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PURPOSE To investigate whether postdiagnosis smoking cessation may affect the risk of death and disease progression in patients with renal cell carcinoma (RCC) who smoked at the time of diagnosis. METHODS Two hundred twelve patients with primary RCC were recruited between 2007 and 2016 from the Urological Department in N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russia. Upon enrollment, a structured questionnaire was completed, and the patients were followed annually through 2020 to repeatedly assess their smoking status and disease progression. Survival probabilities and hazards for all-cause and cancer-specific mortality and disease progression were investigated using extended the Kaplan-Meier method, time-dependent Cox proportional hazards regression, and Fine-Gray competing-risk models. RESULTS Patients were followed for a median of 8.2 years. During this time, 110 cases of disease progression, 100 total deaths, and 77 cancer-specific deaths were recorded. Eighty-four patients (40%) quit smoking after diagnosis. The total person-years at risk for this analysis were 748.2 for continuing smoking and 611.2 for quitting smoking periods. At 5 years of follow-up, both overall survival (85% v 61%) and progression-free survival (80% v 57%) rates were higher during the quitting than continuing smoking periods (both P < .001). In the multivariable time-dependent models, quitting smoking was associated with lower risk of all-cause mortality (hazard ratio [HR], 0.51; 95% CI, 0.31 to 0.85), disease progression (HR, 0.45; 95% CI, 0.29 to 0.71), and cancer-specific mortality (HR, 0.54; 95% CI, 0.31 to 0.93). The beneficial effect of quitting smoking was evident across all subgroups, including light smokers versus moderate-heavy smokers and those with early-stage versus late-stage tumors. CONCLUSION Quitting smoking after RCC diagnosis may significantly improve survival and reduce the risk of disease progression and cancer mortality among patients who smoke.

The frequency of lung cancer detection in the Manchester Lung Health Checks (MLHCs), a community-based screening service, was higher than in the National Lung Screening Trial (NLST) over two screening rounds. We aimed to identify the potential reasons for this difference.We analyzed individual-level data from NLST and MLHCs, restricting to MLHCs participants who met NLST eligibility criteria. We calculated 'detection ratios' comparing the frequency of lung cancer detection in MLHCs vs NLST, first after excluding NLST participants ineligible by MLHC eligibility criteria (6-year lung cancer risk ≥ 1.51 %), and then after standardization to remove the influence of different distributions of baseline lung cancer risk.Among the 1,079 MLHCs participants who met NLST eligibility criteria, 4.7% were diagnosed with lung cancer over two screening rounds compared with 1.7% in NLST, giving an initial detection ratio of 2.6 (95%CI 2.2-3.0). This was reduced to 2.2 (95%CI 1.3-2.3) after imposing the MLHCs eligibility criterion on NLST, and further to 1.6 (95%CI 1.2-2.1) after removing the influence of different risk distributions. In stratified analyses, the standardized detection ratio was particularly elevated in individuals who were older, living in areas of high socioeconomic disadvantage, or had an FEV/FVC ratio less than 60.The 2.6-fold higher lung cancer detection in the community-based MLHCs vs NLST is partly explained by differences in eligibility criteria and baseline risk distributions. The residual 60% increase may relate to higher detection in certain risk groups, including older participants, those with more obstructive lung disease, and those living in areas of socioeconomic disadvantage.

The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample The EarlyCDT-Lung test is a blood-based autoantibody assay intended to identify high-risk individuals for low-dose computed tomography lung cancer screening. However, there is a paucity of evidence on the performance of the EarlyCDT-Lung test in ever-smokers. We conducted a nested case-control study within two prospective cohorts to evaluate the risk-discriminatory performance of the EarlyCDT-Lung test using prediagnostic blood samples from 154 future lung cancer cases and 154 matched controls. Cases were selected from those who had ever smoked and had a prediagnostic blood sample

BackgroundTo evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis.MethodsWe measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only.FindingsThere were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035).InterpretationBlood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information.FundingNo explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry. Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035). Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute ( U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden ( AMP19-962), and Swedish Department of Health Ministry.

Objectives We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals. Methods We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal). Results Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol. Conclusions Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.

