Metabolic Profiles are Principally Different between Cancers of the Liver,Pancreas and Breast

Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.     

Towards an integrated cellular and molecular: definition of breast cancers

Breast cancer is a major health problem as well as scientifically poorly understood. Our knowledge of breast cancer is however rapidly progressing in several directions. First, genomic studies are establishing a new molecular classification of breast cancers. Molecular subtypes have been identified and are being associated with the histoclinical forms of breast cancers. Second, genetic alterations are discovered and classified, generating new potential therapeutical targets. Third, mammary stem cells have been identified in the normal mammary epithelium. Their altered counterparts have been identified in tumors and are being characterized. These combined studies allow a new integrated cellular and molecular definition of breast cancers and a conceptual basis that will help the management of the disease.     

Functional relationship and gene ontology classification of breast cancer biomarkers

Breast cancer is a complex disease that still imposes a significant healthcare burden on women worldwide. The etiology of breast cancer is not known but significant advances have been made in the area of early detection and treatment. The advent of advanced molecular biology techniques, mapping of the human genome and availability of high throughput genomic and proteomic strategies opens up new opportunities and will potentially lead to the discovery of novel biomarkers for early detection and prognostication of breast cancer. Currently, many biomarkers, particularly the hormonal and epidermal growth factor receptors, are being utilized for breast cancer prognosis. Unfortunately, none of the biomarkers in use have sufficient diagnostic, prognostic and/or predictive power across all categories and stages of breast cancer. It is recognized that more useful information can be generated if tumors are interrogated with multiple markers. But choosing the right combination of biomarkers is challenging, because 1) multiple pathways are involved, 2) up to 62 genes and their protein products are potentially involved in breast cancer-related mechanisms and 3) the more markers evaluated, the more the time and cost involved. This review summarizes the current literature on selected biomarkers for breast cancer, discusses the functional relationships, and groups the selected genes based on a Gene Ontology classification.     

Breast cancer clinical and translational research: analogies and implications for prostate cancer

Breast and prostate cancer, respectively, are the most common cancers in women and in men in the United States. The management of locally advanced prostate cancer involves a multidisciplinary approach, bearing similarity to the therapeutic approach to breast cancer. Better understanding of the molecular biology of these cancers and the identification of the role played by the cancer stem cells and the tumor microenvironment may translate into better clinical decision making regarding risk classification and treatment allocation. A systematic assessment is presented of the many parallel evolutions in defining and treating high-risk breast cancer as they pertain to prostate cancer.     

Minireview: nuclear receptors and breast cancer

Until recently, the study of nuclear receptor (NR) function in breast cancer biology has been largely limited to estrogen and progesterone receptors. The development of reliable gene expression arrays, real-time quantitative RT-PCR, and immunohistochemical techniques for studying NR superfamily members in primary human breast cancers has now revealed the presence and potential importance of several additional NRs in the biology of breast cancer. These include receptors for steroid hormones (including androgens and corticosteroids), fat-soluble vitamins A and D, fatty acids, and xenobiotic lipids derived from diet. It is now clear that after NR activation, both genomic and nongenomic NR pathways can coordinately activate growth factor signaling pathways. Advances in our understanding of both NR functional networks and epithelial cell growth factor signaling pathways have revealed a frequent interplay between NR and epithelial cell growth factor family signaling that is clinically relevant to breast cancer. Understanding how growth factor receptors and their downstream kinases are activated by NRs (and vice-versa) is a central goal for maximizing treatment opportunities in breast cancer. In addition to the estrogen receptor, it is predicted that modulating the activity of other NRs will soon provide novel prevention and treatment approaches for breast cancer patients.     

A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g. P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value<0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.     

Systems Biology and Genomics of Breast Cancer

It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or “intrinsic,” biology of breast tumors has been developed; this taxonomy is called the “intrinsic subtypes of breast cancer,” which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process.     

High-risk estrogen-receptor-positive breast cancer: identification and implications for therapy

The estrogen receptor (ER) has long been recognized as a key discriminative feature of breast cancer, which carries profound implications for management. However, recent advances in the understanding of breast cancer heterogeneity have demonstrated the importance of biologic context to the interpretation of ER as a prognostic and predictive factor. The use of tumor subtyping methods and prognostic indicators based on molecular profiling of tumor tissue is now helping to delineate high-risk ER-positive cancer types that have distinct risk and treatment response profiles. These new approaches to breast cancer classification will have a major impact on the conduct of clinical trials and individual patient assessment in the future.     

