Colorectal Cancer Risk Prediction Research Articles

Race adjustments in clinical algorithms can help correct for racial disparities in data quality

Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.

Third-generation PacBio sequencing to explore gut bacteria and gender in colorectal cancer

BackgroundGut bacteria have an important influence on colorectal cancer (CRC). The differences of gut bacteria between genders have been the hot spots. ObjectiveTo analyze the relationship between gut bacteria and gender differences in patients with CRC. MethodsA total of 212 patients with CRC and 212 healthy volunteers were recruited. The subjects' fecal samples were obtained, and the fecal microorganisms were analyzed by the third-generation sequencing PacBio. The composition of gut bacteria was analyzed. Linear discriminant analysis Effect Size (LEfSe) was used to analyze the differences in gut bacteria. Pearson coefficient was used to calculate the correlation between differential bacteria. CRC risk prediction models were used to rank the importance of effective differential bacteria. ResultsEscherichia flexneri and Phocaeicola vulgatus were the most frequent bacteria in both male and female CRC patients. Bacteroides, Verrucomicrobia and Akkermansiaceae were highly enriched in male CRC group, while Bacteroidetes, Phocaeicola and Tissierellales were highly enriched in female CRC group. Peptostreptococcus anaerobius and Phocaeicola vulgatus were important CRC related bacteria in males and females, respectively. Peptostreptococcus anaerobius was the most important characteristic bacterium of males (AUC = 0.951), and the sensitivity and specificity of the discovery set were 78.74 % and 93.98 %, respectively. Blautia stercoris was the most important characteristic bacterium of females (AUC = 0.966), and the sensitivity and specificity of the discovery set were 90.63 % and 90.63 %, respectively. ConclusionGut bacteria varied in different genders. Therefore, gender should be considered when gut bacteria are applied in the diagnose and prevention of CRC.

External validation of the colorectal cancer risk score LiFeCRC using food frequency questions in the HUNT study

PurposeTo mitigate the increasing colorectal cancer (CRC) incidence globally and prevent CRC at the individual level, individual lifestyle information needs to be easily translated into CRC risk assessment. Several CRC risk prediction models exist and their clinical usefulness depends on their ease of use. Our objectives were to assess and externally validate the LiFeCRC score in our independent, unselected population and to investigate the use of simpler food frequency measurements in the score.MethodsIncidental colon and rectal cancer cases were compared to the general population among 78,580 individuals participating in a longitudinal health study in Norway (HUNT). Vegetable, dairy product, processed meat and sugar/confectionary consumption was scored based on food frequency. The LiFeCRC risk score was calculated for each individual.ResultsOver a median of 10 years following participation in HUNT, colon cancer was diagnosed in 1355 patients and rectal cancer was diagnosed in 473 patients. The LiFeCRC score using food frequencies demonstrated good discrimination in CRC overall (AUC 0.77) and in sex-specific models (AUC men 0.76 and women 0.77) in this population also including individuals ≥ 70 years and patients with diabetes. It performed somewhat better in colon (AUC 0.80) than in rectal cancer (AUC 0.72) and worked best for female colon cancer (AUC 0.81).ConclusionReadily available clinical variables and food frequency questions in a modified LiFeCRC score can identify patients at risk of CRC and may improve primary prevention by motivating to lifestyle change or participation in the CRC screening programme.

Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk.

Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.

Predicting Risk of Colorectal Cancer After Adenoma Removal in a Large Community-Based Setting.

