Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

BACKGROUND

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS).

METHODS

A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n = 5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS.

FINDINGS

The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk.

INTERPRETATION

Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold.

FUNDING

This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

BACKGROUND AND OBJECTIVE

Polygenic risk scores (PRSs) have been developed to identify men with the highest risk of prostate cancer. Our aim was to compare the performance of 16 PRSs in identifying men at risk of developing prostate cancer and then to evaluate the performance of the top-performing PRSs in differentiating individuals at risk of aggressive prostate cancer.

METHODS

For this case-control study we downloaded 16 published PRSs from the Polygenic Score Catalog on May 28, 2021 and applied them to Michigan Genomics Initiative (MGI) patients. Cases were matched to the Michigan Urological Surgery Improvement Collaborative (MUSIC) registry to obtain granular clinical and pathological data. MGI prospectively enrolls patients undergoing surgery at the University of Michigan, and MUSIC is a multi-institutional registry that prospectively tracks demographic, treatment, and clinical variables. The predictive performance of each PRS was evaluated using the area under the covariate-adjusted receiver operating characteristic curve (aAUC), and the association between PRS and disease aggressiveness according to prostate biopsy data was measured using logistic regression.

KEY FINDINGS AND LIMITATIONS

We included 18 050 patients in the analysis, of whom 15 310 were control subjects and 2740 were prostate cancer cases. The median age was 66.1 yr (interquartile range 59.9-71.6) for cases and 56.6 yr (interquartile range 42.6-66.7) for control subjects. The PRS performance in predicting the risk of developing prostate cancer according to aAUC ranged from 0.51 (95% confidence interval 0.51-0.53) to 0.67 (95% confidence interval 0.66-0.68). By contrast, there was no association between PRS and disease aggressiveness.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Prostate cancer PRSs have modest real-world performance in identifying patients at higher risk of developing prostate cancer; however, they are limited in distinguishing patients with indolent versus aggressive disease.

PATIENT SUMMARY

Risk scores using data for multiple genes (called polygenic risk scores) can identify men at higher risk of developing prostate cancer. However, these scores need to be refined to be able to identify men with the highest risk for clinically significant prostate cancer.

Tribulations and future opportunities for artificial intelligence in precision medicine

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI.

Effects of joint screening for prostate, lung, colorectal, and ovarian cancer - results from a controlled trial

Background

Although screening is widely used to reduce cancer burden, untargeted cancers are frequently missed after single cancer screening. Joint cancer screening is presumed as a more effective strategy to reduce overall cancer burden.

Methods

Gender-specific screening effects on PLCO cancer incidence, PLCO cancer mortality, all-neoplasms mortality and all-cause mortality were evaluated, and meta-analyses based on gender-specific screening effects were conducted to achieve the pooled effects. The cut-off value of time-dependent receiver-operating-characteristic curve of 10-year combined PLCO cancer risk was used to reclassify participants into low- and high-risk subgroups. Further analyses were conducted to investigate screening effects stratified by risk groups and screening compliance.

Results

After a median follow-up of 10.48 years for incidence and 16.85 years for mortality, a total of 5,506 PLCO cancer cases, 1,845 PLCO cancer deaths, 3,970 all-neoplasms deaths, and 14,221 all-cause deaths were documented in the screening arm, while 6,261, 2,417, 5,091, and 18,516 outcome-specific events in the control arm. Joint cancer screening did not significantly reduce PLCO cancer incidence, but significantly reduced male-specific PLCO cancer mortality (hazard ratio and 95% confidence intervals [HR(95%CIs)]: 0.88(0.82, 0.95)) and pooled mortality [0.89(0.84, 0.95)]. More importantly, joint cancer screening significantly reduced both gender-specific all-neoplasm mortality [0.91(0.86, 0.96) for males, 0.91(0.85, 0.98) for females, and 0.91(0.87, 0.95) for meta-analyses] and all-cause mortality [0.90(0.88, 0.93) for male, 0.88(0.85, 0.92) for female, and 0.89(0.87, 0.91) for meta-analyses]. Further analyses showed decreased risks of all-neoplasm mortality was observed with good compliance [0.72(0.67, 0.77) for male and 0.72(0.65, 0.80) for female] and increased risks with poor compliance [1.61(1.40, 1.85) for male and 1.30(1.13, 1.40) for female].

Conclusion

Joint cancer screening could be recommended as a potentially strategy to reduce the overall cancer burden. More compliance, more benefits. However, organizing a joint cancer screening not only requires more ingenious design, but also needs more attentions to the potential harms.

Trial registration

NCT00002540 (Prostate), NCT01696968 (Lung), NCT01696981 (Colorectal), NCT01696994 (Ovarian).

Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks

BACKGROUND

Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results.

METHODS

We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes.

RESULTS

We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption.

CONCLUSIONS

The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.

