A combination of top-down and bottom-up approaches addressing the inverse occurrence of Alzheimer's disease and cancer enables to classify patients based on their genotypes

The inverse relationship of occurrence of Alzheimer's disease (AD) and cancer has been reported in several population based studies and meta-analysis. The different distribution of exposures to known risk factors in people with cancer or AD was found insufficient to explain, in our previous cohort study, the lower than expected co-occurrence. Therefore, we investigate the genetic basis underlying the inverse relationship by analysing common genetic variants differently expressed in the two diseases. The study explores and identifies the genotypic 'red flags' that could distinguish and classify a priori the AD and cancer cases. A comparative analysis explored the distribution of 545,982 known Single Nucleotide Polymorphisms (SNPs) in the genome of large populations of cancers (N=4409) and AD (N=1292) patients from NIH dbGaP datasets. GWAS, PCA methods, SNPnexus, and ToppGene tools were employed to compare the datasets, to distinguish molecular processes associated with AD and cancer, and to select a set of SNPs differentially distributed in the two diseases. The GWAS analyses identified 300 SNPs (p< 10-5) associated with 213 unique genes (SNPnexus functional annotation). The gene set enrichment analysis (GSE) in ToppGene identified 11 out of 213 genes as significantly (p<10-5) enriched for phospholipid binding (GO:0005543): ABCA1, CADPS, GBF1, KCNQ1, MARCKS, NF1, PLD1, PXK, SNX29, TIAM1, ZFYVE26. Furthermore we tested the SNPs ability to discriminate AD form cancer cases by means of contingency tables and a Receiver Operating Characteristic (ROC) analysis. ROC score performance resulted high (AUC=72.9) for 11 most significant SNPS from the GWAS analysis, while 11 SNPs associated with the phospholipid binding classified even better AD vs. cancer cases (AUC=78.1). The adopted combination of top-down and bottom-up approaches indicates its potential explanatory capabilities. These preliminary results show that genes, identified by SNPs significantly different in the two diseases, are involved in shared biological pathways that, if deregulated, may explain the divergent trajectories towards AD or cancer. The differentially distributed SNPs might have potential clinical applications that could direct future research. Further investigations using other independent genetic datasets are required to confirm these findings that, if successfully replicated in silico, can form the basis for specific in vitro and in vivo studies on the inverse occurrence of the two diseases.