Lorenzo Richiardi

Background: Occupational epidemiological analyses typically focus on a priori hypotheses relating disease to specific exposures or occupations. We used regularized regression models to agnostically identify occupations associated with lung cancer while accounting for known lung carcinogens. Methods: We pooled data from 14 European and Canadian lung cancer case–control studies. The main analysis included 13 587 cases and 16 410 controls for men and 3199 cases and 4408 controls for women. Associations between ever-employment in various occupations and lung cancer risks were evaluated using two selection approaches: a least absolute shrinkage and selection operator regression and a two-stage Bayesian hierarchical horseshoe model accounting for age, study, smoking, and occupational exposure to known lung carcinogens. Unconditional logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for selected occupations. Results: For men, occupations consistently identified as positively associated with lung cancer included miners (OR = 1.28, 95% CI: 1.06–1.54) and bricklayers (OR = 1.30, 95% CI: 1.12–1.52), while secondary education teachers (OR = 0.37, 95% CI: 0.22–0.60), production managers (OR = 0.66, 95% CI: 0.54–0.81), and government executive officials (OR = 0.72, 95% CI: 0.62–0.83) were negatively associated. For women, secondary language and literature teachers (OR = 0.39, 95% CI: 0.20–0.72) and government executive officials (OR = 0.63, 95% CI: 0.48–0.81) showed negative associations. Conclusion: Two agnostic, data-driven models identified several occupations associated with both increased and decreased lung cancer risks. These associations persisted after accounting for known exposures and covariates, highlighting the need to investigate unidentified occupational factors.

Purpose: Prognostic models aim to estimate the individual risk of developing a specific health outcome based on patient characteristics and prognostic factors. A key factor affecting performance over time and across different populations is evolving treatment strategies. These shifts can alter the relationship between predictors and outcome, as well as occurrence of competing events. Counterfactual risk models may yield more reliable estimates across different time periods and treatment regimens than traditional approaches like ignoring treatment, restricting analysis to untreated patients, stratifying, or adjusting for treatment. Patients and methods: In the Turin Prostate Cancer Prognostication cohort study, which includes patients diagnosed with non-metastatic prostate cancer and followed longitudinally, the performance of Memorial Sloan Kettering Cancer Center prediction model was evaluated in predicting metastatic progression up to 5 years after diagnosis in the presence of competing mortality risk. Using the g-formula, time to the two competing events was generated under a counterfactual scenario in which no patients received prostatectomy. The performance of the MSKCC model developed in the TPCP cohort and applied to the counterfactual cohort (applied model) and the model updated under this counterfactual scenario (updated model) were compared. Results: At 5 years post-diagnosis, the updated model had an Area Under the Curve (AUC) of 0.84 (95% CI: 0.82; 0.85) and the applied model had an AUC of 0.83 (95% CI: 0.81, 0.85). The calibration slope for the updated model was 1.02 (95% CI: 0.99, 1.12) compared to 1.07 (95% CI: 0.97, 1.17) for the applied model. Calibration intercepts were 0.00 (95% CI: −0.10, 0.10) and 0.05 (95% CI: −0.04, 0.15) respectively. The predicted probabilities of the outcome of interest under counterfactual treatment strategy varied not only because of the treatment itself, but also due to its impact on competing risks. Conclusion: When a prediction model cannot be validated in a new population due to treatment shifts, counterfactual risks under hypothetical regimens can still be cautiously estimated. In particular, counterfactual modeling should be considered as a useful complementary assessment of model transportability and calibration under clinically relevant treatment scenarios.

Background: Polygenic risk scores (PRS) can provide a proxy for genetic liability to smoking- and alcohol-related behaviors. If these behavior-associated PRS can also predict the risk or survival of head and neck squamous cell carcinoma (HNSCC), then such PRS can provide motivation for behavioral modification during counseling by associating behavior with its consequences.Methods: Based on the VOYAGER Consortium of 3,254 controls and 5,538 HNSCC cases, where 3,194 were HPV-positive, we constructed PRS for multiple smoking and alcoholic metrics, using summary statistics from GSCAN. We evaluated the HPV-stratified HNSCC risk by logistic regression, overall survival by Cox models comparing different percentile strata of PRS and compared these results to observed behaviors.Findings: Each higher PRS predicted greater corresponding smoking and alcohol use behaviors. For HPV-positive diseases, higher PRS for smoking initiation and alcoholic drinks-per-week predicted increased HNSCC risk. Similarly, higher PRS for smoking initiation, cigarettes-per-day, smoking cessation, and alcoholic drinks-per-week each predicted higher HPV-negative HNSCC risk. For overall survival, higher PRS of smoking initiation (HR = 1.08, P=0.017) and alcoholic drinks-per-week (HR=1.14, P=2.66×10-4) predicted poorer prognosis in HPV-positive cases. Illustrative models showed similar results for a genetic counseling purpose. Each PRS predicted its corresponding observed behavior which mediated part of the associations (7.30%-54.6%) between PRSs and HNSCC risk or survival.Interpretation: Smoking- and alcohol-related PRS predicted observed behaviors, greater HNSCC risk and poorer overall survival. These findings support integrating genetic liability with clinical factors to refine counseling strategies for behavior modification.