Clinical proteomics of breast cancer

Despite the lifetimes that increased in breast cancers due to the the early screening programs and new therapeutic strategies, many cases still are being lost due to the metastatic relapses. For this reason, new approaches such as the proteomic techniques have currently become the prime objectives of breast cancer researches. Various omic-based techniques have been applied with increasing success to the molecular characterisation of breast tumours, which have resulted in a more detailed classification scheme and have produced clinical diagnostic tests that have been applied to both the prognosis and the prediction of outcome to the treatment. Implementation of the proteomics-based techniques is also seen as crucial if we are to develop a systems biology approach in the discovery of biomarkers of the early diagnosis, prognosis and prediction of the outcome of the breast cancer therapies. In this review, we discuss the studies that have been conducted thus far, for the discovery of diagnostic, prognostic and predictive biomarkers, and evaluate the potential of the discriminating proteins identified in this research for clinical use as breast cancer biomarkers.     

Application of proteomics in the study of tumor metastasis

Tumor metastasis is the dominant cause of death in cancer patients. However, the molecular and cellular mechanisms underlying tumor metastasis are still elusive.The identification of protein molecules with their expressions correlated to the metastatic process would help to understand the metastatic mechanisms and thus facilitate the development of strategies for the therapeutic interventions and clinical management of cancer. Proteomics is a systematic research approach aiming to provide the global characterization of protein expression and function under given conditions. Proteomic technology has been widely used in biomarker discovery and pathogenetic studies including tumor metastasis. This article provides a brief review of the application of proteomics in identifying molecular factors in tumor metastasis process. The combination of proteomics with other experimental approaches in biochemistry, cell biology, molecular genetics and chemistry, together with the development of new technologies and improvements in existing methodologies will continue to extend its application in studying cancer metastasis.     

Breast cancer gene discovery

Many important advances have been made in the past decade in understanding breast cancer at the molecular level, and two important high-penetrance breast cancer genes--BRCA1 and BRCA2--have been identified. However, germline mutations in these two genes are responsible for only a minority (approximately 5%) of all breast carcinomas, and the genes responsible for the majority of breast cancer cases remain to be identified. There is evidence that there are additional high-to-moderate-penetrance breast cancer susceptibility genes but, given the high degree of molecular heterogeneity in breast carcinomas, it is likely that each of these genes is responsible for only a subset of cases. There are also many candidate low-penetrance breast cancer genes and many more are likely to be identified. In addition to germline, and somatic, sequence alterations, epigenetic changes in many genes are likely to play an important role in the pathobiology of breast cancer. Recently developed genomic technologies and the completion of the human genome sequence provide us with powerful tools to identify novel candidate breast cancer genes that could play an important role in breast tumourigenesis.     

Loss of nuclear BRCA1 protein staining in normal tissue cells derived from BRCA1 and BRCA2 mutation carriers

Enhanced genomic instability has been recently reported in normal cells derived from BRCA1/2 mutation carriers when placed in vitro in non-physiological stress conditions. We present here original data which help to explain the observed genomic instability. Leucocytes from BRCA1/2 mutation carriers, sporadic breast cancer patients and controls were prepared for BRCA1 immunocytochemistry. We show that BRCA1 containing nuclear dot like structures are detectable in about 80% of the leucocytes from controls and sporadic breast cancer patients, but are absent in the majority of normal cells from BRCA1 as well as BRCA2 mutation carriers (also in their normal breast cells). Our results thus indicate that the genomic instability observed in normal cells from BRCA1 and BRCA2 mutation carriers is associated with a down-regulation of nuclear BRCA1 protein accumulation in the dot like structures. These results suggest in addition that immunocytochemical or alternative molecular screening strategies might help to identify women with a high risk for breast (ovarian) cancer even when the underlying genetic defect remains undetectable.     