Risk-stratified screening for the early detection of kidney cancer

Earlier detection and screening for kidney cancer has been identified as a key research priority, however the low prevalence of the disease in unselected populations limits the cost-effectiveness of screening. Risk-stratified screening for kidney cancer may improve early detection by targeting high-risk individuals whilst limiting harms in low-risk individuals, potentially increasing the cost-effectiveness of screening. A number of models have been identified which estimate kidney cancer risk based on both phenotypic and genetic data, and while several of the former have been shown to identify individuals at high-risk of developing kidney cancer with reasonable accuracy, current evidence does not support including a genetic component. Combined screening for lung cancer and kidney cancer has been proposed, as the two malignancies share some common risk factors. A modelling study estimated that using lung cancer risk models (currently used for risk-stratified lung cancer screening) could capture 25% of patients with kidney cancer, which is only slightly lower than using the best performing kidney cancer-specific risk models based on phenotypic data (27%-33%). Additionally, risk-stratified screening for kidney cancer has been shown to be acceptable to the public. The following review summarises existing evidence regarding risk-stratified screening for kidney cancer, highlighting the risks and benefits, as well as exploring the management of potential harms and further research needs.

Association of Polygenic Score With Tumor Molecular Subtypes in Papillary Thyroid Carcinoma

CONTEXT

Genome-wide association studies have identified germline variants associated with elevated PTC risk. It is also known that somatic driver mutations contribute to PTC development and as such PTCs can be further categorized into different molecular subtypes based on their somatic alterations. However, it remains unknown whether identified germline variants predictive of PTC risk are associated with specific molecular subtypes.

OBJECTIVE

The primary goal of the present study is to determine whether germline genetic risk, as assessed using a polygenic score (PGS) is associated with molecular subtypes of papillary thyroid carcinoma (PTC), defined based on tumor driver mutation status.

METHODS

This study was carried out using data from The Cancer Genome Atlas (TCGA) thyroid cancer study. A previously validated 10-single-nucleotide variation PGS for PTC derived from genome-wide association study hits was calculated to ascertain germline genetic risk. The primary molecular subtypes of interest were defined by tumor driver mutation status (BRAFV600E-mutated vs RAS-mutated vs "other"). We also explored associations between PGS and molecular subtypes defined by messenger RNA (mRNA) expression, microRNA expression, and DNA methylation patterns. Polytomous logistic regression analysis was used to assess the association between PGS and PTC molecular subtype with and without adjustment for clinical variables. Odds ratios (ORs) with their 95% CIs were estimated.

RESULTS

A total of 359 patients were included in the study. PGS was significantly associated specific tumor molecular subtypes defined by tumor driver mutation status. Increasing germline risk was associated with having a higher odd of BRAFV600E-mutated PTC compared to PTCs without driver mutations in the "other" category. No significant difference was detected in terms of PGS tumor categorization in the RAS subtype compared to BRAFV600E. In exploratory analyses, PGS was also associated with mRNA-, microRNA-, and DNA methylation-defined molecular subtypes, as defined by the TCGA PTC study.

CONCLUSION

PGS has molecular subtype-specific associations in PTC, which has implications for their use in risk prediction.

Assessing a Polygenic Risk Score for Lung Cancer Susceptibility in Non-Hispanic White and Black Populations

BACKGROUND

Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black populations in model development.

METHODS

We used a previously published lung cancer PRS on the basis of 80 SNPs associated with lung cancer risk in the OncoArray cohort and validated in UK Biobank. The PRS was evaluated for association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of chronic obstructive pulmonary disease, and the top five principal components for genetic ancestry.

RESULTS

Among the 80 PRS SNPs included in the score, 14 were significantly associated with lung cancer risk (P < 0.05) in INHALE White participants, while there were no significant SNPs among INHALE Black participants. After adjusting for covariates, the PRS was significantly associated with risk in Whites (continuous score P = 0.007), but not in Blacks (continuous score P = 0.88). The PRS remained a statistically significant predictor of lung cancer risk in Whites ineligible for lung cancer screening under current U.S. Preventive Services Task Force guidelines (P = 0.02).

CONCLUSIONS

Using a previously validated PRS, we did find some predictive ability for lung cancer in INHALE White participants beyond traditional risk factors. However, this effect was not observed in Black participants, indicating the need to develop and validate ancestry-specific lung cancer risk models.

IMPACT

While a previously published lung cancer PRS was able to stratify White participants into different levels of risk, the model was not predictive in Blacks. Our findings highlight the need to develop and validate ancestry-specific lung cancer risk models.

Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review

BACKGROUND

Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data.

METHODS

We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors.

RESULTS

Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1).

CONCLUSIONS

While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination.

IMPACT

For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.

Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions

Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.

Genetic factors, adherence to healthy lifestyle behaviors, and risk of bladder cancer

BACKGROUND

Genetic and lifestyle factors both contribute to the pathogenesis of bladder cancer, but the extent to which the increased genetic risk can be mitigated by adhering to a healthy lifestyle remains unclear. We aimed to investigate the association of combined lifestyle factors with bladder cancer risk within genetic risk groups.

METHODS

We conducted a prospective study of 375 998 unrelated participants of European ancestry with genotype and lifestyle data and free of cancer from the UK biobank. We generated a polygenic risk score (PRS) using 16 single nucleotide polymorphisms and a healthy lifestyle score based on body weight, smoking status, physical activity, and diet. Cox models were fitted to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of genetic and lifestyle factors on bladder cancer.

