BC tumours. Notably, numerous samples exhibit elevated levels of Hif1A and VEGFR, indicating their potential as valuable biomarkers for assessing metastatic status. Collectively, our RT-qPCR methodology emerges as a powerful diagnostic tool for swiftly identifying BC subtypes and can be complemented with other essential tumorigenic biomarker assessments, such as angiogenesis, to further refine cancer characterisation and inform personalised therapeutic strategies for BC patients. This innovation holds the promise of revolutionising BC diagnosis and treatment, offering expedited and reliable insights for improved patient care.

Prognostic Impact of HER2-Low and HER2-Zero in Resectable Breast Cancer with Different Hormone Receptor Status: A Landmark Analysis of Real-World Data from the National Cancer Center of China

BACKGROUND

The prognostic impact of HER2-low on overall survival (OS) and disease-free survival (DFS) in patients with resectable breast cancer (BC) remains controversial, partly resulting from the hormone receptor (HR) status.

OBJECTIVE

To investigate the prognostic impact of HER2-low in different HR subgroups.

PATIENTS AND METHODS

We retrospectively retrieved medical records of treatment-naive primary HER2-low and HER2-zero BC patients who were diagnosed with invasive ductal carcinoma and underwent surgery in the Cancer Hospital of the Chinese Academy of Medical Sciences from January 2009 to September 2017 (n = 7371). We compared the clinicopathologic features and performed Cox regression and landmark survival analyses to explore the prognostic impact of HER2-low on survival outcomes during distinct post-surgery intervals-36 months, 60 months, and 120 months.

RESULTS

HER2-low BC, compared to HER2-zero BC, exhibited less aggressive clinicopathologic features, such as smaller invasion size, lower grade, increased nerve invasion, higher HR positivity, and a higher proportion of low-Ki67 cases. In the HR-positive subgroup, HER2-low demonstrated improved OS (p = 0.046) and DFS (p = 0.026) within 60 months. Conversely, HER2-low displayed worse DFS (p = 0.046) in the HR-negative subgroup after 36 months from surgery. The findings remained robust in uni- and multi-variable Cox models.

CONCLUSIONS

HER2-low BCs manifested less aggressive clinicopathologic features than the HER2-zero cases. The prognostic impact of HER2-low in resectable BCs exhibits variability contingent upon the patients' HR status.

Clustering Molecular Subtypes in Breast Cancer, Immunohistochemical Parameters and Risk of Axillary Nodal Involvement

(1) Background: To establish similarities in the risk of axillary lymph node metastasis between different groups of women with breast cancer according to immunohistochemical (IHC) parameters. (2) Methods: Data was collected retrospectively, from 2000 to 2013, of 1058 node-positive breast tumours. All patients were divided according to the St Gallen 2013 criteria and IHC features. The proportion of axillary involvement (pN > pN0; pN > pN1mi; pN > pN1) was calculated for each group. Similarities in axillary nodal dissemination were explored by cluster analysis and association between IHC and risk of axillary disease was studied with multivariate analysis. (3) Results: Among clinico-pathological surrogates of intrinsic subtypes, axillary involvement was more frequent in Luminal-B like HER2 negative (45.8%) and less frequent in Luminal-B HER2 positive (33.8%; p = 0.044). Axillary macroscopic involvement was more frequent in Luminal-B like HER2 negative (37.9%) and HER2 positive (37.8%) and less frequent in Luminal-B HER2 positive (25.5%) and Luminal-A like (25.6%; p = 0.002). Axillary involvement ≥pN2 was significantly less frequent in Luminal-A like (7.4%; p < 0.001). Luminal-A with Luminal-B HER2 positive, and triple-negative with Erb-B2 overexpressing tumours were clustered together regarding any axillary involvement, macroscopic disease or ≥pN2. Among the defined subgroups, axillary metastases were more frequent when Ki67 was higher. In a multivariate analysis, Ki67>14% were associated with a risk of axillary metastases (HR: 1.31; 95% CI, 1.51−6.80; p < 0.037). (4) Conclusions: there are two lymphatic drainage pathways of the breast according to the expression of hormone receptor-related genes. Positive-ER tumors are associated with lower axillary involvement and negative-ER tumors and Ki67 > 14% with higher nodal involvement.

The impact of Angiopoietin-2 genetic polymorphisms on susceptibility for malignant breast neoplasms

Breast cancer causes morbidity and mortality among women worldwide, despite much research illuminating the genetic basis of this disease. Anti-angiogenesis therapies have been widely studied, although the association between angiopoietin-2 (ANGPT2) single nucleotide polymorphisms (SNPs) and breast cancer subtypes remains unclear. This case-control study included 464 patients with malignant breast neoplasms and 539 cancer-free females. We explored the effects of ANGPT2 SNPs on the susceptibility for a malignant breast neoplasm in a Chinese Han population. Five ANGPT2 SNPs (rs2442598, rs734701, rs1823375, 11,137,037, and rs12674822) were analyzed using TaqMan SNP genotyping. Carriers of the variant GG allele of rs1823375 were less likely than wild-type carriers to be diagnosed with clinically staged breast cancer, while females with human epidermal growth factor receptor 2 (HER2)-enriched disease carrying the CG or the CG+GG genotype at rs1823375 were significantly less likely than CC genotype carriers to be of lymph node status N1-N3. We also found that the T-T-C-A-T ANGPT2 haplotype significantly increased the risk for developing a malignant breast neoplasm by 1.385-fold (95% CI: 1.025-1.871; p < 0.05). Our study is the first to document a correlation between ANGPT2 polymorphisms and the development and progression of a malignant breast neoplasm in females of Chinese Han ethnicity.

Carnitine palmitoyl transferase 1A is a novel diagnostic and predictive biomarker for breast cancer

BACKGROUND

Carnitine palmitoyl transferase 1A (CPT1A), the key regulator of fatty acid oxidation, contributes to tumor metastasis and therapeutic resistance. We aimed to identify its clinical significance as a biomarker for the diagnosis and prediction of breast cancer.

METHODS

Western blot, ELISA and in silico analysis were used to confirm CPT1A levels in breast cancer cell lines, cell culture medium and breast cancer tissues. Four hundred thirty breast cancer patients, 200 patients with benign breast disease, and 400 healthy controls were enrolled and randomly divided into a training set and a test set with a 7:3 ratio. Training set was used to build diagnostic models and 10-fold cross validation was used to demonstrate the performance of the models. Then test set was aimed to validate the effectiveness of the diagnostic models. ELISA was conducted to detect individual serum CPT1A levels. Receiver operating characteristic (ROC) curves were generated, and binary logistic regression analyses were performed to evaluate the effectiveness of CPT1A as a biomarker in breast cancer diagnosis. CPT1A levels between post-operative and pre-operative samples were also compared.

