Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Impacts of sample weighting on transferability of risk prediction models across EHR-Linked biobanks with different recruitment strategies

OBJECTIVE

To evaluate whether using poststratification weights when training risk prediction models enhances transferability when the external test cohort has a different sampling strategy, a commonly encountered scenario when analyzing electronic health record (EHR)-linked biobanks.

METHODS

PS weights were calculated to align a health system-based biobank, the Michigan Genomics Initiative (MGI; n = 76,757), with a nationally recruited biobank, All of Us (AOU; n = 226,764), which oversamples underrepresented groups. Basic PS weights (PSBASIC) captured age, sex, and race/ethnicity; full PS weights (PSFULL) additionally included smoking, alcohol consumption, BMI, depression, hypertension, and the Charlson Comorbidity Index. Models for esophageal, liver, and pancreatic cancers were developed using EHR data from MGI at 0, 1, 2, and 5 years prior to diagnosis. Phenotype risk scores (PheRS) were constructed using six methods (e.g., regularized regression, random forest) and evaluated alongside covariates, risk factors, and symptoms. Evaluation metrics included the odds ratio (OR) for the top decile vs. the middle 40th-60th percentiles of the risk score distribution and the area under the receiver operating curve (AUC) evaluated in the AOU test cohort when models are trained with and without weighting.

RESULTS

Elastic net and random forest methods generally performed well in risk stratification, but no single PheRS construction method consistently outperformed others. Applying PS weights did not consistently improve risk stratification performance. For example, in liver cancer risk stratification at t = 1, unweighted random forest PheRS yielded an OR of 13.73 (95 % CI: 8.97, 21.01), compared to 14.55 (95 % CI: 9.45, 22.42) with PSBASIC and 13.62 (95 % CI: 8.90, 20.85) with PSFULL.

CONCLUSION

PS weights do not significantly enhance risk model transferability between biobanks. EHR-based PheRS are crucial for risk stratification and should be integrated with other multimodal data for improved risk prediction. Identifying high-risk populations for diseases like liver cancer early through health history mining shows promise.

High familial risks in some rare cancers may pinpoint to hidden germline genetics: focus on esophageal, stomach, small intestinal, testis, thyroid and bone cancers

BACKGROUND

Germline genetic susceptibilities of rare cancers of the esophagus, stomach, small intestine, testis, (nonmedullary) thyroid gland and bone with high familial risks are not well known. Here, we use familial risk data from the Swedish Family-Cancer Database which contains records of cancers in Swedish families obtained over a century. We compare familial risks for offspring diagnosed with any of these cancers when their parent had or had not that cancer. We review the global literature of the reported constitutional variants that may explain part of the familial risk.

MAIN BODY

Familial risks for esophageal and stomach cancers are about 2.0 and apart from early-onset stomach cancer few high-risk variants are known. Genetic studies may be hampered by dominant environmental risk factors for these cancers. Small intestinal carcinoids have a very high familial risk (28 between siblings) but no high-risk genes have been identified to explain this. Low-risk polygenic variants have been identified. Small intestinal adenocarcinoma is a manifestation in Lynch syndrome. Testicular and thyroid cancers are characterized by high familial risk (about 5) which may be explained largely by a polygenic background, although thyroid cancer is a component in a number of rare cancer syndromes. Several predisposing genes have been identified for bone cancer (familial risk 7).

CONCLUSIONS

The discussed cancers are rare and they present with a relatively high familial risk, in spite of lacking identified high-penetrant constitutional variants. It is possible that the polygenic component, already recognized for testis cancer, is stronger than previously expected. Thus polygenic models with rare high/moderate- and low-risk variants could fit the familial risk and shape the germline genetic landscape of these cancers. Polygenic background may have clinical implications.

Age-specific familial risks in cancer as clues to germline genetic and environmental causes: focus on colorectal, endometrial, prostate, kidney, breast and lung cancers

BACKGROUND

The Swedish Family-Cancer Database (FCD) is the largest source of data on familial cancer in the world, including practically complete family structures and individual cancer diagnoses from the high-quality cancer registry. We present a novel application of FCD by analyzing age-specific familial risks and interpreting them through likely causes, such as germline pathogenic variants and/or environmental exposures.

MAIN BODY

The basic assumption for this approach is that a discrete familial clustering in a narrow age-interval is not random but may provide causal clues. For this analysis we selected reasonably common cancers to meaningfully scrutinize familial risk through adulthood in which cancers are diagnosed, that included colorectal (CRC) and endometrial cancers, prostate and kidney cancers and breast and lung cancers. The interpretation is based on the literature. The highest familial relative risks for CRC and endometrial cancers were found at ages 40-44 years, matching the peak impact of mismatch repair gene mutations. However endometrial cancer showed also a small early onset component which could not be explained. Age-related familial risks for breast, prostate and kidney cancers also matched data from large-scale sequencing; these included the early onset component in kidney cancer which was likely due to VHL mutations. Age distribution of familial lung cancer was unique in showing a wide peak extending from middle to old ages, which would be consistent with a combination of direct genetic effects and indirect influence on inheritance of smoking dependence.

CONCLUSIONS

The present review of age-specific familial risks and age-of-onset data from the literature may allow an interpretation that the familial and germline landscapes are reasonably harmonious for relatively early onset cancers but at higher ages no discrete peaks can be found which may implicate attenuated impact of high-risk genes and polygenic influence.