BackgroundWhile lung cancer low-dose computed tomography (LDCT) screening is being rolled out in many regions around the world, differentiation of indeterminate pulmonary nodules between malignant and benign remains to a challenge for screening programs. We conducted one of the first systematic investigations of circulating protein markers for their ability to assess the risk of malignancy for screen-detected pulmonary nodules.MethodsBased on four LDCT screening studies in the United States, Canada and Europe, we assayed 1078 unique protein markers in pre-diagnostic samples based on a nested case-control design with a total of 1253 participants. Protein markers were measured using proximity extension assays and the data were analyzed using multivariate logistic regression, random forest, and penalized regressions.ResultsWe identified 36 potentially informative markers differentiating malignant nodules from benign nodules. Pathway analysis revealed a tightly connected network based on the 36 protein-coding genes. We observed a differential mRNA expression profile of the corresponding 36 mRNAs between lung tumors and adjacent normal tissues using data from The Cancer Genomic Atlas. We prioritized a panel of 9 protein markers through 10-fold nested cross-validations. We observed that circulating protein markers can increase sensitivity to 0.80 for nodule malignancy compared to the Brock model (p-valueConclusionsCirculating protein markers can help to differentiate between malignant and benign pulmonary nodules. Validating these results in an independent CT-screening study will be required prior to clinical implementation.

We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with postscreening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from -27.0% (ITALUNG, 8.3 years follow-up) to 67.2% (DLCST, 5.0 years follow-up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow-up time. The annual changes in Ps were -3.5% and -3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from -14.9% (55-59 years) to 21.7% (70-74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow-up and population characteristics.

Background: Lung cancer is the leading cause of cancer death worldwide. We explored the relationship between socioeconomic status and lung cancer incidence across world regions, using educational level as a proxy for socioeconomic status. Methods: We analyzed the harmonized database of the Lung Cancer Cohort Consortium (LC3). The current study included data from 18 prospective cohorts from 15 countries in the US, Europe, Asia, and Australia. Separately for participants who never or currently/formerly smoked, we estimated the association between educational level and incident lung cancer using Cox proportional hazards models. Information on education was harmonized using the International Standard Classification of Education and then modeled as an ordinal variable in 4 categories. Models were adjusted for age, sex, and for participants who currently/formerly smoked, smoking duration, cigarettes per day, and time since cessation. Results: Among 2.6 million participants from 15 countries, 62,645 developed lung cancer during follow-up (median follow-up = 12.6 years). Among current/former smokers, increased educational level was associated with decreased lung cancer incidence in most cohorts after adjustment for age, sex, and detailed smoking information, with HRs ranging from 0.77 (95%CI: 0.42-1.41) per 1-unit increase in educational level in the Iranian Golestan Cohort Study to 1.02 (95%CI: 0.95-1.09) in the Australian Melbourne Collaborative Cohort Study. When grouping by world region, the association between education and lung cancer incidence among currently/formerly smoking participants was similar for the US (HRpooled=0.88, 95%CI: 0.87-0.89), Europe (HRpooled=0.89, 95%CI: 0.88-0.91), and Asia (HRpooled=0.91, 95%CI: 0.86-0.96), but attenuated in the Australian cohort (HR=1.02, 95%CI: 0.95-1.09). Among never smokers, after adjustment for age and sex, there was no statistically significant association between educational level and lung cancer incidence (p-trend>0.05 in all cohorts), with the exception of the US Southern Community Cohort Study, which comprises primarily African-Americans and showed a HR of 0.75 (95%CI: 0.62-0.90). Conclusion: Among currently and formerly smoking individuals, higher educational level showed a strikingly consistent decreased risk of incident lung cancer across cohorts from 4 continents, after detailed adjustment for smoking. In contrast, among people who never smoked, there was no association between education and lung cancer incidence in any cohort, with the exception of the Southern Community Cohort Study. Further research is needed to clarify the mechanisms, either related or unrelated to smoking, that contribute to the association between education and lung cancer risk. Citation Format: Justina U. Onwuka, Hana Zahed, Xiaoshuang Feng, Karine Alcala, Mattias Johansson, Hilary A. Robbins, Lung Cancer Cohort Consortium. Socioeconomic status and lung cancer incidence: An analysis of data from 15 countries in the Lung Cancer Cohort Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1950.

