Genomic characterization of individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced lung cancer

Single nucleotide polymorphisms (SNPs) may modulate individual susceptibility to carcinogens. We designed a genome-wide association study to characterize individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced non-small cell lung cancer (NSCLC), and we validated our results. We hypothesized that this strategy would enrich the frequencies of the alleles that contribute to the observed traits. We genotyped 2.37 million SNPs in 95 extreme phenotype individuals, that is: heavy smokers that either developed NSCLC at an early age (extreme cases); or did not present NSCLC at an advanced age (extreme controls), selected from a discovery set (n = 3631). We validated significant SNPs in 133 additional subjects with extreme phenotypes selected from databases including >39,000 individuals. Two SNPs were validated: rs12660420 (pcombined  = 5.66 × 10-5 ; ORcombined  = 2.80), mapping to a noncoding transcript exon of PDE10A; and rs6835978 (pcombined  = 1.02 × 10-4 ; ORcombined  = 2.57), an intronic variant in ATP10D. We assessed the relevance of both proteins in early-stage NSCLC. PDE10A and ATP10DmRNA expressions correlated with survival in 821 stage I-II NSCLC patients (p = 0.01 and p < 0.0001). PDE10A protein expression correlated with survival in 149 patients with stage I-II NSCLC (p = 0.002). In conclusion, we validated two variants associated with extreme phenotypes of high and low risk of developing tobacco-induced NSCLC. Our findings may allow to identify individuals presenting high and low risk to develop tobacco-induced NSCLC and to characterize molecular mechanisms of carcinogenesis and resistance to develop NSCLC.

COPD comorbidities network

Multimorbidity frequently affects the ageing population and their co-existence may not occur at random. Understanding their interactions and that with clinical variables could be important for disease screening and management.

In a cohort of 1969 chronic obstructive pulmonary disease (COPD) patients and 316 non-COPD controls, we applied a network-based analysis to explore the associations between multiple comorbidities. Clinical characteristics (age, degree of obstruction, walking, dyspnoea, body mass index) and 79 comorbidities were identified and their interrelationships quantified. Using network visualisation software, we represented each clinical variable and comorbidity as a node with linkages representing statistically significant associations.

The resulting COPD comorbidity network had 428, 357 or 265 linkages depending on the statistical threshold used (p≤0.01, p≤0.001 or p≤0.0001). There were more nodes and links in COPD compared with controls after adjusting for age, sex and number of subjects. In COPD, a subset of nodes had a larger number of linkages representing hubs. Four sub-networks or modules were identified using an inter-linkage affinity algorithm and their display provided meaningful interactions not discernible by univariate analysis.

COPD patients are affected by larger number of multiple interlinked morbidities which clustering pattern may suggest common pathobiological processes or be utilised for screening and/or therapeutic interventions.