Predicting Lung Cancer in Korean Never-Smokers With Polygenic Risk Scores

In the last few decades, genome-wide association studies (GWAS) with more than 10,000 subjects have identified several loci associated with lung cancer and these loci have been used to develop novel risk prediction tools for cancer. The present study aimed to establish a lung cancer prediction model for Korean never-smokers using polygenic risk scores (PRSs); PRSs were calculated using a pruning-thresholding-based approach based on 11 genome-wide significant single nucleotide polymorphisms (SNPs). Overall, the odds ratios tended to increase as PRSs were larger, with the odds ratio of the top 5% PRSs being 1.71 (95% confidence interval: 1.31-2.23) using the 40%-60% percentile group as the reference, and the area under the curve (AUC) of the prediction model being of 0.76 (95% confidence interval: 0.747-0.774). The receiver operating characteristic (ROC) curves of the prediction model with and without PRSs as covariates were compared using DeLong's test, and a significant difference was observed. Our results suggest that PRSs can be valuable tools for predicting the risk of lung cancer.

Rs1347093 regulates microRNA-216/-217 expression and is associated with pancreatic cancer risk

BACKGROUND

Single nucleotide polymorphism (SNP) rs1347093 shows statistically significant association with lung cancer risk, but there is no further rs1347093 expression quantitative trait loci (eQTL) effect information. SNP rs1347093 is located in microRNA-216/-217 (miR-216/-217) locus. In addition, miR-216/-217 have pancreas-enriched expressions. In this study, we examined a potential miR-216/-217 promoter region, and investigated the effect of rs1347093-A allele on the miR-216/-217 promoter activity.

METHODS

Bioinformatics analysis, quantitative real-time PCR, luciferase reporter assay, Western blotting, and cell counting kit-8 (CCK-8) assay were performed.

RESULTS

The miR-216/-217 expressions are down-regulated in pancreatic cancer. In pancreatic cancer patients carrying the rs1347093-A allele, miR-216/-217 expressions were more largely suppressed. We identified a potential promoter region in miR-216/-217 locus and further showed that rs1347093-A allele resulted in significantly reduced promoter activity in pancreatic cancer cells, which could be mediated by MEF2C activities. In terms of mechanism in the pathogenesis of pancreatic cancer, miR-216b-5p expression was down-regulated, thereby preventing it from interacting with beclin-1 mRNA while promoting the survival of pancreatic cancer cells.

CONCLUSIONS

This study may reveal the biological relevance underlying rs1347093-A allele with an increase in pancreatic cancer risk. SNP rs1347093 could be meaningful as a novel biomarker for pancreatic cancer risk.

Undifferentiated endometrial carcinoma diagnosed during perimenopausal hormone therapy: a case report and literature review

Uterine corpus endometrial carcinoma, one of the three most frequent cancers of the female reproductive system, primarily affects women who are perimenopausal or postmenopausal. Moreover, it is an epithelial cancer that develops in the endometrium, which is classified as either estrogen-dependent (type I) or non-estrogen-dependent (type II). Non-estrogen-dependent endometrial cancers include plasma cell carcinoma and undifferentiated/dedifferentiated endometrial carcinoma. Undifferentiated endometrial carcinoma is a rare but aggressive endometrial epithelial cancer that lacks any histologic differentiation and is classified as a high-grade endometrial cancer. This case presents that a patient had uterine corpus endometrial carcinoma during perimenopausal hormone therapy, and the patient was treated with sequential estrogen-progestin treatment for 3 years. During that period, she did not undergo a follow-up examination for the last 2 years due to the pandemic. Undifferentiated endometrial carcinoma is a special type of endometrial cancer that is not hormone-dependent, and whether the occurrence of this case is related to perimenopausal hormone therapy needs to be verified by more evidence-based clinical cases and further studies.

Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk

BACKGROUND

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited.

METHODS

We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium.

RESULTS

The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population.

CONCLUSIONS

These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification.

IMPACT

Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.

Epidemiological and clinical characteristics of lung cancer in Saudi Arabia: a retrospective study in single oncology center

Background

Lung cancer (LC) is one of the most common neoplastic diseases and a leading cause of death in Saudi Arabia. Its incidence in Saudi Arabia has increased by more than 3% within two decades. Our study aimed to describe the epidemiological and genetic landscapes of LC in Al-Madinah city in Saudi Arabia.

Methods

A retrospective analysis was conducted on the medical records of 65 patients diagnosed with lung cancer between 2015 and 2021 at a single medical oncology center in Al-Madinah city of Saudi Arabia.

Results

The mean patients' age was 59.2 years, with 50 (76.9%) males and 15 (23.1%) females; 37 (57%) smokers, and 28 (43%) non-smokers. The number of cases per year has increased gradually over six years from 2015 (n = 3) to 2020 (n = 13). The most prevalent histopathological diagnosis was non-small cell lung cancer (NSCLC) (n = 58, 89%) followed by small cell lung cancer (SCLC) (n = 5, 7.8%). NSCLC was frequently more common in smokers while squamous cell carcinoma was more frequent in non-smokers. Around 89% (n = 58) of the cases were diagnosed in late stage IV and the most common metastatic sites were to pleura and lymph nodes (n = 32, 49.2%). Program Death Legend-1 (PDL-1) was fairly expressed in 7/10 (70%) patients. Epidermal Growth Factor Receptor (EGFR) was mutated in 5/17 (29%) patients. Other mutations detected include Anaplastic Lymphoma Kinase (ALK) and phosphatidylinositol 3-kinase (PIK3C) mutations in two patients.

