A breast-risk score, published in 2016, was developed in white-American women using 92 genetic variants (GRS92), modifiable and non-modifiable risk factors. With the aim of validating the score in ...the Spanish population, 1,732 breast cancer cases and 1,910 controls were studied. The GRS92, modifiable and non-modifiable risk factor scores were estimated via logistic regression. SNPs without available genotyping were simulated as in the aforementioned 2016 study. The full model score was obtained by combining GRS92, modifiable and non-modifiable risk factor scores. Score performances were tested via the area under the ROC curve (AUROC), net reclassification index (NRI) and integrated discrimination improvement (IDI). Compared with non-modifiable and modifiable factor scores, GRS92 had higher discrimination power (AUROC: 0.6195, 0.5885 and 0.5214, respectively). Adding the non-modifiable factor score to GRS92 improved patient classification by 23.6% (NRI = 0.236), while the modifiable factor score only improved it by 7.2%. The full model AUROC reached 0.6244. A simulation study showed the ability of the full model for identifying women at high risk for breast cancer. In conclusion, a model combining genetic and risk factors can be used for stratifying women by their breast cancer risk, which can be applied to individualizing genetic counseling and screening recommendations.
Population-based cancer registries are responsible for collecting incidence and survival data on all reportable neoplasms within a defined geographical area. During the last decades, the role of ...cancer registries has evolved beyond monitoring epidemiological indicators, as they are expanding their activities to studies on cancer aetiology, prevention, and quality of care. This expansion relies also on the collection of additional clinical data, such as stage at diagnosis and cancer treatment. While the collection of data on stage, according to international reference classification, is consolidated almost everywhere, data collection on treatment is still very heterogeneous in Europe. This article combines data from a literature review and conference proceedings together with data from 125 European cancer registries contributing to the 2015 ENCR-JRC data call to provide an overview of the status of using and reporting treatment data in population-based cancer registries. The literature review shows that there is an increase in published data on cancer treatment by population-based cancer registries over the years. In addition, the review indicates that treatment data are most often collected for breast cancer, the most frequent cancer in women in Europe, followed by colorectal, prostate and lung cancers, which are also more common. Treatment data are increasingly being reported by cancer registries, though further improvements are required to ensure their complete and harmonised collection. Sufficient financial and human resources are needed to collect and analyse treatment data. Clear registration guidelines are to be made available to increase the availability of real-world treatment data in a harmonised way across Europe.
Purpose To build models combining circulating microRNAs (miRNAs) able to identify women with breast cancer as well as different types of breast cancer, when comparing with controls without breast ...cancer. Method miRNAs analysis was performed in two phases: screening phase, with a total n = 40 (10 controls and 30 BC cases) analyzed by Next Generation Sequencing, and validation phase, which included 131 controls and 269 cases. For this second phase, the miRNAs were selected combining the screening phase results and a revision of the literature. They were quantified using RT-PCR. Models were built using logistic regression with LASSO penalization. Results The model for all cases included seven miRNAs (miR-423-3p, miR-139-5p, miR-324-5p, miR-1299, miR-101-3p, miR-186-5p and miR-29a-3p); which had an area under the ROC curve of 0.73. The model for cases diagnosed via screening only took in one miRNA (miR-101-3p); the area under the ROC curve was 0.63. The model for disease-free cases in the follow-up had five miRNAs (miR-101-3p, miR-186-5p, miR-423-3p, miR-142-3p and miR-1299) and the area under the ROC curve was 0.73. Finally, the model for cases with active disease in the follow-up contained six miRNAs (miR-101-3p, miR-423-3p, miR-139-5p, miR-1307-3p, miR-331-3p and miR-21-3p) and its area under the ROC curve was 0.82. Conclusion We present four models involving eleven miRNAs to differentiate healthy controls from different types of BC cases. Our models scarcely overlap with those previously reported. Keywords: Breast cancer, Screening, miRNA, Diagnosis, Prognosis
Background
The head and neck cancers (HNCs) incidence differs between Europe and East Asia. Our objective was to determine whether survival of HNC also differs between European and Asian countries.