RESULTS

During a median follow-up of 11.8 years, 880 participants developed bladder cancer. Compared with those with low PRS, participants with intermediate and high PRS had a higher risk of bladder cancer (HR 1.29, 95% CI 1.07-1.56; HR 1.63, 95% CI 1.32-2.02, respectively). An optimal lifestyle was associated with an approximately 50% lower risk of bladder cancer than a poor lifestyle across all genetic strata. Participants with a high genetic risk and a poor lifestyle had 3.6-fold elevated risk of bladder cancer compared with those with a low genetic risk and an optimal lifestyle (HR 3.63, 95% CI 2.23 -5.91).

CONCLUSIONS

Adhering to a healthy lifestyle could substantially reduce the bladder cancer risk across all genetic strata, even for high-genetic risk individuals. For all populations, adopting an intermediate lifestyle is more beneficial than a poor one, and adhering to an optimal lifestyle is the ideal effective strategy for bladder cancer prevention.

A new method for multiancestry polygenic prediction improves performance across diverse populations

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

Overestimated prediction using polygenic prediction derived from summary statistics

BACKGROUND

When polygenic risk score (PRS) is derived from summary statistics, independence between discovery and test sets cannot be monitored. We compared two types of PRS studies derived from raw genetic data (denoted as rPRS) and the summary statistics for IGAP (sPRS).

RESULTS

Two variables with the high heritability in UK Biobank, hypertension, and height, are used to derive an exemplary scale effect of PRS. sPRS without APOE is derived from International Genomics of Alzheimer's Project (IGAP), which records ΔAUC and ΔR2 of 0.051 ± 0.013 and 0.063 ± 0.015 for Alzheimer's Disease Sequencing Project (ADSP) and 0.060 and 0.086 for Accelerating Medicine Partnership - Alzheimer's Disease (AMP-AD). On UK Biobank, rPRS performances for hypertension assuming a similar size of discovery and test sets are 0.0036 ± 0.0027 (ΔAUC) and 0.0032 ± 0.0028 (ΔR2). For height, ΔR2 is 0.029 ± 0.0037.

CONCLUSION

Considering the high heritability of hypertension and height of UK Biobank and sample size of UK Biobank, sPRS results from AD databases are inflated. Independence between discovery and test sets is a well-known basic requirement for PRS studies. However, a lot of PRS studies cannot follow such requirements because of impossible direct comparisons when using summary statistics. Thus, for sPRS, potential duplications should be carefully considered within the same ethnic group.

From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead

Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.

Epidemiology and risk factors for ovarian cancer

Ovarian cancer is a complex disease, mostly observed in postmenopausal women, and is associated with poor survival rates. It is the sixth most common cancer and the fifth most common cause of death due to cancer among women in developed countries. Thus, despite representing less than one third of all gynaecologic cancers, deaths due to ovarian cancer account for more than two thirds of deaths due to gynaecologic cancers. Its prevalence is higher in Western Europe and Northern America than Asia and Africa. In sub-Saharan Africa, there is a considerably lower prevalence of ovarian cancer than other parts of Africa. Ovarian cancer is multifaceted, involving many factors, complex biological processes and unpredictable consequences. Unlike other female cancers that have early warning symptoms, ovarian cancer's symptoms are non-specific. As a result, ovarian cancers are normally undetected until advanced stages (III or IV). The major risk factors for ovarian cancer include older age, genetics, family history, hormone replacement therapy, nulliparity, and dietary fat. Controversial factors include obesity, infertility, talc powder, radiation exposure, fertility medications and in vitro fertilization. The current review discusses the aetiology, epidemiology and risk factors for ovarian cancer. Nevertheless, identification of the main risk factors for ovarian cancer may increase the awareness among women of the general population. This should help to decrease the incidence rate of ovarian cancer and increase the five-year survival rate.

Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit

Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach

Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction. This aggregation approach can potentially increase statistical power for detecting signals from rare variants, which have been hypothesized to be a major source of the missing heritability of cancer. Using germline whole-exome sequencing data from the UK Biobank, we developed risk models for 10 cancer types using known risk variants (cancer-associated SNPs and pathogenic variants in known cancer predisposition genes) as well as models that additionally include the meta-features. The meta-features did not improve the prediction accuracy of models based on known risk variants. It is possible that expanding the approach to whole-genome sequencing can lead to gains in prediction accuracy.

Significance

There is evidence that cancer is partly caused by rare genetic variants that have not yet been identified. We investigate this issue using novel statistical methods and data from the UK Biobank.

Risks of second non-breast primaries following breast cancer in women: a systematic review and meta-analysis

BACKGROUND

Second primary cancer incidence is rising among breast cancer survivors. We examined the risks of non-breast second primaries, in combination and at specific cancer sites, through a systematic review and meta-analysis.

METHODS

We conducted a systematic search of PubMed, Embase, and Web of Science, seeking studies published by March 2022. We included studies that reported standardized incidence ratios (SIRs), with associated standard errors, assessing the combined risk of second non-breast primaries following breast cancer. We performed meta-analyses of combined second primary risks, stratifying by age, follow-up duration, and geographic region. We also assessed second primary risks at several specific sites, stratifying by age. The inverse variance method with DerSimonian-Laird estimators was used in all meta-analyses, assuming a random-effects model. Associated biases and study quality were evaluated using the Newcastle-Ottawa scale.