RESULTS

CPT1A was overexpressed in breast cancer tissues, cell lines and cell culture medium. Serum CPT1A levels were higher in breast cancer patients than in controls and were significantly associated with metastasis, TNM stage, histological grading and molecular subtype. CPT1A levels were decreased in post-operative samples compared with paired pre-operative samples. Moreover, CPT1A exhibited a higher efficacy in differentiating breast cancer patients from healthy controls (training set: area under the curve, AUC, 0.892, 95% CI, 0.872-0.920; test set, AUC, 0.904, 95% CI, 0.869-0.939) than did CA15-3, CEA, or CA125.

CONCLUSION

CPT1A is overexpressed in breast cancer and can be secreted out of breast cancer cell. Serum CPT1A is positively associated with breast cancer progression and could serve as an indicator for disease monitoring. Serum CPT1A displayed a remarkably high diagnostic efficiency for breast cancer and could be a novel biomarker for the diagnosis of breast cancer.

Up-regulation of L Antigen Family Member 3 Associates With Aggressive Progression of Breast Cancer

The role of L Antigen Family Member 3 (LAGE3) in breast cancer (BC) has not been sufficiently studied. In this study, we explored the clinical value and biological functions of LAGE3 in BC. Comprehensive analysis of LAGE3 was carried out on The Cancer Genome Atlas, Molecular Taxonomy of Breast Cancer International Consortium and Gene Expression Omnibus datasets. Results showed that LAGE3 expression was higher in BC tissues than in normal breast tissues of public datasets and our local cohort. Moreover, its expression was higher in BC patients with larger tumor size, significant lymph node metastasis, higher tumor grade, and more advanced disease stage. High expression of LAGE3 was correlated with poor prognosis, and LAGE3 could independently predict survival of BC patients. Functional enrichment analysis revealed a correlation between LAGE3 expression and biochemical metabolism and immune-related terms and cancer-related pathways. Analysis of tumor microenvironment indicated that LAGE3 expression was associated with the immune cell infiltration and anti-cancer immunity cycle. LAGE3 expression was higher in triple-negative breast cancer (TNBC) compared to hormone receptor-positive BC, but not HER2-positive subtype. Suppression of LAGE3 expression inhibited the proliferation and induced apoptosis of TNBC cell lines. Besides, the down-regulation of LAGE3 attenuated the migration and invasion but reduced the expression level of epithelial-mesenchymal-transition related proteins in TNBC cell lines. In conclusion, this study demonstrated for the first time that LAGE3 promotes the progression of BC. Therefore, it may be a potential diagnostic and prognostic biomarker, as well as a treatment target for BC.

Concordance of Immunohistochemistry-Based and Gene Expression-Based Subtyping in Breast Cancer

Background

Use of immunohistochemistry-based surrogates of molecular breast cancer subtypes is common in research and clinical practice, but information on their comparative validity and prognostic capacity is scarce.

Methods

Data from 2 PAM50-subtyped Swedish breast cancer cohorts were used: Stockholm tamoxifen trial–3 with 561 patients diagnosed 1976-1990 and Clinseq with 237 patients diagnosed 2005-2012. We evaluated 3 surrogate classifications; the immunohistochemistry-3 surrogate classifier based on estrogen receptor, progesterone receptor, and HER2 and the St. Gallen and Prolif surrogate classifiers also including Ki-67. Accuracy, kappa, sensitivity, and specificity were computed as compared with PAM50. Alluvial diagrams of misclassification patterns were plotted. Distant recurrence-free survival was assessed using Kaplan-Meier plots, and tamoxifen treatment benefit for luminal subtypes was modeled using flexible parametric survival models.

Results

The concordance with PAM50 ranged from poor to moderate (kappa = 0.36-0.57, accuracy = 0.54-0.75), with best performance for the Prolif surrogate classification in both cohorts. Good concordance was only achieved when luminal subgroups were collapsed (kappa = 0.71-0.69, accuracy = 0.90-0.91). The St. Gallen surrogate classification misclassified luminal A into luminal B; the reverse pattern was seen with the others. In distant recurrence-free survival, surrogates were more similar to each other than PAM50. The difference in tamoxifen treatment benefit between luminal A and B for PAM50 was not replicated with any surrogate classifier.

Conclusions

All surrogate classifiers had limited ability to distinguish between PAM50 luminal A and B, but patterns of misclassifications differed. PAM50 subtyping appeared to yield larger separation of survival between luminal subtypes than any of the surrogate classifications.

Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information

BACKGROUND

A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs.

RESULTS

We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks.

CONCLUSION

Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.

Mutational signatures and mutable motifs in cancer genomes

Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis

As the most commonly occurring cancer in women worldwide, breast cancer poses a formidable public health challenge on a global scale. Breast cancer consists of a group of biologically and molecularly heterogeneous diseases originated from the breast. While the risk factors associated with this cancer varies with respect to other cancers, genetic predisposition, most notably mutations in BRCA1 or BRCA2 gene, is an important causative factor for this malignancy. Breast cancers can begin in different areas of the breast, such as the ducts, the lobules, or the tissue in between. Within the large group of diverse breast carcinomas, there are various denoted types of breast cancer based on their invasiveness relative to the primary tumor sites. It is important to distinguish between the various subtypes because they have different prognoses and treatment implications. As there are remarkable parallels between normal development and breast cancer progression at the molecular level, it has been postulated that breast cancer may be derived from mammary cancer stem cells. Normal breast development and mammary stem cells are regulated by several signaling pathways, such as estrogen receptors (ERs), HER2, and Wnt/β-catenin signaling pathways, which control stem cell proliferation, cell death, cell differentiation, and cell motility. Furthermore, emerging evidence indicates that epigenetic regulations and noncoding RNAs may play important roles in breast cancer development and may contribute to the heterogeneity and metastatic aspects of breast cancer, especially for triple-negative breast cancer. This review provides a comprehensive survey of the molecular, cellular and genetic aspects of breast cancer.

Breast cancer complexity: implications of intratumoral heterogeneity in clinical management

Generation of intratumoral phenotypic and genetic heterogeneity has been attributed to clonal evolution and cancer stem cells that together give rise to a tumor with complex ecosystems. Each ecosystem contains various tumor cell subpopulations and stromal entities, which, depending upon their composition, can influence survival, therapy responses, and global growth of the tumor. Despite recent advances in breast cancer management, the disease has not been completely eradicated as tumors recur despite initial response to treatment. In this review, using data from clinically relevant breast cancer models, we show that the fates of tumor stem cells/progenitor cells in the individual tumor ecosystems comprising a tumor are predetermined to follow a limited (unipotent) and/or unlimited (multipotent) path of differentiation which create conditions for active generation and maintenance of heterogeneity. The resultant dynamic systems respond differently to treatments, thus disrupting the delicate stability maintained in the heterogeneous tumor. This raises the question whether it is better then to preserve stability by preventing takeover by otherwise dormant ecosystems in the tumor following therapy. The ultimate strategy for personalized therapy would require serial assessments of the patient's tumor for biomarker validation during the entire course of treatment that is combined with their three-dimensional mapping to the tumor architecture and landscape.