Colonoscopy surveillance guidelines categorize individuals as high or low risk for future colorectal cancer (CRC) based primarily on their prior polyp characteristics, but this approach is imprecise, and consideration of other risk factors may improve postpolypectomy risk stratification. Among patients who underwent a baseline colonoscopy with removal of a conventional adenoma in 2004-2016, we compared the performance for postpolypectomy CRC risk prediction (through 2020) of a comprehensive model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and prior polyp findings (i.e., adenoma with advanced histology, polyp size ≥10 mm, and sessile serrated adenoma or traditional serrated adenoma) with a polyp model featuring only polyp findings. Models were developed using Cox regression. Performance was assessed using area under the receiver operating characteristic curve (AUC) and calibration by the Hosmer-Lemeshow goodness-of-fit test. Among 95,001 patients randomly divided 70:30 into model development (n = 66,500) and internal validation cohorts (n = 28,501), 495 CRC were subsequently diagnosed; 354 in the development cohort and 141 in the validation cohort. Models demonstrated adequate calibration, and the comprehensive model demonstrated superior predictive performance to the polyp model in the development cohort (AUC 0.71, 95% confidence interval [CI] 0.68-0.74 vs AUC 0.61, 95% CI 0.58-0.64, respectively) and validation cohort (AUC 0.70, 95% CI 0.65-0.75 vs AUC 0.62, 95% CI 0.57-0.67, respectively). A comprehensive CRC risk prediction model featuring patient age, diabetes diagnosis, and baseline colonoscopy indication and polyp findings was more accurate at predicting postpolypectomy CRC diagnosis than a model based on polyp findings alone.

Colorectal Cancer Risk Prediction Using the rs4939827 Polymorphism of the SMAD7 Gene in the Romanian Population.

Colorectal cancer (CRC) is globally recognized as a prevalent malignancy known for its significant mortality rate. Recent years have witnessed a rising incidence trend in colorectal cancer, emphasizing the necessity for early diagnosis. Our study focused on examining the impact of the SMAD7 gene variant rs4939827 on the risk of colorectal cancer occurrence. The composition of our study group included 340 individuals, comprising 170 CRC diagnosed patients and 170 healthy controls. We performed genotyping of all biological samples using the TaqMan assay on the ABI 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). This investigation focused on the rs4939827 gene variant, assessing its association with CRC risk and clinicopathological characteristics. Genotyping results for the SMAD7 gene variant rs4939827 revealed a 42.6% prevalence of the C allele in CRC patients (p = 0.245) and a 22.8% prevalence of the T allele in control subjects (p = 0.109). This study concluded that there was an elevated risk of CRC in the dominant model for CC/CT+TT, with a p-value of 0.113 and an odds ratio (OR) of 2.781, within a 95% confidence interval (CI) of 0.998 to 3.456. The findings of our research indicate a correlation between variants of the SMAD7 gene and the likelihood of developing colorectal cancer in our study population. Consequently, these results could be instrumental in facilitating early diagnosis of colorectal cancer utilizing information on single-nucleotide polymorphism (SNP) and genetic susceptibility to the disease.

Genome-wide polygenic risk scores for colorectal cancer have implications for risk-based screening.

Hereditary factors, including single genetic variants and family history, can be used for targeting colorectal cancer (CRC) screening, but limited data exist on the impact of polygenic risk scores (PRS) on risk-based CRC screening. Using longitudinal health and genomics data on 453,733 Finnish individuals including 8801 CRC cases, we estimated the impact of a genome-wide CRC PRS on CRC screening initiation age through population-calibrated incidence estimation over the life course in men and women. Compared to the cumulative incidence of CRC at age 60 in Finland (the current age for starting screening in Finland), a comparable cumulative incidence was reached 5 and 11 years earlier in persons with high PRS (80-99% and >99%, respectively), while those with a low PRS (< 20%) reached comparable incidence 7 years later. The PRS was associated with increased risk of post-colonoscopy CRC after negative colonoscopy (hazard ratio 1.76 per PRS SD, 95% CI 1.54-2.01). Moreover, the PRS predicted colorectal adenoma incidence and improved incident CRC risk prediction over non-genetic risk factors. Our findings demonstrate that a CRC PRS can be used for risk stratification of CRC, with further research needed to optimally integrate the PRS into risk-based screening.

