Prostate Cancer Screening Trial Research Articles

Adherence to a low-fat dietary pattern reduces head and neck cancer risk: evidence from the PLCO trial

PurposeLow-fat dietary (LFD) pattern refers to a dietary structure with reduced fat intake. The aim was to investigate the association between LFD pattern and risk of head and neck cancer (HNC).MethodsData were derived from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. LFD score was used to assess adherence to an LFD pattern, with higher scores indicating greater adherence. Cox regression was used to evaluate the association between LFD score and risk of HNC and its subtypes. To visualize the trend in risk of HNC and its subtypes with changing LFD score, restricted cubic spline plots were utilized. A series of subgroup analyses were conducted to identify potential confounders. Sensitivity analyses were performed to assess the robustness of the results.ResultsAmong 98,459 participants of PLCO trial, 268 cases with HNC were identified during an average of 8.8 years of follow-up. In the fully adjusted model, participants in the highest compared with the lowest quartiles of LFD score had a lower risk of HNC (HR Q4 vs. Q1: 0.60; 95% CI: 0.40–0.90; P for trend = 0.026) and larynx cancer (HRQ4 vs. Q1: 0.46; 95% CI: 0.22–0.96; P for trend = 0.039). The restricted cubic spline plots demonstrated a linear dose-response relationship between the LFD score and the risk of HNC and its subtypes (all P for nonlinearity > 0.05). The primary association remained robust in the sensitivity analysis.ConclusionOur findings suggest that adherence to an LFD pattern may lower the risk of HNC in the US population.

Prostate cancer screening: Is it time for a new approach? A review article.

Prostate cancer screening has presented a challenge to clinicians. Although the implementation of screening tests such as prostate-specific antigen (PSA) and digital rectal exam (DRE) has had a significant impact on prostate-cancer-specific mortality, these traditional screening tests have a relatively poor positive predictive value of clinically significant prostate cancer (CSPC), leading to unnecessary biopsies and treatment with a host of potential complications. Fortunately, much research has been done to optimize prostate cancer screening. This includes the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, which underwent a secondary analysis to identify an association between PSA level and CSPC, and the IP1-PROSTAGRAM Tri.

Ultra-processed food consumption and renal cell carcinoma incidence and mortality: results from a large prospective cohort

BackgroundGrowing evidence shows that ultra-processed food consumption is associated with the risk of cancer. However, prospective evidence is limited on renal cell carcinoma (RCC) incidence and mortality. In this study, we aimed to examine the association of ultra-processed food consumption and RCC incidence and mortality in a large cohort of US adults.MethodsA population-based cohort of 101,688 participants were included from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Ultra-processed food items were confirmed by using the NOVA food classification system. The consumption of ultra-processed food was expressed as a percentage of total food intake (g/day). Prospective associations were calculated using Cox regression. Restricted cubic spline regression was used to assess nonlinearity. Subgroup analyses were performed to investigate the potential effect modifiers on the incidence and mortality of RCC.ResultsA total of 410 participants developed RCC during a total of 899,731 person-years of follow-up (median 9.41 years) and 230 RCC deaths during 1,533,930 person-years of follow-up (median 16.85 years). In the fully adjusted model, participants in the highest compared with the lowest quintiles of ultra-processed food consumption had a higher risk of RCC (HR quartile 4 vs 1:1.42; 95% CI: 1.06–1.91; Ptrend = 0.004) and mortality (HR quartile 4 vs. quartile 1: 1.64; 95% CI: 1.10–2.43; Ptrend = 0.027). Linear dose–response associations with RCC incidence and mortality were observed for ultra-processed food consumption (all Pnonlinearity > 0.05). The reliability of these results was supported by sensitivity and subgroup analyses.ConclusionIn conclusion, higher consumption of ultra-processed food is associated with an increased risk of RCC incidence and mortality. Limiting ultra-processed food consumption might be a primary prevention method of RCC.

Development and validation of a lung cancer polygenic risk score incorporating susceptibility variants for risk factors.