Interplay Between Genetic and Epigenetic Changes in Breast Cancer Subtypes

Breast cancer is the most common cancer among women and represents one of the top five leading causes of cancer-related mortality. Inherited and acquired genetic mutations as well as epigenetic aberrations are known to be important contributors to the development and progression of breast cancer. Recent developments in high-throughput technologies have increased our understanding of the molecular changes in breast cancer, leading to the identification of distinctive genetic and epigenetic modifications in different breast cancer molecular subtypes. These genetic and epigenetic changes in luminal A, luminal B, ERBB2/HER2-enriched, basal-like, and normal-like breast cancer subtypes are discussed in this chapter. Furthermore, recent epigenome studies provided more information about further stratification of breast cancer subtypes, with essential role in the appropriate diagnosis and treatment of breast cancer. Thus, the inclusion of both genetic and epigenetic information in breast cancer clinical care could provide critical scientific base for precision medicine in breast cancer.

Breast Cancer in Latinas: A Focus on Intrinsic Subtypes Distribution

Breast cancer is the most frequent cancer in women worldwide. It is classified into intrinsic subtypes characterized by different molecular profiles and prognosis. The prevalence of the different intrinsic subtypes varies between population groups. IHC surrogates based on the expression of the estrogen receptor, progesterone receptor, and HER2 have been widely used to study the distribution of intrinsic subtypes in non-Hispanic whites and African Americans, but data are limited for Hispanic/Latina women. Similarly, most studies analyzing gene expression profiles only include women of European descent. This review focuses on studies that describe the distribution of breast cancer subtypes in Hispanic/Latina women and highlights the need for more research in this population. Cancer Epidemiol Biomarkers Prev; 27(1); 3-10. ©2017 AACR.

Molecular mechanisms of breast cancer metastasis by gene expression profile analysis

Metastasis is the main cause of breast cancer‑related mortalities. The present study aimed to uncover the relevant molecular mechanisms of breast cancer metastasis and to explore potential biomarkers that may be used for prognosis. Expression profile microarray data GSE8977, which contained 22 stroma samples (15 were from normal breast and 7 were from invasive ductal carcinoma tumor samples), were obtained from the Gene Expression Omnibus database. Following data preprocessing, differentially expressed genes (DEGs) were selected based on analyses conducted using the linear models for microarray analysis package from R and Bioconductor software. The resulting data were used in subsequent function and pathway enrichment analyses, as well as protein‑protein interaction (PPI) network and subnetwork analyses. Transcription factors (TFs) and tumor‑associated genes were also identified among the DEGs. A total of 234 DEGs were identified, which were enriched in immune response, cell differentiation and cell adhesion‑related functions and pathways. Downregulated DEGs included TFs, such as the proto‑oncogene SPI1, pre‑B‑cell leukemia homeobox 3 (PBX3) and lymphoid enhancer‑binding factor 1 (LEF1), as well as tumor suppressors (TSs), such as capping actin protein, gelsolin like (CAPG) and tumor protein p53‑inducible nuclear protein 1 (TP53INP1). Upregulated DEGs also included TFs and tumor suppressors, consisting of transcription factor 7‑like 2 (TCF7L2) and pleiomorphic adenoma gene‑like 1 (PLAGL1). DEGs that were identified at the hub nodes in the PPI network and the subnetwork were epidermal growth factor receptor (EGFR) and spleen‑associated tyrosine kinase (SYK), respectively. Several genes crucial in the metastasis of breast cancer were identified, which may serve as potential biomarkers, many of which were associated with cell adhesion, proliferation or immune response, and may influence breast cancer metastasis by regulating these function or pathways.

Triple negative invasive lobular carcinoma of the breast presents as small bowel obstruction

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Metastatic breast carcinoma rarely spreads to the Gastrointestinal Tract (GIT).

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GIT breast carcinoma rarely has triple: ER, PR and HER2-neu negative histology.

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We report a case of triple negative breast carcinoma that spread to the jejunum.

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This is the first reported case in the U.S.

Metastasis from breast carcinoma to the gastrointestinal tract (GIT) is very uncommon. To date, only a few cases have been described worldwide. Of those which do metastasize to the GIT, only estrogen receptor (ER), progesterone receptor (PR) and HER2-neu receptor positive cancers have been reported and none have been mentioned in the U.S. We report a case of a 70-year-old white female with history of triple negative lobular carcinoma eight years earlier who presented with solitary jejunal mass causing obstruction.

AMOTL1 Promotes Breast Cancer Progression and Is Antagonized by Merlin

The Hippo signaling network is a key regulator of cell fate. In the recent years, it was shown that its implication in cancer goes well beyond the sole role of YAP transcriptional activity and its regulation by the canonical MST/LATS kinase cascade. Here we show that the motin family member AMOTL1 is an important effector of Hippo signaling in breast cancer. AMOTL1 connects Hippo signaling to tumor cell aggressiveness. We show that both canonical and noncanonical Hippo signaling modulates AMOTL1 levels. The tumor suppressor Merlin triggers AMOTL1 proteasomal degradation mediated by the NEDD family of ubiquitin ligases through direct interaction. In parallel, YAP stimulates AMOTL1 expression. The loss of Merlin expression and the induction of Yap activity that are frequently observed in breast cancers thus result in elevated AMOTL1 levels. AMOTL1 expression is sufficient to trigger tumor cell migration and stimulates proliferation by activating c-Src. In a large cohort of human breast tumors, we show that AMOTL1 protein levels are upregulated during cancer progression and that, importantly, the expression of AMOTL1 in lymph node metastasis appears predictive of the risk of relapse. Hence we uncover an important mechanism by which Hippo signaling promotes breast cancer progression by modulating the expression of AMOTL1.

Oncogenic potential of TSTA3 in breast cancer and its regulation by the tumor suppressors miR-125a-5p and miR-125b

TSTA3 participates in enzyme metabolism and affects glycosylation processes, and abnormal glycosylation influences the malignant transformation of cells and tumor development. However, studies have not examined the molecular biological function of TSTA3 in breast cancer (BC). The expression of TSTA3 was examined in BC tissues and cell lines. Kaplan-Meier survival tests and Cox regression were used to analyze prognosis. TSTA3 depletion was used to analyze cell function. The upstream miRNAs of TSTA3 were predicted, and the downstream target gene was analyzed using a RT2 Profiler™ PCR array. Our results show that TSTA3 was highly expressed in BC tissues and cells and was correlated with poor survival. The expression of TSTA3 was correlated with the TNM status (P < 0.01) and served as an independent prognostic factor (P = 0.041). TSTA3-siRNA decreased cell invasion and proliferation in vitro. miR-125a-5p and miR-125b are upstream targets of TSTA3, and a PCR array revealed that TSTA3 affects the CXCR4-CXCL12 genes. The findings suggest that miR-125a-5p/miR-125b suppress the expression of TSTA3, which controls cell proliferation and invasion by regulating CXCR4 expression. In conclusion, a high expression of TSTA3 exerts a proto-oncogenic effect during carcinogenesis and serves as an independent molecular marker for BC patients.