PURPOSE Seropositivity for the HPV16-E6 oncoprotein is a promising marker for early detection of oropharyngeal cancer (OPC), but the absolute risk of OPC after a positive or negative test is unknown. METHODS We constructed an OPC risk prediction model that integrates (1) relative odds of OPC for HPV16-E6 serostatus and cigarette smoking from the human papillomavirus (HPV) Cancer Cohort Consortium (HPVC3), (2) US population risk factor data from the National Health Interview Survey, and (3) US sex-specific population rates of OPC and mortality. RESULTS The nine HPVC3 cohorts included 365 participants with OPC with up to 10 years between blood draw and diagnosis and 5,794 controls. The estimated 10-year OPC risk for HPV16-E6 seropositive males at age 50 years was 17.4% (95% CI, 12.4 to 28.6) and at age 60 years was 27.1% (95% CI, 19.2 to 45.4). Corresponding 5-year risk estimates were 7.3% and 14.4%, respectively. For HPV16-E6 seropositive females, 10- year risk estimates were 3.6% (95% CI, 2.5 to 5.9) at age 50 years and 5.5% (95% CI, 3.8 to 9.2) at age 60 years and 5-year risk estimates were 1.5% and 2.7%, respectively. Over 30 years, after a seropositive result at age 50 years, an estimated 49.9% of males and 13.3% of females would develop OPC. By contrast, 10-year risks among HPV16-E6 seronegative people were very low, ranging from 0.01% to 0.25% depending on age, sex, and smoking status. CONCLUSION We estimate that a substantial proportion of HPV16-E6 seropositive individuals will develop OPC, with 10-year risks of 17%-27% for males and 4%-6% for females age 50-60 years in the United States. This high level of risk may warrant periodic, minimally invasive surveillance after a positive HPV16-E6 serology test, particularly for males in high-incidence regions. However, an appropriate clinical protocol for surveillance remains to be established., United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) 5U01CA195603-02 U01CA202979, Division of Cancer Epidemiology and Genetics, US NCI, VicHealth, Canadian Institutes of Health Research (CIHR), Cancer Council Victoria, National Health and Medical Research Council (NHMRC) of Australia 209057 396414 1074383, National Cancer Institute P30 CA016056, Einstein Cancer Research Center CA013330, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA, NIH National Heart Lung & Blood Institute (NHLBI) 75N92021D00001 75N92021D00002 75N92021D00003 75N92021D00004 75N92021D00005

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations “partially ameliorate racial disparities in screening eligibility” compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial–like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%–33.4% = 15.0% to 64.5%–48.5% = 16.0%; Asian Americans: 48.3%–35.6% = 12.7% to 64.5%–45.2% = 19.3%; Hispanic Americans: 48.3%–24.8% = 23.5% to 64.5%–37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%–75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1,161 proteins in a nested-case control study within 2 prospective cohorts (n=252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n=479 cases and 479 controls). Eligible participants had any history of smoking and cases were diagnosed within 3 years of blood draw. The Nodule Malignancy project measured 1,077 proteins among participants with a heavy smoking history within 4 LDCT screening studies (n=425 cases within 5 years of blood draw, 398 benign-nodule controls, and 430 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its lung cancer discriminative performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n=1,696 cases and 2,926 subcohort representatives), and in the Nodule Malignancy project within 5 LDCT screening studies (n=675 cases, 648 benign-nodule controls, and 680 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