RESULTS

One prospective and twenty-seven retrospective cohort studies were identified. SIRs for second non-breast primaries combined ranged from 0.84 to 1.84. The summary SIR estimate was 1.24 (95% CI 1.14-1.36, I2: 99%). This varied by age: the estimate was 1.59 (95% CI 1.36-1.85) when breast cancer was diagnosed before age 50, which was significantly higher than in women first diagnosed at 50 or over (SIR: 1.13, 95% CI 1.01-1.36, p for difference: < 0.001). SPC risks were also significantly higher when based on Asian, rather than European, registries (Asia-SIR: 1.47, 95% CI 1.29-1.67. Europe-SIR: 1.16, 95% CI 1.04-1.28). There were significantly increased risks of second thyroid (SIR: 1.89, 95% CI 1.49-2.38), corpus uteri (SIR: 1.84, 95% CI 1.53-2.23), ovary (SIR: 1.53, 95% CI 1.35-1.73), kidney (SIR: 1.43, 95% CI 1.17-1.73), oesophagus (SIR: 1.39, 95% CI 1.26-1.55), skin (melanoma) (SIR: 1.34, 95% CI 1.18-1.52), blood (leukaemia) (SIR: 1.30, 95% CI 1.17-1.45), lung (SIR: 1.25, 95% CI 1.03-1.51), stomach (SIR: 1.23, 95% CI 1.12-1.36) and bladder (SIR: 1.15, 95% CI 1.05-1.26) primaries.

CONCLUSIONS

Breast cancer survivors are at significantly increased risk of second primaries at many sites. Risks are higher for those diagnosed with breast cancer before age 50 and in Asian breast cancer survivors compared to European breast cancer survivors. This study is limited by a lack of data on potentially confounding variables. The conclusions may inform clinical management decisions following breast cancer, although specific clinical recommendations lie outside the scope of this review.

Germline rare deleterious variant load alters cancer risk, age of onset and tumor characteristics

Recent studies show that rare, deleterious variants (RDVs) in certain genes are critical determinants of heritable cancer risk. To more comprehensively understand RDVs, we performed the largest-to-date germline variant calling analysis in a case-control setting for a multi-cancer association study from whole-exome sequencing data of 20,789 participants, split into discovery and validation cohorts. We confirm and extend known associations between cancer risk and germline RDVs in specific gene-sets, including DNA repair (OR = 1.50; p-value = 8.30e-07; 95% CI: 1.28-1.77), cancer predisposition (OR = 1.51; p-value = 4.58e-08; 95% CI: 1.30-1.75), and somatic cancer drivers (OR = 1.46; p-value = 4.04e-06; 95% CI: 1.24-1.72). Furthermore, personal RDV load in these gene-sets associated with increased risk, younger age of onset, increased M1 macrophages in tumor and, increased tumor mutational burden in specific cancers. Our findings can be used towards identifying high-risk individuals, who can then benefit from increased surveillance, earlier screening, and treatments that exploit their tumor characteristics, improving prognosis.

Lifestyle, genetic risk and incidence of cancer: a prospective cohort study of 13 cancer types

BACKGROUND

Genetic and lifestyle factors are associated with cancer risk. We investigated the benefits of adhering to lifestyle advice by the World Cancer Research Fund (WCRF) with the risk of 13 types of cancer and whether these associations differ according to genetic risk using data from the UK Biobank.

METHODS

In 2006-2010, participants aged 37-73 years had their lifestyle assessed and were followed up for incident cancers until 2015-2019. Analyses were restricted to those of White European ancestry with no prior history of malignant cancer (n = 195 822). Polygenic risk scores (PRSs) were computed for 13 cancer types and these cancers combined ('overall cancer'), and a lifestyle index was calculated from WCRF recommendations. Associations with cancer incidence were estimated using Cox regression, adjusting for relevant confounders. Additive and multiplicative interactions between lifestyle index and PRSs were assessed.

RESULTS

There were 15 240 incident cancers during the 1 926 987 person-years of follow-up (median follow-up = 10.2 years). After adjusting for confounders, the lifestyle index was associated with a lower risk of overall cancer [hazard ratio per standard deviation increase (95% CI) = 0.89 (0.87, 0.90)] and of eight specific cancer types. There was no evidence of interactions on the multiplicative scale. There was evidence of additive interactions in risks for colorectal, breast, pancreatic, lung and bladder cancers, such that the recommended lifestyle was associated with greater change in absolute risk for persons at higher genetic risk (P < 0.0003 for all).

CONCLUSIONS

The recommended lifestyle has beneficial associations with most cancers. In terms of absolute risk, the protective association is greater for higher genetic risk groups for some cancers. These findings have important implications for persons most genetically predisposed to those cancers and for targeted strategies for cancer prevention.

Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes

PURPOSE

The contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology.

EXPERIMENTAL DESIGN

Blood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE).

RESULTS

Tissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2-3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively.

CONCLUSIONS

Archival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.

Family History of Prostate and Breast Cancer Integrated with a Polygenic Risk Score Identifies Men at Highest Risk of Dying from Prostate Cancer before Age 75 Years

PURPOSE

Family history of prostate cancer is one of the few universally accepted risk factors for prostate cancer. How much an assessment of inherited polygenic risk for prostate cancer adds to lifetime risk stratification beyond family history is unknown.