Prognostic Relevance of Circulating Tumor Cells in Molecular Subtypes of Breast Cancer

Circulating tumor cells (CTCs) can be detected in the peripheral blood of breast cancer patients with early and metastatic disease. Recent data suggest that immune pathologic characteristics between the primary tumor, metastatic colonies and CTCs are discordant and that CTCs possess an independent phenotype that is associated with prognosis and treatment efficacy. Large scale gene expression analysis has provided the possibility to stratify breast cancer according to the gene expression fingerprint of primary tumor tissue into five intrinsic molecular subtypes which can be associated with different clinical outcome. As a consequence of the different prognostic power of primary tumors' characteristics and CTCs several groups have started to investigate if CTCs might be disseminated differentially within these breast cancer subtypes. They determined the CTC number in immunohistochemical subtypes to validate if CTCs may provide differential and more specific prognostic information within each subtype. This review provides an overview of the outcome of some recently published data gathered from early and metastatic breast cancer.

Molecular portrait of breast cancer in China reveals comprehensive transcriptomic likeness to Caucasian breast cancer and low prevalence of luminal A subtype

The recent dramatic increase in breast cancer incidence across China with progressive urbanization and economic development has signaled the urgent need for molecular and clinical detailing of breast cancer in the Chinese population. Our analyses of a unique transethnic collection of breast cancer frozen specimens from Shanghai Fudan Cancer Center (Chinese Han) profiled simultaneously with an analogous Caucasian Italian series revealed consistent transcriptomic data lacking in batch effects. The prevalence of Luminal A subtype was significantly lower in Chinese series, impacting the overall prevalence of estrogen receptor (ER)-positive disease in a large cohort of Chinese/Caucasian patients. Unsupervised and supervised comparison of gene and microRNA (miRNA) profiles of Chinese and Caucasian samples revealed extensive similarity in the comprehensive taxonomy of transcriptional elements regulating breast cancer biology. Partition of gene expression data using gene lists relevant to breast cancer as "intrinsic" and "extracellular matrix" genes identified Chinese and Caucasian subgroups with equivalent global gene and miRNA profiles. These findings indicate that in the Chinese and Caucasian groups, breast neoplasia and the surrounding stromal characteristics undergo the same differentiation and molecular processes. Transcriptional similarity across transethnic cohorts may simplify translational medicine approaches and clinical management of breast cancer patients worldwide.

Overcome support vector machine diagnosis overfitting

Support vector machines (SVMs) are widely employed in molecular diagnosis of disease for their efficiency and robustness. However, there is no previous research to analyze their overfitting in high-dimensional omics data based disease diagnosis, which is essential to avoid deceptive diagnostic results and enhance clinical decision making. In this work, we comprehensively investigate this problem from both theoretical and practical standpoints to unveil the special characteristics of SVM overfitting. We found that disease diagnosis under an SVM classifier would inevitably encounter overfitting under a Gaussian kernel because of the large data variations generated from high-throughput profiling technologies. Furthermore, we propose a novel sparse-coding kernel approach to overcome SVM overfitting in disease diagnosis. Unlike traditional ad-hoc parametric tuning approaches, it not only robustly conquers the overfitting problem, but also achieves good diagnostic accuracy. To our knowledge, it is the first rigorous method proposed to overcome SVM overfitting. Finally, we propose a novel biomarker discovery algorithm: Gene-Switch-Marker (GSM) to capture meaningful biomarkers by taking advantage of SVM overfitting on single genes.

Hereditary breast cancer: clinical, pathological and molecular characteristics

Pathogenic mutations in BRCA1 or BRCA2 are only detected in 25% of families with a strong history of breast cancer, though hereditary factors are expected to be involved in the remaining families with no recognized mutation. Molecular characterization is expected to provide new insight into the tumor biology to guide the search of new high-risk alleles and provide better classification of the growing number of BRCA1/2 variants of unknown significance (VUS). In this review, we provide an overview of hereditary breast cancer, its genetic background, and clinical implications, before focusing on the pathologically and molecular features associated with the disease. Recent transcriptome and genome profiling studies of tumor series from BRCA1/2 mutation carriers as well as familial non-BRCA1/2 will be discussed. Special attention is paid to its association with molecular breast cancer subtypes as well as the latest advances in predicting BRCA1/2 involvement (BRCAness) using molecular signatures, for improved diagnostics and selection of patients sensitive to targeted therapeutics.

How many etiological subtypes of breast cancer: two, three, four, or more?

Breast cancer is a heterogeneous disease, divisible into a variable number of clinical subtypes. A fundamental question is how many etiological classes underlie the clinical spectrum of breast cancer? An etiological subtype reflects a grouping with a common set of causes, whereas a clinical subtype represents a grouping with similar prognosis and/or prediction. Herein, we review the evidence for breast cancer etiological heterogeneity. We then evaluate the etiological evidence with mRNA profiling data. A bimodal age distribution at diagnosis with peak frequencies near ages 50 and 70 years is a fundamental characteristic of breast cancer for important tumor features, clinical characteristics, risk factor profiles, and molecular subtypes. The bimodal peak frequencies at diagnosis divide breast cancer overall into a "mixture" of two main components in varying proportions in different cancer populations. The first breast cancer tends to arise early in life with modal age-at-diagnosis near 50 years and generally behaves aggressively. The second breast cancer occurs later in life with modal age near 70 years and usually portends a more indolent clinical course. These epidemiological and molecular data are consistent with a two-component mixture model and compatible with a hierarchal view of breast cancers arising from two main cell types of origin. Notwithstanding the potential added value of more detailed categorizations for personalized breast cancer treatment, we suggest that the development of better criteria to identify the two proposed etiologic classes would advance breast cancer research and prevention.

Breast cancer in China

The health burden of cancer is increasing in China, with more than 1·6 million people being diagnosed and 1·2 million people dying of the disease each year. As in most other countries, breast cancer is now the most common cancer in Chinese women; cases in China account for 12·2% of all newly diagnosed breast cancers and 9·6% of all deaths from breast cancer worldwide. China's proportional contribution to global rates is increasing rapidly because of the population's rising socioeconomic status and unique reproductive patterns. In this Review we present an overview of present control measures for breast cancer across China, and emphasise epidemiological and socioeconomic diversities and disparities in access to care for various subpopulations. We describe demographic differences between China and high-income countries, and also within geographical and socioeconomic regions of China. These disparities between China and high-income countries include younger age at onset of breast cancer; the unique one-child policy; lower rates of provision and uptake for screening for breast cancer; delays in diagnosis that result in more advanced stage of disease at presentation; inadequate resources; and a lack of awareness about breast cancer in the Chinese population. Finally, we recommend key measures that could contribute to improved health outcomes for patients with breast cancer in China.

