Ovarian Cancer Screening Trial Research Articles

Association of dietary patterns derived by reduced-rank regression with colorectal cancer risk and mortality.

Unhealthy dietary patterns contribute to an increased risk of colorectal cancer (CRC). Limited prior research has used reduced rank regression (RRR) to assess dietary patterns relative to CRC risk. This study aimed to identify dietary patterns derived by RRR and assess their associations with CRC risk and mortality. We used data from the multicentre Prostate, Lung, Colorectal, and Ovarian Cancer Screening (PLCO) trial. Dietary intake was assessed using a Dietary History Questionnaire. In the RRR intake of fibre, folate, and the percentage of energy from carbohydrates, saturated and unsaturated fatty acids were used as response variables. Cox models and competing risk survival regression, with age as the time scale, were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CRC risk and mortality, respectively. The median follow-up time for CRC risk (n = 1044) and mortality (n = 499) was 9.4 years (Interquartile Range: 8. 0, 10.1) and 16.9 years (11.9, 18.6), respectively. Two dietary patterns were identified: the first was characterised by high carbohydrate, folate and low fatty acid intake, and the second by high fibre and unsaturated fatty acid. Compared to participants in the first tertile of the high fibre and unsaturated fatty acid pattern, those in the third tertile had a lower risk of CRC (HR = 0.88; 95% CI: 0.76, 1.03), and colon cancer (HR = 0.85; 95% CI: 0.72, 1.01). Conversely, the high carbohydrate, high folate and low fatty acid pattern had no association with CRC outcomes. None of the dietary patterns showed associations with rectal cancer or CRC mortality. A diet enriched with high fibre and unsaturated fatty acids may reduce the risk of CRC. These results highlight the potential protective effect of adequate fibre intake in conjunction with high consumption of unsaturated fatty acids against CRC.

Survival Stacking Ensemble Model for Lung Cancer Risk Prediction.

The most well-established risk factor for lung cancer (LC) is smoking, responsible for approximately 85% of cases. The Lung Cancer Risk Assessment Tool (LCRAT) is a key advancement in this field, which predicts individual risk based on factors like smoking habits, demographic details, personal and family medical history, and environmental exposures. This paper proposes a model with fewer features that improves state of the art performance, using a simplified stacking ensemble, making it more accessible and easier to implement in routine healthcare practice. The data used in this work were derived from two cohorts in the United States: The National Lung Screening Trial (NLST) and the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Both our model and LCRAT achieve an AUC of 0.799 and 0.782 on test respectively. In terms of percentage of positives, in the 50% of the population, both detect 0.766 and 0.754 of the cases. The ensemble of different survival models enhances robustness by mitigating the weakness of individual models and directly impacts the efficiency of the model, increasing the efficiency and generalizability.

Association between sulfur microbial diet and the risk of esophageal cancer: a prospective cohort study in 101,752 American adults

BackgroundSulfur microbial diet (SMD) is a dietary pattern closely related to the intestinal load of sulfur-metabolizing microbes in humans. Diet and microbes may play an important role in the carcinogenesis of esophagus. However, epidemiological studies on SMD and esophageal cancer (EC) risk are scarce. Here, we evaluated this association based on a large American cohort.MethodsIn the cohort of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, a SMD score was calculated to evaluate participants’ compliance of SMD pattern, with higher scores presenting greater adherence. Cox hazards regression model was used to explore the association between the SMD score and the incidence of EC, esophageal squamous cell carcinoma (ESCC), and esophageal adenocarcinoma (EA). Subgroup analyses were conducted to figure out potential modifiers interacting with SMD on EC. Sensitivity analyses were used to testify the robustness of our main result.ResultsAmong 101,752 participants, 154 EC cases, consisted of 41 ESCC cases and 97 EA cases, were identified with mean follow-up of 8.9 years. In the fully adjusted model, the highest versus the lowest quartiles of the SMD score were found to be associated with an increased risk of EC and ESCC (EC: HRQ4 vs. Q1: 1.64; 95% CI: 1.05, 2.56; P = 0.016 for trend; ESCC: HRQ4 vs. Q1: 2.37; 95% CI: 1.02, 5.47; P = 0.031 for trend), while not significantly associated with increases risk of EA (HRQ4 vs. Q1: 1.41; P = 0.144 for trend). The main result remained through a series of sensitivity analyses. Subgroup analyses showed a stronger association between SMD and EC in participants with no regular consumption of aspirin (HRQ4 vs. Q1: 1.90; 95% CI: 1.04, 3.47) than in those using aspirin regularly (HRQ4 vs. Q1: 1.37; 95% CI: 0.71, 2.66) (P = 0.008 for interaction).ConclusionAdherence to the SMD pattern may be associated with increased risks of EC and ESCC, particularly for EC in individuals who do not regularly consume aspirin.