...Methods
We used population-based cancer registry data to calculate 5-year relative survival (RS) for the oral cavity, hypopharynx, larynx, nasal cavity, and major salivary gland in Europe, Taiwan, and Japan. We modeled RS with a generalized linear model adjusting for time since diagnosis, sex, age, subsite, and histological grouping. Analyses were performed using federated learning, which enables analyses without sharing sensitive data.
Findings
Five-year RS for HNC varied between geographical areas. For each HNC site, Europe had a lower RS than both Japan and Taiwan. HNC subsites and histologies distribution and survival differed between the three areas. Differences between Europe and both Asian countries persisted even after adjustments for all HNC sites but nasal cavity and paranasal sinuses, when comparing Europe and Taiwan.
Interpretation
Survival differences can be attributed to different factors including different period of diagnosis, more advanced stage at diagnosis, or different availability/access of treatment. Cancer registries did not have stage and treatment information to further explore the reasons of the observed survival differences. Our analyses have confirmed federated learning as a feasible approach for data analyses that addresses the challenges of data sharing and urge for further collaborative studies including relevant prognostic factors.
Socioeconomic inequalities in cancer incidence are not well documented in southern Europe. We aim to study the association between socioeconomic status (SES) and colorectal, lung, and breast cancer ...incidence in Spain. We conducted a multilevel study using data from Spanish population-based cancer registries, including incident cases diagnosed for the period 2010–2013 in nine Spanish provinces. We used Poisson mixed-effects models, including the census tract as a random intercept, to derive cancer incidence rate ratios by SES, adjusted for age and calendar year. Male adults with the lowest SES, compared to those with the highest SES, showed weak evidence of being at increased risk of lung cancer (risk ratio (RR): 1.18, 95% CI: 0.94–1.46) but showed moderate evidence of being at reduced risk of colorectal cancer (RR: 0.84, 95% CI: 0.74–0.97). Female adults with the lowest SES, compared to those with the highest SES, showed strong evidence of lower breast cancer incidence with 24% decreased risk (RR: 0.76, 95% CI: 0.68–0.85). Among females, we did not find evidence of an association between SES and lung or colorectal cancer. The associations found between SES and cancer incidence in Spain are consistent with those obtained in other European countries.
Central nervous system (CNS) neoplasms are highly frequent solid tumours in children and adolescents. While some studies have shown a rise in their incidence in Europe, others have not. Survival ...remains limited. We addressed two questions about these tumours in Spain: (1) Is incidence increasing? and (2) Has survival improved?
This population-based study included 1635 children and 328 adolescents from 11 population-based cancer registries with International Classification of Childhood Cancer Group III tumours, incident in 1983-2007. Age-specific and age-standardised (world population) incidence rates (ASRws) were calculated. Incidence time trends were characterised using annual percent change (APC) obtained with Joinpoint. Cases from 1991 to 2005 (1171) were included in Kaplan-Meier survival analyses, and the results were evaluated with log-rank and log-rank for trend tests. Children's survival was age-standardised using: (1) the age distribution of cases and the corresponding trends assessed with Joinpoint; and (2) European weights for comparison with Europe.
ASRw 1983-2007: children: 32.7 cases/10
; adolescents: 23.5 cases/10
. The overall incidence of all tumours increased across 1983-2007 in children and adolescents. Considering change points, the APCs were: (1) children: 1983-1993, 4.3%^ (1.1; 7.7); 1993-2007, -0.2% (-1.9; 1.6); (2) adolescents: 1983-2004: 2.9%^ (0.9; 4.9); 2004-2007: -7.7% (-40; 41.9). For malignant tumours, the trends were not significant. 5-year survival was 65% (1991-2005), with no significant trends (except for non-malignant tumours).