Conclusions

Our study revealed that lung cancer is a significant burden in Al-Madinah city of Saudi Arabia. If the risk factors are not controlled, the number of cases may increase considerably. Health education about the risk factors and cancer prevention helps in early lung cancer detection.

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

Using polygenic risk modification to improve breast cancer prevention: study protocol for the PRiMo multicentre randomised controlled trial

INTRODUCTION

Established personal and familial risk factors contribute collectively to a woman's risk of breast or ovarian cancer. Existing clinical services offer genetic testing for pathogenic variants in high-risk genes to investigate these risks but recent information on the role of common genomic variants, in the form of a Polygenic Risk Score (PRS), has provided the potential to further personalise breast and ovarian cancer risk assessment. Data from cohort studies support the potential of an integrated risk assessment to improve targeted risk management but experience of this approach in clinical practice is limited.

METHODS AND ANALYSIS

The polygenic risk modification trial is an Australian multicentre prospective randomised controlled trial of integrated risk assessment including personal and family risk factors with inclusion of breast and ovarian PRS vs standard care. The study will enrol women, unaffected by cancer, undergoing predictive testing at a familial cancer clinic for a pathogenic variant in a known breast cancer (BC) or ovarian cancer (OC) predisposition gene (BRCA1, BRCA2, PALB2, CHEK2, ATM, RAD51C, RAD51D). Array-based genotyping will be used to generate breast cancer (313 SNP) and ovarian cancer (36 SNP) PRS. A suite of materials has been developed for the trial including an online portal for patient consent and questionnaires, and a clinician education programme to train healthcare providers in the use of integrated risk assessment. Long-term follow-up will evaluate differences in the assessed risk and management advice, patient risk management intentions and adherence, patient-reported experience and outcomes, and the health service implications of personalised risk assessment.

ETHICS AND DISSEMINATION

This study has been approved by the Human Research Ethics Committee of Peter MacCallum Cancer Centre and at all participating centres. Study findings will be disseminated via peer-reviewed publications and conference presentations, and directly to participants.

TRIAL REGISTRATION NUMBER

ACTRN12621000009819.

Genetic variation across and within individuals

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BACKGROUND

The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.

RESULTS

We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.

CONCLUSION

We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions

Here, in a multi-ancestry genome-wide association study meta-analysis of kidney cancer (29,020 cases and 835,670 controls), we identified 63 susceptibility regions (50 novel) containing 108 independent risk loci. In analyses stratified by subtype, 52 regions (78 loci) were associated with clear cell renal cell carcinoma (RCC) and 6 regions (7 loci) with papillary RCC. Notably, we report a variant common in African ancestry individuals ( rs7629500 ) in the 3' untranslated region of VHL, nearly tripling clear cell RCC risk (odds ratio 2.72, 95% confidence interval 2.23-3.30). In cis-expression quantitative trait locus analyses, 48 variants from 34 regions point toward 83 candidate genes. Enrichment of hypoxia-inducible factor-binding sites underscores the importance of hypoxia-related mechanisms in kidney cancer. Our results advance understanding of the genetic architecture of kidney cancer, provide clues for functional investigation and enable generation of a validated polygenic risk score with an estimated area under the curve of 0.65 (0.74 including risk factors) among European ancestry individuals.

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).

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

The Impact of Inherited Genetic Variation on DNA Methylation in Prostate Cancer and Benign Tissues of African American and European American Men

BACKGROUND

American men of African ancestry (AA) have higher prostate cancer incidence and mortality rates compared with American men of European ancestry (EA). Differences in genetic susceptibility mechanisms may contribute to this disparity.

METHODS

To gain insights into the regulatory mechanisms of prostate cancer susceptibility variants, we tested the association between SNPs and DNA methylation (DNAm) at nearby CpG sites across the genome in benign and cancer prostate tissue from 74 AA and 74 EA men. Genome-wide SNP data (from benign tissue) and DNAm were generated using Illumina arrays.

RESULTS

Among AA men, we identified 6,298 and 2,641 cis-methylation QTLs (meQTL; FDR of 0.05) in benign and tumor tissue, respectively, with 6,960 and 1,700 detected in EA men. We leveraged genome-wide association study (GWAS) summary statistics to identify previously reported prostate cancer GWAS signals likely to share a common causal variant with a detected meQTL. We identified nine GWAS-meQTL pairs with strong evidence of colocalization (four in EA benign, three in EA tumor, two in AA benign, and three in AA tumor). Among these colocalized GWAS-meQTL pairs, we identified colocalizing expression quantitative trait loci (eQTL) impacting four eGenes with known roles in tumorigenesis.

CONCLUSIONS

These findings highlight epigenetic regulatory mechanisms by which prostate cancer-risk SNPs can modify local DNAm and/or gene expression in prostate tissue.

IMPACT

Overall, our findings showed general consistency in the meQTL landscape of AA and EA men, but meQTLs often differ by tissue type (normal vs. cancer). Ancestry-based linkage disequilibrium differences and lack of AA representation in GWAS decrease statistical power to detect colocalization for some regions.

Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking

PURPOSE

Pancreatic cancer (PC) is a deadly disease most often diagnosed in late stages. Identification of high-risk subjects could both contribute to preventative measures and help diagnose the disease at earlier timepoints. However, known risk factors, assessed independently, are currently insufficient for accurately stratifying patients. We use large-scale data from the UK Biobank (UKB) to identify genetic variant-smoking interaction effects and show their importance in risk assessment.