INNOVATIVE APPROACH TO CRC PREVENTION: THE ROLE OF LOW-PENETRATION GENES

Colorectal cancer (CRC), the most common malignancy of the gastrointestinal tract, ranks as the second leading cause of cancer-related deaths worldwide. In this context, the investigation of low-penetration gene variants associated with an increased risk of CRC represents a novel and crucial approach to enhancing prevention strategies and clinical surveillance. By focusing on these genetic variants, there is potential for more accurate prediction of individual CRC risk, which could revolutionize current screening and prophylactic programs. The aim of this case-control study was to explore the association between SIRT1 polymorphisms and CRC risk, a line of research that could lead to new insights into carcinogenesis. We analyzed three SNPs (single nucleotide polymorphisms) - rs12778366 (T/C), rs3758391 (C/T), and rs7895833 (A/G) - in the promoter region of the SIRT1 gene, which may influence SIRT1 expression and thus play a role in cancer development. Our study included 200 patients with colorectal adenocarcinoma and 115 controls. Genomic DNA was extracted from blood samples, and SIRT1 SNP analysis was performed using qPCR method – endpoint genotyping. Although our findings did not reveal statistically significant differences in SIRT1 gene polymorphism frequencies between the CRC group and the control group, we observed a trend that suggests further investigation in larger cohorts is warranted. This research underscores the importance of understanding low-penetration genetic factors in CRC, highlighting their potential to inform more personalized and effective prevention strategies.

From Algorithms to Clinical Utility: A Systematic Review of Individualized Risk Prediction Models for Colorectal Cancer

Individualized risk prediction models for colorectal cancer (CRC) play a pivotal role in shaping risk-based screening approaches, garnering attention for use in informed decision making by patients and clinicians. While the incorporation of new predictors and the development of advanced yet complex prediction models can enhance model performance, their practical implementation in clinical settings remains challenging. This systematic review assessed individualized CRC risk prediction models for their validity and potential clinical utility. Utilizing the Cochrane Collaboration methods and PROBAST tool, we conducted comprehensive searches across key databases and risk of bias assessment, respectively. Out of 41 studies included evaluating 44 risk prediction models, 12 conventional and 3 composite models underwent external validation. All risk models exhibited varying discriminatory accuracy, with the area under the curve (AUCs) ranging from 0.57 to 0.90. However, most studies showed an unclear or high risk of bias, with concerns about applicability. Of the five models with promising clinical utility, only two underwent external validation and one employed a decision curve analysis. These models demonstrated a discriminating and well-calibrated performance. While high-performing CRC risk prediction models exist, a need for transparent reporting of performance metrics and their clinical utility persists. Further research on this area is needed to facilitate the integration of these models into clinical practice, particularly in CRC screening.

Abstract B145: Validation of a colorectal cancer risk prediction model in US Black women

Abstract Background: The National Cancer Institute’s Colorectal Cancer Risk Assessment Tool (NCI-CCRAT) estimates an individual's five-year risk of developing colorectal cancer. It was developed by estimating relative risks from a population of US White women and men and incorporating race, gender, and age-specific incidence data from population-based cancer registries. However, the model performance has not been assessed for Black Americans, who have the highest colorectal cancer incidence and mortality rates of any US racial/ethnic group. Methods: The NCI-CCRAT estimates the absolute risk of colorectal cancer based on an individual’s age, sex, family history of colorectal cancer, sigmoidoscopy/colonoscopy, polyps, smoking, physical activity, regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use, vegetable intake, body mass index, and hormone replacement therapy use. Validation of the NCI-CCRAT was performed using prospective data from 57,558 participants ages 40 to 84 years in the Black Women's Health Study (BWHS). We predicted 5-year absolute risk using data from 2001 through 2015 in three 5-year prediction periods: 2001-2005, 2006-2010, and 2011-2015. To assess model performance, we computed calibration by comparing the expected number of colorectal cancer cases predicted by the model to the observed number of cases (E/O) in the BWHS overall and in subgroups of the population. We assessed discriminatory accuracy by calculating the area under the curve (AUC). Results: During follow-up from 2001 through 2015 (634,512 person-years), 433 BWHS participants developed invasive colorectal cancer. Based on the NCI-CCRAT, 543 cases were expected. The NCI-CCRAT was well-calibrated in the first 5-year prediction period (2001-2005 E/O=1.03, 95% CI: 0.88–1.21), but the model overestimated risk in the subsequent 5-year prediction periods, with E/O=1.30 for 2006-2010 and E/O=1.44 for 2011-2015. The overall E/O ratio was 1.25 (95% confidence interval: 1.14–1.38), indicating that NCI-CCRAT overestimated the number of cases in this population of Black women. Of note, associations with colorectal cancer were weaker in the BWHS than in the development cohort for vigorous physical activity, aspirin/NSAID use, vegetable intake, and body mass index. We estimated discriminatory accuracy only for the first 5-year prediction period (2001-2005, N cases=151) for which the model was well calibrated, and obtained an age-adjusted AUC of 0.61 (95% CI: 0.57–0.66). Conclusions: The NCI-CCRAT overestimated the overall number of colorectal cancer cases by 25% in this prospective cohort of Black women, possibly due to population-specific differences in the relative risks of key factors driving colorectal cancer incidence. Discriminatory accuracy was similar to measures from the original validation cohort of White women and men. Impact: This study suggests that further research is needed to determine the most important predictors of colorectal cancer etiology in Black women and to develop and test a colorectal cancer risk prediction model for Black Americans. Citation Format: Jessica L. Petrick, Nelsy Castro-Webb, Gary R. Zirpoli, Kerrie P. Nelson, Julie R. Palmer, Ruth M. Pfeiffer. Validation of a colorectal cancer risk prediction model in US Black women [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B145.

