Xu, Wei

Importance A subset of Union for International Cancer Control ( UICC )/ American Joint Committee on Cancer ( AJCC ) eighth edition TNM stage I and II human papillomavirus–positive oropharyngeal carcinoma has undesirable outcomes, which might have contributed to a lack of success in phase III deintensification trials. Refining clinical stage groups, especially in the overabundant cN1/stage I group, has become important for treatment selection. Objective To assess the prognostic importance of pretreatment lymph node (LN) characteristics to optimize case distribution and outcome homogeneity within the N classification system. Design, Setting, and Participants This is an international multi-institutional retrospective prognostic cohort study. Analysis of human papillomavirus–positive oropharyngeal carcinoma treated curatively from 4 institutions (International Collaboration of Oropharyngeal Cancer Network for N-Classification [ICON-N] dataset) provided a refined clinical staging proposal; an independent dataset (Centre Hospitalier de l’Université de Montréal [CHUM] dataset) validated the proposal. Neuroradiologists reviewed pretreatment computed tomography and/or magnetic resonance imaging for nodal features, including presence or absence of abnormal LN(s), retropharyngeal LN, laterality, number of abnormal LN, and imaging-detected extranodal extension (iENE). Data were collected from February to May 2023, and data were analyzed from June to July 2023. Exposures Definitive chemoradiotherapy/radiotherapy or definitive surgery with or without postoperative chemoradiotherapy/radiotherapy. Main Outcomes and Measures The primary end point was overall survival. A Cox proportional hazards multivariable model was used to estimate adjusted hazard ratios (AHRs) and to derive an optimal clinical TNM stage classification (AHR-stage schema) incorporating the strongest prognostic nodal features within the UICC/AJCC eighth edition TNM framework after confirming the prognostication of iENE status. The performance (according to overall normalized scores and ranking) of the AHR-stage schema against the current UICC/AJCC eighth edition TNM staging system was evaluated for hazard consistency, hazard discrimination, prognostic importance, and sample size balance. Validation was performed in the CHUM dataset. Results The ICON-N dataset comprised 2053 patients, including 1898 (92.5%) with cN-positive disease and 155 (7.5%) with cN0 disease; a total of 298 (14.5%) were female, and the mean (SD) age was 60.6 (9.3) years. iENE-positive disease was identified in 710 of 1898 patients with cN-positive disease (37.4%). The median (range) follow-up was 5.1 (0.1-14.7) years. iENE was the strongest prognostic nodal feature in multivariable analysis; the AHR for iENE-positive vs iENE-positive disease was 2.43 (95% CI, 1.96-3.03) in the ICON-N dataset and 2.04 (95% CI, 1.28-3.23) in the CHUM dataset (n = 451). Reclassifying iENE-positive cases 1 stratum higher for N categorization without altering iENE-negative cases yielded an AHR-stage schema that outperformed the current TNM staging system in disease-free and overall survival with a lower (ie, better) overall normalized score (2 vs 3). Conclusions and Relevance In this study, reclassifying each N category 1 stratum higher for iENE-positive disease resulted in better disease-free and overall survival. The proposed new classification outperformed the currently TNM staging system in risk stratification and may facilitate future clinical trial design, outcomes research, and patient care.

Abstract Background: Somatic EGFR mutations define a subset of non–small cell lung cancers (NSCLC) that have clinical impact on NSCLC risk and outcome. However, EGFR-mutation-status is often missing in epidemiologic datasets. We developed and tested pragmatic approaches to account for EGFR-mutation-status based on variables commonly included in epidemiologic datasets and evaluated the clinical utility of these approaches. Methods: Through analysis of the International Lung Cancer Consortium (ILCCO) epidemiologic datasets, we developed a regression model for EGFR-status; we then applied a clinical-restriction approach using the optimal cut-point, and a second epidemiologic, multiple imputation approach to ILCCO survival analyses that did and did not account for EGFR-status. Results: Of 35,356 ILCCO patients with NSCLC, EGFR-mutation-status was available in 4,231 patients. A model regressing known EGFR-mutation-status on clinical and demographic variables achieved a concordance index of 0.75 (95% CI, 0.74–0.77) in the training and 0.77 (95% CI, 0.74–0.79) in the testing dataset. At an optimal cut-point of probability-score = 0.335, sensitivity = 69% and specificity = 72.5% for determining EGFR-wildtype status. In both restriction-based and imputation-based regression analyses of the individual roles of BMI on overall survival of patients with NSCLC, similar results were observed between overall and EGFR-mutation-negative cohort analyses of patients of all ancestries. However, our approach identified some differences: EGFR-mutated Asian patients did not incur a survival benefit from being obese, as observed in EGFR-wildtype Asian patients. Conclusions: We introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC. Impact: The proposed method is generalizable in the common occurrence in which EGFR-status data are missing.

