Elevated MACC1 expression in colorectal cancer is driven by chromosomal instability and is associated with molecular subtype and worse patient survival

Metastasis-Associated in Colon Cancer 1 (MACC1) is a strong prognostic biomarker inducing proliferation, migration, invasiveness, and metastasis of cancer cells. The context of MACC1 dysregulation in cancers is, however, still poorly understood. Here, we investigated whether chromosomal instability and somatic copy number alterations (SCNA) frequently occurring in CRC contribute to MACC1 dysregulation, with prognostic and predictive impacts. Using the Oncotrack and Charité CRC cohorts of CRC patients, we showed that elevated MACC1 mRNA expression was tightly dependent on increased MACC1 gene SCNA and was associated with metastasis and shorter metastasis free survival. Deep analysis of the COAD-READ TCGA cohort revealed elevated MACC1 expression due to SCNA for advanced tumors exhibiting high chromosomal instability (CIN), and predominantly classified as CMS2 and CMS4 transcriptomic subtypes. For that cohort, we validated that elevated MACC1 mRNA expression correlated with reduced disease-free and overall survival. In conclusion, this study gives insights into the context of MACC1 expression in CRC. Increased MACC1 expression is largely driven by CIN, SCNA gains, and molecular subtypes, potentially determining the molecular risk for metastasis that might serve as a basis for patient-tailored treatment decisions

E2F1 and KIAA0191 expression predicts breast cancer patient survival

<p>Abstract</p> <p>Background</p> <p>Gene expression profiling of human breast tumors has uncovered several molecular signatures that can divide breast cancer patients into good and poor outcome groups. However, these signatures typically comprise many genes (~50-100), and the prognostic tests associated with identifying these signatures in patient tumor specimens require complicated methods, which are not routinely available in most hospital pathology laboratories, thus limiting their use. Hence, there is a need for more practical methods to predict patient survival.</p> <p>Methods</p> <p>We modified a feature selection algorithm and used survival analysis to derive a 2-gene signature that accurately predicts breast cancer patient survival.</p> <p>Results</p> <p>We developed a tree based decision method that segregated patients into various risk groups using <it>KIAA0191 </it>expression in the context of <it>E2F1 </it>expression levels. This approach led to highly accurate survival predictions in a large cohort of breast cancer patients using only a 2-gene signature.</p> <p>Conclusions</p> <p>Our observations suggest a possible relationship between <it>E2F1 </it>and <it>KIAA0191 </it>expression that is relevant to the pathogenesis of breast cancer. Furthermore, our findings raise the prospect that the practicality of patient prognosis methods may be improved by reducing the number of genes required for analysis. Indeed, our <it>E2F1/KIAA0191 </it>2-gene signature would be highly amenable for an immunohistochemistry based test, which is commonly used in hospital laboratories.</p

Core module biomarker identification with network exploration for breast cancer metastasis

<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data

<p>Abstract</p> <p>Background</p> <p>Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms.</p> <p>Methods</p> <p>We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-κB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival.</p> <p>Results</p> <p>Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint^®.</p> <p>Conclusion</p> <p>Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.</p

Biological Convergence of Cancer Signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties

Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes

Background: Recent advances in whole-genome association studies (WGASs) for human cancer risk are beginning to provide the part lists of low-penetrance susceptibility genes. However, statistical analysis in these studies is complicated by the vast number of genetic variants examined and the weak effects observed, as a result of which constraints must be incorporated into the study design and analytical approach. In this scenario, biological attributes beyond the adjusted statistics generally receive little attention and, more importantly, the fundamental biological characteristics of low-penetrance susceptibility genes have yet to be determined. Methods: We applied an integrative approach for identifying candidate low-penetrance breast cancer susceptibility genes, their characteristics and molecular networks through the analysis of diverse sources of biological evidence. Results: First, examination of the distribution of Gene Ontology terms in ordered WGAS results identified asymmetrical distribution of Cell Communication and Cell Death processes linked to risk. Second, analysis of 11 different types of molecular or functional relationships in genomic and proteomic data sets defined the 'omic' properties of candidate genes: i/ differential expression in tumors relative to normal tissue; ii/ somatic genomic copy number changes correlating with gene expression levels; iii/ differentially expressed across age at diagnosis; and iv/ expression changes after BRCA1 perturbation. Finally, network modeling of the effects of variants on germline gene expression showed higher connectivity than expected by chance between novel candidates and with known susceptibility genes, which supports functional relationships and provides mechanistic hypotheses of risk. Conclusion: This study proposes that cell communication and cell death are major biological processes perturbed in risk of breast cancer conferred by low-penetrance variants, and defines the common omic properties, molecular interactions and possible functional effects of candidate genes and proteins

No loss of sst receptors gene expression in advanced stages of colorectal cancer.

International audienceAs demonstrated by several studies, the pan-inhibitory peptide somatostatin (SS) is implicated in a large variety of physiological processes in the gastrointestinal tractus. SS inhibits hormonal and gastric acid secretions, and decreases gastric and intestinal motility, mesenteric blood flow and intestinal absorption. In vitro and in vivo studies showed also that the antiproliferative potency of SS analogs may be a target to improve the prognosis of colorectal cancer. Here we report the expression profile of the five SS receptor subtypes (hsst1-5) mRNAs in a large set of tumoral and normal colon. Using reverse transcription-PCR, we showed that hsst5, hsst1 and hsst2 mRNA subtypes were the most frequently expressed hsst mRNA subtypes in normal and pathological colon. Interestingly, we found that the frequency of hsst5 mRNA expression in the left colon was significantly higher in tumors than in normal samples: 81. 2% (13/16) and 36.4% (4/11) respectively (0.025>P>0.01, chi2 test with Yates' correction). We did not find any influence of Dukes' stage on hsst mRNAs expression. Of interest, no loss of hsst2 and hsst5 mRNA expression in advanced stages was noted. Some differences in the frequency of expression of hsst mRNAs according to the origin of the tissue (left or right colon) were evident. The expression of hsst5 and hsst2 mRNA in advanced colorectal carcinoma associated with the development of new SS analogs boost the relevance of colorectal cancer treatment by somatostatin analogs