Background: Tobacco exposure causes 8 of 10 lung cancers, and identifying additional risk factors is challenging due to confounding introduced by smoking in traditional observational studies. Materials and Methods: We used Mendelian randomization (MR) to screen 207 metabolites for their role in lung cancer predisposition using independent genome-wide association studies (GWAS) of blood metabolite levels (n = 7,824) and lung cancer risk (n = 29,266 cases/56,450 controls). A nested case–control study (656 cases and 1,296 matched controls) was subsequently performed using prediagnostic blood samples to validate MR association with lung cancer incidence data from population-based cohorts (EPIC and NSHDS). Results: An MR-based scan of 207 circulating metabolites for lung cancer risk identified that blood isovalerylcarnitine (IVC) was associated with a decreased odds of lung cancer after accounting for multiple testing (log10-OR = 0.43; 95% CI, 0.29–0.63). Molar measurement of IVC in prediagnostic blood found similar results (log10-OR = 0.39; 95% CI, 0.21–0.72). Results were consistent across lung cancer subtypes. Conclusions: Independent lines of evidence support an inverse association of elevated circulating IVC with lung cancer risk through a novel methodologic approach that integrates genetic and traditional epidemiology to efficiently identify novel cancer biomarkers. Impact: Our results find compelling evidence in favor of a protective role for a circulating metabolite, IVC, in lung cancer etiology. From the treatment of a Mendelian disease, isovaleric acidemia, we know that circulating IVC is modifiable through a restricted protein diet or glycine and L-carnatine supplementation. IVC may represent a modifiable and inversely associated biomarker for lung cancer.

IntroductionLow-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOM2012 and Liverpool Lung Project model (LLPv2)) and National Lung Screening Trial (NLST) eligibility criteria in a community-based screening programme.MethodsEver-smokers aged 55–74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOM2012 score ≥1.51%) were offered annual LDCT screening over two rounds. LLPv2 score was calculated but not used for screening selection; ≥2.5% and ≥5% thresholds were used for analysis.ResultsPLCOM2012 ≥1.51% selected 56% (n=1429) of LHC attendees for screening. LLPv2 ≥2.5% also selected 56% (n=1430) whereas NLST (47%, n=1188) and LLPv2 ≥5% (33%, n=826) selected fewer. Over two screening rounds 62 individuals were diagnosed with LC; representing 87% (n=62/71) of 6-year incidence predicted by mean PLCOM2012 score (5.0%). 26% (n=16/62) of individuals with LC were not eligible for screening using LLPv2 ≥5%, 18% (n=11/62) with NLST criteria and 7% (n=5/62) with LLPv2 ≥2.5%. NLST eligible Manchester attendees had 2.5 times the LC detection rate than NLST participants after two annual screens (≈4.3% (n=51/1188) vs 1.7% (n=438/26 309); padjOR 2.50, 95% CI 1.11 to 5.64; p=0.028).ConclusionProspective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCOM2012 model may underestimate risk in deprived UK populations; further research focused on model calibration is required.

Background: Country-specific evidence is needed to guide decisions regarding whether and how to implement lung cancer screening in different settings. For this study, we estimated the potential numbers of individuals screened and lung cancer deaths prevented in Brazil after applying different strategies to define screening eligibility. Methods: We applied the Lung Cancer Death Risk Assessment Tool (LCDRAT) to survey data on current and former smokers (ever-smokers) in 15 Brazilian state capital cities that comprise 18% of the Brazilian population. We evaluated three strategies to define eligibility for screening: (1) pack-years and cessation time (≥30 pack-years and

INTRODUCTION: The risk of prostate cancer among persons living with HIV (PWH) is not well understood and may be obscured by different opportunities for detection. METHODS: We identified 123,472 (37,819 PWH and 85,653 comparators) men enrolled in the Veterans Aging Cohort Study, a prospective national cohort of PWH and demographically-matched, uninfected comparators in 2000–2015. We calculated rates of PSA testing by HIV status and fit multivariable Poisson models comparing the rates of PSA testing, prostate biopsy, and cancer incidence. RESULTS: The mean age at enrollment was 52 years. Rates of PSA testing were lower in PWH versus uninfected comparators (0.58 versus 0.63 tests per person-year, respectively). Adjusted rates of PSA screening and prostate biopsy were lower among PWH (incidence rate ratio [IRR] 0.87, 95% CI 0.75–0.84) and IRR 0.79 (0.74–0.83), respectively. The crude IRR for prostate cancer was lower in PWH versus controls (IRR 0.90, 95% CI 0.83–0.97). However, in a multivariable model adjusting for PSA testing, cancer incidence was similar by HIV status (IRR=0.93, 95% CI 0.86–1.01, p=0.08). Among patients who received a prostate biopsy, incidence of prostate cancer did not differ significantly by HIV status (IRR 1.06, 95% CI 0.98–1.15, p=0.15). Among incident cancers, there were significant differences in the distributions of Gleason Grade (p=0.05), but not cancer stage (p=0.14) by HIV status. CONCLUSIONS: When accounting for less PSA testing among PWH, the incidence of prostate cancer was similar by HIV status. These findings suggest that less screening contributed to lower observed incidence of prostate cancer in PWH.

