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Key Points The architecture of inherited genetic susceptibility to cancer is defined by a spectrum of predisposition alleles that have differing frequencies and impact. Genome-wide association studies (GWAS) provide an agnostic approach to the identification of genetic variation influencing cancer risk. For most cancers, GWAS have been performed, and hundreds of risk alleles have been identified, most of which are common and individually confer a modest increase in risk. Most cancer risk loci identified through GWAS locate to non-coding regions of the genome and influence gene expression through diverse mechanisms. As well as improving our understanding of cancer, information from GWAS has direct clinical relevance in identifying nongenetic aetiological risk factors, optimising population screening, identifying therapeutic targets, drug repositioning and prognostication. Although challenging, deciphering the biological basis of identified associations is necessary to fully realise the potential of GWAS. Genome-wide association studies (GWAS) uncover the impact of genetic variation on the risk of many common cancers. This Review discusses current insights and how understanding the biological basis of these associations is required to maximise the clinical benefit of GWAS. Genome-wide association studies (GWAS) provide an agnostic approach for investigating the genetic basis of complex diseases. In oncology, GWAS of nearly all common malignancies have been performed, and over 450 genetic variants associated with increased risks have been identified. As well as revealing novel pathways important in carcinogenesis, these studies have shown that common genetic variation contributes substantially to the heritable risk of many common cancers. The clinical application of GWAS is starting to provide opportunities for drug discovery and repositioning as well as for cancer prevention. However, deciphering the functional and biological basis of associations is challenging and is in part a barrier to fully unlocking the potential of GWAS.

Genome-wide association studies (GWAS) have identified associations between common genetic variants, single nucleotide polymorphisms (SNPs) and the risk of developing different cancers1,2,3. Proponents argue that polygenic risk score (PRS) testing, based on panels of risk SNPs, will revolutionize the prevention and early detection of cancer through individualised risk management strategies and streamlining of the current ‘one-size-fits all’ population screening programs4. Such a model is highly seductive for the rationalisation of healthcare provision. UK government enthusiasm for PRSs is well demonstrated within the recent Genome UK report and 2020 update to the Life Sciences Strategy5. Indeed, reflecting governmental endorsement of predictive genomics, the UK government’s Secretary of State for Health and Social Care, Matthew Hancock, rather questionably enthused that his recent PRS-derived lifetime prostate cancer risk estimate of 15% (compared to a prior of 13%) “may have saved his life”6,7. To establish the requisite governance and data infrastructure for population-level genomic profiling, national projects such as the 100,000 Genomes Project (>70,000 NHS patients) and the Accelerating Detection of Disease programme (up to 5 million volunteers) were initiated8,9. Following initial discontinuation by the U.S. Food and Drug Administration of the 23andMe PRS service10,11, there has been a resurgence within the direct-to-consumer genomics market of PRS predictions for many diseases. While the value of additional biomarkers to improve the targeting of measures for cancer prevention and early detection is indisputable, for PRS to be clinically useful, two assertions must be proven correct. The first assertion is that PRSs provides sufficient risk discrimination. The second is that this risk discrimination is meaningful in the context of absolute risk of that cancer and applicable in the context of respective tools available for prevention and early detection.

