Ensemble inference by integrative cancer networks

Genetic Association of rs2237572 Cyclin-Dependent Kinase 6 Gene with Breast Cancer in Iraq

This case-control study is aimed to evaluate serum concentration of Cyclin-Dependent Kinase 6 (CDK6) and the genetic association between rs2237572 CDK6 gene and breast cancer (BC) in Iraq. To attain this goal, 80 patients with BC as cases and 80 healthy individuals as controls were included. Further, BC patients were sorted according to the molecular classification into four subtypes of Luminal A, Luminal B, Her2/neu enriched and TPN. Serum concentration of CDK6 enzyme, allelic and genotypic frequencies of rs2237572 CDK6, and the occurrence of BC phenotype and its subtypes in the studied population were investigated. ELISA technique was used to perform the biochemical testing, while the molecular analysis was achieved by real-time PCR, high resolution melting analysis, conventional PCR, as well as sequencing analysis. The results revealed no significant difference in serum concentration of CDK6 enzyme between patients and healthy controls (p > 0.05). Also, no significant differences were shown between BC patients subtypes (p > 0.05). The rs2237572 CDK6 genotypes were associated with the BC and affirmed that allele C was inherited as a recessive risk factor. Moreover, a highly significant difference between patients' subtypes in the genotypic frequency of rs2237572 (p < 0.01) was noted. Furthermore, the association of rs2237572 genotypes and CDK6 serum concentration in BC patients showed a considered significant difference between C/C and T/T, C/C and T/C and the CDK6 level (p < 0.05). Nevertheless, T/T and T/C did not show any significant difference with the CDK6 level. Hence, it was concluded that the rs2237572 of CDK6 gene is significantly correlated with BC.

Clinical and molecular aspects of breast cancer: Targets and therapies

Breast Cancer is a complex disease characterized by the occurrence of multiple molecular alterations. Currently, some molecular markers are in use for breast cancer diagnostic, prognostic, and predictive purposes. Thus, genetic signatures are available for improving the decision-making. The biomarkers are also essential as therapeutic approaches, but many questions remain due to the lack of efficacy on breast cancer treatment, mainly for triple-negative breast cancer subtype. Since the genetic profile of breast cancer can also be related to different ethnic groups and geographic areas, the reference populations of the genetic assays and clinical trials need to include a broader population beyond the European and North American patients. In this review, we analyzed the current and potential molecular markers that could help to improve the strategies for breast cancer therapy.

Time-course gene profiling and networks in demethylated retinoblastoma cell line

Retinoblastoma, a very aggressive cancer of the developing retina, initiatiates by the biallelic loss of RB1 gene, and progresses very quickly following RB1 inactivation. While its genome is stable, multiple pathways are deregulated, also epigenetically. After reviewing the main findings in relation with recently validated markers, we propose an integrative bioinformatics approach to include in the previous group new markers obtained from the analysis of a single cell line subject to epigenetic treatment. In particular, differentially expressed genes are identified from time course microarray experiments on the WERI-RB1 cell line treated with 5-Aza-2′-deoxycytidine (decitabine; DAC). By inducing demethylation of CpG island in promoter genes that are involved in biological processes, for instance apoptosis, we performed the following main integrative analysis steps: i) Gene expression profiling at 48h, 72h and 96h after DAC treatment; ii) Time differential gene co-expression networks and iii) Context-driven marker association (transcriptional factor regulated protein networks, master regulatory paths). The observed DAC-driven temporal profiles and regulatory connectivity patterns are obtained by the application of computational tools, with support from curated literature. It is worth emphasizing the capacity of networks to reconcile multi-type evidences, thus generating testable hypotheses made available by systems scale predictive inference power. Despite our small experimental setting, we propose through such integrations valuable impacts of epigenetic treatment in terms of gene expression measurements, and then validate evidenced apoptotic effects.

Immunomediated Pan-cancer Regulation Networks are Dominant Fingerprints After Treatment of Cell Lines with Demethylation

Pan-cancer studies are particularly relevant not only for addressing the complexity of the inherently observed heterogeneity but also for identifying clinically relevant features that may be common to the cancer types. Immune system regulations usually reveal synergistic modulation with other cancer mechanisms and in combination provide insights on possible advances in cancer immunotherapies. Network inference is a powerful approach to decipher pan-cancer systems dynamics. The methodology proposed in this study elucidates the impacts of epigenetic treatment on the drivers of complex pan-cancer regulation circuits involving cell lines of five cancer types. These patterns were observed from differential gene expression measurements following demethylation with 5-azacytidine. Networks were built to establish associations of phenotypes at molecular level with cancer hallmarks through both transcriptional and post-transcriptional regulation mechanisms. The most prominent feature that emerges from our integrative network maps, linking pathway landscapes to disease and drug-target associations, refers primarily to a mosaic of immune-system crosslinked influences. Therefore, characteristics initially evidenced in single cancer maps become motifs well summarized by network cores and fingerprints.

Immuno-regulated common markers but different network signatures in two associated cancers: evidences from epigenetic treatment

BACKGROUND

Networks are now widely accepted inference tools in translational oncology. Besides providing agnostic model frameworks for complex data-driven clinical problems of diagnostic, therapeutic and prognostic impacts, networks mainly support insights, testable hypotheses and decision processes on the basis of their topological configurations and connectivity patterns.

METHODS

The purpose of this study is to emphasize the role of both gene and network signatures in two specific cancers. Retinoblastoma (RB) and osteosarcoma are associated to some extent. It is known that patients who carry germline mutations in the RB1 gene, and who survive RB, are typically at an increased risk of early-onset second cancers, including osteosarcomas. Gene signatures are widely used, but also criticized for their partial lack of reproducibility. Network signatures include gene association dynamics by identifying modules or communities in which subsets of genes functionally belong.

RESULTS

Two cancer cell lines (one per cancer type) were subjected to a similar epigenetic treatment regimen, using a demethylation agent (DAC, and including similar dose and time course administration). A minimal set of shared differentially expressed (DEG) genes was identified in cancer-specific cell lines from microarray analyses. However, the identified immune signatures were observed to translate into much diversified network signatures.

CONCLUSIONS

Our evidence is relevant to therapeutic developments, indicating that preference should be assigned to the assessment of bio-entities in a connected environment rather than considering single entities alone.

CMV-induced pathology: pathway and gene-gene interaction analysis

Mucoepidermoid carcinoma (MEC) is the most prevalent malignant tumor in major and minor salivary glands (SGs). We have recently identified human cytomegalovirus (hCMV) as a principle component in the multifactorial causation of SG-MEC. This finding is corroborated by the ability of the purified mouse CMV (mCMV) to induce malignant transformation of SG cells in a three-dimensional in vitro mouse model, using a similar oncogenic signaling pathway. Our prior studies indicate that the core tumor microenvironment (TME) is a key regulator of pathologic progression, particularly the cancer-associated fibroblast (CAF) component. Studies of early CAFs immunodetect aberrant expression of ECM components, as well as multiple growth factors, cytokines and transcription factors. Here we present the mechanistic insight derived from a mathematical structure ("wiring diagram") used to model complex relationships between a highly relevant (p=9.43×10(-12)) global "cancer network" of 32 genes and their known links. Detailed characterization of the functional architecture of the examined "cancer network" exposes the critical crosstalk and compensatory pathways that limit the efficacy of targeted anti-kinase therapies.