Incorporating both genetic and tobacco smoking data to identify high-risk smokers for lung cancer screening

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification

BACKGROUND

Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored.

METHODS

Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold.

RESULTS

Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74).

CONCLUSIONS

Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Health effects associated with smoking: a Burden of Proof study

As a leading behavioral risk factor for numerous health outcomes, smoking is a major ongoing public health challenge. Although evidence on the health effects of smoking has been widely reported, few attempts have evaluated the dose-response relationship between smoking and a diverse range of health outcomes systematically and comprehensively. In the present study, we re-estimated the dose-response relationships between current smoking and 36 health outcomes by conducting systematic reviews up to 31 May 2022, employing a meta-analytic method that incorporates between-study heterogeneity into estimates of uncertainty. Among the 36 selected outcomes, 8 had strong-to-very-strong evidence of an association with smoking, 21 had weak-to-moderate evidence of association and 7 had no evidence of association. By overcoming many of the limitations of traditional meta-analyses, our approach provides comprehensive, up-to-date and easy-to-use estimates of the evidence on the health effects of smoking. These estimates provide important information for tobacco control advocates, policy makers, researchers, physicians, smokers and the public.

Risk of Colorectal Cancer Associated With Lifetime Excess Weight

Importance

Excess weight is associated with increased cancer risk, but the risk may have been underestimated, as previous studies did not consider cumulative lifetime exposure.

Objective

To assess the association of cumulative lifetime excess weight with risk of colorectal cancer (CRC).

Design, Setting, and Participants

In a population-based case-control study conducted since 2003 in Germany, height and self-reported weight documented in 10-year increments starting at age 20 years up to the current age were obtained from 5635 individuals with CRC and 4515 persons serving as controls. Body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, was calculated for each year of age from age 20 years to the current age by linear interpolation. Excess BMI (eBMI) at each year of age was determined as BMI - 25 and summed across ages to obtain the weighted number of years lived with overweight or obesity (WYOs), determined as year × eBMI. The eBMI was set to 0 in case of a BMI below 25. Associations with CRC risk were estimated for BMI at various ages and for WYOs by multiple logistic regression. Data analyses were performed from June 4, 2021, to December 17, 2021.

Main Outcomes and Measures

Relative risk of CRC according to lifetime exposure to excess weight compared with relative risks according to BMI at various ages.

Results

The mean (SD) age of the patients with CRC (n = 5635) was 68.4 (10.9) years; 3366 were men (59.7%); mean (SD) age of the control participants (n = 4515) was 68.5 (10.6) years; 2759 were men (61.1%). An association was observed between WYOs and CRC risk, with adjusted odds ratios (ORs) increasing from 1.25 (95% CI, 1.09-1.44) to 2.54 (95% CI, 2.24-2.89) from the first to the fourth quartile of WYOs compared with participants who remained within the normal weight range. Each SD increment in WYOs was associated with an increase of CRC risk by 55% (adjusted OR, 1.55; 95% CI, 1.46-1.64). This OR was higher than the OR per SD increase of eBMI at any single point of time, which ranged from 1.04 (95% CI, 0.93-1.16) to 1.27 (95% CI 1.16-1.39).

Conclusions and Relevance

The results of this case-control study suggest a greater role of cumulative lifetime excess weight for CRC risk than estimated by traditional analyses based on BMI measures taken at a single point.

Gene-gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer

BACKGROUND

Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility.

AIM

To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent.

METHODS

Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant × variant interaction. All analyses were performed for overall lung cancer and for subgroups.

RESULTS

No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR  = 1.20; 95% CI 1.13-1.27; p  = 5.6 × 10-10) and never smokers (e.g., maker rs1133683 Axin2; OR  = 1.27; 95% CI 1.19-1.35; p  = 1.0 × 10-12). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants.

CONCLUSIONS

The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers.

Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine

In recent years there has been a growth in the role of prevention in controlling the disease burden. Increasing efforts have been conveyed in the screening implementation and public health policies, and the spreading knowledge on risk factors reflects on major attention to health checks. Despite this, lifestyle changes are difficult to be adopted and the adherence to current public health services like screening and vaccinations remains suboptimal. Additionally, the prevalence and outcome of different chronic diseases and cancers is burdened by social disparities. P4 [predictive, preventive, personalized, participatory] medicine is the conceptualization of a new health care model, based on multidimensional data and machine-learning algorithms in order to develop public health intervention and monitoring the health status of the population with focus on wellbeing and healthy ageing. Each of the characteristics of P4 medicine is relevant to occupational medicine, and indeed the P4 approach appears to be particularly relevant to this discipline. In this review, we discuss the potential applications of P4 to occupational medicine, showing examples of its introduction on workplaces and hypothesizing its further implementation at the occupational level.

Elevated Stratifin promotes cisplatin-based chemotherapy failure and poor prognosis in non-small cell lung cancer

Drug resistance is a key factor in the treatment failure of clinical non-small cell lung cancer (NSCLC) patients after adjuvant chemotherapy. Here, our results provide the first evidence that eukaryotic translation initiation factor 2b subunit delta (EIF2B4)-Stratifin (SFN) fusion and increased SFN expression are associated with chemotherapy tolerance and activation of the phosphatidylinositol 3 kinase/v-akt murine thymoma viral oncogene (PI3K/Akt) signaling pathway in NSCLC patients, suggesting that SFN might have potential prognostic value as a tumor biomarker for the prognosis of patients with NSCLC.