Background The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. Methods We analysed current and former smokers aged 40–80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). Results Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81–0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79–0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79–0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14–1.27) to 2.16 for LLPv2 (95% CI = 2.05–2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). Conclusion In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.

Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates “prescreening risk” (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%–0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%–0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%–2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.

Background Current guidelines for lung cancer screening via low-dose computed tomography recommend annual screening for all candidates meeting basic eligibility criteria. However, lung cancer risk of eligible screening participants can vary widely, and further risk stratification could be used to individually optimize screening intervals in view of expected benefits, possible harms and financial costs. To this effect, models have been developed in the US National Lung Screening Trial based on self-reported lung cancer risk factors and imaging data. We evaluated these models using data from an independent screening trial in Germany. Methods We examined the Polynomial model by Schreuder et al., the Lung Cancer Risk Assessment Tool extended by CT characteristics (LCRAT + CT) by Robbins et al., and a criterion of presence vs. absence of pulmonary nodules ≥4 mm (Patz et al.), applied to sub-sets of screening participants according to eligibility criteria. Discrimination was evaluated via the receiver operating characteristic curve. Delayed diagnoses and false positive results were calculated at various thresholds of predicted risk. Model calibration was assessed by comparing mean predicted risk versus observed incidence. Results One thousand five hundred and six participants were eligible for the validation of the LCRAT + CT model, and 1,889 for the validation of the Polynomial model and Patz criterion, yielding areas under the receiver operating characteristic curve of 0.73 (95% CI: 0.63, 0.82), 0.75 (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping 50% annual screenings (participants within the 5 lowest risk deciles by LCRAT + CT in any round or by the Polynomial model; baseline screening round), would have avoided 75% (21.9%, 98.7%) and 40% (21.8%, 61.1%) false positive screen tests and delayed 10% (1.8%, 33.1%) or no (0%, 32.1%) diagnoses, respectively. Using the Patz criterion, referring 63.2% (61.0% to 65.4%) of participants to biennial screening would have avoided 4% (0.2% to 22.3%) of false positive screen tests but delayed 55% (24.6% to 81.9%) diagnoses. Conclusions In this German trial, the LCRAT + CT and Polynomial models showed useful discrimination of screening participants for one-year lung cancer risk following CT examination. Our results illustrate the remaining heterogeneity in risk within screening-eligible subjects and the trade-off between a low-frequency screening approach and delayed detection.

Imbalances of blood biomarkers are associated with disease, and biomarkers may also vary non-pathologically across population groups. We described variation in concentrations of biomarkers of one-carbon metabolism, vitamin status, inflammation including tryptophan metabolism, and endothelial and renal function among cancer-free older adults. We analyzed 5167 cancer-free controls aged 40–80 years from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). Centralized biochemical analyses of 40 biomarkers in plasma or serum were performed. We fit multivariable linear mixed effects models to quantify variation in standardized biomarker log-concentrations across four factors: age, sex, smoking status, and body mass index (BMI). Differences in most biomarkers across most factors were small, with 93% (186/200) of analyses showing an estimated difference lower than 0.25 standard-deviations, although most were statistically significant due to large sample size. The largest difference was for creatinine by sex, which was − 0.91 standard-deviations lower in women than men (95%CI − 0.98; − 0.84). The largest difference by age was for total cysteine (0.40 standard-deviation increase per 10-year increase, 95%CI 0.36; 0.43), and by BMI was for C-reactive protein (0.38 standard-deviation increase per 5-kg/m2 increase, 95%CI 0.34; 0.41). For 31 of 40 markers, the mean difference between current and never smokers was larger than between former and never smokers. A statistically significant (p