METHODS

We draw data from 15,086,830 genetic variants and 315,512 individuals in the UKB. There are 765 cases of PC. Crucially, robust resampling corrections are used to overcome well-known challenges in hypothesis testing for interactions. Replication analysis is conducted in two independent cohorts totaling 793 cases and 570 controls. Integration of functional annotation data and construction of polygenic risk scores (PRS) demonstrate the additional insight provided by interaction effects.

RESULTS

We identify the genome-wide significant variant rs77196339 on chromosome 2 (per minor allele odds ratio in never-smokers, 2.31 [95% CI, 1.69 to 3.15]; per minor allele odds ratio in ever-smokers, 0.53 [95% CI, 0.30 to 0.91]; P = 3.54 × 10-8) as well as eight other loci with suggestive evidence of interaction effects (P < 5 × 10-6). The rs77196339 region association is validated (P < .05) in the replication sample. PRS incorporating interaction effects show improved discriminatory ability over PRS of main effects alone.

CONCLUSION

This study of genome-wide germline variants identified smoking to modify the effect of rs77196339 on PC risk. Interactions between known risk factors can provide critical information for identifying high-risk subjects, given the relative inadequacy of models considering only main effects, as demonstrated in PRS. Further studies are necessary to advance toward comprehensive risk prediction approaches for PC.

Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis

Background

While polygenic risk scores (PRS) could enable the streamlining of organised cancer screening programmes, its current discriminative ability is limited. We conducted a cost-effectiveness analysis to trade-off the benefits and harms of PRS-stratified cancer screening in China.

Methods

The validated National Cancer Center (NCC) modelling framework for six cancers (lung, liver, breast, gastric, colorectum, and oesophagus) was used to simulate cancer incidence, progression, stage-specific cancer detection, and risk of death. We estimated the number of cancer deaths averted, quality-adjusted life-years (QALY) gained, number needed to screen (NNS), overdiagnosis, and incremental cost-effectiveness ratio (ICER) of one-time PRS-stratified screening strategy (screening 25% of PRS-defined high-risk population) for a birth cohort at age 60 in 2025, compared with unstratified screening strategy (screening 25% of general population) and no screening strategy. We applied lifetime horizon, societal perspective, and 3% discount rate. An ICER less than $18,364 per QALY gained is considered cost-effective.

Findings

One-time cancer screening for population aged 60 was the most cost-effective strategy compared to screening at other ages. Compared with an unstratified screening strategy, the PRS-stratified screening strategy averted more cancer deaths (61,237 vs. 40,329), had a lower NNS to prevent one death (307 vs. 451), had a slightly higher overdiagnosis (14.1% vs. 13.8%), and associated with an additional 130,045 QALYs at an additional cost of $1942 million, over a lifetime horizon. The ICER for all six cancers combined was $14,930 per QALY gained, with the ICER varying from $7928 in colorectal cancer to $39,068 in liver cancer. ICER estimates were sensitive to changes in risk threshold and cost of PRS tools.

Interpretation

PRS-stratified screening strategy modestly improves clinical benefit and cost-effectiveness of organised cancer screening programmes. Reducing the costs of polygenic risk stratification is needed before PRS implementation.

Funding

The Chinese Academy of Medical Sciences, the Jing-jin-ji Special Projects for Basic Research Cooperation, and the Sanming Project of the Medicine in Shenzhen.

Germline determinants of aberrant signaling pathways in cancer

Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.

Polymorphisms in transcription factor binding sites and enhancer regions and pancreatic ductal adenocarcinoma risk

Genome-wide association studies (GWAS) are a powerful tool for detecting variants associated with complex traits and can help risk stratification and prevention strategies against pancreatic ductal adenocarcinoma (PDAC). However, the strict significance threshold commonly used makes it likely that many true risk loci are missed. Functional annotation of GWAS polymorphisms is a proven strategy to identify additional risk loci. We aimed to investigate single-nucleotide polymorphisms (SNP) in regulatory regions [transcription factor binding sites (TFBSs) and enhancers] that could change the expression profile of multiple genes they act upon and thereby modify PDAC risk. We analyzed a total of 12,636 PDAC cases and 43,443 controls from PanScan/PanC4 and the East Asian GWAS (discovery populations), and the PANDoRA consortium (replication population). We identified four associations that reached study-wide statistical significance in the overall meta-analysis: rs2472632(A) (enhancer variant, OR 1.10, 95%CI 1.06,1.13, p = 5.5 × 10-8), rs17358295(G) (enhancer variant, OR 1.16, 95%CI 1.10,1.22, p = 6.1 × 10-7), rs2232079(T) (TFBS variant, OR 0.88, 95%CI 0.83,0.93, p = 6.4 × 10-6) and rs10025845(A) (TFBS variant, OR 1.88, 95%CI 1.50,1.12, p = 1.32 × 10-5). The SNP with the most significant association, rs2472632, is located in an enhancer predicted to target the coiled-coil domain containing 34 oncogene. Our results provide new insights into genetic risk factors for PDAC by a focused analysis of polymorphisms in regulatory regions and demonstrating the usefulness of functional prioritization to identify loci associated with PDAC risk.

Epidemiology of Cancer

BACKGROUND

Cancers are a large and heterogeneous group of malignant tumors that collectively accounted for approximately 600 000 US deaths in 2020; only heart disease claimed more lives. A large amount of knowledge has accumulated regarding the epidemiology of most cancer types, including their causes.