Microarray-based RNA profiling of breast cancer: batch effect removal improves cross-platform consistency

Microarray is a powerful technique used extensively for gene expression analysis. Different technologies are available, but lack of standardization makes it challenging to compare and integrate data. Furthermore, batch-related biases within datasets are common but often not tackled. We have analyzed the same 234 breast cancers on two different microarray platforms. One dataset contained known batch-effects associated with the fabrication procedure used. The aim was to assess the significance of correcting for systematic batch-effects when integrating data from different platforms. We here demonstrate the importance of detecting batch-effects and how tools, such as ComBat, can be used to successfully overcome such systematic variations in order to unmask essential biological signals. Batch adjustment was found to be particularly valuable in the detection of more delicate differences in gene expression. Furthermore, our results show that prober adjustment is essential for integration of gene expression data obtained from multiple sources. We show that high-variance genes are highly reproducibly expressed across platforms making them particularly well suited as biomarkers and for building gene signatures, exemplified by prediction of estrogen-receptor status and molecular subtypes. In conclusion, the study emphasizes the importance of utilizing proper batch adjustment methods when integrating data across different batches and platforms.

Lessons learned from the intrinsic subtypes of breast cancer in the quest for precision therapy

BACKGROUND

Wide variability in breast cancer, between patients and within each individual neoplasm, adds confounding complexity to the treatment of the disease. In clinical practice, hormone receptor status has been used to classify breast tumours and to guide treatment. Modern classification systems should take the wide tumour heterogeneity into account to improve patient outcome.

METHODS

This article reviews the identification of the intrinsic molecular subtypes of breast cancer, their prognostic and therapeutic implications, and the impact of tumour heterogeneity on cancer progression and treatment. The possibility of functionally addressing tumour-specific characteristics in in vivo models to inform decisions for precision therapies is also discussed.

RESULTS

Despite the robust breast tumour classification system provided by gene expression profiling, heterogeneity is also evident within these molecular portraits. A complicating factor in breast cancer classification is the process of selective clonality within developing neoplasms. Phenotypically and functionally distinct clones representing the intratumour heterogeneity might confuse molecular classification. Molecular portraits of the heterogeneous primary tumour might not necessarily reflect the subclone of cancer cells that causes the disease to relapse. Studies of reciprocal relationships between cancer cell subpopulations within developing tumours are therefore needed, and are possible only in genetically engineered mouse models or patient-derived xenograft models, in which the treatment-induced selection pressure on individual cell clones can be mimicked.

CONCLUSION

In the future, more refined classifications, based on integration of information at several molecular levels, are required to improve treatment guidelines. Large-scale translational research efforts paved the way for identification of the intrinsic subtypes, and are still fundamental for ensuring future progress in cancer care.

[Personalized medicine and breast cancer: anticipatory medicine, prognostic evaluation and therapeutic targeting]

Breast cancer is now considered as a large collection of distinct biological entities, the management of which is increasingly personalized. Personalized medicine - defined as a medicine, which uses molecular profiles, notably genetic profiles, from patients and/or tumors to tailor therapeutic decisions - is now introduced in the management of breast cancer at any stages: screening and prevention of hereditary forms, prognostic and predictive evaluation of early breast cancer, and, more recently, novel clinical trials in advanced breast cancer, where genetic characterization of tumor tissue based on genomics, including next-generation sequencing tools, is used to drive specific therapeutic targeting.

Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics

BACKGROUND

Data are lacking to describe gene expression-based breast cancer intrinsic subtype patterns for population-based patient groups.

METHODS

We studied a diverse cohort of women with breast cancer from the Life After Cancer Epidemiology and Pathways studies. RNA was extracted from 1 mm punches from fixed tumor tissue. Quantitative reverse-transcriptase PCR was conducted for the 50 genes that comprise the PAM50 intrinsic subtype classifier.

RESULTS

In a subcohort of 1,319 women, the overall subtype distribution based on PAM50 was 53.1% luminal A, 20.5% luminal B, 13.0% HER2-enriched, 9.8% basal-like, and 3.6% normal-like. Among low-risk endocrine-positive tumors (i.e., estrogen and progesterone receptor positive by immunohistochemistry, HER2 negative, and low histologic grade), only 76.5% were categorized as luminal A by PAM50. Continuous-scale luminal A, luminal B, HER2-enriched, and normal-like scores from PAM50 were mutually positively correlated. Basal-like score was inversely correlated with other subtypes. The proportion with non-luminal A subtype decreased with older age at diagnosis, P Trend < 0.0001. Compared with non-Hispanic Whites, African American women were more likely to have basal-like tumors, age-adjusted OR = 4.4 [95% confidence intervals (CI), 2.3-8.4], whereas Asian and Pacific Islander women had reduced odds of basal-like subtype, OR = 0.5 (95% CI, 0.3-0.9).

CONCLUSIONS

Our data indicate that over 50% of breast cancers treated in the community have luminal A subtype. Gene expression-based classification shifted some tumors categorized as low risk by surrogate clinicopathologic criteria to higher-risk subtypes.

IMPACT

Subtyping in a population-based cohort revealed distinct profiles by age and race.

RNA profiling reveals familial aggregation of molecular subtypes in non-BRCA1/2 breast cancer families

BACKGROUND

In more than 70% of families with a strong history of breast and ovarian cancers, pathogenic mutation in BRCA1 or BRCA2 cannot be identified, even though hereditary factors are expected to be involved. It has been proposed that tumors with similar molecular phenotypes also share similar underlying pathophysiological mechanisms. In the current study, the aim was to investigate if global RNA profiling can be used to identify functional subgroups within breast tumors from families tested negative for BRCA1/2 germline mutations and how these subgroupings relate to different breast cancer patients within the same family.

METHODS

In the current study we analyzed a collection of 70 frozen breast tumor biopsies from a total of 58 families by global RNA profiling and promoter methylation analysis.

RESULTS

We show that distinct functional subgroupings, similar to the intrinsic molecular breast cancer subtypes, exist among non-BRCA1/2 breast cancers. The distribution of subtypes was markedly different from the distribution found among BRCA1/2 mutation carriers. From 11 breast cancer families, breast tumor biopsies from more than one affected family member were included in the study. Notably, in 8 of these families we found that patients from the same family shared the same tumor subtype, showing a tendency of familial aggregation of tumor subtypes (p-value = 1.7e-3). Using our previously developed BRCA1/2-signatures, we identified 7 non-BRCA1/2 tumors with a BRCA1-like molecular phenotype and provide evidence for epigenetic inactivation of BRCA1 in three of the tumors. In addition, 7 BRCA2-like tumors were found.

CONCLUSIONS

Our finding indicates involvement of hereditary factors in non-BRCA1/2 breast cancer families in which family members may carry genetic susceptibility not just to breast cancer but to a particular subtype of breast cancer. This is the first study to provide a biological link between breast cancers from family members of high-risk non-BRCA1/2 families in a systematic manner, suggesting that future genetic analysis may benefit from subgrouping families into molecularly homogeneous subtypes in order to search for new high penetrance susceptibility genes.