EXPERIMENTAL DESIGN

We followed 10,120 men in the Health Professionals Follow-up Study with existing genotype data for risk of prostate cancer and prostate cancer-specific death. We assessed to what extent family history of prostate or breast cancer, combined with a validated polygenic risk score (PRS) including 269 prostate cancer risk variants, identifies men at risk of prostate cancer and prostate cancer death across the age span.

RESULTS

During 20 years of follow-up, 1,915 prostate cancer and 166 fatal prostate cancer events were observed. Men in the top PRS quartile with a family history of prostate or breast cancer had the highest rate of both prostate cancer and prostate cancer-specific death. Compared with men at lowest genetic risk (bottom PRS quartile and no family history), the HR was 6.95 [95% confidence interval (CI), 5.57-8.66] for prostate cancer and 4.84 (95% CI, 2.59-9.03) for prostate cancer death. Men in the two upper PRS quartiles (50%-100%) or with a family history of prostate or breast cancer (61.8% of the population) accounted for 97.5% of prostate cancer deaths by age 75 years.

CONCLUSIONS

Our study shows that prostate cancer risk stratification on the basis of family history and inherited polygenic risk can identify men at highest risk of dying from prostate cancer before age 75 years.

The current state of genetic risk models for the development of kidney cancer: a review and validation

OBJECTIVE

To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models.

METHODS

Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925).

RESULTS

A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers.

CONCLUSIONS

Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.

Assessment of genetic susceptibility to multiple primary cancers through whole-exome sequencing in two large multi-ancestry studies

BACKGROUND

Up to one of every six individuals diagnosed with one cancer will be diagnosed with a second primary cancer in their lifetime. Genetic factors contributing to the development of multiple primary cancers, beyond known cancer syndromes, have been underexplored.

METHODS

To characterize genetic susceptibility to multiple cancers, we conducted a pan-cancer, whole-exome sequencing study of individuals drawn from two large multi-ancestry populations (6429 cases, 165,853 controls). We created two groupings of individuals diagnosed with multiple primary cancers: (1) an overall combined set with at least two cancers across any of 36 organ sites and (2) cancer-specific sets defined by an index cancer at one of 16 organ sites with at least 50 cases from each study population. We then investigated whether variants identified from exome sequencing were associated with these sets of multiple cancer cases in comparison to individuals with one and, separately, no cancers.

RESULTS

We identified 22 variant-phenotype associations, 10 of which have not been previously discovered and were significantly overrepresented among individuals with multiple cancers, compared to those with a single cancer.

CONCLUSIONS

Overall, we describe variants and genes that may play a fundamental role in the development of multiple primary cancers and improve our understanding of shared mechanisms underlying carcinogenesis.

Performance of the Use of Genetic Information to Assess the Risk of Colorectal Cancer in the Basque Population

Although the genetic contribution to colorectal cancer (CRC) has been studied in various populations, studies on the applicability of available genetic information in the Basque population are scarce. In total, 835 CRC cases and 940 controls from the Basque population were genotyped and genome-wide association studies were carried out. Mendelian Randomization analyses were used to discover the effect of modifiable risk factors and microbiota on CRC. In total, 25 polygenic risk score models were evaluated to assess their performance in CRC risk calculation. Moreover, 492 inflammatory bowel disease cases were used to assess whether that genetic information would not confuse both conditions. Five suggestive (p < 5 × 10−6) loci were associated with CRC risk, where genes previously associated with CRC were located (e.g., ABCA12, ATIC or ERBB4). Moreover, the analyses of CRC locations detected additional genes consistent with the biology of CRC. The possible contribution of cholesterol, BMI, Firmicutes and Cyanobacteria to CRC risk was detected by Mendelian Randomization. Finally, although polygenic risk score models showed variable performance, the best model performed correctly regardless of the location and did not misclassify inflammatory bowel disease cases. Our results are consistent with CRC biology and genetic risk models and could be applied to assess CRC risk in the Basque population.

Multiethnic polygenic risk prediction in diverse populations through transfer learning

Polygenic risk scores (PRS) leverage the genetic contribution of an individual’s genotype to a complex trait by estimating disease risk. Traditional PRS prediction methods are predominantly for the European population. The accuracy of PRS prediction in non-European populations is diminished due to much smaller sample size of genome-wide association studies (GWAS). In this article, we introduced a novel method to construct PRS for non-European populations, abbreviated as TL-Multi, by conducting a transfer learning framework to learn useful knowledge from the European population to correct the bias for non-European populations. We considered non-European GWAS data as the target data and European GWAS data as the informative auxiliary data. TL-Multi borrows useful information from the auxiliary data to improve the learning accuracy of the target data while preserving the efficiency and accuracy. To demonstrate the practical applicability of the proposed method, we applied TL-Multi to predict the risk of systemic lupus erythematosus (SLE) in the Asian population and the risk of asthma in the Indian population by borrowing information from the European population. TL-Multi achieved better prediction accuracy than the competing methods, including Lassosum and meta-analysis in both simulations and real applications.

Can polygenic risk scores contribute to cost-effective cancer screening? A systematic review

PURPOSE

Polygenic risk influences susceptibility to cancer. We assessed whether polygenic risk scores could be used in conjunction with other predictors of future disease status in cost-effective risk-stratified screening for cancer.