1512 Background: Tobacco smoking profoundly impacts LC risk; however, data are limited as to what extent SC prior to diagnosis impacts LC overall survival (OS) and lung cancer specific survival (LCSS). LC screening offers a possible teachable moment, but there is uncertainty of SC benefits after a lifetime of smoking. We use the ILCCO database to answer if SC prior to LC dx is associated with better OS and LCSS, considering time since smoking cessation (TSSC). Methods: Using individual data, analysis was performed on 17 ILCCO studies with available TSSC to estimate survival using univariable analysis and models of stage-adjusted and cumulative smoking-adjusted multivariable analysis. Adjusted Hazard Ratios (aHR) from Cox models, cubic spline smooth curves and Kaplan-Meier curves were created. Sensitivity analysis was performed for TSSC and LCSS on 13 studies. Results: Of 34649 patients, 14322 (41%) were current smokers 14273 (41%) ex-smokers and 6054 (18%) never smokers at diagnosis. We confirmed that ex-smokers (aHR 0.88 CI 0.86-0.91) and never smokers (aHR 0.76 CI 0.73-0.8) improved OS compared to current smokers. Amongst ex-smokers, < 2y TSSC (aHR 0.88 CI 0.82-0.94), 2-5y TSSC (aHR 0.83 CI 0.77-0.90) and > 5y TSSC (aHR 0.8 CI 0.76-0.84) had improved OS compared to CS. Sensitivity analysis showed a trend towards improved LCSS survival for < 2y TSSC (aHR 0.95 CI 0.86-1.05) and 2-5y TSSC (aHR 0.93 CI 0.83-1.04), whereas > 5y TSSC significantly improved LCSS by 15% (aHR 0.85 CI 0.78-0.92). To mimic the LC screening participants, in analysis of > 30 pack-years individuals, associations were strikingly strong: < 2y TSSC had improved OS by 14% (aHR 0.86 CI 0.80-0.93); 2-5y TSSC by 17% (aHR 0.83 CI 0.76-0.90); and > 5 TSSC by 22% (aHR 0.78 CI 0.74-0.83), compared to current smokers; for < 30 packs-years, a trend towards better OS was observed for < 2y TSSC (aHR 0.95 CI 0.92-1.02) and 2-5y TSSC (aHR 0.86 CI 0.74-1.01), whereas > 5y TSSC improved OS by 23% (aHR 0.77 CI 0.72-0.82). Conclusions: Among ex-smokers, the risk of overall death was reduced by 12% on < 2y TSSC, 17% on 2-5y TSSC and 20% > 5y TSSC, whereas for LCSS, the benefit was significant only for > 5y TSCC, compared to current smokers at time of diagnosis. Here we demonstrate that convincing screening participants to quit smoking at any point of their trajectory, even just prior to dx such as < 2y TSSC, improved OS, and LCSS benefit was present beyond 5y of quitting. These relationships are independent of pack-years, age, across all stages and other prognostic variables.

1562 Background: In Caucasian-predominant populations, overweight or obese NSCLC pts (BMI≥25-kg/m2) have better prognosis while underweight (BMI≤18.5-kg/m2) pts have a worse prognosis. A large pooled sample allowed us to evaluate the role of ethnicity in this BMI-NSCLC OS relationship. Methods: Using individual data, survival analysis was performed on 15 ILCCO studies, assessing the interactions between ethnicity and BMI on overall survival (OS). Adjusted Hazard Ratios (aHR) from Cox models and adjusted penalized smoothing spline plots were generated. Results: Among 13416 (77%) Caucasian, 2975 (17%) Asian, and 935 (5%) Black NSCLC pts analyzed, we confirmed that for all pts, being underweight at NSCLC diagnosis was associated with worse OS (aHR=1.68, CI 1.5-1.8, p<0.001), while overweight/obese pts had improved survival (aHR 0.89, CI 0.8-0.9, p<0.001), when compared to pts with normal BMI. In general, Black pts had poorer OS than Caucasian pts (aHR 1.26 CI 1.1-1.4, p<0.001). However, the BMI-OS relationship differed according to ethnicity (BMI-ethnicity interaction, p=0.009): Caucasian underweight pts had poorer OS (aHR 1.67, CI 1.5-1.8, P<0.001) while overweight/obese pts had improved OS (aHR 0.89, CI 0.8-0.9, P<0.001). In Asian pts, these two associations were aHR 1.15, CI 0.8-1.5, P=0.33, and aHR 0.91, CI 0.7-1.1, P=0.32, respectively. In Black pts, these two associations were aHR 1.06, CI 0.7-1.5, p=0.73 and 0.75, CI 0.6-0.8 p<0.001, respectively. For overweight/obese patients, as BMI rises from 25-kg/m2 through 45-kg/m2, the prognosis worsens for Caucasian pts, remains stable for Asian pts, but improves for Black pts. Conclusions: InCaucasian pts, being underweight had a greater negative impact on OS than for Asian or Black pts, while being obese had a greater beneficial impact in Blacks than in other ethnic groups. These ethnic differences likely reflect genetically-informed muscle/adipose tissue distributions, where Black pts may have less sarcopenic obesity than other ethnicities. In future prognostic studies, BMI relationships must account for ethnic differences.

