The beta regression model (BRM) is a widely applied modeling approach for data bounded within the open interval (0, 1), and it is extensively used in fields such as chemistry, environmental science, medicine, and biology. Parameter estimation in these models is conventionally performed using the maximum likelihood estimator (MLE). However, the MLE is known to be sensitive to multicollinearity among predictors and the presence of outliers, which can bias coefficient estimates, inflate their variance, and increase the mean squared error (MSE), potentially leading to erroneous inferences. To address these issues, this paper introduces new robust biased estimators for BRM that are named robust ridge-type estimators. These estimators are designed to handle the adverse effects of multicollinearity and outliers. We used a theoretical comparison to compare the proposed estimators against the MLE and existing robust ridge estimators. Furthermore, a simulation study was performed under different conditions to evaluate the performance of the proposed estimators. Both the theoretical comparison and simulation results demonstrate the superior performance of the proposed estimators in the presence of multicollinearity and outliers. The practical utility of the methods was further validated through an application to a real-world dataset on breast cancer data. The results confirm that the proposed estimators provide greater reliability and robustness compared to existing methods. These results emphasize the importance of using robust biased estimation techniques to enhance the accuracy and reliability of regression models, especially in empirical research involving multicollinear and outlier data.

Objective: To investigate the expression of microfibril-associated protein 5 (MFAP5) in ovarian cancer and its influence on malignant behavior of ovarian cancer cells. Methods: GEPIA, CSIOVDB and Kaplan-Meier Plotter online databases were used to analyze the expression of MFAP5 in various tumor tissues, especially in ovarian cancer. Immunohistochemistry was used to detect the expression level of MFAP5 in ovarian cancer tissue chip (The tissue microarray was commissioned to Zhongke Guanghua [Xi'an] Intelligent Biotechnology Co., Ltd. for processing. The specimens were sourced from Henan Provincial People's Hospital from January 2018 to March 2023), and the relationship between MFAP5 expression and the clinical characteristics of patients with ovarian cancer was analyzed using SPSS software. Kaplan-Meier Plotter online database was used to analyze the relationship between MFAP5 expression and survival prognosis of ovarian cancer patients. The ovarian cancer data sets GSE9891 and TCGA594 were downloaded from GEO and TCGA respectively, and ssGSEA was used to analyze the scores of gene sets related to epithelial mesenchymal transition, migration and invasion in ovarian cancer data sets. Next, the relationship between MFAP5 expression and the above scores was analyzed using GraphPad Prism. The expression of MFAP5 in normal fibroblasts (NFs) and cancer associated fibroblasts (CAFs) was verified by western blot. MFAP5 expression in CAFs was reduced by MFAP5-si RNA, and influence of CAFs with high or low expression of MFAP5 on malignant behavior of ovarian cancer cell SKOV3 was verified by transwell test. The influence of CAFs with high or low expression of MFAP5 on epithelial mesenchymal transition markers of ovarian cancer cell SKOV3 was verified by western blot. The CCK8 assay and 3D co-culture model were used to verify the effects of CAFs with high and low MFAP5 expression on the chemoresistance of ovarian cancer cells SKOV3. Transwell assay, western blot, and 3D co-culture model were used to explore and verify the related pathways through which MFAP5 affects the malignant behavior and drug resistance of ovarian cancer cells SKOV3. Results: Online data analysis showed that the expression of MFAP5 in ovarian cancer tissue was significantly higher than that in normal ovarian tissue (P＜0.001). Immunohistochemistry showed that the expression of MFAP5 was mainly concentrated in ovarian cancer stroma, and the later stage and the higher grade ovarian cancer tissues showed higher MFAP5 expression. Survival analysis showed that the high expression of MFAP5 was related to the poor prognosis of patients, and it was only significant in later stages and higher grades. ssGSEA analysis showed that the expression of MFAP5 was positively correlated with the scores of gene sets related to epithelial-mesenchymal transition, migration and invasion of ovarian cancer. In vitro experiments showed that reducing the expression of MFAP5 in CAFs could not only partially reduce its supportive effect on the malignant behaviors such as migration and invasion of ovarian cancer epithelial cells SKOV3 (The number of migrating cells in the control group, si-NC group, and si-MFAP5-group was 73.44±7.80, 199.74±18.26, and 91.21±6.70, respectively. The number of invading cells in the control group, si-NC group, and si-MFAP5-group was 61.62±8.76, 174.81±15.23, and 67.17±9.83, respectively), but also increase the sensitivity of ovarian cancer epithelial cells SKOV3 to chemotherapy and maintenance therapy. Further research showed that MFAP5 could activate the AKT signaling pathway in ovarian cancer cells SKOV3, and inhibiting the AKT pathway could block the supportive effect of MFAP5 on the malignant behavior and drug resistance of ovarian cancer cells. Conclusions: MFAP5 is highly expressed in ovarian cancer, especially in the stromal tissue of ovarian cancer, where the expression level is the highest. High expression of MFAP5 often indicates a poor prognosis for ovarian cancer patients. By reducing the expression of MFAP5 in CAFs, the supportive effect on the malignant biological behavior and drug resistance of ovarian cancer epithelial cells can be weakened. The underlying mechanism of this phenomenon may be related to the blockade of the AKT signaling pathway in ovarian cancer cells.