METHODS

We undertook a systematic review of papers that evaluated the cost-effectiveness of screening interventions informed by polygenic risk scores compared with more conventional screening modalities. We included papers reporting cost-effectiveness outcomes with no restriction on type of cancer or form of polygenic risk modeled. We evaluated studies using the Quality of Health Economic Studies checklist.

RESULTS

A total of 10 studies were included in the review, which investigated 3 cancers: prostate (n = 5), colorectal (n = 3), and breast (n = 2). Of the 10 papers, 9 scored highly (score >75 on a 0-100 scale) when assessed using the quality checklist. Of the 10 studies, 8 concluded that polygenic risk-informed cancer screening was likely to be more cost-effective than alternatives.

CONCLUSION

Despite the positive conclusions of the included studies, it is unclear if polygenic risk stratification will contribute to cost-effective cancer screening given the absence of robust evidence on the costs of polygenic risk stratification, the effects of differential ancestry, potential downstream economic sequalae, and how large volumes of polygenic risk data would be collected and used.

The Fabrication of Oleophobic Coating and Its Application in Particulates Filtration

The stir-frying process in Chinese cooking has produced serious emissions of oily particles, which are an important source of urban air pollution. In particular, the complex composition of fine particulate may pose a threat to human respiratory and immune systems. However, current filtration methods for oily particulate fumes have low filtration efficiency, high resistance, and high equipment costs. In polypropylene (PP) electret filters, efficiency rapidly decreases and pressure drop (wind resistance) sharply increases after the adsorption of oily particles, due to the oleophilic properties of the PP fibre. We addressed this issue of filter performance degradation by fabricating a sodium perfluorooctanoate (SPFO) oleophobic coating on polyvinylidene fluoride (PVDF) fibre membranes for oily particle filtration. The SPFO coating showed a promising oleophobic effect even at low concentrations, which suggests it has oleophobic properties for different oil types and can be modified for different substrates. This fabricated oleophobic coating is thermostable and the oleophobic effect is unaffected by temperatures from 0 to 100 °C. By modifying the SPFO coating on the PVDF membrane, a high filtration efficiency (89.43%) and low wind resistance (69 Pa) was achieved without oil adhesion, so the proposed coating can be applied in the filtration and purification of oily fine particles and offers a potential strategy for preventing atmospheric oil pollution.

PolarMorphism enables discovery of shared genetic variants across multiple traits from GWAS summary statistics

MOTIVATION

Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis.

RESULTS

We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods.

AVAILABILITY AND IMPLEMENTATION

code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Independent evaluation of melanoma polygenic risk scores in UK and Australian prospective cohorts

BACKGROUND

Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS-based risk prediction, particularly assessment of calibration (comparing predicted to observed risks).

OBJECTIVES

To evaluate PRS-based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry.

METHODS

We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case-cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single-nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta-analysis (PRS68), 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment.

RESULTS

Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62-0·68] and MCCS (E/O = 0·63, 95% CI 0·56-0·72). For UKB, calibration was improved by PRS adjustment, with PRS50-adjusted risks E/O = 0·91, 95% CI 0·87-0·95. The discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07-0·10, P < 0·0001), and higher than for PRS45-adjusted risks (Δ area under the curve 0·02-0·04, P < 0·001).

CONCLUSIONS

A PRS derived from a larger, more diverse meta-analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Prostate cancer polygenic risk score and prediction of lethal prostate cancer

Polygenic risk scores (PRS) for prostate cancer incidence have been proposed to optimize prostate cancer screening. Prediction of lethal prostate cancer is key to any stratified screening program to avoid excessive overdiagnosis. Herein, PRS for incident prostate cancer was evaluated in two population-based cohorts of unscreened middle-aged men linked to cancer and death registries: the Västerbotten Intervention Project (VIP) and the Malmö Diet and Cancer study (MDC). SNP genotypes were measured by genome-wide SNP genotyping by array followed by imputation or genotyping of selected SNPs using mass spectrometry. The ability of PRS to predict lethal prostate cancer was compared to PSA and a commercialized pre-specified model based on four kallikrein markers. The PRS was associated with incident prostate cancer, replicating previously reported relative risks, and was also associated with prostate cancer death. However, unlike PSA, the PRS did not show stronger association with lethal disease: the hazard ratio for prostate cancer incidence vs. prostate cancer metastasis and death was 1.69 vs. 1.65 in VIP and 1.25 vs. 1.25 in MDC. PSA was a much stronger predictor of prostate cancer metastasis or death with an area-under-the-curve of 0.78 versus 0.63 for the PRS. Importantly, addition of PRS to PSA did not contribute additional risk stratification for lethal prostate cancer. We have shown that a PRS that predicts prostate cancer incidence does not have utility above and beyond that of PSA measured at baseline when applied to the clinically relevant endpoint of prostate cancer death. These findings have implications for public health policies for delivery of prostate cancer screening. Focusing polygenic risk scores on clinically significant endpoints such as prostate cancer metastasis or death would likely improve clinical utility.

