A comparison of frailty measures in population-based data for patients with colorectal cancer.

BACKGROUND: Numerous studies have revealed age-related inequalities in colorectal cancer care. Increasing levels of frailty in an ageing population may be contributing to this, but quantifying frailty in population-based studies is challenging. OBJECTIVE: To assess the feasibility, validity and reliability of the Hospital Frailty Risk Score (HFRS), the Secondary Care Administrative Records Frailty (SCARF) index and the frailty syndromes (FS) measures in a national colorectal cancer cohort. DESIGN: Retrospective population-based study using 136,008 patients with colorectal cancer treated within the English National Health Service. METHODS: Each measure was generated in the dataset to assess their feasibility. The diagnostic codes used in each measure were compared with those in the Charlson Comorbidity Index (CCI). Validity was assessed using the prevalence of frailty and relationship with 1-year survival. The Brier score and the c-statistic were used to assess performance and discriminative ability of models with included each measure. RESULTS: All measures demonstrated feasibility, validity and reliability. Diagnostic codes used in SCARF and CCI have considerable overlap. Prevalence of frailty determined by each differed; SCARF allocating 55.4% of the population to the lowest risk group compared with 85.1% (HFRS) and 81.2% (FS). HFRS and FS demonstrated the greatest difference in 1-year overall survival between those with the lowest and highest measured levels of frailty. Differences in model performance were marginal. CONCLUSIONS: HFRS, SCARF and FS all have value in quantifying frailty in routine administrative health care datasets. The most suitable measure will depend on the context and requirements of each individual epidemiological study.

Risks of second primary cancers among 584,965 female and male breast cancer survivors in England: a 25-year retrospective cohort study.

Background: Second primary cancers (SPCs) after breast cancer (BC) present an increasing public health burden, with little existing research on socio-demographic, tumour, and treatment effects. We addressed this in the largest BC survivor cohort to date, using a novel linkage of National Disease Registration Service datasets. Methods: The cohort included 581,403 female and 3562 male BC survivors diagnosed between 1995 and 2019. We estimated standardized incidence ratios (SIRs) for combined and site-specific SPCs using incidences for England, overall and by age at BC and socioeconomic status. We estimated incidences and Kaplan-Meier cumulative risks stratified by age at BC, and assessed risk variation by socio-demographic, tumour, and treatment characteristics using Cox regression. Findings: Both genders were at elevated contralateral breast (SIR: 2.02 (95% CI: 1.99-2.06) females; 55.4 (35.5-82.4) males) and non-breast (1.10 (1.09-1.11) females, 1.10 (1.00-1.20) males) SPC risks. Non-breast SPC risks were higher for females younger at BC diagnosis (SIR: 1.34 (1.31-1.38) <50 y, 1.07 (1.06-1.09) ≥50 y) and more socioeconomically deprived (SIR: 1.00 (0.98-1.02) least deprived quintile, 1.34 (1.30-1.37) most). Interpretation: Enhanced SPC surveillance may benefit BC survivors, although specific recommendations require more detailed multifactorial risk and cost-benefit analyses. The associations between deprivation and SPC risks could provide clinical management insights. Funding: CRUK Catalyst Award CanGene-CanVar (C61296/A27223). Cancer Research UK grant: PPRPGM-Nov 20∖100,002. This work was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312)]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

The comprehensive English National Lynch Syndrome Registry: development and description of a new genomics data resource.