Background: Tobacco, alcohol, and human papillomavirus (HPV) are major risk factors for oral cavity and oropharyngeal cancers, yet their specific contributions to the tumor molecular landscape remain incompletely understood. Methods: We analyzed somatic alterations in 1086 tumors (606 oral cavity cancers and 480 oropharyngeal cancers, including 287 HPV-positive oropharyngeal cancers) profiled with a 1109-gene panel and evaluated their associations with exposures. Results: PIK3CA alterations were widespread but context specific: HPV-positive oropharyngeal cancers featured APOBEC-driven mutations and co-occurrence of FGFR3 mutations in a subset of cases, while HPV-negative oropharyngeal cancers and oral cavity cancers were marked by 3q26/28 (PIK3CA/TP63/SOX2) amplifications. TP53 was the most frequently mutated gene in HPV-negative tumors, with strong enrichment among smokers in HPV-negative oropharyngeal cancers, whereas TP53 mutation frequencies in oral cavity cancers were consistently high, regardless of smoking status. CCND1 amplifications were largely restricted to HPV-negative tumors and were most frequent in individuals reporting combined tobacco and alcohol use. This combined effect was replicated in The Cancer Genome Atlas oral cavity cancer–oropharyngeal cancer cohort and mirrored at the transcriptomic level, where CCND1 messenger RNA expression was highest in dual tobacco-alcohol users. Within oral cavity cancers, characterization of 1 of the largest cohorts of tumors from never smoker–never drinkers to date (n = 100) showed that patients were more likely to be female and to exhibit enrichment for FAT1 and CASP8 mutations compared with tumors from ever smoker–ever drinkers (n = 353). Conclusions: Together, these findings define distinct, exposure-driven patterns of somatic alterations by subsite and HPV status and provide a molecular framework that may inform etiological-based risk stratification in head and neck cancer.

Background: Prognostic models are crucial for prostate cancer (PCa) treatment decision making at the time of diagnosis, particularly for distinguishing active surveillance (AS) candidates from those requiring curative treatment. While several models exist, their ability to predict metastatic disease—the primary driver of PCa mortality—remains underexplored. Methods: We analysed the Turin Prostate Cancer Prognostication cohort, which includes 891 unselected PCa patients diagnosed between 2008 and 2013 in Turin, Italy. Three widely used prognostic models—D’Amico, CAPRA, and MSKCC—were updated and compared based on optimism-corrected discrimination and overall prediction error for metastatic PCa (mPCa) within five years of diagnosis, accounting for competing risks. Overall survival was also assessed. Additionally, we investigated whether replacing standard AS eligibility criteria with nomogram-based risk thresholds could better identify patients at low risk of metastasis, maximizing AS uptake while minimising metastatic risk. Results: The MSKCC nomogram (optimism-corrected AUC _t : 0.81; scaled Brier score: 0.15) outperforming the CAPRA score (AUC _t : 0.77; Brier score: 0.11) and the D’Amico classification (AUC _t 0.64; Brier score: 0.03) in predicting mPCa. The same ranking was observed for overall mortality prediction. When 95th percentile of MSKCC’s predicted probabilities among patients selected for six different AS protocols was used as a threshold, the proportion of potentially eligible patients increased from 7.8% when UCSF criterion was used to 57.0% without substantially increasing metastatic risk (observed 5-year risk: 1.7%). Conclusions: The MSKCC nomogram outperformed other models in predicting mPCa and overall mortality. Implementing risk-based AS eligibility thresholds derived from MSKCC could enhance patient selection while facilitating shared decision-making between patients and clinicians.

Epigenetics represents a key mechanism through which environmental factors can influence the risk of developing chronic diseases. DNA methylation is tissue

Supplementary Figure from Association Study between Polymorphisms in DNA Methylation–Related Genes and Testicular Germ Cell Tumor Risk

Supplementary Data from Association Study between Polymorphisms in DNA Methylation–Related Genes and Testicular Germ Cell Tumor Risk

Supplementary Figure from Occupational Exposure to Polycyclic Aromatic Hydrocarbons and Lung Cancer Risk: Results from a Pooled Analysis of Case–Control Studies (SYNERGY)