Hepatocellular carcinoma (HCC) is a growing burden particularly in rural, regional and remote areas, but samples from these communities are underrepresented in public cancer data repositories. It remains unclear whether the findings of large, commonly studied cohorts such as The Cancer Genome Atlas (TCGA) are applicable to these remote communities. We profiled paired tumour and adjacent non-tumour liver biopsies from 19 patients admitted to the Townsville University Hospital in rural Australia. We used RNA-seq to characterize transcriptomic and mutational features and compared these with the TCGA Liver Hepatocellular Carcinoma (LIHC) cohort. Furthermore, we used this data to test a transcriptome-only adaptation of our TARGET-SL pipeline for low-cost drug target prediction. Differential expression analysis identified 923 genes altered in our cohort, of which 64% overlapped with TCGA-LIHC, and the cohort-mean gene expression correlated strongly (Spearman Rho = 0.96). Somatic variant calling from RNA highlighted mutational heterogeneity, with CTNNB1 (47%) and TP53 (21%) the most frequently mutated genes, consistent with TCGA findings. Copy number inference detected recurrent deletions on 8p, 6q, and 17p, congruous with known HCC patterns. We ran TARGET-SL solely on RNA-Seq to identify personalised driver genes in these patients and were able to identify a drug candidate in 63% of patients. Our results demonstrate that NQ HCC shares core molecular features with larger TCGA cohorts, and that a transcriptome-based approach can feasibly support precision oncology in resource-limited regional settings.

To investigate the causal relationship between gut microbiota, T-cell function, and the risk of colorectal cancer. Gut microbiota data from the MiBioGen database and T-cell and colorectal cancer data from publicly available GWAS datasets were obtained for analyzing the causality between gut microbiota, T-cell subsets, and the risk of colorectal cancer with two-sample Mendelian randomization (MR) analyses, using inverse variance weighting as the primary analytical method supplemented with MR-Egger, weighted median, simple mode, and weighted mode methods. Horizontal pleiotropy was assessed using MR-PRESSO and MR-Egger regression. Cochran's Q test was used to evaluate heterogeneity, and sensitivity analysis was performed using the leave-one-out method. In the Forward MR analysis of gut microbiota and T cells, 11 gut microbiota species showed causal relationships. Six of these species exhibited positive correlations with T cells, including Prevotella7 (P=0.003), Ruminococcaceae UCG011 (P=0.033), Ruminococcaceae UCG004 (0.010), Ebacterium brachy group (P=0.005), Lachnospiraceae FCS020 group (P=0.028), and Coprobacter (P=0.033), and the remaining 5 species showed negative correlations with T cells. Forward MR analysis of T cells and colorectal cancer suggested that CD25++CD45RA-CD4+ non-regulatory T cells were negatively correlated with colorectal cancer risk (IVW: OR=0.935, 95% CI: 0.878-0.995; P=0.035). The analysis of gut microbiota and colorectal cancer suggested that 11 gut microbiota species were causally associated with colorectal cancer, and 6 of them (Eubacterium xylanophilum group, P=0.039; Selenomonadales, P=0.014; Negativicutes, P=0.014; Bifidobacteriaceae, P=0.048; Bifidobacteriales, P=0.048; and Coprococcus1, P=0.033) showed positive correlations and the remaining 5 showed negative correlations. Coprobacter spp. and Eubacterium xylanophilum group spp. are causally associated with both T cell activity and colorectal cancer risk, and the former bacteria induce inactivation of CD25++CD45RA-CD4+ non-regulatory T cells to promote colorectal cancer progression, whereas the latter bacteria promote CD25++CD45RA-CD4+ non-regulatory T cell activity to inhibit colorectal cancer development.