Incorporating susceptibility genetic variants of risk factors has been reported to enhance the risk prediction of polygenic risk score (PRS). However, it remains unclear whether this approach is effective for lung cancer. Hence, we aimed to construct a meta polygenic risk score (metaPRS) of lung cancer and assess its prediction of lung cancer risk and implication for risk stratification. Here, a total of 2180 genetic variants were used to develop nine PRSs for lung cancer, three PRSs for different histopathologic subtypes, and 17 PRSs for lung cancer-related risk factors, respectively. These PRSs were then integrated into a metaPRS for lung cancer using the elastic-net Cox regression model in the UK Biobank (N = 442,508). Furthermore, the predictive effects of the metaPRS were assessed in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial (N = 108,665). The metaPRS was associated with lung cancer risk with a hazard ratio of 1.33 (95% confidence interval: 1.27-1.39) per standard deviation increased. The metaPRS showed the highest C-index (0.580) compared with the previous nine PRSs (C-index: 0.513-0.564) in PLCO. Besides, smokers in the intermediate risk group predicted by the clinical risk model (1.34%-1.51%) with the intermediate-high genetic risk had a 6-year average absolute lung cancer risk that exceeded the clinical risk model threshold (≥1.51%). The addition of metaPRS to the clinical risk model showed continuous net reclassification improvement (continuous NRI = 6.50%) in PLCO. These findings suggest the metaPRS can improve the predictive efficiency of lung cancer compared with the previous PRSs and refine risk stratification for lung cancer.

Genetic variants of LRRC8C, OAS2, and CCL25 in the T cell exhaustion-related genes are associated with non-small cell lung cancer survival.

T cell exhaustion is a state in which T cells become dysfunctional and is associated with a decreased efficacy of immune checkpoint inhibitors. Lung cancer has the highest mortality among all cancers. However, the roles of genetic variants of the T cell exhaustion-related genes in the prognosis of non-small cell lung cancer (NSCLC) patients has not been reported. We conducted a two-stage multivariable Cox proportional hazards regression analysis with two previous genome-wide association study (GWAS) datasets to explore associations between genetic variants in the T cell exhaustion-related genes and survival of NSCLC patients. We also performed expression quantitative trait loci analysis for functional validation of the identified variants. Of all the 52,103 single nucleotide polymorphisms (SNPs) in 672 T cell exhaustion-related genes, 1,721 SNPs were found to be associated with overall survival (OS) of 1185 NSCLC patients of the discovery GWAS dataset from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, and 125 of these 1,721 SNPs remained significant after validation in an additional independent replication GWAS dataset of 984 patients from the Harvard Lung Cancer Susceptibility (HLCS) Study. In multivariable stepwise Cox model analysis, three independent SNPs (i.e., LRRC8C rs10493829 T>C, OAS2 rs2239193 A>G, and CCL25 rs3136651 T>A) remained significantly associated with OS with hazards ratios (HRs) of 0.86 (95% confidence interval (CI) = 0.77-0.96, P = 0.008), 1.48 (95% CI = 1.18-1.85, P < 0.0001) and 0.78 (95% CI = 0.66-0.91, P = 0.002), respectively. Further combined analysis for these three SNPs suggested that an unfavorable genotype score was associated with a poor OS and disease-specific survival. Expression quantitative trait loci analysis suggested that the LRRC8C rs10493829 C allele was associated with elevated LRRC8C mRNA expression levels in normal lymphoblastoid cells, lung tissue, and whole blood. Our findings suggested that these functional SNPs in the T cell exhaustion-related genes may be prognostic predictors for survival of NSCLC patients, possibly via a mechanism of modulating corresponding gene expression.

Serum concentrations of per- and polyfluorinated substances and risk of B-cell non-Hodgkin lymphoma

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants that are detectable in the serum of most U.S. adults. Some studies of highly-exposed individuals have suggested positive associations between PFAS and B-cell non-Hodgkin lymphoma (B-NHL). To investigate whether associations exist at lower exposure levels, we conducted a nested case-control study investigating serum PFAS concentrations and B-NHL within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. We measured pre-diagnostic serum concentrations of five PFAS among 706 cases (age at diagnosis = 55–93 years, median 73 years) and 706 controls individually matched on age at blood draw, sex, self-reported race and ethnicity, study center, and year of blood collection (the median follow-up years = 10). We estimated odds ratios (ORs) and 95 % confidence intervals (CIs) for PFAS concentrations in relation to B-NHL, both overall and for selected histologic subtypes [diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), and marginal zone lymphoma (MZL)] using conditional logistic regression. We found no evidence of a positive association with B-NHL for any of the five PFAS. In analyses of histologic subtypes, perfluorohexane sulfonate (PFHxS) was significantly associated with DLBCL in a model adjusting for all other PFAS (OR for highest vs. lowest quintile = 2.19, 95 % CI = 1.21, 3.95; Ptrend = 0.02), but not in a model without mutual adjustment (OR = 1.37, 95 % CI = 0.82, 2.29; Ptrend = 0.26). We also observed an inverse association between perfluorononanoate and DLBCL (mutually-adjusted OR = 0.83, 95 % CI = 0.69, 0.99 per doubling in concentration), although the association was null among participants with blood drawn prior to 1997 (OR<1997 = 1.00, 95 % CI = 0.82, 1.21; OR≥1997 = 0.65, 95 % CI = 0.53, 0.79; Pinteraction = 0.0003). In conclusion, our findings from a prospective cohort study with PFAS serum concentrations comparable to that of the general population do not support an association with increased risk of B-NHL overall. The suggestive evidence of a positive association between PFHxS and DLBCL warrants further investigation.

