Somatic defects at five loci, WT1, CTNNB1, WTX, TP53 and the imprinted 11p15 region, are implicated in Wilms tumor, the commonest childhood kidney cancer. In this study we analysed all five loci in ...120 Wilms tumors. We identified epigenetic 11p15 abnormalities in 69% of tumors, 37% were H19 epimutations and 32% were paternal uniparental disomy (pUPD). We identified mutations of WTX in 32%, CTNNB1 in 15%, WT1 in 12% and TP53 in 5% of tumors. We identified several significant associations: between 11p15 and WTX (P=0.007), between WT1 and CTNNB1 (P less than 0.001), between WT1 and pUPD 11p15 (P=0.01), and a strong negative association between WT1 and H19 epimutation (P less than 0.001). We next used these data to stratify Wilms tumor into three molecular Groups, based on the status at 11p15 and WT1. Group 1 tumors (63%) were defined as 11p15-mutant and WT1-normal; a third also had WTX mutations. Group 2 tumors (13%) were WT1-mutant. They either had 11p15 pUPD or were 11p15-normal. Almost all had CTNNB1 mutations but none had H19 epimutation. Group 3 tumors (25%) were defined as 11p15-normal and WT1-normal and were typically normal at all five loci (P less than 0.001). We also identified a novel clinical association between H19 epimutation and bilateral disease (P less than 0.001). These data provide new insights into the pattern, order, interactions and clinical associations of molecular events in Wilms tumor.
Purpose: To assess the contribution of germline pathogenic variants (PVs) in population-based series of breast cancers and the best strategy to improve detection rates. Methods: Three cohort studies ...were utilized, including a hospital-based series identified from new UK mainstream testing criteria (group-1), offering testing to all women (group-2-BReast CAncer BRCA-DIRECT), and a Greater Manchester cohort study recruited from the mammography screening population (group-3-Predicting Risk of Cancer at Screening). DNA samples from women with breast cancer were sequenced for PVs in BRCA1, BRCA2, and Partner and Localiser of BRCA2 (PALB2). The Manchester score (MS) was used at different points thresholds. Current mainstream criteria include women diagnosed <40 years and all triple negative <60 years or an MS ≥15. Results: Thirty-six PVs (BRCA1 = 9, BRCA2 = 18, PALB2 = 9) were identified among 1061 women with breast cancer (3.4%). Mainstreaming criteria identified 21 of 36 (58%) of PVs by testing 190 women; detection rate (8.4%), specificity = 83.5%. A better detection rate was found using an MS threshold of 12-points with 66.7% (24/36) sensitivity and 85.7% specificity in 171 women. No PVs were identified in 158 women with grade-1 invasive cancers. The best strategy to detect all PVs was an MS ≥3 with specificity of 32.6%. Conclusion: In order to detect higher PV rates on a population basis the best strategy is to reduce the MS threshold for genetic testing.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and ...breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer OR = 1.31; 95% confidence interval (CI), 1.08-1.59 compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.
Genomics in medicine Turnbull, Clare
Medicine (Abingdon. 1995, UK ed.),
December 2018, 2018-12-00, Volume:
46, Issue:
12
Journal Article
Peer reviewed
Nearly all diseases have a genomic basis. This varies from rare ‘single-gene disorders’ such as Duchenne muscular dystrophy, to ‘complex’ or ‘polygenic’ diseases such as type II diabetes mellitus and ...ulcerative colitis. Cancer is a genomic disease in which tumour evolution is driven by serial acquisition of somatic mutations. Investigation of genetic disorders has been predicated on available technologies, which until recently have been expensive, slow, low-throughput and therefore limited in availability. The technology shift to ‘next-generation sequencing’ has massively expanded the capacity for genomic sequencing, resulting in dramatic advances and opportunities for the clinical application of genomics. Rather than testing genes one by one to identify the underlying cause of a rare paediatric disorder, whole-genome sequencing of all genes in the child and both parents can now be routinely performed as an early investigation. Sequencing of tumour material is routine to identify mutations that provide information related to tumour behaviour, prognosis and response to drugs. Sequencing of pathogen genomes enables tracking of disease outbreaks and monitoring of antimicrobial resistance.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