Aim Lung cancer screening reduces mortality. We aim to validate the performance of Lung EpiCheck, a six-marker panel methylation-based plasma test, in the detection of lung cancer in European and Chinese samples. Methods A case–control European training set (n=102 lung cancer cases, n=265 controls) was used to define the panel and algorithm. Two cut-offs were selected, low cut-off (LCO) for high sensitivity and high cut-off (HCO) for high specificity. The performance was validated in case–control European and Chinese validation sets (cases/controls 179/137 and 30/15, respectively). Results The European and Chinese validation sets achieved AUCs of 0.882 and 0.899, respectively. The sensitivities/specificities with LCO were 87.2%/64.2% and 76.7%/93.3%, and with HCO they were 74.3%/90.5% and 56.7%/100.0%, respectively. Stage I nonsmall cell lung cancer (NSCLC) sensitivity in European and Chinese samples with LCO was 78.4% and 70.0% and with HCO was 62.2% and 30.0%, respectively. Small cell lung cancer (SCLC) was represented only in the European set and sensitivities with LCO and HCO were 100.0% and 93.3%, respectively. In multivariable analyses of the European validation set, the assay's ability to predict lung cancer was independent of established risk factors (age, smoking, COPD), and overall AUC was 0.942. Conclusions Lung EpiCheck demonstrated strong performance in lung cancer prediction in case–control European and Chinese samples, detecting high proportions of early-stage NSCLC and SCLC and significantly improving predictive accuracy when added to established risk factors. Prospective studies are required to confirm these findings. Utilising such a simple and inexpensive blood test has the potential to improve compliance and broaden access to screening for at-risk populations., Lung EpiCheck, a simple blood test, detected 85% of early-stage lung cancers with specificity of 64% in high-risk population, reaching AUC of 0.942 when combined with risk factors. This could improve efficiency of implementing lung cancer screening. https://bit.ly/3jWhLOn

Background: Melanoma aetiology has been proposed to have two pathways, which are determined by naevi and type of sun exposure and related to the anatomical site where melanoma develops. Objectives: We examined associations with melanoma by anatomical site for a comprehensive set of risk factors including pigmentary and naevus phenotypes, ultraviolet radiation exposure and polygenic risk. Methods: We analysed harmonized data from 2617 people with incident first invasive melanoma and 975 healthy controls recruited through two population-based case–control studies in Australia and the UK. Questionnaire data were collected by interview using a single protocol, and pathway-specific polygenic risk scores were derived from DNA samples. We estimated adjusted odds ratios using unconditional logistic regression that compared melanoma cases at each anatomical site with all controls. Results: When cases were compared with control participants, there were stronger associations for many naevi vs. no naevi for melanomas on the trunk, and upper and lower limbs than on the head and neck (P-heterogeneity < 0·001). Very fair skin (vs. olive/brown skin) was more weakly related to melanoma on the trunk than to melanomas at other sites (P-heterogeneity = 0·04). There was no significant difference by anatomical site for polygenic risk. Increased weekday sun exposure was positively associated with melanoma on the head and neck but not on other sites. Conclusions: We found evidence of aetiological heterogeneity for melanoma, supporting the dual pathway hypothesis. These findings enhance understanding of risk factors for melanoma and can guide prevention and skin examination education and practices.