Phenome-wide association study (PheWAS) of colorectal cancer risk SNP effects on health outcomes in UK Biobank

BACKGROUND

Associations between colorectal cancer (CRC) and other health outcomes have been reported, but these may be subject to biases, or due to limitations of observational studies.

METHODS

We set out to determine whether genetic predisposition to CRC is also associated with the risk of other phenotypes. Under the phenome-wide association study (PheWAS) and tree-structured phenotypic model (TreeWAS), we studied 334,385 unrelated White British individuals (excluding CRC patients) from the UK Biobank cohort. We generated a polygenic risk score (PRS) from CRC genome-wide association studies as a measure of CRC risk. We performed sensitivity analyses to test the robustness of the results and searched the Danish Disease Trajectory Browser (DTB) to replicate the observed associations.

RESULTS

Eight PheWAS phenotypes and 21 TreeWAS nodes were associated with CRC genetic predisposition by PheWAS and TreeWAS, respectively. The PheWAS detected associations were from neoplasms and digestive system disease group (e.g. benign neoplasm of colon, anal and rectal polyp and diverticular disease). The results from the TreeWAS corroborated the results from the PheWAS. These results were replicated in the observational data within the DTB.

CONCLUSIONS

We show that benign colorectal neoplasms share genetic aetiology with CRC using PheWAS and TreeWAS methods. Additionally, CRC genetic predisposition is associated with diverticular disease.

10 Years of GWAS discovery in endometrial cancer: Aetiology, function and translation

Endometrial cancer is a common gynaecological cancer with increasing incidence and mortality. In the last decade, endometrial cancer genome-wide association studies (GWAS) have provided a resource to explore aetiology and for functional interpretation of heritable risk variation, informing endometrial cancer biology. Indeed, GWAS data have been used to assess relationships with other traits through correlation and Mendelian randomisation analyses, establishing genetic relationships and potential risk factors. Cross-trait GWAS analyses have increased statistical power and identified novel endometrial cancer risk variation related to other traits. Functional analysis of risk loci has helped prioritise candidate susceptibility genes, revealing molecular mechanisms and networks. Lastly, risk scores generated using endometrial cancer GWAS data may allow for clinical translation through identification of patients at high risk of disease. In the next decade, this knowledge base should enable substantial progress in our understanding of endometrial cancer and, potentially, new approaches for its screening and treatment.

Simple Linear Cancer Risk Prediction Models With Novel Features Outperform Complex Approaches

PURPOSE

The ability to accurately predict an individual's risk for cancer is critical to the implementation of precision prevention measures. Current cancer risk predictions are frequently made with simple models that use a few proven risk factors, such as the Gail model for breast cancer, which are easy to interpret, but may theoretically be less accurate than advanced machine learning (ML) models.

METHODS

With the UK Biobank, a large prospective study, we developed models that predicted 13 cancer diagnoses within a 10-year time span. ML and linear models fit with all features, linear models fit with 10 features, and externally developed QCancer models, which are available to more than 4,000 general practices, were assessed.

RESULTS

The average area under the receiver operator curve (AUC) of the linear models (0.722, SE = 0.015) was greater than the average AUC of the ML models (0.720, SE = 0.016) when all 931 features were used. Linear models with only 10 features generated an average AUC of 0.706 (SE 0.015), which was comparable to the complex models using all features and greater than the average AUC of the QCancer models (0.684, SE 0.021). The high performance of the 10-feature linear model may be caused by the consideration of often omitted feature types, including census records and genetic information.

CONCLUSION

The high performance of the 10-feature linear models indicate that unbiased selection of diverse features, not ML models, may lead to impressively accurate predictions, possibly enabling personalized screening schedules that increase cancer survival.

Investigating the effect of sexual behaviour on oropharyngeal cancer risk: a methodological assessment of Mendelian randomization

BACKGROUND

Human papilloma virus infection is known to influence oropharyngeal cancer (OPC) risk, likely via sexual transmission. However, sexual behaviour has been correlated with other risk factors including smoking and alcohol, meaning independent effects are difficult to establish. We aimed to evaluate the causal effect of sexual behaviour on the risk of OPC using Mendelian randomization (MR).

METHODS

Genetic variants robustly associated with age at first sex (AFS) and the number of sexual partners (NSP) were used to perform both univariable and multivariable MR analyses with summary data on 2641 OPC cases and 6585 controls, obtained from the largest available genome-wide association studies (GWAS). Given the potential for genetic pleiotropy, we performed a number of sensitivity analyses: (i) MR methods to account for horizontal pleiotropy, (ii) MR of sexual behaviours on positive (cervical cancer and seropositivity for Chlamydia trachomatis) and negative control outcomes (lung and oral cancer), (iii) Causal Analysis Using Summary Effect estimates (CAUSE), to account for correlated and uncorrelated horizontal pleiotropic effects, (iv) multivariable MR analysis to account for the effects of smoking, alcohol, risk tolerance and educational attainment.