5-Hydroxymethylated Biomarkers in Cell-Free DNA Predict Occult Colorectal Cancer up to 36 Months Before Diagnosis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis. We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio. We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]). Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Oral Microbiome and Subsequent Risk of Head and Neck Squamous Cell Cancer

The oral microbiota may be involved in development of head and neck squamous cell cancer (HNSCC), yet current evidence is largely limited to bacterial 16S amplicon sequencing or small retrospective case-control studies. To test whether oral bacterial and fungal microbiomes are associated with subsequent risk of HNSCC development. Prospective nested case-control study among participants providing oral samples in 3 epidemiological cohorts, the American Cancer Society Cancer Prevention Study II Nutrition Cohort, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and the Southern Community Cohort Study. Two hundred thirty-six patients who prospectively developed HNSCC were identified during a mean (SD) of 5.1 (3.6) years of follow-up. Control participants who remained HNSCC free were selected by 2:1 frequency matching on cohort, age, sex, race and ethnicity, and time since oral sample collection. Data analysis was conducted in 2023. Characterization of the oral bacterial microbiome using whole-genome shotgun sequencing and the oral fungal microbiome using internal transcribed spacer sequencing. Association of bacterial and fungal taxa with HNSCC was assessed by analysis of compositions of microbiomes with bias correction. Association with red and orange oral pathogen complexes was tested by logistic regression. A microbial risk score for HNSCC risk was calculated from risk-associated microbiota. The primary outcome was HNSCC incidence. The study included 236 HNSCC case participants with a mean (SD) age of 60.9 (9.5) years and 24.6% women during a mean of 5.1 (3.6) years of follow-up, and 485 matched control participants. Overall microbiome diversity at baseline was not related to subsequent HNSCC risk; however 13 oral bacterial species were found to be differentially associated with development of HNSCC. The species included the newly identified Prevotella salivae, Streptococcus sanguinis, and Leptotrichia species, as well as several species belonging to beta and gamma Proteobacteria. The red/orange periodontal pathogen complex was moderately associated with HNSCC risk (odds ratio, 1.06 per 1 SD; 95% CI, 1.00-1.12). A 1-SD increase in microbial risk score (created based on 22 bacteria) was associated with a 50% increase in HNSCC risk (multivariate odds ratio, 1.50; 95% CI, 1.21-1.85). No fungal taxa associated with HNSCC risk were identified. This case-control study yielded compelling evidence that oral bacteria are a risk factor for HNSCC development. The identified bacteria and bacterial complexes hold promise, along with other risk factors, to identify high-risk individuals for personalized prevention of HNSCC.

Association between female infertility and stroke mortality: evidence from the PLCO cancer screening trial.

While infertility affects about 15% of women during their reproductive years, its long-term impact on stroke mortality after this period remains unclear. This study aims to investigate the association between infertility and stroke mortality in women using data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. We analyzed data from 75,778 female participants aged 55-74 years with a median follow-up of 16.84 years. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for stroke mortality, adjusting for potential confounders. Among participants, 14.53% reported infertility. During follow-up, 1,159 women died from stroke. Compared to women without infertility, those with infertility had a higher risk of stroke mortality (HR 1.21, 95% CI 1.04-1.41, p = 0.016). This association remained statistically significant after adjusting for age, race, education level, marital status, smoking status, body mass index, history of hypertension, history of heart attack, history of diabetes mellitus, birth control pill use, hormone replacement therapy, endometriosis, first menstrual period and pregnancy history (HR 1.20, 95% CI 1.02-1.42, p = 0.029). Sensitivity and subgroup analyses yielded consistent results. The findings of this study indicate that infertility is associated with an increased risk of stroke mortality in women. Further research is needed to confirm these findings and elucidate the underlying mechanisms.