Background: Some uses of “race correction” in clinical algorithms and prediction models unfairly reduce access to care, resulting in calls to remove racial/ethnic variables from all models and algorithms. However, for models that are based on unbiased, high-quality, and plentiful data, removing racial/ethnic variables may reduce prediction accuracy for minorities. We model racial/ethnic disparities in screening eligibility from augmenting USPSTF-2021 guidelines (ages 50-80, ≥20 pack-years, ≤15 quit-years) to also include individuals selected by an NCCN-recommended risk model that includes race (PLCOM2012) versus the same model with race/ethnicity removed (PLCOM2012_NoRace). Methods: We used previously published methodology to model the performance of lung cancer screening using 6915 ever-smokers ages 50-80 from the US-representative 2015 National Health Interview Survey (NHIS). Individuals were considered eligible for screening if they are eligible by USPSTF-2021 guidelines or by PLCOM2012 (“USPSTF+PLCOM2012”), versus being eligible by USPSTF-2021 or PLCOM2012_NoRace (“USPSTF+PLCOM2012_NoRace”). Both models used the NCCN-recommended ≥1.3% 6-year risk-threshold for eligibility. We evaluated model accuracy (average percent over/under-estimation) by race/ethnicity, estimated the proportion of life-years gainable achieved by each eligible cohort (LYG), and evaluated the LYG disparity (difference in LYG between whites and each minority). Results: USPSTF+PLCOM2012 and USPSTF+PLCOM2012_NoRace identified similar numbers of minorities as eligible for screening (~2.7 million). However, USPSTF+PLCOM2012_NoRace selected 125% more Hispanic-Americans and 31% less African-Americans. LYG disparities decreased using USPSTF+PLCOM2012_NoRace versus USPSTF+PLCOM2012 for Hispanic Americans (LYG: 33% to 29%). However, LYG disparities for African Americans increased (LYG: 16% to 18%). PLCOM2012 underestimated lung cancer risk by 49% for Hispanic-Americans, whereas PLCOM2012_NoRace performed well (4% overestimation). However, PLCOM2012underestimated risk in African-Americans by only 6%, PLCOM2012_NoRace underestimated risk in African-Americans by 36%. Conclusion: The model that was most accurate for a minority group was projected to reduce disparities the most for that group. Removing race from the PLCOM2012 model substantially underestimated risk for African-Americans and may increase disparities. Inexplicably, PLCOM2012 substantially underestimated risk in Hispanic-Americans despite including race/ethnicity, which was alleviated by removing race/ethnicity. Great care must be taken when removing racial/ethnic variables from models, because this will assign minorities risk estimates that may be largely, or entirely, based on the majority population. Citation Format: Corey D. Young, Li C. Cheung, Christine D. Berg, Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki, Rebecca Landy. Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-13.

Low-dose CT (LDCT) screening of high-risk smokers reduces lung cancer (LC) specific mortality. Determining screening eligibility using individualised risk may improve screening effectiveness and reduce harm. Here, we compare the performance of two risk prediction models (PLCOEver-smokers aged 55-74, from deprived areas of Manchester, were invited to a Lung Health Check (LHC). Individuals at higher risk (PLCOPLCOProspective comparisons of risk prediction tools are required to optimise screening selection in different settings. The PLCO

This exploratory study analyzes the cost-effectiveness of incorporating biomarkers into eligibility assessment for lung screening.

Background: Men who have sex with men (MSM) are at increased risk for anal cancer. In cervical cancer screening, patterns of repeated cytology results are used to identify low- and high-risk women, but little is known about these patterns for anal cytology among MSM. Methods: We analyzed Multicenter AIDS Cohort Study (MACS) data for MSM who were offered anal cytology testing annually (HIV-positive) or every 2 years (HIV-negative) for 4 years. Results: Following an initial negative (normal) cytology, the frequency of a second negative cytology was lower among HIV-positive MSM with CD4 ≥ 500 (74%) or CD4

Background The incidence of HPV-related head and neck squamous cell carcinoma (HPV-HNSCC) is increasing. Oral samples are easy and non-invasive to collect, but the diagnostic accuracy of oral HPV detection methods for classifying HPV-positive HNSCC tumors has not been well explored. Methods In a systematic review, we identified eight studies of HNSCC patients meeting our eligibility criteria of having: (1) HPV detection in oral rinse or oral swab samples, (2) tumor HPV or p16 testing, (3) a publication date within the last 10 years (January 2007–May 2017, as laboratory methods change), and (4) at least 15 HNSCC cases. Data were abstracted from each study and a meta-analysis performed to calculate sensitivity and specificity. Results Eight articles meeting inclusion criteria were identified. Among people diagnosed with HNSCC, oral HPV detection has good specificity (92%, 95% CI = 82–97%) and moderate sensitivity (72%, 95% CI = 45–89%) for HPV-positive HNSCC tumor. Results were similar when restricted to studies with only oropharyngeal cancer cases, with oral rinse samples, or testing for HPV16 DNA (instead of any oncogenic HPV) in the oral samples. Discussion Among those who already have HNSCC, oral HPV detection has few false-positives but may miss one-half to one-quarter of HPV-related cases (false-negatives). Given these findings in cancer patients, the utility of oral rinses and swabs as screening tests for HPV-HNSCC among healthy populations is probably limited.