During the COVID-19 lockdown, referrals via the 2-week-wait urgent pathway for suspected cancer in England, UK, are reported to have decreased by up to 84%. We aimed to examine the impact of different scenarios of lockdown-accumulated backlog in cancer referrals on cancer survival, and the impact on survival per referred patient due to delayed referral versus risk of death from nosocomial infection with severe acute respiratory syndrome coronavirus 2. In this modelling study, we used age-stratified and stage-stratified 10-year cancer survival estimates for patients in England, UK, for 20 common tumour types diagnosed in 2008–17 at age 30 years and older from Public Health England. We also used data for cancer diagnoses made via the 2-week-wait referral pathway in 2013–16 from the Cancer Waiting Times system from NHS Digital. We applied per-day hazard ratios (HRs) for cancer progression that we generated from observational studies of delay to treatment. We quantified the annual numbers of cancers at stage I–III diagnosed via the 2-week-wait pathway using 2-week-wait age-specific and stage-specific breakdowns. From these numbers, we estimated the aggregate number of lives and life-years lost in England for per-patient delays of 1–6 months in presentation, diagnosis, or cancer treatment, or a combination of these. We assessed three scenarios of a 3-month period of lockdown during which 25%, 50%, and 75% of the normal monthly volumes of symptomatic patients delayed their presentation until after lockdown. Using referral-to-diagnosis conversion rates and COVID-19 case-fatality rates, we also estimated the survival increment per patient referred. Across England in 2013–16, an average of 6281 patients with stage I–III cancer were diagnosed via the 2-week-wait pathway per month, of whom 1691 (27%) would be predicted to die within 10 years from their disease. Delays in presentation via the 2-week-wait pathway over a 3-month lockdown period (with an average presentational delay of 2 months per patient) would result in 181 additional lives and 3316 life-years lost as a result of a backlog of referrals of 25%, 361 additional lives and 6632 life-years lost for a 50% backlog of referrals, and 542 additional lives and 9948 life-years lost for a 75% backlog in referrals. Compared with all diagnostics for the backlog being done in month 1 after lockdown, additional capacity across months 1–3 would result in 90 additional lives and 1662 live-years lost due to diagnostic delays for the 25% backlog scenario, 183 additional lives and 3362 life-years lost under the 50% backlog scenario, and 276 additional lives and 5075 life-years lost under the 75% backlog scenario. However, a delay in additional diagnostic capacity with provision spread across months 3–8 after lockdown would result in 401 additional lives and 7332 life-years lost due to diagnostic delays under the 25% backlog scenario, 811 additional lives and 14 873 life-years lost under the 50% backlog scenario, and 1231 additional lives and 22 635 life-years lost under the 75% backlog scenario. A 2-month delay in 2-week-wait investigatory referrals results in an estimated loss of between 0·0 and 0·7 life-years per referred patient, depending on age and tumour type. Prompt provision of additional capacity to address the backlog of diagnostics will minimise deaths as a result of diagnostic delays that could add to those predicted due to expected presentational delays. Prioritisation of patient groups for whom delay would result in most life-years lost warrants consideration as an option for mitigating the aggregate burden of mortality in patients with cancer. None.

It is proposed that, through restriction to individuals delineated as high risk, polygenic risk scores (PRSs) might enable more efficient targeting of existing cancer screening programmes and enable extension into new age ranges and disease types. To address this proposition, we present an overview of the performance of PRS tools (ie, models and sets of single nucleotide polymorphisms) alongside harms and benefits of PRS-stratified cancer screening for eight example cancers (breast, prostate, colorectal, pancreas, ovary, kidney, lung, and testicular cancer). For this modelling analysis, we used age-stratified cancer incidences for the UK population from the National Cancer Registration Dataset (2016–18) and published estimates of the area under the receiver operating characteristic curve for current, future, and optimised PRS for each of the eight cancer types. For each of five PRS-defined high-risk quantiles (ie, the top 50%, 20%, 10%, 5%, and 1%) and according to each of the three PRS tools (ie, current, future, and optimised) for the eight cancers, we calculated the relative proportion of cancers arising, the odds ratios of a cancer arising compared with the UK population average, and the lifetime cancer risk. We examined maximal attainable rates of cancer detection by age stratum from combining PRS-based stratification with cancer screening tools and modelled the maximal impact on cancer-specific survival of hypothetical new UK programmes of PRS-stratified screening. The PRS-defined high-risk quintile (20%) of the population was estimated to capture 37% of breast cancer cases, 46% of prostate cancer cases, 34% of colorectal cancer cases, 29% of pancreatic cancer cases, 26% of ovarian cancer cases, 22% of renal cancer cases, 26% of lung cancer cases, and 47% of testicular cancer cases. Extending UK screening programmes to a PRS-defined high-risk quintile including people aged 40–49 years for breast cancer, 50–59 years for colorectal cancer, and 60–69 years for prostate cancer has the potential to avert, respectively, a maximum of 102, 188, and 158 deaths annually. Unstratified screening of the full population aged 48–49 years for breast cancer, 58–59 years for colorectal cancer, and 68–69 years for prostate cancer would use equivalent resources and avert, respectively, an estimated maximum of 80, 155, and 95 deaths annually. These maximal modelled numbers will be substantially attenuated by incomplete population uptake of PRS profiling and cancer screening, interval cancers, non-European ancestry, and other factors. Under favourable assumptions, our modelling suggests modest potential efficiency gain in cancer case detection and deaths averted for hypothetical new PRS-stratified screening programmes for breast, prostate, and colorectal cancer. Restriction of screening to high-risk quantiles means many or most incident cancers will arise in those assigned as being low-risk. To quantify real-world clinical impact, costs, and harms, UK-specific cluster-randomised trials are required. The Wellcome Trust.