Abstract A115: Utilizing digital storytelling to improve the recruitment of Black and Hispanic patients for a genetic predisposition to prostate cancer screening trial

Abstract Introduction: Clinical genomics leads to more effective cancer prevention, diagnosis, and treatment. Prostate cancer (PC) significantly impacts Black and Hispanic men, but participation in clinical trials and PC risk management methods like genetic testing and screening remains low. Barriers to diversity and inclusion in clinical trials include cultural and socioeconomic factors. Digital storytelling is an effective tool for building trust and increasing community engagement. We aim to develop and assess the use of digital storytelling to improve recruitment of Black and Hispanic patients in a trial for men at high genetic risk for developing PC (clinicaltrials.gov NCT03805919) and raise awareness of genetic testing and enrollment in clinical trials within these communities. Methods: Guided by the Assessing Community Engagement Conceptual Model, we will establish a community advisory panel (CAP) comprised of patient advocates, community, and clinical partners with a strong interest in increasing clinical trial representation of Black and Hispanic patients at high genetic risk for developing PC. CAP participants will be identified from existing partners and through snowballing techniques, which involve asking partners to recommend other CAP participants. The CAP will co-design storytelling interview questions, baseline, and outcome assessments and collectively determine effective methods for digital story dissemination. To evaluate the impact of storytelling on awareness, interest, and participation in genetic testing, medical management, and clinical trial recruitment among Black and Hispanic men, outcome assessments co-designed with the CAP will be administered pre and post-video viewing and include viewer qualitative interviews. Results: Filmed interviews with Black and Hispanic participants will be conducted. Interviews will focus on participant experiences with clinical trials, genetic testing, and cancer risk management. Interview transcripts will be in English and Spanish with forward and back translation to ensure accuracy using National Institutes of Health resources. Local and national partners, community organizations, faith-based institutions, healthcare facilities, and advocacy groups identified by the CAP will facilitate dissemination. Digital stories will be distributed across various multimedia platforms to maximize outreach and accessibility, including community web platforms, social media channels, podcasts, local radio stations, educational workshops, health fairs, cultural events, churches, and support groups. Conclusion: Building strategies for improving patient-centered care delivery and community engagement within culturally diverse populations will enhance clinical trial diversity and inclusion. Using digital storytelling as a recruitment strategy for clinical trials can foster trust, engagement, and participation within Black and Hispanic communities. Study findings will contribute to future clinical trial design and recruitment to enhance underrepresented minority participation in clinical research. Citation Format: Yi Liu, Brenda Adjei, Anna Couvillon, Kathleen Calzone, Fatima Karzai. Utilizing digital storytelling to improve the recruitment of Black and Hispanic patients for a genetic predisposition to prostate cancer screening trial [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A115.

Pooling controls from nested case-control studies with the proportional risks model.

The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study.

Risk-stratified screening and colorectal cancer incidence and mortality: A retrospective study from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

ObjectiveTo determine whether risk stratification can optimize the benefits of flexible sigmoidoscopy (FSG) screening. MethodsThe Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial was conducted from 1993 to 2001 in the United States. A colorectal cancer (CRC) risk stratification tool was developed in the control arm (n = 64,207) from the PLCO cohort and validated in the UK Biobank (n = 270,726). PLCO participants (n = 130,021) were classified into low-, medium-, and high-risk groups. Cumulative incidence and mortality were estimated using the Kaplan-Meier method. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between screening and CRC incidence and mortality. ResultsThe CRC risk stratification tool was based on age, gender, body mass index, smoking status, family history of CRC, diabetes, regular use of aspirin, and CRC screening history. Compared with the control arm, FSG screening was significantly associated with a reduction in mortality in both the medium-risk (HR = 0.76, 95% CI = 0.63–0.92) and high-risk groups (0.58, 0.46–0.73), but not in the low-risk group (0.85, 0.61–1.19). FSG screening also reduced distal CRC incidence and mortality in the medium-risk and high-risk groups. Furthermore, it was associated with a reduction in incidence (0.74, 0.59–0.92) and mortality (0.59, 0.40–0.87) of proximal colon cancer in the high-risk group. ConclusionsFSG screening yielded more benefits for the high-risk group than for the low-risk and medium-risk groups, supporting the development of a risk-stratified CRC screening strategy.