CNS tumour incidence in Spain was found to be similar to that in Europe. Rises in incidence may be mostly attributable to changes in the registration of non-malignant tumours. The overall malignant CNS tumour trend was compatible with reports for Southern Europe. Survival was lower than in Europe, without improvement over time. We provide a baseline for assessing current paediatric oncology achievements and incidence in respect of childhood and adolescent CNS tumours.
La supervivencia relativa se ha utilizado habitualmente como medida de la evolución temporal del exceso de riesgo de mortalidad en cohortes de pacientes diagnosticados de cáncer, teniendo en cuenta ...la mortalidad de una población de referencia. Una vez estimado el exceso de riesgo de mortalidad pueden calcularse tres probabilidades acumuladas a un tiempo T: 1) la probabilidad de fallecer asociada a la causa de diagnóstico inicial (enfermedad en estudio), 2) la probabilidad de fallecer asociada a otras causas, y 3) la probabilidad de supervivencia absoluta en la cohorte a un tiempo T. Este trabajo presenta la aplicación WebSurvCa (https://shiny.snpstats.net/WebSurvCa/), mediante la cual los registros de cáncer de base hospitalaria y poblacional, y los registros de otras enfermedades, estiman dichas probabilidades en sus cohortes seleccionando como población de referencia la mortalidad de la comunidad autónoma que consideren.
La supervivencia relativa se ha utilizado habitualmente como medida de la evolución temporal del exceso de riesgo de mortalidad en cohortes de pacientes diagnosticados de cáncer, teniendo en cuenta ...la mortalidad de una población de referencia. Una vez estimado el exceso de riesgo de mortalidad pueden calcularse tres probabilidades acumuladas a un tiempo T: 1) la probabilidad de fallecer asociada a la causa de diagnóstico inicial (enfermedad en estudio), 2) la probabilidad de fallecer asociada a otras causas, y 3) la probabilidad de supervivencia absoluta en la cohorte a un tiempo T. Este trabajo presenta la aplicación WebSurvCa (https://shiny.snpstats.net/WebSurvCa/), mediante la cual los registros de cáncer de base hospitalaria y poblacional, y los registros de otras enfermedades, estiman dichas probabilidades en sus cohortes seleccionando como población de referencia la mortalidad de la comunidad autónoma que consideren.
Relative survival has been used as a measure of the temporal evolution of the excess risk of death of a cohort of patients diagnosed with cancer, taking into account the mortality of a reference population. Once the excess risk of death has been estimated, three probabilities can be computed at time T: 1) the crude probability of death associated with the cause of initial diagnosis (disease under study), 2) the crude probability of death associated with other causes, and 3) the probability of absolute survival in the cohort at time T. This paper presents the WebSurvCa application (https://shiny.snpstats.net/WebSurvCa/), whereby hospital-based and population-based cancer registries and registries of other diseases can estimate such probabilities in their cohorts by selecting the mortality of the relevant region (reference population).
Myeloid malignancies (MMs) are a heterogeneous group of hematologic malignancies presenting different incidence, prognosis and survival.1–3 Changing classifications (FAB 1994, WHO 2001 and WHO 2008) ...and few available epidemiological data complicate incidence comparisons.4,5 Taking this into account, the aims of the present study were: a) to calculate the incidence rates and trends of MMs in the Province of Girona, northeastern Spain, between 1994 and 2008 according to the WHO 2001 classification; and b) to predict the number of MMs cases in Spain during 2013. Data were extracted from the population-based Girona Cancer Registry (GCR) located in the north-east of Catalonia, Spain, and covering a population of 731,864 inhabitants (2008 census). Cases were registered according to the rules of the European Network for Cancer Registries and the Manual for Coding and Reporting Haematological Malignancies (HAEMACARE project). To ensure the complete coverage of MMs in the GCR, and especially myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS), a retrospective search was performed. The ICD-O-2 (1990) codes were converted into their corresponding ICD-O-3 (2000) codes, including MDS, polycythemia vera (PV) and essential thrombocythemia (ET) as malignant diseases. Results of crude rate (CR) and European standardized incidence rate (ASRE) were expressed per 100,000 inhabitants/year
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