Transcending Boundaries: Integrating Genetic Insights for Improved Colorectal Cancer Risk Assessment in Multiethnic Populations

Colorectal cancer (CRC) remains a significant global health concern, with variations in incidence and genetic susceptibility observed among diverse ethnic populations. This article presents a groundbreaking study titled "Transcending Boundaries: Integrating Genetic Insights for Improved Colorectal Cancer Risk Assessment in Multiethnic Populations." The research aims to enhance precision medicine approaches by synthesizing genome-wide association studies (GWAS) data from various ancestral backgrounds to improve CRC risk prediction.&#x0D; The study employs a comprehensive meta-analysis methodology, consolidating genetic data from Asian and European populations. By transcending traditional ethnic boundaries, the research seeks to identify shared and unique genetic markers associated with CRC risk across diverse groups. This integrative approach not only facilitates a more nuanced understanding of the genetic architecture of CRC but also allows for the development of a unified risk assessment model applicable to multiethnic populations.&#x0D; The findings underscore the importance of considering genetic diversity in CRC risk prediction. The integrated model demonstrates superior accuracy compared to conventional approaches, showcasing its potential for more effective and inclusive screening strategies. Moreover, the study sheds light on the need for personalized screening protocols that account for the genetic heterogeneity present in multiethnic societies.&#x0D; This research represents a significant step toward advancing the field of genetic epidemiology and precision medicine, emphasizing the importance of collaborative efforts to address health disparities across populations. The insights gained from this study hold promise for informing public health initiatives, guiding clinicians in risk assessment, and ultimately improving preventive strategies for colorectal cancer on a global scale.

The effect of aspirin and eicosapentaenoic acid on urinary biomarkers of prostaglandin E2 synthesis and platelet activation in participants of the seAFOod polyp prevention trial.

Urinary prostaglandin (PG) E metabolite (PGE-M) and 11-dehydro (d)-thromboxane (TX) B2 are biomarkers of cyclooxygenase-dependent prostanoid synthesis. We investigated (1) the effect of aspirin 300 mg daily and eicosapentaenoic acid (EPA) 2000 mg daily, alone and in combination, on urinary biomarker levels and, (2) whether urinary biomarker levels predicted colorectal polyp risk, during participation in the seAFOod polyp prevention trial. Urinary PGE-M and 11-d-TXB2 were measured by liquid chromatography-tandem mass spectrometry. The relationship between urinary biomarker levels and colorectal polyp outcomes was investigated using negative binomial (polyp number) and logistic (% with one or more polyps) regression models. Despite wide temporal variability in PGE-M and 11-d-TXB2 levels within individuals, both aspirin and, to a lesser extent, EPA decreased levels of both biomarkers (74% [P ≤ .001] and 8% [P ≤ .05] reduction in median 11-d-TXB2 values, respectively). In the placebo group, a high (quartile [Q] 2-4) baseline 11-d-TXB2 level predicted increased polyp number (incidence rate ratio [IRR] [95% CI] 2.26 [1.11,4.58]) and risk (odds ratio [95% CI] 3.56 [1.09,11.63]). A low (Q1) on-treatment 11-d-TXB2 level predicted reduced colorectal polyp number compared to placebo (IRR 0.34 [0.12,0.93] for combination aspirin and EPA treatment) compared to high on-treatment 11-d-TXB2 values (0.61 [0.34,1.11]). Aspirin and EPA both inhibit PGE-M and 11-d-TXB2 synthesis in keeping with shared in vivo cyclooxygenase inhibition. Colorectal polyp risk and treatment response prediction by 11-d-TXB2 is consistent with a role for platelet activation during early colorectal carcinogenesis. The use of urinary 11-d-TXB2 measurement for a precision approach to colorectal cancer risk prediction and chemoprevention requires prospective evaluation.