Re: Prostate Cancers in the Prostate-specific Antigen Interval of 1.8-3 ng/ml: Results from the Göteborg-2 Prostate Cancer Screening Trial

Re: Prostate Cancers in the Prostate-specific Antigen Interval of 1.8-3 ng/ml: Results from the Göteborg-2 Prostate Cancer Screening Trial

How a population-based cohort of men estimate lifetime risk of prostate cancer in a survey before entering a prostate cancer screening trial in Sweden?

ObjectivesInvestigating men’s perceived lifetime risk of prostate cancer.DesignSurvey-based study to men invited for prostate-specific antigen (PSA) screening in the GÖTEBORG-2 trial between September 2015 and June 2020.Setting38 775 men in...

Potentially functional variants of ERRFI1 in hypoxia-related genes predict survival of non-small cell lung cancer patients.

Hypoxia is often involved in tumor microenvironment, and the hypoxia-induced signaling pathways play a key role in aggressive cancer phenotypes, including angiogenesis, immune evasion, and therapy resistance. However, it is unknown what role genetic variants in the hypoxia-related genes play in survival of patients with non-small cell lung cancer (NSCLC). We evaluated the associations between 16,092 single-nucleotide polymorphisms (SNPs) in 182 hypoxia-related genes and survival outcomes of NSCLC patients. Data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial were used as the discovery dataset, and the Harvard Lung Cancer Susceptibility (HLCS) Study served as the replication dataset. We also performed additional linkage disequilibrium analysis and a stepwise multivariable Cox proportional hazards regression analysis in the PLCO dataset. An independent SNP, ERRFI1 rs28624 A > C, was identified with an adjusted hazards ratio (HR) of 1.31 (95% CI = 1.14-1.51, p = 0.0001) for overall survival (OS). In further analyses, unfavorable genotypes AC and CC, compared with the AA genotype, were associated a worse OS (HR = 1.20, 95% CI = 1.03-1.39, p = 0.014) and disease-specific survival (HR = 1.21, 95% CI = 1.04-1.42, p = 0.016). Further expression quantitative trait loci analysis indicated that ERRFI1 rs28624C genotypes were significantly associated with higher ERRFI1 mRNA expression levels in the whole blood. Additional analysis showed that high ERRFI1 mRNA expression levels were associated with a worse OS in patients with lung adenocarcinoma. Our findings suggest that genetic variants in the hypoxia-related gene ERRFI1 may modulate NSCLC survival, potentially through their effect on the gene expression.

Estimating sojourn time and sensitivity of screening for ovarian cancer using a Bayesian framework.

Ovarian cancer is among the leading causes of gynecologic cancer-related death. Past ovarian cancer screening trials using combination of cancer antigen 125 testing and transvaginal ultrasound failed to yield statistically significant mortality reduction. Estimates of ovarian cancer sojourn time-that is, the period from when the cancer is first screen detectable until clinical detection-may inform future screening programs. We modeled ovarian cancer progression as a continuous time Markov chain and estimated screening modality-specific sojourn time and sensitivity using a Bayesian approach. Model inputs were derived from the screening arms (multimodal and ultrasound) of the UK Collaborative Trial of Ovarian Cancer Screening and the Prostate, Lung, Colorectal and Ovarian cancer screening trials. We assessed the quality of our estimates by using the posterior predictive P value. We derived histology-specific sojourn times by adjusting the overall sojourn time based on the corresponding histology-specific survival from the Surveillance, Epidemiology, and End Results Program. The overall ovarian cancer sojourn time was 2.1 years (posterior predictive P value = .469) in the Prostate, Lung, Colorectal and Ovarian studies, with 65.7% screening sensitivity. The sojourn time was 2.0 years (posterior predictive P value = .532) in the United Kingdom Collaborative Trial of Ovarian Cancer Screening's multimodal screening arm and 2.4 years (posterior predictive P value = .640) in the ultrasound screening arm, with sensitivities of 93.2% and 64.5%, respectively. Stage-specific screening sensitivities in the Prostate, Lung, Colorectal and Ovarian studies were 39.1% and 82.9% for early-stage and advanced-stage disease, respectively. The histology-specific sojourn times ranged from 0.8 to 1.8 years for type II ovarian cancer and 2.9 to 6.6 years for type I ovarian cancer. Annual screening is not effective for all ovarian cancer subtypes. Screening sensitivity for early-stage ovarian cancers is not sufficient for substantial mortality reduction.