Classification moléculaire des cancers du sein : utilité en clinique

In spite of the progress of treatments and the discovery of targeted therapies, breast cancer remains the leading cause of cancer mortality among women. It is also the first cancer to get benefits from target therapies against hormone receptors and now HER2. The anatomoclinical classification used to optimize the therapeutics is not always accurate and is a cause of overtreatments or of inappropriate treatments. Tumour cells genomic studies have shown the relationship between genomic alterations and the prognosis and the efficacy of treatments. The molecular classification which results from those studies allowed the emergence of numerous diagnostic tests. They use different technologies and different clinical approaches which should allow a better classification in order to be able to propose a personalized therapy. In this review, eight molecular tests are estimated compared to their scientific validation and to their clinical utility. Protein assays are also reviewed as uPA/PAI-1, the only prognostic marker validated with a level of evidence 1.     

Genomic landscape of gastric cancer: molecular classification and potential targets

Gastric cancer imposes a considerable health burden worldwide, and its mortality ranks as the second highest for all types of cancers. The limited knowledge of the molecular mechanisms underlying gastric cancer tumorigenesis hinders the development of therapeutic strategies. However, ongoing collaborative sequencing efforts facilitate molecular classification and unveil the genomic landscape of gastric cancer. Several new drivers and tumorigenic pathways in gastric cancer, including chromatin remodeling genes, RhoA-related pathways, TP53 dysregulation, activation of receptor tyrosine kinases, stem cell pathways and abnormal DNA methylation, have been revealed. These newly identified genomic alterations await translation into clinical diagnosis and targeted therapies. Considering that loss-of-function mutations are intractable, synthetic lethality could be employed when discussing feasible therapeutic strategies. Although many challenges remain to be tackled, we are optimistic regarding improvements in the prognosis and treatment of gastric cancer in the near future.     

基因组学与表观遗传学在肿瘤标志物研究中的应用

肿瘤标志物是一类能反映肿瘤存在与生长状态的生化物质,它们在肿瘤的早期筛查、辅助诊断、预后判断、疗效评价、复发和转移监测中具有重要意义. 随着细胞与分子生物学领域的发展,人类基因组计划大量研究成果的涌现,人们对肿瘤的发生和发展机理有了越来越清楚的认识,肿瘤标志物的研究也得到了极大的拓展与更新,特别是基因组学和表观遗传学的进展大大促进了新型肿瘤标志物的发现. 文章就肿瘤标志物发展历程,常用肿瘤标志物的种类和临床应用进行介绍,分析了现有肿瘤标志物的局限性,重点阐述了新型肿瘤标志物,如多种表观遗传学标志物及循环核酸的研究及应用. 同时,对肿瘤标志物的研究前景提出了一些观点.     

Polygenic modeling of genome-wide association studies: an application to prostate and breast cancer

Genome-wide association studies (GWAS) have successfully detected and replicated associations with numerous diseases, including cancers of the prostate and breast. These findings are helping clarify the genomic basis of such diseases, but appear to explain little of disease heritability. This limitation might reflect the focus of conventional GWAS on a small set of the most statistically significant associations with disease. More information might be obtained by analyzing GWAS using a polygenic model, which allows for the possibility that thousands of genetic variants could impact disease. Furthermore, there may exist common polygenic effects between potentially related phenotypes (e.g., prostate and breast cancer). Here we present and apply a polygenic model to GWAS of prostate and breast cancer. Our results indicate that the polygenic model can explain an increasing--albeit low--amount of heritability for both of these cancers, even when excluding the most statistically significant associations. In addition, nonaggressive prostate cancer and breast cancer appear to share a common polygenic model, potentially reflecting a similar underlying biology. This supports the further development and application of polygenic models to genomic data.     

Integration of diverse statistical evidence of gene-trait association in systems biology studies

The rapid advancement of high-throughput genomic assay technologies has generated large amounts of diverse genomic data in disparate human populations and diseases. These data provide a unique opportunity for biomedical investigators to systematically study multifaceted aspects of genes' involvement in the biological processes underlying important traits from the systems biology perspective. An important component in such a study is the inference that integrates diverse lines of statistical evidence for gene-trait association from the observed trait values and the massive numbers of measured genomic features. A novel integrated statistical analysis procedure is developed in this paper and is illustrated by an application in studying childhood leukemia.