Background Human papillomavirus type 16 (HPV16)-E6 antibodies are detectable in peripheral blood before diagnosis in the majority of HPV16-driven oropharyngeal squamous cell carcinoma (OPSCC), but the timing of seroconversion is unknown. Patients and methods We formed the HPV Cancer Cohort Consortium which comprises nine population cohorts from Europe, North America and Australia. In total, 743 incident OPSCC cases and 5814 controls provided at least one pre-diagnostic blood sample, including 111 cases with multiple samples. Median time between first blood collection and OPSCC diagnosis was 11.4 years (IQR = 6–11 years, range = 0–40 years). Antibodies against HPV16-E6 were measured by multiplex serology (GST fusion protein based Luminex assay). Results HPV16-E6 seropositivity was present in 0.4% of controls (22/5814; 95% CI 0.2% to 0.6%) and 26.2% (195/743; 95% CI 23.1% to 29.6%) of OPSCC cases. HPV16-E6 seropositivity increased the odds of OPSCC 98.2-fold (95% CI 62.1–155.4) in whites and 17.2-fold (95% CI 1.7–170.5) in blacks. Seropositivity in cases was more frequent in recent calendar periods, ranging from 21.9% pre-1996 to 68.4% in 2005 onwards, in those with blood collection near diagnosis (lead time 30 years (N = 24), 20–30 years (N = 148), 10–20 years (N = 228), and

Background In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules. Methods First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category. Results Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4–8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05–0.67). Conclusions If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.

Background: For the same age and smoking history as whites, minorities have substantially different lung-cancer risk. However, current US Preventive Services Task Force (USPSTF) lung-cancer screening recommendations make no allowance for race/ethnicity and may induce health disparities. Incorporating individualized prediction-models into USPSTF guidelines may reduce racial/ethnic disparities in lung-cancer screening eligibility. We examine whether expanding current USPSTF lung cancer screening eligibility to include ever-smokers whose risk (calculated by an individualized prediction model) exceeded a threshold would reduce racial/ethnic disparities induced by current USPSTF guidelines. Methods: We used the US- representative 2015 National Health Interview Survey to examine screening eligibility. We identified the thresholds for each of 5 models: lung-cancer risk (Bach, PLCOM2012 and LCRAT models), lung-cancer death risk (LCDRAT model), and life- years gained by attending screening (LYFS-CT model), which select the same number of ever-smokers aged 50-80yrs as USPSTF guidelines. We defined 5 cohorts of ever- smokers as eligible for screening if they were eligible by each screening model or USPSTF guidelines. Among each race/ethnicity, we calculated the number eligible for screening, proportion of preventable lung-cancer deaths prevented (LCD sensitivity), proportion of gainable life-years gained (LYG sensitivity) and screening effectiveness (the number needed to screen to prevent one lung-cancer death). Results: USPSTF criteria performed best for whites (20% eligible, preventing 55% of preventable lung- cancer deaths). Asian-Americans had the least effective screening (NNS=419), only 13% of African-Americans were eligible despite having the most effective screening (NNS=135), and Hispanic-Americans had the lowest percentages eligible (9%) and deaths preventable (30%). Augmenting USPSTF criteria with LCDRAT or LYFS-CT prediction-models nearly equalized the performance of screening for African- Americans with that of whites, doubling the number of African-Americans eligible and increasing the number of preventable deaths and life-years gained by nearly 80%, although at a 25% loss in effectiveness. Prediction-models improved all screening metrics for Asian-Americans and Hispanic-Americans. However models estimated risk more accurately for whites than minorities. Conclusions: Augmenting USPSTF criteria with the LCDRAT or LYFS-CT prediction-models nearly eliminated the white/African-American disparity. All screening metrics were substantially improved for Asian/Hispanic-Americans. Citation Format: Rebecca Landy, Corey D. Young, Martin Skarzynski, Li C. Cheung, Christine D. Berg, M. Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki. Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-247.