CONTENT

The cancer types most frequently diagnosed among adults in most high-income countries are lung, colorectal, female breast, cutaneous melanoma, and prostate. In general cancer incidence and mortality is very low in children and adolescents, rising exponentially with increasing age during adulthood. There is marked international variation in the incidence of most cancers. The most important causes of cancer are tobacco use (primarily cigarette use), excess alcohol consumption, obesity, lack of physical activity, diets low in fruits and vegetables, infectious agents, and sun exposure. Early detection can reduce the chances that a person will die of cancers of the female breast, uterine cervix, colon and rectum, lung, and prostate.

SUMMARY

Although the most common cancers in the United States continue to have a substantial impact on public health, they are caused in whole or part by factors over which people and governments have control through choices they make. Among these are tobacco and alcohol use, obesity, diets low in fruits and vegetables and lack of physical activity, and sun exposure. Thus, a very large proportion of cancer's impact could be ameliorated if more people avoided these exposures.

From Hazard Rate to Age-at-Onset Distribution: Mind the Gap

A recent study published in the journal claimed that genetic susceptibility to breast cancer occurs mainly due to rare inherited variants. The claim relies on a set of deductive arguments following observations on patterns of age-at-onset distribution of the disease among twin pairs. In this brief commentary, we point out a major gap in the given argument due to the interchangeable use of hazard rates and age-at-onset distribution, and thus conclude that the published study does not provide any evidence against polygenic risk of breast cancer due to common variants. See related article by Yasui et al., p. 1518.

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.

Polygenic scores in cancer

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

Endometrial Cancer Risk Among Germline BRCA1/2 Pathogenic Variant Carriers: Review of Our Current Understanding and Next Steps

PURPOSE

To review the literature exploring endometrial cancer (EC) risk among surgical candidates with germline BRCA1/2 pathogenic variants (PVs) to guide decisions around risk-reducing (rr) hysterectomy in this population.

DESIGN

A comprehensive review was conducted of the current literature that influences clinical practice and informs expert consensus. We present our understanding of EC risk among BRCA1/2 PV carriers, the risk-modifying factors specific to this patient population, and the available research technology that may guide clinical practice in the future. Limitations of the existing literature are outlined.

RESULTS

Patients with BRCA1/2 PVs, those with a personal history of tamoxifen use, those who desire long-term hormone replacement therapy, and/or have an elevated BMI are at higher risk of EC, primarily endometrioid EC and/or uterine papillary serous carcinoma, and may benefit from rr-hysterectomy. Although prescriptive clinical guidelines specific to BRCA1/2 PV carriers could inform decisions around rr-hysterectomy, limitations of the current literature prevent more definitive guidance at this time. A large population-based study of a contemporary cohort of BRCA1/2 PV carriers with lifetime follow-up compared with cancer-gene negative controls would advance this topic and facilitate care decisions.

CONCLUSION

This review validates a potential role for rr-hysterectomy to address EC risk among surgical candidates with BRCA1/2 PVs. Evidence-based clinical guidelines for rr-hysterectomy in BRCA1/2 PV carriers are essential to ensure equitable access to this preventive measure, supporting insurance coverage for patients with either BRCA1 or BRCA2 PVs to pursue rr-hysterectomy. Overall, this review highlights the complexity of EC risk in BRCA1/2 PV carriers and offers a comprehensive framework to shared decision making to inform rr-hysterectomy for BRCA1/2 PV carriers.

Optimization of polygenic risk scores in BRCA1/2 pathogenic variant heterozygotes in epithelial ovarian cancer

PURPOSE

A third of familial epithelial ovarian cancer (EOC) is explained by BRCA1/2 pathogenic variants. Polygenic risk scores (PRSs) for BRCA1/2 heterozygotes associated with EOC have been created, but impact of combination with clinical and hormonal risk factors is unclear.

METHODS

We genotyped 300 cases and 355 controls and constructed modified PRSs based on those validated by Barnes et al. Model discrimination and EOC risk was assessed by area under the curve (AUC) values and difference between lowest and highest quintile odds ratios (ORs). We investigated model optimization using logistic regression to combine models with clinical and hormonal data.

RESULTS

Unadjusted AUC values ranged from 0.526 to 0.551 with 2.2- to 2.3-fold increase in OR between lowest and highest quintiles (BRCA1 heterozygotes) and 0.574 to 0.585 AUC values with a 6.3- to 7.7-fold increase (BRCA2 heterozygotes). The optimized model (parity, age at menarche, menopause, and first full-term pregnancy) estimated AUC values of 0.872 to 0.876 and 21- to 23-fold increase in OR (BRCA1 heterozygotes) and AUC values of 0.857 to 0.867 and 40- to 41-fold increase (BRCA2 heterozygotes).

CONCLUSION

The combination of PRS with age, family history, and hormonal factors significantly improved the EOC risk discrimination ability. However, the contribution of the PRS was small. Larger prospective studies are needed to assess if combined-PRS models could provide information to inform risk-reducing decisions.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Association between a polymorphic variant in the CDKN2B-AS1/ANRIL gene and pancreatic cancer risk

