A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

The retroviral oncoprotein Tax targets the coiled-coil centrosomal protein TAX1BP2 to induce centrosome overduplication

Emerging evidence suggests that supernumerary centrosomes drive genome instability and oncogenesis. Human T-cell leukaemia virus type I (HTLV-I) is etiologically associated with adult T-cell leukaemia (ATL). ATL cells are aneuploid, but the causes of aneuploidy are incompletely understood. Here, we show that centrosome amplification is frequent in HTLV-I-transformed cells and that this phenotype is caused by the viral Tax oncoprotein. We also show that the fraction of Tax protein that localizes to centrosomes interacts with TAX1BP2, a novel centrosomal protein composed almost entirely of coiled-coil domains. Overexpression of TAX1BP2 inhibited centrosome duplication, whereas depletion of TAX1BP2 by RNAi resulted in centrosome hyperamplification. Our findings suggest that the HTLV-I Tax oncoprotein targets TAX1BP2 causing genomic instability and aneuploidy. © 2006 Nature Publishing Group.postprin

The basal epithelial marker P-cadherin associates with breast cancer cell populations harboring a glycolytic and acid-resistant phenotype

"BMC Cancer 2014 14:734"BACKGROUND: Cancer stem cells are hypoxia-resistant and present a preponderant glycolytic metabolism. These characteristics are also found in basal-like breast carcinomas (BLBC), which show increased expression of cancer stem cell markers.Recently, we demonstrated that P-cadherin, a biomarker of BLBC and a poor prognostic factor in this disease, mediates stem-like properties and resistance to radiation therapy. Thus, the aim of the present study was to evaluate if P-cadherin expression was associated to breast cancer cell populations with an adapted phenotype to hypoxia. METHODS: Immunohistochemistry was performed to address the expression of P-cadherin, hypoxic, glycolytic and acid-resistance biomarkers in primary human breast carcinomas. In vitro studies were performed using basal-like breast cancer cell lines. qRT-PCR, FACS analysis, western blotting and confocal microscopy were used to assess the expression of P-cadherin after HIF-1a stabilization, achieved by CoCl2 treatment. siRNA-mediated knockdown was used to silence the expression of several targets and qRT-PCR was employed to evaluate the effects of P-cadherin on HIF-1a signaling. P-cadherin high and low breast cancer cell populations were sorted by FACS and levels of GLUT1 and CAIX were assessed by FACS and western blotting. Mammosphere forming efficiency was used to determine the stem cell activity after specific siRNA-mediated knockdown, further confirmed by western blotting. RESULTS: We demonstrated that P-cadherin overexpression was significantly associated with the expression of HIF-1a, GLUT1, CAIX, MCT1 and CD147 in human breast carcinomas. In vitro, we showed that HIF-1a stabilization was accompanied by increased membrane expression of P-cadherin and that P-cadherin silencing led to a decrease of the mRNA levels of GLUT1 and CAIX. We also found that the cell fractions harboring high levels of P-cadherin were the same exhibiting more GLUT1 and CAIX expression. Finally, we showed that P-cadherin silencing significantly decreases the mammosphere forming efficiency in the same range as the silencing of HIF-1a, CAIX or GLUT1, validating that all these markers are being expressed by the same breast cancer stem cell population. CONCLUSIONS: Our results establish a link between aberrant P-cadherin expression and hypoxic, glycolytic and acid-resistant breast cancer cells, suggesting a possible role for this marker in cancer cell metabolismo.This work was funded by FEDER funds through the COMPETE Program (Programa Operacional Factores de Competitividade) and by national funds through FCT (Portuguese Foundation for Science and Technology, Portugal), mainly in the context of the scientific project PTDC/SAU-GMG/120049/2010-FCOMP-01-0124-FEDER-021209, and partially by PTDC/SAU-FCF/104347/2008. FCT funded the research grants of BS (SFRH/BD/69353/2010), ASR (SFRH/BPD/75705/2011), ARN (grant from the project PTDC/SAU-GMG/120049/2010), CP (SFRH/BPD/69479/2010), AV (SFRH/BPD/90303/2012), as well as JP, with Programa Ciencia 2007 (Contratacao de Doutorados para o SCTN - financiamento pelo POPH - QREN - Tipologia 4.2 - Promocao do Emprego Cientifico, comparticipado pelo Fundo Social Europeu e por fundos nacionais do MCTES) and Programa IFCT (FCT Investigator). IPATIMUP is an Associate Laboratory of the Portuguese Ministry of Science, Technology and Higher Education and is partially supported by FCT

Tumor mutational burden is a determinant of immune-mediated survival in breast cancer

Mounting evidence supports a role for the immune system in breast cancer outcomes. The ability to distinguish highly immunogenic tumors susceptible to anti-tumor immunity from weakly immunogenic or inherently immune-resistant tumors would guide development of therapeutic strategies in breast cancer. Genomic, transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohorts were used to examine statistical associations between tumor mutational burden (TMB) and the survival of patients whose tumors were assigned to previously-described prognostic immune subclasses reflecting favorable, weak or poor immune-infiltrate dispositions (FID, WID or PID, respectively). Tumor immune subclasses were associated with survival in patients with high TMB (TMB-Hi, P < 0.001) but not in those with low TMB (TMB-Lo, P = 0.44). This statistical relationship was confirmed in the METABRIC cohort (TMB-Hi, P = 0.047; TMB-Lo, P = 0.39), and also found to hold true in the more-indolent Luminal A tumor subtype (TMB-Hi, P = 0.011; TMB-Lo, P = 0.91). In TMB-Hi tumors, the FID subclass was associated with prolonged survival independent of tumor stage, molecular subtype, age and treatment. Copy number analysis revealed the reproducible, preferential amplification of chromosome 1q immune-regulatory genes in the PID immune subclass. These findings demonstrate a previously unappreciated role for TMB as a determinant of immune-mediated survival of breast cancer patients and identify candidate immune-regulatory mechanisms associated with immunologically cold tumors. Immune subtyping of breast cancers may offer opportunities for therapeutic stratification.fals

SIGNATURE: A workbench for gene expression signature analysis

<p>Abstract</p> <p>Background</p> <p>The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of these genes provides an opportunity to develop more precise and varied sets of phenotypes. However, to use this approach requires computational methods that are difficult to implement and apply, and thus there is a critical need for intelligent software tools that can reduce the technical burden of the analysis. Tools for gene expression analyses are unusually difficult to implement in a user-friendly way because their application requires a combination of biological data curation, statistical computational methods, and database expertise.</p> <p>Results</p> <p>We have developed SIGNATURE, a web-based resource that simplifies gene expression signature analysis by providing software, data, and protocols to perform the analysis successfully. This resource uses Bayesian methods for processing gene expression data coupled with a curated database of gene expression signatures, all carried out within a GenePattern web interface for easy use and access.</p> <p>Conclusions</p> <p>SIGNATURE is available for public use at <url>http://genepattern.genome.duke.edu/signature/</url>.</p

Comparison of data-merging methods with SVM attribute selection and classification in breast cancer gene expression

Background: DNA microarray data are used to identify genes which could be considered prognostic markers. However, due to the limited sample size of each study, the signatures are unstable in terms of the composing genes and may be limited in terms of performances. It is therefore of great interest to integrate different studies, thus increasing sample size.Results: In the past, several studies explored the issue of microarray data merging, but the arrival of new techniques and a focus on SVM based classification needed further investigation. We used distant metastasis prediction based on SVM attribute selection and classification to three breast cancer data sets.Conclusions: The results showed that breast cancer classification does not benefit from data merging, confirming the results found by other studies with different technique