Development and validation of colorectal cancer risk prediction tools: A comparison of models

BackgroundIdentification of individuals at elevated risk can improve cancer screening programmes by permitting risk-adjusted screening intensities. Previous work introduced a prognostic model using sex, age and two preceding faecal haemoglobin concentrations to predict the risk of colorectal cancer (CRC) in the next screening round. Using data of 3 screening rounds, this model attained an area under the receiver-operating-characteristic curve (AUC) of 0.78 for predicting advanced neoplasia (AN). We validated this existing logistic regression (LR) model and attempted to improve it by applying a more flexible machine-learning approach. MethodsWe trained an existing LR and a newly developed random forest (RF) model using updated data from 219,257 third-round participants of the Dutch CRC screening programme until 2018. For both models, we performed two separate out-of-sample validations using 1,137,599 third-round participants after 2018 and 192,793 fourth-round participants from 2020 onwards. We evaluated the AUC and relative risks of the predicted high-risk groups for the outcomes AN and CRC. ResultsFor third-round participants after 2018, the AUC for predicting AN was 0.77 (95% CI: 0.76–0.77) using LR and 0.77 (95% CI: 0.77–0.77) using RF. For fourth-round participants, the AUCs were 0.73 (95% CI: 0.72–0.74) and 0.73 (95% CI: 0.72–0.74) for the LR and RF models, respectively. For both models, the 5% with the highest predicted risk had a 7-fold risk of AN compared to average, whereas the lowest 80% had a risk below the population average for third-round participants. ConclusionThe LR is a valid risk prediction method in stool-based screening programmes. Although predictive performance declined marginally, the LR model still effectively predicted risk in subsequent screening rounds. An RF did not improve CRC risk prediction compared to an LR, probably due to the limited number of available explanatory variables. The LR remains the preferred prediction tool because of its interpretability.

Aging characteristics of colorectal cancer based on gut microbiota.

Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age-differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age-differentiated bacteria. The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age-related differential bacteria and model of CRC patients based on the 58 age-related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease-differentiated bacteria and the age-differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.

Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms.

We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

Analyzing Big EHR Data—Optimal Cox Regression Subsampling Procedure with Rare Events

Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors, including a time-dependent coefficient, which transforms the dataset into “pseudo-observation” form, thus, critically inflating its size. A popular way for coping with massive datasets is downsampling them, such that the computational resources can be afforded by the researcher. Cox regression has remained one of the most popular statistical models for the analysis of survival data to-date. This work addresses the settings of right censored and possibly left-truncated data with rare events, such that the observed failure times constitute only a small portion of the overall sample. We propose Cox regression subsampling-based estimators that approximate their full-data partial-likelihood-based counterparts, by assigning optimal sampling probabilities to censored observations, and including all observed failures in the analysis. The suggested methodology is applied on the UK-biobank for building a colorectal cancer risk-prediction model, while reducing the computation time and memory requirements. Asymptotic properties of the proposed estimators are established under suitable regularity conditions, and simulation studies are carried out to evaluate their finite sample performance. Supplementary materials for this article are available online.

Ability of a polygenic risk score to refine colorectal cancer risk in Lynch syndrome

BackgroundPolygenic risk scores (PRSs) have been used to stratify colorectal cancer (CRC) risk in the general population, whereas its role in Lynch syndrome (LS), the most common type of hereditary...

Gut bacteria and sex differences in colorectal cancer.

