Single-nucleotide polymorphisms based genetic risk score in the prediction of pancreatic cancer risk

BACKGROUND Disease-related single nucleotide polymorphisms(SNPs)based genetic risk score(GRS)has been proven to provide independent inherited risk other than family history in multiple cancer types.AIM To evaluate the potential of GRS in the prediction of pancreatic cancer risk.METHODS In this case-control study(254 cases and 1200 controls),we aimed to evaluate the association between GRS and pancreatic ductal adenocarcinoma(PDAC)risk in the Chinese population.The GRS was calculated based on the genotype information of 18 PDAC-related SNPs for each study subject(personal genotyping information of the SNPs)and was weighted by external odd ratios(ORs).RESULTS GRS was significantly different in cases and controls(1.96±3.84 in PDACs vs 1.09±0.94 in controls,P<0.0001).Logistic regression revealed GRS to be associated with PDAC risk[OR=1.23,95%confidence interval(CI):1.13-1.34,P<0.0001].GRS remained significantly associated with PDAC(OR=1.36,95%CI:1.06-1.74,P=0.015)after adjusting for age and sex.Further analysis revealed an association of increased risk for PDAC with higher GRS.Compared with low GRS(<1.0),subjects with high GRS(2.0)were 99%more likely to have PDAC(OR:1.99,95%CI:1.30-3.04,P=0.002).Participants with intermediate GRS(1.0-1.9)were 39%more likely to have PDAC(OR:1.39,95%CI:1.03-1.84,P=0.031).A positive trend was observed(P trend=0.0006).CONCLUSION GRS based on PDAC-associated SNPs could provide independent information on PDAC risk and may be used to predict a high risk PDAC population.

Risk assessment of venous thromboembolism in inflammatory bowel disease by inherited risk in a population-based incident cohort

Inflammatory bowel disease(IBD),including Crohn’s disease(CD)and ulcerative colitis(UC),is a chronic inflammatory disease of the digestive tract with increasing prevalence globally.Although venous thromboembolism(VTE)is a major complication in IBD patients,it is often underappreciated with limited tools for risk stratification.AIM To estimate the proportion of VTE among IBD patients and assess genetic risk factors(monogenic and polygenic)for VTE.METHODS Incident VTE was followed for 8465 IBD patients in the UK Biobank(UKB).The associations of VTE with F5 factor V leiden(FVL)mutation,F2 G20210A prothrombin gene mutation(PGM),and polygenic score(PGS003332)were tested using Cox hazards regression analysis,adjusting for age at IBD diagnosis,gender,and genetic background(top 10 principal components).The performance of genetic risk factors for discriminating VTE diagnosis was estimated using the area under the receiver operating characteristic curve(AUC).RESULTS The overall proportion of incident VTE was 4.70%in IBD patients and was similar for CD(4.46%),UC(4.49%),and unclassified(6.42%),and comparable to that of cancer patients(4.66%)who are well-known at increased risk for VTE.Mutation carriers of F5/F2 had a significantly increased risk for VTE compared to non-mutation carriers,hazard ratio(HR)was 1.94,95%confidence interval(CI):1.42-2.65.In contrast,patients with the top PGS decile had a considerably higher risk for VTE compared to those with intermediate scores(middle 8 deciles),HR was 2.06(95%CI:1.57-2.71).The AUC for differentiating VTE diagnosis was 0.64(95%CI:0.61-0.67),0.68(95%CI:0.66-0.71),and 0.69(95%CI:0.66-0.71),respectively,for F5/F2 mutation carriers,PGS,and combined.CONCLUSION Similar to cancer patients,VTE complications are common in IBD patients.PGS provides more informative risk information than F5/F2 mutations(FVL and PGM)for personalized thromboprophylaxis.

Personalized prostate cancer care： from screening totreatment

Unprecedented progress has been made in genomic personalized medicine in the last several years, allowing for more individualized healthcare assessments and recommendations than ever before. However, most of this progress in prostate cancer （PCa） care has focused on developing and selecting therapies for late-stage disease. To address this issue of limited focus, we propose a model for incorporating genomic-based personalized medicine into all levels of PCa care, from prevention and screening to diagnosis, and ultimately to the treatment of both early-stage and late-stage cancers. We have termed this strategy the ＂Pyramid Model＂ of personalized cancer care. In this perspective paper, our objective is to demonstrate the potential application of the Pyramid Model to PCa care. This proactive and comprehensive personalized cancer care approach has the potential to achieve three important medical goals： reducing mortality, improving quality of life and decreasing both individual and societal healthcare costs.

