Prediction of cancer prognosis with the genetic basis of transcriptional variations

A Genome-Wide Analysis of a Sudden Cardiac Death Cohort: Identifying Novel Target Variants in the Era of Molecular Autopsy

Sudden cardiac death (SCD) is an unexpected natural death due to cardiac causes, usually happening within one hour of symptom manifestation or in individuals in good health up to 24 h before the event. Genomic screening has been increasingly applied as a useful approach to detecting the genetic variants that potentially contribute to SCD and helping the evaluation of SCD cases in the post-mortem setting. Our aim was to identify the genetic markers associated with SCD, which might enable its target screening and prevention. In this scope, a case-control analysis through the post-mortem genome-wide screening of 30 autopsy cases was performed. We identified a high number of novel genetic variants associated with SCD, of which 25 polymorphisms were consistent with a previous link to cardiovascular diseases. We ascertained that many genes have been already linked to cardiovascular system functioning and diseases and that the metabolisms most implicated in SCD are the lipid, cholesterol, arachidonic acid, and drug metabolisms, suggesting their roles as potential risk factors. Overall, the genetic variants pinpointed herein might be useful markers of SCD, but the novelty of these results requires further investigations.

Associations between genetic polymorphisms of membrane transporter genes and prognosis after chemotherapy: meta-analysis and finding from Seoul Breast Cancer Study (SEBCS)

Membrane transporters can be major determinants of the pharmacokinetic profiles of anticancer drugs. The associations between genetic variations of ATP-binding cassette (ABC) and solute carrier (SLC) genes and cancer survival were investigated through a meta-analysis and an association study in the Seoul Breast Cancer Study (SEBCS). Including the SEBCS, the meta-analysis was conducted among 38 studies of genetic variations of transporters on various cancer survivors. The population of SEBCS consisted of 1338 breast cancer patients who had been treated with adjuvant chemotherapy. A total of 7750 SNPs were selected from 453 ABC and/or SLC genes typed by an Affymetrix 6.0 chip. ABCB1 rs1045642 was associated with poor progression-free survival in a meta-analysis (HR = 1.33, 95% CI: 1.07-1.64). ABCB1, SLC8A1, and SLC12A8 were associated with breast cancer survival in SEBCS (P_gene < 0.05). ABCB1 rs1202172 was differentially associated with survival depending on the chemotherapy (P_interaction = 0.035). Our finding provides suggestive associations of membrane transporters on cancer survival.

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

BACKGROUND

Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting.

RESULTS

Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging.

CONCLUSIONS

The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.