Dietary pattern, sputum DNA methylation, and lung health: an epidemiological study in people who ever smoked

BackgroundWe previously identified a panel of sputum DNA methylation that predicts lung ageing and risk for lung cancer. Research QuestionCan the sputum methylation panel be used as a readout to derive a dietary pattern beneficial for lung health? Is this dietary pattern associated with various subjective and objective lung health phenotypes? Does this relationship vary among people who currently (current smoker) versus previously smoked (former smoker)? Study Design and MethodsUsing the Lovelace Smoker Cohort (LSC), we employed the LASSO regularized Poisson regression to define a dietary pattern for sputum. Associations of the dietary pattern with objective and subjective lung health measurements were examined using generalized linear and Cox models in the LSC and the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening trial. ResultsThe Dietary Pattern for Healthy Lung (DiPHeaL) includes low consumption of processed meat, and high consumption of dark-green vegetables, tea, alcohol, and fruit juice. In the LSC, higher DiPHeaL score (one standard deviation) was associated with better FEV1 (by 96.1 mL/s), FEV1/FVC ratio (by 1.83%), and respiratory quality of life (by 4.9 for activity score), and decreased cardiopulmonary mortality (by 47%) in former smokers (all Ps<0.05), but not in current smokers. Moreover, effect sizes of the DiPHeaL score on respiratory quality of life measures were greater among former smokers with airway obstruction compared to those without. Associations with cardiovascular and respiratory mortality were replicated in PLCO former smokers. Greater DiPHeaL score was also associated with lower lung cancer incidence in former smokers, as well as reduced COPD incidence and lung cancer mortality regardless of smoking status in the PLCO. InterpretationWe defined a novel dietary pattern for lung epigenetic aging, which linked to lung health measurements. Former smokers, especially those with airway obstruction, may benefit the most from nutritional modification.

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.

B-308 Analytical and clinical performance of a novel ovarian cancer screening test in asymptomatic women

Abstract Background Ovarian cancer (OC) ranks among the deadliest gynecological cancers, often diagnosed at advanced stages when treatment options are limited. Costly sequencing methods and established immunoassay and imaging-based methods lack early-stage sensitivity, highlighting the need for a cost-effective test with improved performance. Extracellular vesicles (EVs) produced in abundance by all cells can potentially improve test sensitivity and detection of multiple colocalized cancer specific markers can improve test specificity. The randomized controlled trial UKCTOCS (United Kingdom Collaborative Trial of Ovarian Cancer Screening) where over 200,000 postmenopausal asymptomatic women were randomly assigned to three arms revealed no significant reduction in mortality with OC screening. We report on an EV-based OC Test performance in serum samples from asymptomatic postmenopausal women in UKCTOCS. Methods The OC Test was previously locked (biomarkers, method, cutoff) and validated in an independent cohort. We evaluated the OC Test in a blinded case-control study nested within the no screening (NS) and annual ultrasound screening (USS) arms of UKCTOCS. All UKCTOCS participants provided a blood sample at study entry. Cases were all women who developed high grade serous ovarian cancer (HGSOC) within 36 months following sample donation (spun within 28 hours). They were matched by age at collection (+/-2 years) to women who had no OC detected either before or within the trial follow up period (controls). Where a case had &amp;gt;1 sample within the 0-36 months period, the control sample was matched to the case sample closest to diagnosis. The OC Test was run with a locked method and classifier algorithm utilizing 3 independent biomarker combinations tested in duplicate. OC Test results were calculated based on a previously determined cutoff prior to unblinding of clinical outcomes. Run controls were prepared by harvesting conditioned media from COV413A cell cultures (an ovarian epithelial-serous carcinoma cell line), purifying EVs and spiking into pre-screened OC Test negative human plasma at three levels. Run controls were run twice per day, one replicate per run for 17 days. Results We retrospectively assessed 116 samples from 108 cases (63 clinical Dx NS, 15 clinical Dx USS, 30 screen detected USS) women with HGSOC and 1310 samples from 1310 controls. OC test sensitivity for HGSOC was 84% (36/43; 95% CI 69%-93%), Stage I/II sensitivity was 89% (8/9; 95% CI 51%-99%) and the AUC (area under the curve) was 0.93 for case samples drawn 0-12 months prior to clinical Dx and all control samples. The OC Test specificity was 98% (1099/1176, 95% CI 97%-98%). Within laboratory CVs were 20.9%, 12.7% and 0.6% for low negative, high negative and high positive run controls respectively. Data from 143 samples (10%, 9 cases, 134 controls) were excluded due to operator error, low sample volume or quality issues. Conclusions The OC Test is capable of highly sensitive detection of HGSOC within one year of clinical diagnosis including at the earliest stages of disease while maintaining exceptionally high specificity. The results demonstrate the potential application of the OC Test for ovarian cancer screening in asymptomatic women.