Patterns of TPD52 overexpression in multiple human solid tumor types analyzed by quantitative PCR

Tumor protein D52 (TPD52) is located at chromosome 8q21, a region that is frequently gained or amplified in multiple human cancer types. TPD52 has been suggested as a potential target for new anticancer therapies. In order to analyze TPD52 expression in the most prevalent human cancer types, we employed quantitative PCR to measure TPD52 mRNA levels in formalin-fixed tissue samples from more than 900 cancer tissues obtained from 29 different human cancer types. TPD52 was expressed at varying levels in all tested normal tissues, including skin, lymph node, lung, oral mucosa, breast, endometrium, ovary, vulva, myometrium, liver, pancreas, stomach, kidney, prostate, testis, urinary bladder, thyroid gland, brain, muscle and fat tissue. TPD52 was upregulated in 18/29 (62%) tested cancer types. Strongest expression was found in non-seminoma (56-fold overexpression compared to corresponding normal tissue), seminoma (42-fold), ductal (28-fold) and lobular breast cancer (14-fold). In these tumor types, TPD52 upregulation was found in the vast majority (>80%) of tested samples. Downregulation was found in 11 (38%) tumor types, most strongly in papillary renal cell cancer (-8-fold), leiomyosarcoma (-6-fold), clear cell renal cell cancer (-5-fold), liposarcoma (-5-fold) and lung cancer (-4-fold). These results demonstrate that TPD52 is frequently and strongly upregulated in many human cancer types, which may represent candidate tumor types for potential anti-TPD52 therapies.

Prognostic value of circulating tumor cells according to immunohistochemically defined molecular subtypes in advanced breast cancer

BACKGROUND

Breast cancer is a heterogeneous disease. Circulating tumor cell (CTC) enumeration might be useful to identify different risk categories within each molecular subtype.

METHODS

We retrospectively analyzed 203 consecutive patients with metastatic breast cancer with baseline CTC enumeration performed with CellSearch (Veridex Corp, Warren, NJ) between March 2005 and July 2011. Patients were categorized into 3 prognostic groups based on the number of CTCs (0, 1-4, and ≥ 5) and into 5 categories based on tumor biological characteristics: luminal-A (estrogen receptor [ER] and progesterone receptor [PR] > 1%, grade 1/2, human epidermal growth factor 2 [HER2]-negative [HER2(-)], Ki67 value < 14%); luminal-B (ER and/or PR > 1%, grade 3, HER2(-), Ki67 value > 14%); luminal-B HER2-positive [HER2(+)] (ER and/or PR > 1%, any grade, HER2(+), Ki-67 value any); HER2(+) (HER2 overexpressed/fluorescence in situ hybridization [FISH] amplified, ER and PR absent); triple negative (TN) (ER and PR 0%, HER2 not overexpressed/FISH not amplified).

RESULTS

Median age was 57 years (range 31-78 years). Twenty-seven patients (13.3%) had luminal-A category, 105 (51.7%) patients had luminal-B, 29 (14.3%) patients had luminal-B HER2(+), 24 patients (11.8%) had HER2(+), and 18 patients (8.9%) had TN. CTCs were mostly found in patients with luminal-A/luminal-B HER2(-) subtype. At multivariable analysis, CTC count was a significant predictive factor for overall survival (OS) in all molecular subtypes (log-rank P < .01). Patients with 0 CTCs/7.5 mL blood and all subtypes, except HER2(+), seem to perform better compared with other categories.

CONCLUSION

These findings confirm CTCs as an important prognostic factor for metastatic breast cancer in all molecular subtypes. Larger studies could help identify metastatic breast cancer subgroups in which CTC analysis would be particularly useful.

Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement

The 2012 IMPAKT task force investigated the medical usefulness of current methods for the classification of breast cancer into the 'intrinsic' molecular subtypes (luminal A, luminal B, basal-like and HER2). A panel of breast cancer and/or gene expression profiling experts evaluated the analytical validity, clinical validity and clinical utility of two approaches for molecular subtyping of breast cancer: the prediction analysis of microarray (PAM)50 assay and an immuno-histochemical (IHC) surrogate panel including oestrogen receptor (ER), HER2 and Ki67. The panel found the currently available evidence on the analytical validity and clinical utility of Ki67 based on a 14% cut-off and PAM50 to be inadequate. The majority of the working group members found the available evidence on the analytical validity, clinical validity and clinical utility of ER/HER2 to be convincing. The panel concluded that breast cancer classification into molecular subtypes based on the IHC assessment of ER, HER2 and Ki67 with a 14% cut-off and on the PAM50 test does not provide sufficiently robust information to modify systemic treatment decisions, and recommended the use IHC for ER and HER2 for the identification of clinically relevant subtypes of breast cancers. Methods for breast cancer classification into molecular subtypes should, however, be incorporated into clinical trial design.

Descriptive analysis of molecular subtypes in Tunisian breast cancer

AIM

The objective is to report the correlation between pathology and molecular subtype classifications of breast cancer in Tunisian women.

METHODS

This retrospective study concerned data of 966 breast cancer cases collected from 2007 to 2009 at Salah Azaiz Institute of Tunis. These cases were classified by an immunohistochemistry test for estrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2) status in the four molecular subtypes, namely luminal A, luminal B, HER2+ and triple negative. The molecular classifications were correlated with the clinicopathological characteristics of the tumors.

RESULTS

Luminal A (50.7% of cases) was the most common subtype, with triple negative subtype 22.5%, luminal B 13.4% and HER2+ 13.4%. Triple negative and HER2+ subtypes were significantly associated with large tumor size (>5 cm, P < 0.001), younger age (<40 years, P < 0.03) and high grade (P < 0.001). Conversely, there was no correlation with the lymph node status.

CONCLUSION

Our data demonstrated that the luminal A subtype, associated with a favorable prognosis, was the most frequent subtype in the Tunisian population; however the triple negative subtype occurred at a high incidence in Tunisia compared to Western countries. The molecular subtypes are correlated to the tumor size, histological grade and patient's age.

Pathway-based classification of cancer subtypes

Background

Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols.

Results

The present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type_1_diabetes mellitus, Cytokine-cytokine_receptor_interaction and Hedgehog_signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively.

Conclusions

Standardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications.

Reviewers

This article was reviewed by John McDonald (nominated by I. King Jordon), Eugene Koonin, Nathan Bowen (nominated by I. King Jordon), and Ekaterina Kotelnikova (nominated by Mikhail Gelfand).