Data-integration with pseudoweights and survey-calibration: application to developing US-representative lung cancer risk models for use in screening

Abstract Accurate cancer risk estimation is crucial to clinical decision-making, such as identifying high-risk people for screening. However, most existing cancer risk models incorporate data from epidemiologic studies, which usually cannot represent the target population. While population-based health surveys are ideal for making inference to the target population, they typically do not collect time-to-cancer incidence data. Instead, time-to-cancer specific mortality is often readily available on surveys via linkage to vital statistics. We develop calibrated pseudoweighting methods that integrate individual-level data from a cohort and a survey, and summary statistics of cancer incidence from national cancer registries. By leveraging individual-level cancer mortality data in the survey, the proposed methods impute time-to-cancer incidence for survey sample individuals and use survey calibration with auxiliary variables of influence functions generated from Cox regression to improve robustness and efficiency of the inverse-propensity pseudoweighting method in estimating pure risks. We develop a lung cancer incidence pure risk model from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial using our proposed methods by integrating data from the National Health Interview Survey and cancer registries.

Modification Role of Dietary Antioxidants in the Association of High Red Meat Intake and Lung Cancer Risk: Evidence from a Cancer Screening Trial.

Evidence on the association between red meat consumption and lung cancer risk is weak. This study examined the associations between red meat and lung cancer across levels of antioxidant intake from foods or supplements. Cox proportional hazard models were applied to assess hazard ratios (HRs) for lung cancer incidence in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. Baseline food frequency questionnaires measured red meat and antioxidant intake. The food-based Composite Dietary Antioxidant Index (fCDAI) evaluated the overall natural intake of vitamin A, vitamin C, vitamin E, zinc, magnesium, and selenium. During 13 years of follow-up, 95,647 participants developed 1599 lung cancer cases. Higher red meat consumption was associated with a higher risk of lung cancer (HRQ4vsQ1 1.43, 95%CI 1.20-1.71, p-trend < 0.001). We observed similar trends across groups with low or medium levels of antioxidant intake. However, no association was noticed in the group with the highest fCDAI (HRQ4vsQ1 1.24, 95%CI 0.90-1.72, p-trend = 0.08) and highest independent natural antioxidant intake. The attenuated risk was not consistently observed among groups with high supplement use. Lastly, we did not notice evidence of interactions between red meat and antioxidant intake. Our findings emphasize the importance of limiting red meat in lung cancer prevention.

A Linear Relationship between the Number of Cancers among First-Degree Relatives and the Risk of Multiple Primary Cancers.

With advances in the early detection and treatment of cancer, the incidence of multiple primary cancers (MPC) or second primary cancers has increased over time. Characterization of etiologic risk factors, including family history of cancer, within the general population is critical for assessing MPC risk in patients. We examined the association between family history of cancer among first-degree relatives and MPC risk in a prospective study of 139,958 participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Cox proportional hazard models were used to calculate HRs and 95% confidence intervals (95% CI), adjusting for potential confounders. Over a median follow-up of 16 years (IQR: 11-19 years), 6,170 participants were diagnosed with MPC. Having a family history of cancer increased the risk of MPC by 18% (HR, 1.18; 95% CI, 1.12-1.24). A positive linear trend was observed between the reported number of cancers in the family history and MPC risk with HRs (95% CI) of 1.13 (1.07-1.20), 1.23 (1.14-1.33), 1.29 (1.15-1.45), and 1.42 (1.20-1.70) for one, two, three, and four or more cancers among first-degree relatives, respectively (Ptrend = 2.36 × 10-13). No significant differences were observed by cancer histology or specific cancer types reported in the family history. Our study demonstrates that the family history of cancer is an important risk factor for the development of MPCs and that a comprehensive assessment of the number of cancers reported among first-degree relatives may identify those at higher risk who may benefit from targeted cancer prevention and screening strategies. Prevention Relevance: Our study makes a substantial contribution to the understanding of risk factors for MPCs in the general population. It demonstrates that individuals with a strong family history of cancer are at higher risk for MPCs and may benefit from more targeted cancer prevention and screening interventions.