Polymorphisms in the promoter of the BRM gene, a critical subunit of the chromatin remodeling SWI/SNF complex, have previously been implicated in risk and prognosis in Caucasian‐predominant lung, head and neck, esophageal, and pancreatic cancers, and in hepatocellular cancers in Asians. We investigated the role of these polymorphisms in hepatocellular carcinoma (HCC) risk and prognosis. HCC cases were recruited in a comprehensive cancer center while the matched controls were recruited from family practice units from the same catchment area. For risk analyses, unconditional logistic regression analyses were performed in HCC patients and matched healthy controls. Overall survival analyses were performed using Cox proportional hazard models, Kaplan‐Meier curves, and log‐rank tests. In 266 HCC cases and 536 controls, no association between either BRM promoter polymorphism (BRM‐741 or BRM‐1321) and risk of HCC was identified (P > 0.10 for all comparisons). There was significant worsening of overall survival as the number of variant alleles increased: BRM‐741 per variant allele adjusted hazards ratio (aHR) 5.77, 95% confidence interval (CI) 2.89‐11.54 and BRM‐1321 per variant allele aHR 4.09, 95%CI 2.22‐7.51. The effects of these two polymorphisms were at least additive, where individuals who were double homozygotes for the variant alleles had a 45‐fold increase in risk of death when compared to those who were double wild‐type for the two polymorphisms. Two BRM promoter polymorphisms were strongly associated with HCC prognosis but were not associated with increased HCC susceptibility. The association was strongest in double homozygotes for the allele variants.

42 Background: Methods of stratifying esophageal adenocarcinoma patients into prognostic groups are needed, as are new insights into genetic determinants of disease behaviour. Prognosis is likely to have non-negligible genetic influences, as mediated by host responses to tumor, resistance to therapeutic side-effects, and/or an influence on tumor development. Prior studies have used candidate-gene approaches. We took an alternative approach, using an unbiased, genome-wide approach, and novel analytic methods that may be better able to detect multi-gene interactions, which may contribute the majority of genetic effects for many clinical phenotypes. Methods: Germline DNA from a Toronto-based cohort of EAC patients (n=270) was analyzed by Omni1 Quad microarray as part of the BEAGESS initiative. Quality control and analysis was performed using PLINK, R, and GenABEL software packages. A Cox proportional hazards (CPH) model for progression-free survival tested each polymorphism for independent effects at a genome-wide significance level of P < 1E-07, adjusting for population stratification. While classical analysis has limited ability to detect gene-gene interactions, a Random Survival Forest algorithm was used to detect effects based on the complex interactions among top 1,000 polymorphisms by p-value ranking. Results: After data cleaning and standard GWAS quality control procedures, there were 735,309 SNPs and 245 patients remaining for analysis. The CPH model, adjusted for population stratification, produced a satisfactory Q-Q plot, and showed one SNP (rs7844673, Chr 8) that was significant at p=7.8E-8. In addition, Random Forest based variable selection produced a set of 20 polymorphisms that (1) reproduced 86% of the predictive ability of the full 1000 variables, and (2) also included the #3 ranked polymorphism by CPH modeling (rs9290822, Chr 3) upstream of the IGF2BP2 gene. Conclusions: A genome-wide approach has discovered two previously undescribed SNPs with a potential influence on EAC prognosis via a combination of independent and interactive effects. Validation in an independent cohort is currently being pursued.