Relationship between tumor DNA methylation status and patient characteristics in African-American and European-American women with breast cancer

Aberrant DNA methylation is critical for development and progression of breast cancer. We investigated the association of CpG island methylation in candidate genes and clinicopathological features in 65 African-American (AA) and European-American (EA) breast cancer patients. Quantitative methylation analysis was carried out on bisulfite modified genomic DNA and sequencing (pyrosequencing) for promoter CpG islands of p16, ESR1, RASSF1A, RARβ2, CDH13, HIN1, SFRP1 genes and the LINE1 repetitive element using matched paired non-cancerous and breast tumor specimen (32 AA and 33 EA women). Five of the genes, all known tumor suppressor genes (RASSF1A, RARβ2, CDH13, HIN1 and SFRP1), were found to be frequently hypermethylated in breast tumor tissues but not in the adjacent non-cancerous tissues. Significant differences in the CDH13 methylation status were observed by comparing DNA methylation between AA and EA patients, with more obvious CDH13 methylation differences between the two patient groups in the ER- disease and among young patients (age<50). In addition, we observed associations between CDH13, SFRP1, and RASSF1A methylation and breast cancer subtypes and between SFRP1 methylation and patient's age. Furthermore, tumors that received neoadjuvant therapy tended to have reduced RASSF1A methylation when compared with chemotherapy naïve tumors. Finally, Kaplan Meier survival analysis showed a significant association between methylation at 3 loci (RASSF1A, RARβ2 and CDH13) and reduced overall disease survival. In conclusion, the DNA methylation status of breast tumors was found to be significantly associated with clinicopathological features and race/ethnicity of the patients.

[Genomics and clinical research for breast cancer]

Genomics of breast cancer is paving the way towards more and more tailored treatments. The number of molecularly targeted therapies under development is increasing. In parallel, the high-throughput analyses revealed the molecular heterogeneity of disease, and identified several very different molecular subtypes, numerous and sometimes very scarce molecular alterations, and multigenic signatures predictive for clinical outcome, some of which are being tested in prospective clinical trials. This molecular segmentation of breast cancer and the multitude of new drugs to be tested (alone and in combination) lead to develop clinical trials based on the molecular profile of tumors to guide the patient towards the most suitable drug.

Dissecting the heterogeneity of triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 15% to 20% of breast cancers. It is a heterogeneous disease, not only on the molecular level, but also on the pathologic and clinical levels. TNBC is associated with a significantly higher probability of relapse and poorer overall survival in the first few years after diagnosis when compared with other breast cancer subtypes. This is observed despite its usual high sensitivity to chemotherapy. In the advanced setting, responses observed with chemotherapy lack durability. Early-stage clinical studies suggested impressive potential when a poly (ADP-ribose) polymerase (PARP) inhibitor is given for the treatment of advanced TNBC with BRCA gene dysfunction. The molecular complexity of TNBC has led to proposed subclassifications, which will be of great value for the development of targeted therapies. In this review, we discuss the biology of TNBC at the pathologic and the molecular levels. We also elaborate on the role of systemic therapies and the results of the first phase III clinical trial evaluating the addition of iniparib, a novel investigational anticancer agent that does not possess characteristics typical of the PARP inhibitor class, in combination with chemotherapy in advanced TNBC.

Epidemiology of triple negative breast cancers

Triple negative (TN) breast cancers fail to express the three most common breast cancer receptors; i.e., estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Accumulating data demonstrate that epidemiological risk factor profiles also vary between TN (ER-PR-HER2-) and other breast cancers, especially the so-called Luminal A breast cancers (ER+PR ± HER2-) [1]. A more comprehensive understanding of the epidemiology of TN breast cancers has important public health implications for risk assessment [2], prevention and treatment. The epidemiology of TN breast cancers can be first understood in the age-related reproductive risk factor patterns for ER, PR, and HER2. For example, there is a clear and strong association between older age at diagnosis (and therefore postmenopausal status) and the development of ER positive, PR positive, and HER2 negative breast cancers. On the other hand, younger age at diagnosis (and premenopausal status) is related to the development of ER negative, PR negative, and HER2 positive breast cancers. This gives rise to the somewhat counterintuitive suggestion that menopause has a greater relative impact upon hormone receptor negative than positive breast cancers [3,4]. Throughout this review, we will primarily contrast ER-PR-HER2- (TN) with ER+PR ± HER2- (Luminal A) breast cancers. We will first summarize the population-based age-specific incidence rate patterns and clinical outcomes, and then will review the available analytical studies. Information sources for this review included the National Cancer Institute's Surveillance, Epidemiology, and End Results 13 Registries Public-Use Database [5], CANCERLIT, Index Medicus, and PubMed.

Basal breast cancer: a complex and deadly molecular subtype

During the last decade, gene expression profiling of breast cancer has revealed the existence of five molecular subtypes and allowed the establishment of a new classification. The basal subtype, which represents 15-25% of cases, is characterized by an expression profile similar to that of myoepithelial normal mammary cells. Basal tumors are frequently assimilated to triple-negative (TN) breast cancers. They display epidemiological and clinico-pathological features distinct from other subtypes. Their pattern of relapse is characterized by frequent and early relapses and visceral locations. Despite a relative sensitivity to chemotherapy, the prognosis is poor. Recent characterization of their molecular features, such as the dysfunction of the BRCA1 pathway or the frequent expression of EGFR, provides opportunities for optimizing the systemic treatment. Several clinical trials dedicated to basal or TN tumors are testing cytotoxic agents and/or molecularly targeted therapies. This review summarizes the current state of knowledge of this aggressive and hard-to-treat subtype of breast cancer.

A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments

Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate relapse for patients with the major subtypes of breast cancer as classified using immunohistochemical assay and to investigate the patterns of benefit from the therapies over the past years. The study population included primary, operable 2,118 breast cancer patients, all non-specific infiltrative ductal carcinoma, with the median age of 53.2 years. All patients underwent local and/or systemic treatments. The clinicopathological characteristics and clinical outcomes were retrospectively reviewed. The expression of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 were analyzed by immunohistochemistry. All patients were classified into the following categories: luminal A, luminal B, HER2 overexpressing, basal-like, and unclassified subtypes. Ki-67 was detected in luminal A subtype. The median follow-up time was 67.9 months. Luminal A tumors had the lowest rate of relapse (12.7%, P < 0.001), while luminal B, HER2 overexpression, and basal-like subtypes were associated with an increased risk of relapse (15.7, 19.1, 20.9%). Molecular subtypes retained independent prognostic significance (P < 0.001). In luminal A subtype, adjunctive radiotherapy could decrease the risk of relapse (P = 0.005), Ki67 positive was a high-risk factor for relapse (P < 0.001), and adjuvant chemotherapies could reduce the relapse for the patients with risk factors (P < 0.001). Adjuvant hormone therapy was an effective treatment for ER-positive tumors (P < 0.001). Molecular subtypes of breast cancer could robustly identify the risk of recurrence and were significant in therapeutic decision making. The model combined subtype and clinical pathology was a significant improvement. Luminal A tumors might represent two distinct subsets which demonstrated distinct prognosis and therapy response.