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.

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.

Associations of minerals intake with colorectal cancer risk in the prostate, lung, colorectal, ovarian cancer screening trial.

Exploring the association between common mineral intake and the risk of colorectal cancer (CRC). We utilized the multivariate Cox proportional hazards model to assess the association between intake of minerals and the risk of CRC, estimating hazard ratios (HRs) and 95% confidence intervals (CIs). A total of 101,686 eligible participants were included in the analysis of this study, including 1,100 CRC cases. After adjusting for potential confounders, we found that total zinc intake (HRQ4vs.Q1: 0.79, 95%CI 0.67-0.93; P for trend <0.05), iron intake (HRQ4vs.Q1: 0.81, 95%CI 0.68-0.96; P for trend <0.05), copper intake (HRQ4vs.Q1: 0.80, 95%CI 0.68-0.95; P for trend <0.05), selenium intake (HRQ4vs.Q1: 0.83, 95%CI 0.69-0.98; P for trend <0.05) were significantly negatively associated with the incidence of CRC, but magnesium intake in the appropriate range is associated with a reduced risk of CRC (HRQ3vs.Q1: 0.77, 95%CI 0.65-0.91; P for trend >0.05). Our findings suggested that an appropriate intake of total zinc, iron, copper, selenium and magnesium were associated with lower CRC risk.

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.

Identification of a serum proteomic biomarker panel using diagnosis specific ensemble learning and symptoms for early pancreatic cancer detection.

The grim (<10% 5-year) survival rates for pancreatic ductal adenocarcinoma (PDAC) are attributed to its complex intrinsic biology and most often late-stage detection. The overlap of symptoms with benign gastrointestinal conditions in early stage further complicates timely detection. The suboptimal diagnostic performance of carbohydrate antigen (CA) 19-9 and elevation in benign hyperbilirubinaemia undermine its reliability, leaving a notable absence of accurate diagnostic biomarkers. Using a selected patient cohort with benign pancreatic and biliary tract conditions we aimed to develop a data analysis protocol leading to a biomarker signature capable of distinguishing patients with non-specific yet concerning clinical presentations, from those with PDAC. 539 patient serum samples collected under the Accelerated Diagnosis of neuro Endocrine and Pancreatic TumourS (ADEPTS) study (benign disease controls and PDACs) and the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, healthy controls) were screened using the Olink Oncology II panel, supplemented with five in-house markers. 16 specialized base-learner classifiers were stacked to select and enhance biomarker performances and robustness in blinded samples. Each base-learner was constructed through cross-validation and recursive feature elimination in a discovery set comprising approximately two thirds of the ADEPTS and UKCTOCS samples and contrasted specific diagnosis with PDAC. The signature which was developed using diagnosis-specific ensemble learning demonstrated predictive capabilities outperforming CA19-9, the only biomarker currently accepted by the FDA and the National Comprehensive Cancer Network guidelines for pancreatic cancer, and other individual biomarkers and combinations in both discovery and held-out validation sets. An AUC of 0.98 (95% CI 0.98-0.99) and sensitivity of 0.99 (95% CI 0.98-1) at 90% specificity was achieved with the ensemble method, which was significantly larger than the AUC of 0.79 (95% CI 0.66-0.91) and sensitivity 0.67 (95% CI 0.50-0.83), also at 90% specificity, for CA19-9, in the discovery set (p = 0.0016 and p = 0.00050, respectively). During ensemble signature validation in the held-out set, an AUC of 0.95 (95% CI 0.91-0.99), sensitivity 0.86 (95% CI 0.68-1), was attained compared to an AUC of 0.80 (95% CI 0.66-0.93), sensitivity 0.65 (95% CI 0.48-0.56) at 90% specificity for CA19-9 alone (p = 0.0082 and p = 0.024, respectively). When validated only on the benign disease controls and PDACs collected from ADEPTS, the diagnostic-specific signature achieved an AUC of 0.96 (95% CI 0.92-0.99), sensitivity 0.82 (95% CI 0.64-0.95) at 90% specificity, which was still significantly higher than the performance for CA19-9 taken as a single predictor, AUC of 0.79 (95% CI 0.64-0.93) and sensitivity of 0.18 (95% CI 0.03-0.69) (p = 0.013 and p = 0.0055, respectively). Our ensemble modelling technique outperformed CA19-9, individual biomarkers and indices developed with prevailing algorithms in distinguishing patients with non-specific but concerning symptoms from those with PDAC, with implications for improving its early detection in individuals at risk.

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.