RESULTS

In univariable MR, we found evidence supportive of an effect of both later AFS (IVW OR = 0.4, 95%CI (0.3, 0.7), per standard deviation (SD), p = < 0.001) and increasing NSP (IVW OR = 2.2, 95%CI (1.3, 3.8) per SD, p = < 0.001) on OPC risk. These effects were largely robust to sensitivity analyses accounting for horizontal pleiotropy. However, negative control analysis suggested potential violation of the core MR assumptions and subsequent CAUSE analysis implicated pleiotropy of the genetic instruments used to proxy sexual behaviours. Finally, there was some attenuation of the univariable MR results in the multivariable models (AFS IVW OR = 0.7, 95%CI (0.4, 1.2), p = 0.21; NSP IVW OR = 0.9, 95%CI (0.5 1.7), p = 0.76).

CONCLUSIONS

Despite using genetic variants strongly related sexual behaviour traits in large-scale GWAS, we found evidence for correlated pleiotropy. This emphasizes a need for multivariable approaches and the triangulation of evidence when performing MR of complex behavioural traits.

Associations of genetic susceptibility to 16 cancers with risk of breast cancer overall and by intrinsic subtypes

Certain genetic variants are associated with risks of multiple cancers. We investigated breast cancer risk with overall genetic susceptibility to each of 16 other cancers. We constructed polygenic risk scores (PRS) for 16 cancers using risk variants identified by genome-wide association studies. We evaluated the associations of these PRSs with breast cancer risk (overall and by subtypes) using Breast Cancer Association Consortium data, including 106,278 cases and 91,477 controls of European ancestry. Odds ratios (OR) and 95% confidence intervals (CIs) were estimated to measure the association of each PRS with breast cancer risk. Data from the UK Biobank, including 4,337 cases and 209,983 non-cases, were used to replicate the findings. A 5%–8% significantly elevated risk of overall breast cancer was associated with per unit increase of the PRS for glioma and cancers of the corpus uteri, stomach, or colorectum. Analyses by subtype revealed that the PRS for corpus uteri cancer (OR = 1.09; 95% CI, 1.03–1.15) and stomach cancer (OR = 1.07; 95% CI, 1.03–1.12) were associated with estrogen receptor-positive breast cancer, while ovarian cancer PRS was associated with triple-negative breast cancer (OR = 1.25; 95% CI, 1.01–1.55). UK Biobank data supported the positive associations of overall breast cancer risk with PRS for melanoma and cancers of the stomach, colorectum, and ovary. Our study provides strong evidence for shared genetic susceptibility of breast cancer with several other cancers. Results from our study help uncover the genetic basis for breast and other cancers and identify individuals at high risk for multiple cancers.

Melanoma susceptibility: an update on genetic and epigenetic findings

Malignant melanoma is one of the most highly ranked cancers in terms of years of life lost. Hereditary melanoma with its increased familial susceptibility is thought to affect up to 12% of all melanoma patients. In the past, only a few high-penetrance genes associated with familial melanoma, such as CDKN2A and CDK4, have been clinically tested. However, findings now indicate that melanoma is a cancer most likely to develop not only due to high-penetrance variants but also due to polygenic inheritance patterns, leaving no clear division between the hereditary and sporadic development of malignant melanoma. Various pathogenic low-penetrance variants were recently discovered through genome-wide association studies, and are now translated into polygenic risk scores. These can show superior sensitivity rates for the prediction of melanoma susceptibility and related mixed cancer syndromes than risk scores based on phenotypic traits of the patients, with odds ratios of up to 5.7 for patients in risk groups. In addition to describing genetic findings, we also review the first results of epigenetic research showing constitutional methylation changes that alter the susceptibility to cutaneous melanoma and its risk factors.

Genomic Risk Factors for Cervical Cancer

Cervical cancer is the fourth common cancer amongst women worldwide. Infection by high-risk human papilloma virus is necessary in most cases, but not sufficient to develop invasive cervical cancer. Despite a predicted genetic heritability in the range of other gynaecological cancers, only few genomic susceptibility loci have been identified thus far. Various case-control association studies have found corroborative evidence for several independent risk variants at the 6p21.3 locus (HLA), while many reports of associations with variants outside the HLA region remain to be validated in other cohorts. Here, we review cervical cancer susceptibility variants arising from recent genome-wide association studies and meta-analysis in large cohorts and propose 2q14 (PAX8), 17q12 (GSDMB), and 5p15.33 (CLPTM1L) as consistently replicated non-HLA cervical cancer susceptibility loci. We further discuss the available evidence for these loci, knowledge gaps, future perspectives, and the potential impact of these findings on precision medicine strategies to combat cervical cancer.

Challenges of Prevention for a Sustainable Personalized Medicine

The development and implementation of the approaches of personalized medicine for disease prevention are still at infancy, although preventive activities in healthcare represent a key pillar to guarantee health system sustainability. There is an increasing interest in finding informative markers that indicate the disease risk before the manifestation of the disease (primary prevention) or for early disease detection (secondary prevention). Recently, the systematic collection and study of clinical phenotypes and biomarkers consented to the advance of Rehabilomics in tertiary prevention. It consents to identify relevant molecular and physiological factors that can be linked to plasticity, treatment response, and natural recovery. Implementation of these approaches would open avenues to identify people at high risk and enable new preventive lifestyle interventions or early treatments targeted to their individual genomic profile, personalizing prevention and rehabilitation. The integration of personalized medicine into prevention may benefit citizens, patients, healthcare professionals, healthcare authorities, and industry, and ultimately will seek to contribute to better health and quality of life for Europe’s citizens.