A systems biology approach to defining metastatic biomarkers and signaling pathways

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Molecular Mechanisms Driving Cholangiocarcinoma Invasiveness: An Overview

The acquisition of invasive functions by tumor cells is a first and crucial step toward the development of metastasis, which nowadays represents the main cause of cancer-related death. Cholangiocarcinoma (CCA), a primary liver cancer originating from the biliary epithelium, typically develops intrahepatic or lymph node metastases at early stages, thus preventing the majority of patients from undergoing curative treatments, consistent with their very poor prognosis. As in most carcinomas, CCA cells gradually adopt a motile, mesenchymal-like phenotype, enabling them to cross the basement membrane, detach from the primary tumor, and invade the surrounding stroma. Unfortunately, little is known about the molecular mechanisms that synergistically orchestrate this proinvasive phenotypic switch. Autocrine and paracrine signals (cyto/chemokines, growth factors, and morphogens) permeating the tumor microenvironment undoubtedly play a prominent role in this context. Moreover, a number of recently identified signaling systems are currently drawing attention as putative mechanistic determinants of CCA cell invasion. They encompass transcription factors, protein kinases and phosphatases, ubiquitin ligases, adaptor proteins, and miRNAs, whose aberrant expression may result from either stochastic mutations or the abnormal activation of upstream pro-oncogenic pathways. Herein we sought to summarize the most relevant molecules in this field and to discuss their mechanism of action and potential prognostic relevance in CCA. Hopefully, a deeper knowledge of the molecular determinants of CCA invasiveness will help to identify clinically useful biomarkers and novel druggable targets, with the ultimate goal to develop innovative approaches to the management of this devastating malignancy.

Genetic variants in cell cycle control pathway confer susceptibility to aggressive prostate carcinoma

BACKGROUND

Because a significant number of patients with prostate cancer (PCa) are diagnosed with disease unlikely to cause harm, genetic markers associated with clinically aggressive PCa have potential clinical utility. Since cell cycle checkpoint dysregulation is crucial for the development and progression of cancer, we tested the hypothesis that common germ-line variants within cell cycle genes were associated with aggressive PCa.

METHODS

Via a two-stage design, 364 common sequence variants in 88 genes were tested. The initial stage consisted of 258 aggressive PCa patients and 442 controls, and the second stage added 384 aggressive PCa Patients and 463 controls. European-American and African-American samples were analyzed separately. In the first stage, SNPs were typed by Illumina Goldengate assay while in the second stage SNPs were typed by Pyrosequencing assays. Genotype frequencies between cases and controls were compared using logistical regression analysis with additive, dominant and recessive models.

RESULTS

Eleven variants within 10 genes (CCNC, CCND3, CCNG1, CCNT2, CDK6, MDM2, SKP2, WEE1, YWHAB, YWHAH) in the European-American population and nine variants in 7 genes (CCNG1, CDK2, CDK5, MDM2, RB1, SMAD3, TERF2) in the African-American population were found to be associated with aggressive PCa using at least one model. Of particular interest, CCNC (rs3380812) was associated with risk in European-American cohorts from both institutions. CDK2 (rs1045435) and CDK5 (rs2069459) were associated with risk in the African-American cohorts from both institutions. Lastly, variants within MDM2 and CCNG1 were protective for aggressive PCa in both ethnic groups.

CONCLUSIONS

This study confirms that polymorphisms within cell cycle genes are associated with clinically aggressive PCa. Validation of these markers in additional populations is necessary, but these markers may help identify patients at risk for potentially lethal carcinoma.

Systems biology for molecular life sciences and its impact in biomedicine

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.