Genes carrying high-penetrance germline mutations may also be associated with cancer susceptibility through common low-penetrance genetic variants. To increase the knowledge on genetic pancreatic ductal adenocarcinoma (PDAC) aetiology, the common genetic variability of PDAC familial genes was analysed in our study. We conducted a multiphase study analysing 7745 single nucleotide polymorphisms (SNPs) from 29 genes reported to harbour a high-penetrance PDAC-associated mutation in at least one published study. To assess the effect of the SNPs on PDAC risk, a total of 14 666 PDAC cases and 221 897 controls across five different studies were analysed. The T allele of the rs1412832 polymorphism, that is situated in the CDKN2B-AS1/ANRIL, showed a genome-wide significant association with increased risk of developing PDAC (OR = 1.11, 95% CI = 1.07-1.15, P = 5.25 × 10-9 ). CDKN2B-AS1/ANRIL is a long noncoding RNA, situated in 9p21.3, and regulates many target genes, among which CDKN2A (p16) that frequently shows deleterious somatic and germline mutations and deregulation in PDAC. Our results strongly support the role of the genetic variability of the 9p21.3 region in PDAC aetiopathogenesis and highlight the importance of secondary analysis as a tool for discovering new risk loci in complex human diseases.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Learning from cancer to address COVID-19

Patients with cancer have been disproportionately affected by the novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Knowledge collected during the last three decades of cancer research has helped the medical research community worldwide to respond to many of the challenges raised by COVID-19, during the pandemic. The review, briefly summarizes the underlying biology and risk factors of COVID-19 and cancer, and aims to present recent evidence on cellular and molecular relationship between the two diseases, with a focus on those that are related to the hallmarks of cancer and uncovered in the first less than three years of the pandemic (2020-2022). This may not only help answer the question "Why cancer patients are considered to be at a particularly high risk of developing severe COVID-19 illness?", but also helped treatments of patients during the COVID-19 pandemic. The last session highlights the pioneering mRNA studies and the breakthrough discovery on nucleoside-modifications of mRNA by Katalin Karikó, which led to the innovation and development of the mRNA-based SARSCoV-2 vaccines saving lives of millions and also opened the door for a new era of vaccines and a new class of therapeutics.

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

Evaluating Approaches for Constructing Polygenic Risk Scores for Prostate Cancer in Men of African and European Ancestry

Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS 269 ). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS 269 . Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS 269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS 269 constructed with fine-mapping.

LINC01589 serves as a potential tumor-suppressor and immune-related biomarker in endometrial cancer: A review

Currently, increasing attention is being paid to biomarkers in endometrial cancer. Immune infiltration of the tumor microenvironment has been shown to significantly affect the overall survival (OS) of uterine corpus endometrial carcinoma (UCEC) patients. LINC01589 is a long non-coding RNA (lncRNA) that is rarely reported in cancer and is assumed to play a role in immune regulation. We therefore evaluated the role of LINC01589 in UCEC using the Cancer Genome Atlas (TCGA) database. We analyzed the expression of LINC01589 using the gene expression profiles of LINC01589 in the UCEC projects in TCGA. Comparisons between the differentially expressed genes (DEGs) of the cancer and adjacent normal tissues of the UCEC projects revealed that LINC01589 expression was decreased in UCEC tissues. A multivariate cox regression analysis indicated that LINC01589 upregulation could serve as an independent prognostic factor for survival. Furthermore, there was a positive correlation between LINC01589 expression and B cell, T cell, NK cell, monocytic lineage, and myeloid dendritic cell infiltration in UCEC patients. In addition, 5 clusters of hub genes were detected by comparison of different expression levels of LINC01589 in the UCEC groups. The analysis of the reactome pathway using gene set enrichment analysis (GSEA) revealed immune-related pathways, including CD22-mediated B cell receptor (BCR) regulation and antigen-activated BCRs, leading to the generation of second messengers and complement cascade pathways that were significantly enriched in the high LINC01589 expression group. Thus, LINC01589 may serve as a prognostic biomarker, as it is associated with immune infiltration in UCEC.

Polygenic Risk Score in African populations: progress and challenges

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

The inherited genetic contribution and polygenic risk score for risk of CLL and MBL: a narrative review

Chronic lymphocytic leukemia (CLL) is a neoplasm of B-cells in the blood and monoclonal B-cell lymphocytosis (MBL) is a precursor state to CLL. This narrative review provides an overview of the genetic studies that identified 43 common variants associated with risk of CLL among individuals of European ancestry. Emerging studies found that ∼50% of these variants are associated with MBL risk. Moreover, the polygenic risk score (PRS) calculated from these CLL variants has been shown to be a robust predictor for both CLL and MBL risk among European ancestry individuals but a weak predictor among African ancestry individuals. By summarizing these genetic studies, we conclude that additional studies are needed in other race/ethnic populations to identify race-specific susceptibility variants, that functional studies are needed to validate the biological mechanisms of the variants, and that the clinical utility of the PRS is limited until preventive strategies for CLL are developed.

Common Germline Risk Variants Impact Somatic Alterations and Clinical Features across Cancers

Aggregation of genome-wide common risk variants, such as polygenic risk score (PRS), can measure genetic susceptibility to cancer. A better understanding of how common germline variants associate with somatic alterations and clinical features could facilitate personalized cancer prevention and early detection. We constructed PRSs from 14 genome-wide association studies (median n = 64,905) for 12 cancer types by multiple methods and calibrated them using the UK Biobank resources (n = 335,048). Meta-analyses across cancer types in The Cancer Genome Atlas (n = 7,965) revealed that higher PRS values were associated with earlier cancer onset and lower burden of somatic alterations, including total mutations, chromosome/arm somatic copy-number alterations (SCNA), and focal SCNAs. This contrasts with rare germline pathogenic variants (e.g., BRCA1/2 variants), showing heterogeneous associations with somatic alterations. Our results suggest that common germline cancer risk variants allow early tumor development before the accumulation of many somatic alterations characteristic of later stages of carcinogenesis.

