A Review of Cancer Risk Prediction Models with Genetic Variants

Wang, Xuexia; Oldani, Michael; Zhao, Xingwang; Huang, Xiaohui; Qian, Dajun

doi:10.4137/cin.s13788

Cited by 19 publications

(18 citation statements)

References 67 publications

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“…A baseline binomial model with only age, sex, and smoking pack-years as predictors attained an AUC of 0.607 (#1 in Table 3). Consistent with previous findings (7), adding top SNPs implicated in overall LC GWAS hardly increased AUC, by around 0.01 (#2 in Table 3). Diminishing returns came from further adding smoking interactions with these SNPs (#3 in Table 3).…”

Section: Resultssupporting

confidence: 90%

“…These findings suggest that genetic considerations may be more important for identifying cases, while epidemiologic considerations may be more important for identifying controls. Genetics-informed LC prediction models have reported AUCs, but not sensitivities and specificities at discrete ROC curve cutoffs against which to further compare (7, 12–16). …”

Section: Resultsmentioning

confidence: 99%

“…The major LC prediction models upon which most subsequent models (7) expand are the Bach model (8), the Spitz model (9), the Liverpool Lung Project (LLP) model (10), and the Tammemagi model (11). Although they differ slightly in population study design, analysis of smokers versus non-smokers, and epidemiologic risk factors under consideration, all of the models agree that ascertaining airborne exposures and history of respiratory illnesses is useful for prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations

Qian

Han

Byun

et al. 2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways. Methods For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant single-nucleotide polymorphism (SNP) variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation, and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC). Results Compared to typical binomial prediction models which have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001). Conclusions Our biologically informed approach demonstrated a larger increase in AUC over non-genetic counterpart models relative to previous approaches that incorporate variants. Impact This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations

Qian

Han

Byun

et al. 2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…In genetic studies, to account for population stratification or identify differences by race/ethnicity, empiric analysis or agnostic investigations such as GWAS that comprehensively search for associations between all available genetic data and disease without a prior hypothesis often are conducted in separate racial/ethnic groups, and are coupled with replication studies to test whether significant findings in one racial/ethnic group also are significant in other groups . Empiric analyses, such as GWAS studies, have proven to be hypothesis‐generating for the study of PCa disparities, identifying potentially important genetic differences in black and white men that may inform the identification of high‐risk populations for advanced PCa . Similarly, in neighborhood studies, we previously designed a novel neighborhood‐wide association study (NWAS), which is a multiphase, successively more stringent, empiric variable selection method derived from GWAS that agnostically identified 17 neighborhood variables (out of >14,000 US Census variables) that are significantly associated with advanced PCa in white men .…”

Section: Introductionmentioning

confidence: 99%

Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood‐wide association study “hits” in black and white men

Lynch

Sorice

Tagai

et al. 2020

Cancer

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BACKGROUND: Black men are more likely to die of prostate cancer (PCa) compared with white men. Factors ranging from genetics to neighborhood environment contribute to these disparities. However, unlike genetics, agnostic investigations that identify candidate variables from large-scale data, and that allow for empiric investigations into differential associations between neighborhood and PCa by race/ethnicity, to the authors' knowledge have not been well explored. Thus, herein, the authors built on their previously developed, empiric neighborhood-wide association study (NWAS) in white men and conducted a NWAS in black men to determine whether findings differed by race. METHODS: Pennsylvania Cancer Registry data were linked to US Census data. For the NWAS in non-Hispanic black men, the authors evaluated the association between 14,663 neighborhood census variables and advanced PCa (11 high-stage and/or high-grade cases and 8632 low-stage and/or low-grade cases), adjusting for age, diagnosis year, spatial correlation, and multiple testing. Odds ratios and 95% credible intervals were reported. Replication of NWAS findings across black and white races was assessed using Bayesian mixed effects models. RESULTS: Five variables related to housing (3 variables), education (1 variable), and employment and/or transportation (1 variable) were found to be significantly associated with advanced PCa in black men compared with 17 socioeconomic variables (mostly related to poverty and/or income) in white men. The top hit in black men was related to crowding in renter-occupied housing (odds ratio, 1.10; 95% credible interval, 1.001-1.12). Nine of 22 NWAS hits (4 of 5 hits in black men) were replicated across racial/ethnic groups. CONCLUSIONS: Different neighborhood variables, or "candidates," were identified across race-specific NWASs. These findings and empiric approaches warrant additional study and may inform PCa racial disparities, particularly future gene-environment studies aimed at identifying patients and/or communities at risk of advanced PCa.

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“…Since the Framingham study in 1976, yielding a first risk prediction model for coronary heart disease, a number of prediction models have been reported for various medical conditions, including cancer. [1][2][3][4][5] In pancreatic ductal adenocarcinoma (PDAC), few such models have been designed, including the ones for absolute risk prediction [6][7][8][9][10][11][12] and gene carrier status prediction, 13 as well as prediction models in groups at risk. 14,15 Recently, two independent models to determine the risk of PDAC in patients in new-onset diabetes (NOD) cohort have also been reported.…”

Section: Introductionmentioning

confidence: 99%

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

et al. 2019

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BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.

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A Review of Cancer Risk Prediction Models with Genetic Variants

Cited by 19 publications

References 67 publications

A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations

A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations

Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood‐wide association study “hits” in black and white men

Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients

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