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population

Risk prediction models including the Prostate Health Index(phi)for prostate cancer have been well established and evaluated in the Western population.The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performanee in predicting prostate cancer(PCa)and high-grade PCa(Gleason score 27)in the Chinese population.We developed risk calculators based on 635 men who underwent initial prostate biopsy.Then,we validated the performance of prostate-specific antigen(PSA),phi,and the risk calculators in an additional observational cohort of 1045 men.We observed that the phi-based risk calculators(risk calculators 2 and 4)outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort.In the validation study,the area under the receiver operating characteristic curve(AUC)for risk calculators 2 and 4 reached 0.91 and 0.92,respectively,for predicting PCa and high-grade PCa,respectively;the AUC values were better than those for risk calculator 1(PSA-based model with an AUC of 0.81 and 0.82,respectively)(all P<0.001).Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml^-1 to 10.0 ng ml^-1.Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators.In this study,we showed that,compared to risk calculators without phi,phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population.Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Population-standardized genetic risk score： the SNP-based method of choice for inherited risk assessment of prostate cancer

Several different approaches are available to clinicians for determining prostate cancer （PCa） risk. The clinical validity of various PCa risk assessment methods utilizing single nucleotide polymorphisms （SNPs） has been established; however, these SNP-based methods have not been compared. The objective of this study was to compare the three most commonly used SNP-based methods for PCa risk assessment. Participants were men （n = 1654） enrolled in a prospective study of PCa development. Genotypes of 59 PCa risk-associated SNPs were available in this cohort. Three methods of calculating SNP-based genetic risk scores （GRSs） were used for the evaluation of individual disease risk such as risk allele count （GRS-RAC）, weighted risk allele count （GRS-wRAC）, and population-standardized genetic risk score （GRS-PS）. Mean GRSs were calculated, and performances were compared using area under the receiver operating characteristic curve （AUC） and positive predictive value （PPV）. All SNP-based methods were found to be independently associated with PCa （all P 〈 0.05; hence their clinical validity）. The mean GRSs in men with or without PCa using GRS-RAC were 55.15 and 53.46, respectively, using GRS-wRAC were 7.42 and 6.97, respectively, and using GRS-PS were 1.12 and 0.84, respectively （all P 〈 0.05 for differences between patients with or without PCa）. All three SNP-based methods performed similarly in discriminating PCa from non-PCa based on AUC and in predicting PCa risk based on PPV （all P 〉 0.05 for comparisons between the three methods）, and all three SNP-based methods had a significantly higher AUC than family history （all P 〈 0.05）. Results from this study suggest that while the three most commonly used SNP-based methods performed similarly in discriminating PCa from non-PCa at the population level, GRS-PS is the method of choice for risk assessment at the individual level because its value （where 1.0 represents average population risk） can be easily interpreted regardless of the number of risk-associated SNPs used in the calculation.

Prostate volume does not provide additional predictive value to prostate health index for prostate cancer or clinically significant prostate cancer:results from a multicenter study in China

To evaluate whether prostate volume(PV)would provide additional predictive utility to the prostate health index(phi)for predicting prostate cancer(PCa)or clinically significant prostate cancer,we designed a prospective,observational multicenter study in two prostate biopsy cohorts.Cohort 1 included 595 patients from three medical centers from 2012 to 2013,and Cohort 2 included 1025 patients from four medical centers from 2013 to 2014.Area under the receiver operating characteristic curves(AUC)and logistic regression models were used to evaluate the predictive performance of PV-based derivatives and models.Linear regression analysis showed that both total prostate-specific antigen(tPSA)and free PSA(fPSA)were significantly correlated with PV(all P<0.05).[-2]proPSA(p2PSA)was significantly correlated with PV in Cohort 2(P<0.001)but not in Cohort 1(P=0.309),while no significant association was observed between phi and PV.When combining phi with PV,phi density(PHID)and another phi derivative(PHIV,calculated as phi/PV°5)did not outperform phi for predicting PCa or clinically significant PCa in either Cohort 1 or Cohort 2.Logistic regression analysis also showed that phi and PV were independent predictors for both PCa and clinically significant PCa(all P<0.05);however,PV did not provide additional predictive value to phi when combining these derivatives in a regression model(all models vs phi were not statistically significant,all P>0.05).In conclusion,PV-based derivatives(both PHIV and PHID)and models incorporating PV did not improve the predictive abilities of phi for either PCa or clinically significant PCa.

Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases

Genetic risk score （GRS） based on disease risk-associated single nucleotide polymorphisms （SNPs） is an informative tool that can be used to provide inherited information for specific diseases in addition to family history, However, it is still unknown whether only SNPs that are implicated in a specific racial group should be used when calculating GRSs. The objective of this study is to compare the performance of race-specific GRS and nonrace-specitic GRS for predicting prostate cancer （PCa） among 1338 patients underwent prostate biopsy in Shanghai, China. A race-specific GRS was calculated with seven PCa risk-associated SNPs implicated in East Asians （GRS7）, and a nonrace-specific GRS was calculated based on 76 PCa risk-associated SNPs implicated in at least one racial group （GRS76）. The means of GRS7 and GRS76 were 1.19 and 1.85, respectively, in the study population. Higher GRS7 and GRS76 were independent predictors for PCa and high-grade PCa in univariate and multivariate analyses. GRS7 had a better area under the receiver-operating curve （AUC） than GRS76 for discriminating PCa （0.602 vs 0.573） and high-grade PCa （0.603 vs 0.575） but did not reach statistical significance. GRS7 had a better （up to 13% at different cutoffs） positive predictive value （PPV） than GRS76. In conclusion, a race-specific GRS is more robust and has a better performance when predicting PCa in East Asian men than a GRS calculated using SNPs that are not shown to be associated with East Asians.

Performance of the Prostate Health Index in predicting prostate biopsy outcomes among men with a negative digital rectal examination and transrectal ultrasonography

The [-2]proPSA （p2PSA） and its derivatives, the p2PSA-to-free PSA ratio （%p2PSA）, and the Prostate Health Index （PHI） have greatly improved discrimination between men with and without prostate cancer （PCa） in prostate biopsies. However, little is known about their performance in cases where a digital rectal examination （DRE） and transrectal ultrasonography （TRUS） are negative. A prospective cohort of 261 consecutive patients in China with negative DRE and TRUS were recruited and underwent prostate biopsies. A serum sample had collected before the biopsy was used to measure various PSA derivatives, including total prostate-specific antigen （tPSA）, free PSA, and p2PSA. For each patient, the free-to-total PSA ratio （%fPSA）, PSA density （PSAD）, p2PSA-to-free PSA ratio （%p2PSA）, and PHI were calculated. Discriminative performance was assessed using the area under the receiver operating characteristic curve （AUC） and the biopsy rate at 91% sensitivity. The AUC scores within the entire cohort with respect to age, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI were 0.598, 0.751, 0.646, 0.789, 0.814, 0.808, and 0.853, respectively. PHI was the best predictor of prostate biopsy results, especially in patients with a tPSA of 10.1-20 ng ml-1. Compared with other markers, at a sensitivity of 91%, PHI was the most useful for determining which men did not need to undergo biopsy, thereby avoiding unnecessary procedures. The use of PHI could improve the accuracy of PCa detection by predicting prostate biopsy outcomes among men with a negative DRE and TRUS in China.

DNA alterations in the tumor genome and their associations with clinical outcome in prostate cancer

Although most prostate cancer （PCa） cases are not life-threatening, approximately 293 000 men worldwide die annually due to PCa. These lethal cases are thought to be caused by coordinated genomic alterations that accumulate over time. Recent genome-wide analyses of DNA from subjects with PCa have revealed most, if not all, genetic changes in both germline and PCa tumor genomes. In this article, I first review the major, somatically acquired genomic characteristics of various subtypes of PCa. I then recap key findings on the relationships between genomic alterations and clinical parameters, such as biochemical recurrence or clinical relapse, metastasis and cancer-specific mortality. Finally, I outline the need for, and challenges with, validation of recent findings in prospective studies for clinical utility. It is clearer now than ever before that the landscape of somatically acquired aberrations in PCa is highlighted by DNA copy number alterations （CNAs） and TMPRSS2-ERG fusion derived from complex rearrangements, numerous single nucleotide variations or mutations, tremendous heterogeneity, and continuously punctuated evolution. Genome-wide CNAs, PTEN loss, MYC gain in primary tumors, and TP53 loss/mutation and AR amplification/mutation in advanced metastatic PCa have consistently been associated with worse cancer prognosis. With this recently gained knowledge, it is now an opportune time to develop DNA-based tests that provide more accurate patient stratification for prediction of clinical outcome, which will ultimately lead to more personalized cancer care than is possible at present.

