Dataset Information

The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program.

ABSTRACT: Background:In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios. Methods:We estimated the model's predicted CRC risk distribution in the Australian population. Predicted CRC risks were categorized into screening recommendations under 3 proposed scenarios to compare with current recommendations: 1) highly tailored, 2) 3 risk categories, and 3) 4 sex-specific risk categories. Under each scenario, for 35- to 74-year-olds, we calculated the number of CRC screens by immunochemical fecal occult blood testing (iFOBT) and colonoscopy and the proportion of predicted CRCs over 10 years in each screening group. Results:Currently, 1.1% of 35- to 74-year-olds are recommended screening colonoscopy and 56.2% iFOBT, and 5.7% and 83.2% of CRCs over 10 years were predicted to occur in these groups, respectively. For the scenarios, 1) colonoscopy was recommended to 8.1% and iFOBT to 37.5%, with 36.1% and 50.1% of CRCs in each group; 2) colonoscopy was recommended to 2.4% and iFOBT to 56.0%, with 13.2% and 76.9% of cancers in each group; and 3) colonoscopy was recommended to 5.0% and iFOBT to 54.2%, with 24.5% and 66.5% of cancers in each group. Conclusions:A highly tailored CRC screening scenario results in many fewer screens but more cancers in those unscreened. Category-based scenarios may provide a good balance between number of screens and cancers detected and are simpler to implement.

SUBMITTER: Saya S

PROVIDER: S-EPMC7583148 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program.

Saya Sibel S Emery Jon D JD Dowty James G JG McIntosh Jennifer G JG Winship Ingrid M IM Jenkins Mark A MA

JNCI cancer spectrum 20200718 5

<h4>Background</h4>In many countries, population colorectal cancer (CRC) screening is based on age and family history, though more precise risk prediction could better target screening. We examined the impact of a CRC risk prediction model (incorporating age, sex, lifestyle, genomic, and family history factors) to target screening under several feasible screening scenarios.<h4>Methods</h4>We estimated the model's predicted CRC risk distribution in the Australian population. Predicted CRC risks w ...[more]

PMID: 33134836

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Project description:PURPOSE:Reducing colorectal cancer incidence and mortality through early detection would improve efficacy if targeted. We developed a colorectal cancer risk prediction model incorporating personal, family, genetic, and environmental risk factors to enhance prevention. METHODS:A familial risk profile (FRP) was calculated to summarize individuals' risk based on detailed cancer family history (FH), family structure, probabilities of mutation in major colorectal cancer susceptibility genes, and a polygenic component. We developed risk models, including individuals' FRP or binary colorectal cancer FH, and colorectal cancer risk factors collected at enrollment using population-based colorectal cancer cases (N = 4,445) and controls (N = 3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). Model validation used CCFRC follow-up data for population-based (N = 12,052) and clinic-based (N = 5,584) relatives with no cancer history at recruitment to assess model calibration [expected/observed rate ratio (E/O)] and discrimination [area under the receiver-operating-characteristic curve (AUC)]. RESULTS:The E/O [95% confidence interval (CI)] for FRP models for population-based relatives were 1.04 (0.74-1.45) for men and 0.86 (0.64-1.20) for women, and for clinic-based relatives were 1.15 (0.87-1.58) for men and 1.04 (0.76-1.45) for women. The age-adjusted AUCs (95% CI) for FRP models for population-based relatives were 0.69 (0.60-0.78) for men and 0.70 (0.62-0.77) for women, and for clinic-based relatives were 0.77 (0.69-0.84) for men and 0.68 (0.60-0.76) for women. The incremental values of AUC for FRP over FH models for population-based relatives were 0.08 (0.01-0.15) for men and 0.10 (0.04-0.16) for women, and for clinic-based relatives were 0.11 (0.05-0.17) for men and 0.11 (0.06-0.17) for women. CONCLUSIONS:Both models calibrated well. The FRP-based model provided better risk stratification and risk discrimination than the FH-based model. IMPACT:Our findings suggest detailed FH may be useful for targeted risk-based screening and clinical management.

Project description:BackgroundThe database used for the NHS Bowel Cancer Screening Programme (BCSP) derives participant information from primary care records. Combining predictors with FOBTs has shown to improve referral decisions and accuracy. The richer data available from GP databases could be used to complement screening referral decisions by identifying those at greatest risk of colorectal cancer. We determined the availability of data for key predictors and whether this information could be used to inform more accurate screening referral decisions.MethodsAn English BCSP cohort was derived using the electronic notifications received from the BCSP database to GP records. The cohort covered a period between 13th May 2009 to 17th January 2017. Completeness of variables and univariable associations were assessed. Risk prediction models were developed using Cox regression and multivariable fractional polynomials with backwards elimination. Optimism adjusted performance metrics were reported. The sensitivity and specificity of a combined approach using the negative FOBT model plus FOBT positive patients was determined using a probability equivalent to a 3% PPV NICE guidelines level.Results292,059 participants aged 60-74 were derived for the BCSP screening cohort. A model including the screening test result had a C-statistic of 0.860, c-slope of 0.997, and R2 of 0.597. A model developed for negative screening results only had a C-statistic of 0.597, c-slope of 0.940, and R2 of 0.062. Risk predictors included in the models included; age, sex, alcohol consumption, IBS diagnosis, family history of gastrointestinal cancer, smoking status, previous negatives and whether a GP had ordered a blood test. For the combined screening approach, sensitivity increased slightly from 53.90% (FOBT only) to 58.82% but at the expense of an increased referral rate.ConclusionsThis research has identified several potential predictors for CRC in a BCSP population. A risk prediction model developed for BCSP FOBT negative patients was not clinically useful due to a low sensitivity and increased referral rate. The predictors identified in this study should be investigated in a refined algorithm combining the quantitative FIT result. Combining data from multiple sources enables fuller patient profiles using the primary care and screening database interface.

Project description:BackgroundColorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.MethodsWe retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.ResultsThe model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.ConclusionsOverall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.

Dataset Information

The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program.

Publications

The Impact of a Comprehensive Risk Prediction Model for Colorectal Cancer on a Population Screening Program.

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