For many cancers there are only a few well-established risk factors. Here, we use summary data from genome-wide association studies (GWAS) in a Mendelian randomisation (MR) phenome-wide association study (PheWAS) to identify potentially causal relationships for over 3,000 traits. Our outcome datasets comprise 378,142 cases across breast, prostate, colorectal, lung, endometrial, oesophageal, renal, and ovarian cancers, as well as 485,715 controls. We complement this analysis by systematically mining the literature space for supporting evidence. In addition to providing supporting evidence for well-established risk factors (smoking, alcohol, obesity, lack of physical activity), we also find sex steroid hormones, plasma lipids, and telomere length as determinants of cancer risk. A number of the molecular factors we identify may prove to be potential biomarkers. Our analysis, which highlights aetiological similarities and differences in common cancers, should aid public health prevention strategies to reduce cancer burden. We provide a R/Shiny app to visualise findings. Mendelian randomisation can identify potential risk factors from large populations. Here, the authors analyse 3000 traits across multiple cancer types to search for potential risk factors and molecular biomarkers.

Multiple myeloma (MM) is an incurable malignancy of plasma cells. Epidemiological studies indicate a substantial heritable component, but the underlying mechanisms remain unclear. Here, in a genome-wide association study totaling 10,906 cases and 366,221 controls, we identify 35 MM risk loci, 12 of which are novel. Through functional fine-mapping and Mendelian randomization, we uncover two causal mechanisms for inherited MM risk: longer telomeres; and elevated levels of B-cell maturation antigen (BCMA) and interleukin-5 receptor alpha (IL5RA) in plasma. The largest increase in BCMA and IL5RA levels is mediated by the risk variant rs34562254-A at TNFRSF13B . While individuals with loss-of-function variants in TNFRSF13B develop B-cell immunodeficiency, rs34562254-A exerts a gain-of-function effect, increasing MM risk through amplified B-cell responses. Our results represent an analysis of genetic MM predisposition, highlighting causal mechanisms contributing to MM development. Multiple myeloma (MM) is an incurable blood malignancy. Here, the authors report 35 MM risk loci and two causal mechanisms for genetic MM risk: longer telomeres and elevated plasma B-cell maturation antigen (BCMA) and interleukin−5 receptor alpha (IL5RA) levels.

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients. The genomic landscape of clear cell renal cell carcinoma (ccRCC) remains to be comprehensively characterised. Here, whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project was used to identify potential drivers and clinical correlations to inform the development of therapies.

Diffuse gliomas are the commonest malignant primary brain tumour in adults. Herein, we present analysis of the genomic landscape of adult glioma, by whole genome sequencing of 403 tumours (256 glioblastoma, 89 astrocytoma, 58 oligodendroglioma; 338 primary, 65 recurrence). We identify an extended catalogue of recurrent coding and non-coding genetic mutations that represents a source for future studies and provides a high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and extrachromosomal DNA. Finally, we relate these to clinical outcome. As well as identifying drug targets for treatment of glioma our findings offer the prospect of improving treatment allocation with established targeted therapies. The genomic landscape of diffuse gliomas remains to be characterised. Here, the authors perform whole genome sequencing of 403 tumours and identify recurrent coding and non-coding genetic mutations, their associations with clinical outcomes and potential therapeutic targets.

Although treatment options for B-cell malignancies have expanded, many patients continue to face limited response rates, highlighting an urgent need for new therapeutic targets. To prioritize candidate drug targets for B-cell malignancies, we employed Mendelian Randomization to estimate potentially causal relationships between 445 immune cell traits and six B-cell cancers: follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL), Hodgkin lymphoma (HL), marginal zone lymphoma (MZL), chronic lymphocytic leukemia (CLL), and multiple myeloma (MM), totaling 22,922 cases and 394,204 controls. 163 traits showed a suggestive association with at least one B-cell malignancy ( P  < 0.05), with 34 traits being significant after correction for multiple testing ( P  < 2 × 10 −4 ). By integrating findings with observational data and clinical trial evidence to support drug target candidacy, 24 cell surface markers were identified as druggable targets. In addition to established therapeutic targets such as CD3, CD20 and CD38, our analysis highlights BAFF-R and CD39 in HL, CD25 in MM, CD27 in CLL, CD80/86 in DLBCL, and CCR2 in FL and MZL as promising candidates for therapeutic inhibition. Our findings provide further support for the potential of human genetics to guide the identification of drug targets and address a productivity-limiting step.