Introduction. Differences in gut bacteria that are associated with the occurrence and development of colorectal cancer (CRC) exist between sexes, and males have a higher morbidity of CRC.Gap Statement. Clinical data for the relationship between gut bacteria and sexes in patients with CRC are not available and are needed to support individualized screening and treatment programmes.Aim. To analyse the relationship between gut bacteria and sexes in patients with CRC.Methodology. A total of 6 077 samples recruited by Fudan University's Academy of Brain Artificial Intelligence Science and Technology were included, and the gut bacteria composition mainly shows the top 30 genera. Linear discriminant analysis Effect Size (LEfSe) was used to analyse the differences in gut bacteria. Pearson correlation coefficients were calculated to demonstrate the relationship of discrepant bacteria. CRC risk prediction models were used to rank the importance of valid discrepant bacteria.Results. Bacteroides, Eubacterium and Faecalibacterium were the top three bacteria in males with CRC, while Bacteroides, Subdoligranulum and Eubacterium were the top three bacteria in females with CRC. The abundance of gut bacteria (Escherichia, Eubacteriales, Clostridia, etc.) was higher in males with CRC compared with that in females with CRC. In addition, Dorea and Bacteroides were important CRC-related bacteria (P<0.001). Finally, the importance of discrepant bacteria was ranked based on CRC risk prediction models. Blautia, Barnesiella and Anaerostipes were the top three important discrepant bacteria between males with CRC and females with CRC. The value of AUC was 1.0, the sensitivity was 92.0 %, the specificity was 68.4 %, and the accuracy was 83.3 % in the discovery set.Conclusion. Gut bacteria were correlated with sexes and CRC. It is necessary to consider gender when gut bacteria are used to treat and predict CRC.

A predictive role of C-reactive protein in colorectal cancer risk: an updated meta-analysis from 780,985 participants and 11,289 cancer cases.

This meta-analysis is aimed at understanding the potential role of circulating C-reactive protein (CRP) in the prediction of colorectal cancer (CRC) risk and the potential effect of relevant variables, with specific concern to determine the incorporation of CRP into a CRC risk prediction model. Relevant articles on the association between circulating CRP and CRC risk were searched from PubMed, Embase, Web of Science, and Cochrane Database of Systematic Reviews through August 2022. Random-effects models were used to estimate the pooled relative risk (RR) for the highest versus lowest CRP categories. Linear and non-linear trend analyses were conducted to explore the dose-response associations between CRP and CRC risk. Twenty-three articles including 780,985 participants and 11,289 cancer cases met the selection criteria. The overall result demonstrated a remarkable association between elevated CRP levels and CRC risk (RR, 1.259; 95% CI, 1.060-1.457), but not in dose-response analysis (RR, 1.002 (95% CI, 0.964-1.041) per natural log unit change in CRP). Subgroup analyses indicated a significant difference when grouped by study location, the length of follow-up, and gender composition. No evidence of publication bias was observed. The predictive role of CRP in CRC incidence is limited to colon cancer and a period of 10years after the initial discovery of CRP elevation. The result did not support the etiological role of CRP in CRC and the inclusion of CRP into the CRC risk prediction model.

Risk-Stratified Screening for Colorectal Cancer Using Genetic and Environmental Risk Factors: A Cost-Effectiveness Analysis Based on Real-World Data

Previous studies on the cost-effectiveness of personalized colorectal cancer (CRC) screening were based on hypothetical performance of CRC risk prediction and did not consider the association with competing causes of death. In this study, we estimated the cost-effectiveness of risk-stratified screening using real-world data for CRC risk and competing causes of death. Risk predictions for CRC and competing causes of death from a large community-based cohort were used to stratify individuals into risk groups. A microsimulation model was used to optimize colonoscopy screening for each risk group by varying the start age (40-60 years), end age (70-85 years), and screening interval (5-15 years). The outcomes included personalized screening ages and intervals and cost-effectiveness compared with uniform colonoscopy screening (ages 45-75, every 10 years). Key assumptions were varied in sensitivity analyses. Risk-stratified screening resulted in substantially different screening recommendations, ranging from a one-time colonoscopy at age 60 for low-risk individuals to a colonoscopy every 5 years from ages 40 to 85 for high-risk individuals. Nevertheless, on a population level, risk-stratified screening would increase net quality-adjusted life years gained (QALYG) by only 0.7% at equal costs to uniform screening or reduce average costs by 1.2% for equal QALYG. The benefit of risk-stratified screening improved when it was assumed to increase participation or costs less per genetic test. Personalized screening for CRC, accounting for competing causes of death risk, could result in highly tailored individual screening programs. However, average improvements across the population in QALYG and cost-effectiveness compared with uniform screening are small.