Clinical validity and utility of genetic risk scores in prostate cancer

Current issues related to prostate cancer （PCa） clinical care （e.g., over-screening, over-diagnosis, and over-treatment of nonaggressive PCa） call for risk assessment tools that can be combined with family history （FH） to stratify disease risk among men in the general population. Since 2007, genome-wide association studies （GWASs） have identified more than 100 SNPs associated with PCa susceptibility. In this review, we discuss （1） the validity of these PCa risk-associated SNPs, individually and collectively; （2） the various methods used for measuring the cumulative effect of multiple SNPs, including genetic risk score （GRS）; （3） the adequate number of SNPs needed for risk assessment; （4） reclassification of risk based on evolving numbers of SNPs used to calculate genetic risk, （5） risk assessment for men from various racial groups, and （6） the clinical utility of genetic risk assessment. In conclusion, data available to date support the clinical validity of PCa risk-associated SNPs and GRS in risk assessment among men with or without FH. PCa risk-associated SNPs are not intended for diagnostic use; rather, they should be used the same way as FH. Combining GRS and FH can significantly improve the performance of risk assessment. Improved risk assessment may have important clinical utility in targeted PCa testing. However, clinical trials are urgently needed to evaluate this clinical utility as well as the acceptance of GRS by patients and physicians.

Translation of genomics and epigenomics in prostate cancer： progress and promising directions

During the last several years, exciting discoveries have been madein prostate cancer （PCa） as a result of significant advances in genomic technology and information. For example, using genome-wide association studies, more than 100 inherited genetic variants associated with PCa risk have been identified. Similarly, with the use of next-generation sequencing, various types of recurrent somatic DNA alterations in prostate tumors have been revealed. Some of these discoveries have potential clinical application to supplement existing tools for better decision-making regarding the need for screening, biopsy, and treatment of PCa. However, because of the complexity of these genomic findings and incomplete understanding of the genetics of this multifactorial disease, this potential has not yet been fully realized.

Association between diacylglycerol kinase kappa variants and hypospadias susceptibility in a Han Chinese population

Previous genome-wide association studies have identified variants in the diacylglycerol kinase kappa （DGKtO gene associated with hypospadias in populations of European descent. However, no variants of DGKKwere confirmed to be associated with hypospadias in a recent Han Chinese study population, likely due to the limited number of single-nucleotide polymorphisms （SNPs） included in the analysis. In this study, we aimed to address the inconsistent results and evaluate the association between DGKK and hypospadias in the Han Chinese population through a more comprehensive analysis of DGKK variants. We conducted association analyses for 17 SNPs in or downstream of DGKKwith hypospadias among 322 cases （58 mild, 113 moderate, 128 severe, and 23 unknown） and 1008 controls. Five SNPs （rs2211122, rs4554617, rs7058226, rs7063116, and rs5915254） in DGKK were significantly associated with hypospadias （P 〈 0.05）, with odds ratios （ORs） of 1.64-1.76. When only mild and moderate cases were compared to controls, 10 SNPs in DGKKwere significant （P〈 0.05）, with ORs of 1.56-2.13. No significant SNP was observed when only severe cases were compared to controls. This study successfully implicated DGKK variants in hypospadias risk among a Han Chinese population, especially for mild/moderate cases. Severe forms of hyposDadias are likely due to other genetic factors.

Clinically available RNA profiling tests of prostate tumors： utility and comparison

In the postscreening era, physicians are in need of methods to discriminate aggressive from nonaggressive prostate cancer （PCa） to reduce overdiagnosis and overtreatment. However, studies have shown that prognoses （e.g., progression and mortality） differ even among individuals with similar clinical and pathological characteristics. Existing risk classifiers （TMN grading system, Gleason score, etc.） are not accurately enough to represent the biological features of PCa. Using new genomic technologies, novel biomarkers and classifiers have been developed and shown to add value to clinical or pathological risk factors for predicting aggressive disease. Among them, RNA testing （gene expression analysis） is useful because it can not only reflect genetic variations but also reflect epigenetic regulations. Commercially available RNA profiling tests （Oncotype Dx, Prolaris, and Decipher） have demonstrated strong abilities to discriminate PCa with poor prognosis from less aggressive diseases. For instance, these RNA profiling tests can predict disease progression in active surveillance patients or early recurrence after radical treatments. These tests may offer more dependable methods for PCa prognosis prediction to make more accurate and personal medical decisions.