SIGNIFICANCE

Meta-analyses across cancers show that common germline risk variants affect not only cancer predisposition but the age of cancer onset and burden of somatic alterations, including total mutations and copy-number alterations.

Screening Programs for Breast Cancer: Toward Individualized, Risk-Adapted Strategies of Early Detection

Early detection of breast cancer (BC) comprises two approaches: screening of asymptomatic women in a specified target population at risk (usually a target age range for women at average risk), and early diagnosis for women with BC signs and symptoms. Screening for BC is a key health intervention for early detection. While population-based screening programs have been implemented for age-selected women, the pivotal clinical trials have not addressed the global utility nor the improvement of screening performance by utilizing more refined parameters for patient eligibility, such as individualized risk stratification. In addition, with the exception of the subset of women known to carry germline pathogenetic mutations in (high- or moderately-penetrant) cancer predisposition genes, such as BRCA1 and BRCA2, there has been less success in outreach and service provision for the unaffected relatives of women found to carry a high-risk mutation (i.e., "cascade testing") as it is in these individuals for whom such actionable information can result in cancers (and/or cancer deaths) being averted. Moreover, even in the absence of clinical cancer genetics services, as is the case for the immediate and at least near-term in most countries globally, the capacity to stratify the risk of an individual to develop BC has existed for many years, is available for free online at various sites/platforms, and is increasingly being validated for non-Caucasian populations. Ultimately, a precision approach to BC screening is largely missing. In the present chapter, we aim to address the concept of risk-adapted screening of BC, in multiple facets, and understand if there is a value in the implementation of adapted screening strategies in selected women, outside the established screening prescriptions, in the terms of age-range, screening modality and schedules of imaging.

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

Non-Invasive Biomarkers for Early Lung Cancer Detection

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.

A Prism Vote method for individualized risk prediction of traits in genotype data of Multi-population

Multi-population cohorts offer unprecedented opportunities for profiling disease risk in large samples, however, heterogeneous risk effects underlying complex traits across populations make integrative prediction challenging. In this study, we propose a novel Bayesian probability framework, the Prism Vote (PV), to construct risk predictions in heterogeneous genetic data. The PV views the trait of an individual as a composite risk from subpopulations, in which stratum-specific predictors can be formed in data of more homogeneous genetic structure. Since each individual is described by a composition of subpopulation memberships, the framework enables individualized risk characterization. Simulations demonstrated that the PV framework applied with alternative prediction methods significantly improved prediction accuracy in mixed and admixed populations. The advantage of PV enlarges as genetic heterogeneity and sample size increase. In two real genome-wide association data consists of multiple populations, we showed that the framework considerably enhanced prediction accuracy of the linear mixed model in five-group cross validations. The proposed method offers a new aspect to analyze individual's disease risk and improve accuracy for predicting complex traits in genotype data.

Development and evaluation of polygenic risk scores for prediction of endometrial cancer risk in European women

PURPOSE

Single-nucleotide variations (SNVs) (formerly single-nucleotide polymorphism [SNV]) influence genetic predisposition to endometrial cancer. We hypothesized that a polygenic risk score (PRS) comprising multiple SNVs may improve endometrial cancer risk prediction for targeted screening and prevention.

METHODS

We developed PRSs from SNVs identified from a systematic review of published studies and suggestive SNVs from the Endometrial Cancer Association Consortium. These were tested in an independent study of 555 surgically-confirmed endometrial cancer cases and 1202 geographically-matched controls from Manchester, United Kingdom and validated in 1676 cases and 116,960 controls from the UK Biobank (UKBB).

RESULTS

Age and body mass index predicted endometrial cancer in both data sets (Manchester: area under the receiver operator curve [AUC] = 0.77, 95% CI = 0.74-0.80; UKBB: AUC = 0.74, 95% CI = 0.73-0.75). The AUC for PRS19, PRS24, and PRS72 were 0.58, 0.55, and 0.57 in the Manchester study and 0.56, 0.54, and 0.54 in UKBB, respectively. For PRS19, women in the third tertile had a 2.1-fold increased risk of endometrial cancer compared with those in the first tertile of the Manchester study (odds ratio = 2.08, 95% CI = 1.61-2.68, P_trend = 5.75E-9). Combining PRS19 with age and body mass index improved discriminatory power (Manchester study: AUC = 0.79, 95% CI = 0.76-0.82; UKBB: AUC =0.75, 95% CI = 0.73-0.76).

CONCLUSION

An endometrial cancer risk prediction model incorporating a PRS derived from multiple SNVs may help stratify women for screening and prevention strategies.

Germline Aberrations in Pancreatic Cancer: Implications for Clinical Care

Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis and represents a major public health issue, as both its incidence and mortality are expecting to increase steeply over the next years. Effective screening strategies are lacking, and most patients are diagnosed with unresectable disease precluding the only chance of cure. Therapeutic options for advanced disease are limited, and the treatment paradigm is still based on chemotherapy, with a few rare exceptions to targeted therapies. Germline variants in cancer susceptibility genes-particularly those involved in mechanisms of DNA repair-are emerging as promising targets for PDAC treatment and prevention. Hereditary PDAC is part of the spectrum of several syndromic disorders, and germline testing of PDAC patients has relevant implications for broad cancer prevention. Germline aberrations in BRCA1 and BRCA2 genes are predictive biomarkers of response to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitor olaparib and platinum-based chemotherapy in PDAC, while mutations in mismatch repair genes identify patients suitable for immune checkpoint inhibitors. This review provides a timely and comprehensive overview of germline aberrations in PDAC and their implications for clinical care. It also discusses the need for optimal approaches to better select patients for PARP inhibitor therapy, novel therapeutic opportunities under clinical investigation, and preclinical models for cancer susceptibility and drug discovery.