Background: Lynch Syndrome (LS) is a cancer predisposition syndrome caused by constitutional pathogenic variants in the mismatch repair (MMR) genes. To date, fragmentation of clinical and genomic data has restricted understanding of national LS ascertainment and outcomes, and precluded evaluation of NICE guidance on testing and management. To address this, via collaboration between researchers, the National Disease Registration Service (NDRS), NHS Genomic Medicine Service Alliances (GMSAs), and NHS Regional Clinical Genetics Services, a comprehensive registry of LS carriers in England has been established. Methods: For comprehensive ascertainment of retrospectively identified MMR pathogenic variant (PV) carriers (diagnosed prior to January 1, 2023), information was retrieved from all clinical genetics services across England, then restructured, amalgamated, and validated via a team of trained experts in NDRS. An online submission portal was established for prospective ascertainment from January 1, 2023. The resulting data, stored in a secure database in NDRS, were used to investigate the demographic and genetic characteristics of the cohort, censored at July 25, 2023. Cancer outcomes were investigated via linkage to the National Cancer Registration Dataset (NCRD). Findings: A total of 11,722 retrospective and 570 prospective data submissions were received, resulting in a comprehensive English National Lynch Syndrome Registry (ENLSR) comprising 9030 unique individuals. The most frequently identified pathogenic MMR genes were MSH2 and MLH1 at 37.2% (n = 3362) and 29.1% (n = 2624), respectively. 35.9% (n = 3239) of the ENLSR cohort received their LS diagnosis before their first cancer diagnosis (presumptive predictive germline test). Of these, 6.3% (n = 204) developed colorectal cancer, at a median age of initial diagnosis of 51 (IQR 40-62), compared to 73 years (IQR 64-80) in the general population (p < 0.0001). Interpretation: The ENLSR represents the first comprehensive national registry of PV carriers in England and one of the largest cohorts of MMR PV carriers worldwide. The establishment of a secure, centralised infrastructure and mechanism for routine registration of newly identified carriers ensures sustainability of the data resource. Funding: This work was funded by the Wellcome Trust, Cancer Research UK and Bowel Cancer UK. The funder of this study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Germline mismatch repair (MMR) gene analyses from English NHS regional molecular genomics laboratories 1996-2020: development of a national resource of patient-level genomics laboratory records.

OBJECTIVE: To describe national patterns of National Health Service (NHS) analysis of mismatch repair (MMR) genes in England using individual-level data submitted to the National Disease Registration Service (NDRS) by the NHS regional molecular genetics laboratories. DESIGN: Laboratories submitted individual-level patient data to NDRS against a prescribed data model, including (1) patient identifiers, (2) test episode data, (3) per-gene results and (4) detected sequence variants. Individualised per-laboratory algorithms were designed and applied in NDRS to extract and map the data to the common data model. Laboratory-level MMR activity audit data from the Clinical Molecular Genetics Society/Association of Clinical Genomic Science were used to assess early years' missing data. RESULTS: Individual-level data from patients undergoing NHS MMR germline genetic testing were submitted from all 13 English laboratories performing MMR analyses, comprising in total 16 722 patients (9649 full-gene, 7073 targeted), with the earliest submission from 2000. The NDRS dataset is estimated to comprise >60% of NHS MMR analyses performed since inception of NHS MMR analysis, with complete national data for full-gene analyses for 2016 onwards. Out of 9649 full-gene tests, 2724 had an abnormal result, approximately 70% of which were (likely) pathogenic. Data linkage to the National Cancer Registry demonstrated colorectal cancer was the most frequent cancer type in which full-gene analysis was performed. CONCLUSION: The NDRS MMR dataset is a unique national pan-laboratory amalgamation of individual-level clinical and genomic patient data with pseudonymised identifiers enabling linkage to other national datasets. This growing resource will enable longitudinal research and can form the basis of a live national genomic disease registry.

Evolutionary non-linear modelling for selecting vaccines against antigenically variable viruses.

MOTIVATION: In vitro and in vivo selection of vaccines is time consuming, expensive and the selected vaccines may not be able to provide protection against broad-spectrum viruses because of emerging antigenically novel disease strains. A powerful computational model that incorporates these protein/DNA or RNA level fluctuations can effectively predict antigenically variant strains, and can minimize the amount of resources spent on exclusive serological testing of vaccines and make wide spectrum vaccines possible for many diseases. However, in silico vaccine prediction remains a grand challenge. To address the challenge, we investigate the use of linear and non-linear regression models to predict the antigenic similarity in foot-and-mouth disease virus strains and in influenza strains, where the structure and parameters of the non-linear model are optimized using an evolutionary algorithm (EA). In addition, we examine two different scoring methods for weighting the type of amino acid substitutions in the linear and non-linear models. We also test our models with some unseen data. RESULTS: We achieved the best prediction results on three datasets of SAT2 (Foot-and-Mouth disease), two datasets of serotype A (Foot-and-Mouth disease) and two datasets of influenza when the scoring method based on biochemical properties of amino acids is employed in combination with a non-linear regression model. Models based on substitutions in the antigenic areas performed better than those that took the entire exposed viral capsid proteins. A majority of the non-linear regression models optimi Z: ed with the EA: performed better than the linear and non-linear models whose parameters are estimated using the least-squares method. In addition, for the best models, optimi Z: ed non-linear regression models consist of more terms than their linear counterparts, implying a non-linear nature of influences of amino acid substitutions. Our models were also tested on five recently generated FMDV datasets and the best model was able to achieve an 80% agreement rate.