CD44(+)/CD24(-) cells are transit progenitors and do not determine the molecular subtypes and clinical parameters in breast carcinomas

CD44(+)/CD24(-) cells have been associated with breast cancer stem/progenitor cell features. However, the status of this phenotype cells in normal, benign and malignant breast tissues has not been studied, and the clinical correlation of this subpopulation in breast cancer is not fully understood. The present study sought to identify these cells in a series of normal, benign, and malignant breast tissues and explore their correlation to the molecular subtypes of breast carcinoma and conventional pathological features. Double-staining immunohistochemistry (DIHC) of CD44 and CD24 was performed on 30 normal breast tissues, 30 breast fibroadenomas (FA), 60 breast invasive ductal carcinomas (IDC), and 3 breast cancer cell lines (MCF-7, MDA-MB-468, and MDA-MB-231). In the normal breast tissues and FAs, three phenotypes were observed including CD44(+)/CD24(+), CD44(+)/CD24(-), and CD44(-)/CD24(-) cells. In the IDCs, CD44(-)/CD24(+) cells were detected, in addition to the three aforementioned phenotypes. The strong positive rate (+++, incidence >60%) of CD44(+)/CD24(-) was significantly increased from normal breast tissue, FAs to IDCs (0.0%-->6.7%-->21.7%). However, the CD44(+)/CD24(-) cells didn't correlate with ages of patients, lymph node metastasis, tumor size, molecular subtypes, and the expression of ER, PR, HER-2, PS2, Bcl-2, nm23. The proportion of CD44(+)/CD24(-) cells in MCF-7, MDA-MB-468, and MDA-MB-231 was about 1, 5, and 80%, respectively. The results indicate that the CD44(+)/CD24(-) cells are transit progenitors and have no association with the molecular subtypes and clinicopathological parameters in the IDCs.

Molecular assays in breast cancer pathology

Recent advances in understanding the molecular pathology of breast cancer offer significant potential to identify patients who may benefit from adjuvant therapies. To date, few of these advances are utilised in a routine setting. We review molecular assays that are currently in use or are in the advanced stages of development, which may be used as predictive or prognostic biomarkers in breast cancer.The only widely used breast cancer molecular assay is in situ hybridisation (ISH) for human epidermal growth factor receptor 2 (HER2) gene amplification and we highlight key issues with the interpretation of this assay, with particular attention to the difficulties of the equivocal category. New molecular assays such as ISH for the topoisomerase II alpha (TOP2A) gene and for the aberrations in the copy number of the centromeric region of chromosome 17 are readily performed in a standard histopathology laboratory, but to date there are insufficient data to support their routine use. We also review the current data on two commercially available multigene expression assays, Oncotype DX and MammaPrint and discuss their potential use. Overall, while new molecular assays have significant potential to improve patient selection for therapy, well-performed histopathology with reliable interpretation of standard hormone and HER2 assays provides the most important predictive and prognostic information in early breast cancer.

Genomic Grade Index: An important tool for assessing breast cancer tumor grade and prognosis

Different multi-gene expression signatures have been shown to outperform classic histopathologic variables and therefore represent an important step towards personalizing breast cancer treatment. In particular, gene profiles overcome many of the limitations observed with classic histopathologic variables. The Genomic Grade Index (GGI) is a gene expression signature developed to better define histologic grade assessment. GGI divides classic histologic grade into low and high risk, instead of grades 1, 2 and 3. The ability of GGI to predict response to chemotherapy and separate hormone receptor positive breast cancer subtypes has also been demonstrated. This article critically reviews the limitations inherent in classic histologic grade evaluation; it also reviews the process of gene signature development in general and then focuses on GGI, its biologic significance, comparison with different gene signatures, and its applicability to clinical practise.

Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery

BACKGROUND

Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.

METHODS

We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-based feature selection methods such as principal component analysis (PCA), independent component analysis (ICA), and nonnegative matrix factorization (NMF) by avoiding their global feature-selection mechanism. In addition to demonstrating the effectiveness of the multi-resolution independent component analysis in meaningful biomarker discovery, we present a multi-resolution independent component analysis based support vector machines (MICA-SVM) and linear discriminant analysis (MICA-LDA) to attain high-performance classifications in low-dimensional spaces.

RESULTS

We have demonstrated the superiority and stability of our algorithms by performing comprehensive experimental comparisons with nine state-of-the-art algorithms on six high-dimensional heterogeneous profiles under cross validations. Our classification algorithms, especially, MICA-SVM, not only accomplish clinical or near-clinical level sensitivities and specificities, but also show strong performance stability over its peers in classification. Software that implements the major algorithm and data sets on which this paper focuses are freely available at https://sites.google.com/site/heyaumapbc2011/.

CONCLUSIONS

This work suggests a new direction to accelerate microarray technologies into a clinical routine through building a high-performance classifier to attain clinical-level sensitivities and specificities by treating an input profile as a 'profile-biomarker'. The multi-resolution data analysis based redundant global feature suppressing and effective local feature extraction also have a positive impact on large scale 'omics' data mining.

Hormone Receptors and HER-2 Status as Surrogates for Breast Cancer Molecular Subtypes Prognosticate for Disease Control in Node Negative Asian Patients Treated with Breast Conservation Therapy

Introduction: Our study investigates whether an approximation of breast cancer molecular subtypes using the hormone receptors and HER-2 status prognosticates for disease control after breast conservation therapy (BCT) in node-negative Asian breast cancer patients. Methods and Materials: We retrospectively reviewed 541 women with node-negative breast cancers treated with BCT between 1989 and 2007. Hormone receptors and HER-2 status were obtained from patients’ histological report. All patients received radiotherapy. Thirty-six percent and 68% of women received chemotherapy and hormonal treatment respectively. Results: Median follow-up of patients is 72 months. Five-year local recurrence free survival (LRFS) is 97.2% for the cohort but differs between subtypes: luminal A, 0.8%; luminal B, 1.4%; HER-2, 3.6% and basal-like, 12.7% (P = 0.047). The 5-year distant disease free survival (DDFS) is 96.4% for the cohort but differs between subtypes: luminal A, 98.2%; luminal B, 92.6%; HER-2, 89.5% and basal-like, 91.5% (P = 0.019). The 5-year disease free survival (DFS) is 94.4% for the cohort but differs between subtypes: luminal A, 97.4%; luminal B, 92.7%; HER-2, 86.3% and basal-like, 85.0% (P = 0.007). Univariate analysis with luminal A as baseline revealed an association of the other 3 subtypes with decreased DFS (P = 0.007), Hazard Ratio (HR) of 2.2, 4.4 and 3.3 to Luminal B, HER-2 and basal subtypes, respectively. On multivariate analysis, HER-2 subtype (AHR = 3.3, 95% CI, 1.1 to 9.8, P = 0.036) and basal-like subtype (HR = 3.5, 95% CI, 1.2 to 9.9, P = 0.019) prognosticate adversely for DFS. Conclusion: The combination of hormone receptors and HER-2 status can be used as surrogates for molecular subtypes in Asian breast cancer patients with node-negative disease to prognosticate LRFS, DFS and DDFS. Keywords: Histological subtypes, Lumpectomy, Outcomes, Prognostic factors