Integration of rare expression outlier-associated variants improves polygenic risk prediction

Polygenic risk scores (PRSs) quantify the contribution of multiple genetic loci to an individual's likelihood of a complex trait or disease. However, existing PRSs estimate this likelihood with common genetic variants, excluding the impact of rare variants. Here, we report on a method to identify rare variants associated with outlier gene expression and integrate their impact into PRS predictions for body mass index (BMI), obesity, and bariatric surgery. Between the top and bottom 10%, we observed a 20.8% increase in risk for obesity (p = 3 × 10-14), 62.3% increase in risk for severe obesity (p = 1 × 10-6), and median 5.29 years earlier onset for bariatric surgery (p = 0.008), as a function of expression outlier-associated rare variant burden when controlling for common variant PRS. We show that these predictions were more significant than integrating the effects of rare protein-truncating variants (PTVs), observing a mean 19% increase in phenotypic variance explained with expression outlier-associated rare variants when compared with PTVs (p = 2 × 10-15). We replicated these findings by using data from the Million Veteran Program and demonstrated that PRSs across multiple traits and diseases can benefit from the inclusion of expression outlier-associated rare variants identified through population-scale transcriptome sequencing.

Polygenic risk scores to stratify cancer screening should predict mortality not incidence

Population-based cancer screening programs such as mammography or colonscopy generally directed at all healthy individuals in a given age stratum. It has recently been proposed that cancer screening could be restricted to a high-risk subgroup based on polygenic risk scores (PRSs) using panels of single-nucleotide polymorphisms (SNPs). These PRSs were, however, generated to predict cancer incidence rather than cancer mortality and will not necessarily address overdiagnosis, a major problem associated with cancer screening programs. We develop a simple net-benefit framework for evaluating screening approaches that incorporates overdiagnosis. We use this methodology to demonstrate that if a PRS does not differentially discriminate between incident and lethal cancer, restricting screening to a subgroup with high scores will only improve screening outcomes in a small number of scenarios. In contrast, restricting screening to a subgroup defined as high-risk based on a marker that is more strongly predictive of mortality than incidence will often afford greater net benefit than screening all eligible individuals. If PRS-based cancer screening is to be effective, research needs to focus on identifying PRSs associated with cancer mortality, an unchartered and clinically-relevant area of research, with a much higher potential to improve screening outcomes.

Genetics of Type 2 Diabetes: Implications from Large-Scale Studies

PURPOSE OF REVIEW

Type 2 diabetes (T2D) is a multifactorial, heritable syndrome characterized by dysregulated glucose homeostasis that results from impaired insulin secretion and insulin resistance. Genetic association studies have successfully identified hundreds of T2D risk loci implicating many genes in disease pathogenesis. In this review, we provide an overview of the recent T2D genetic studies from the past 3 years with particular focus on the effects of sample size and ancestral diversity on genetic discovery as well as discuss recent work on the use and limitations of genetic risk scores (GRS) for T2D risk prediction.

RECENT FINDINGS

Recent large-scale, multi-ancestry genetic studies of T2D have identified over 500 novel risk loci. The genetic variants (i.e., single nucleotide polymorphisms (SNPs)) marking these novel loci in general have smaller effect sizes than previously discovered loci. Inclusion of samples from diverse ancestral backgrounds shows a few ancestry specific loci marked by common variants, but overall, the majority of loci discovered are common across ancestries. Inclusion of common variant GRS, even with hundreds of loci, does not substantially increase T2D risk prediction over standard clinical risk factors such as age and family history. Common variant association studies of T2D have now identified over 700 T2D risk loci, half of which have been discovered in the past 3 years. These recent studies demonstrate that inclusion of ancestrally diverse samples can enhance locus discovery and improve accuracy of GRS for T2D risk prediction. GRS based on common variants, however, only minimally enhances risk prediction over standard clinical risk factors.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response–related risk predictive signature for forecasting the prognosis of patients with LUAD.

Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response–related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting.

Results: We constructed a novel inflammatory response–related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells’ sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression.

Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

CRISPR signal conductor 2.0 for redirecting cellular information flow

A key challenge in designing intelligent artificial gene circuits is generating flexible connections between arbitrary components and directly coupling them with endogenous signaling pathways. The CRISPR signal conductor based on conditionally inducible artificial transcriptional regulators can link classic cellular protein signals with targeted gene expression, but there are still problems with multiple signal processing and gene delivery. With the discovery and characterization of new Cas systems and long noncoding RNA (lncRNA) functional motifs, and because of the compatibility of guide RNA with noncoding RNA elements at multiple sites, it is increasingly possible to solve these problems. In this study, we developed CRISPR signal conductor version 2.0 by integrating various lncRNA functional motifs into different parts of the crRNA in the CRISPR-dCasΦ system. This system can directly regulate the expression of target genes by recruiting cellular endogenous transcription factors and efficiently sense a variety of protein signals that are not detected by a classical synthetic system. The new system solved the problems of background leakage and insensitive signaling responses and enabled the construction of logic gates with as many as six input signals, which can be used to specifically target cancer cells. By rewiring endogenous signaling networks, we further demonstrated the effectiveness and biosafety of this system for in vivo cancer gene therapy.