Metabolic dysfunction-associated steatotic liver disease (MASLD) shares metabolic and inflammatory pathways with heart failure. However, prior studies with small samples and broad cardiometabolic risk factor (CMRF) groupings have left the relationship unclear. We aimed to assess the independent effect of MASLD and the combined impact of specific CMRFs on heart failure incidence. We analysed data from 218 605 adults in the Korean National Health Insurance Service screening cohort between 2009 and 2010, excluding those with other liver diseases, excessive alcohol use, cancer, cirrhosis or missing data. MASLD was defined based on a Fatty Liver Index ≥30 with at least one CMRF. Participants were categorised by MASLD and CMRF status, and incident heart failure was identified during follow-up until 2019. Adjusted HRs were estimated using multivariable adjusted Cox proportional hazards models. Among the 218 605 participants, 32% had MASLD with less favourable cardiometabolic profiles than those without steatotic liver disease. During a median follow-up of 9.6 years, 16 340 incident heart failure cases occurred. Patients with MASLD had a significantly higher risk of heart failure than individuals without steatotic liver disease and CMRFs (adjusted HR 2.62, 95% CI 2.39 to 2.86). Heart failure risk increased progressively with an increasing number of CMRFs (per additional CMRF: adjusted HR 1.24, 95% CI 1.22 to 1.25). MASLD is independently associated with incident heart failure beyond traditional CMRFs. Therefore, MASLD's role may warrant consideration in future heart failure risk stratification.

IntroductionGene expression analysis is crucial for understanding cell identity and function. While single-cell RNA sequencing (scRNA-seq) successfully unveiled cellular heterogeneity, it critically loses the spatial context where cells reside and interact. To address this, spatial transcriptomics (ST) techniques have emerged. Among these, fluorescence in situ hybridization (FISH) based methods have garnered significant attention for their ability to quantify hundreds of genes with subcellular resolution. These methods generate rich transcript point cloud data that incorporates both spatial coordinates and gene identity.A fundamental challenge in ST analysis is to assign each transcript to its corresponding cell to construct accurate cell-level gene expression profiles. Conventional methods often rely on the segmentation of stained images of the nuclei and membrane. However, these image-based approaches suffer from major limitations: low image quality, the need for labor intensive staining, and poor performance in densely packed or noisy tissue regions. Therefore, there is a need for a robust method that can estimate cell properties directly from transcript data without depending on image morphology.In this study, we propose a method to estimate the positions and types of individual cells directly from transcript point clouds. Unlike conventional approaches, our method requires no auxiliary imaging and aims to provide reliable cell-level assignments in noisy or densely packed tissues.Proposed methodOur method estimates the locations and types of individual cells directly from the input transcript point cloud data, where each point is defined by its two-dimensional spatial coordinates and gene identity. Our approach relies on two important properties of tissue organization: tissues are hierarchically structured from cell types down to individual cells, and each cell type is characterized by a distinctive gene expression signature. Cells of the same type therefore form spatially coherent domains that can be distinguished by their expression profiles.The approach consists of two steps. First, we partition the tissue space into hierarchical regions using a quadtree [1] to approximate coarse cell-type-specific domains. This enables the identification of transcript subsets likely belonging to a given type. Second, within each subset, we apply a probabilistic mixture model to identify individual cells. Each component is modeled by a two-dimensional Gaussian distribution for spatial positions and a categorical distribution for gene identities. The parameters and transcript assignments are estimated using an Expectation–Maximization (EM) algorithm. An overview of the proposed method is shown in Fig. 1 (a).To distinguish cell types, our method requires reference data showing the gene expression pattern of each cell type, for example, annotated scRNA-seq data. The output is an assignment of transcripts to unique cell IDs and cell type labels. By directly leveraging spatial and gene expression signatures, our method eliminates the need for auxiliary images, ensuring effectiveness in noisy or densely packed tissues.ExperimentWe evaluated our method on a human lung cancer dataset acquired with the Xenium platform [2]. Annotated scRNA-seq data [3] covering multiple cell types was used as reference data. As a baseline, we compared against the image-based segmentation provided by the Xenium Onboard Analysis pipeline [4].Our results are shown in Fig. 1 (b) and (c), where hierarchical partitioning and clustering were used to define transcript assignments and cell regions. Furthermore, as illustrated in Fig. 2 (a), image-based segmentation failed to recognize cells in certain regions because of weak or ambiguous staining. In contrast, our method was able to reliably detect the cells in these regions (Fig. 2 (b), (c)). This confirms that our approach can robustly identify cells by leveraging transcript distributions alone.Quantitatively, our method achieved a significantly higher transcript-to-cell assignment rate of 0.9996, compared to 0.7891 achieved by the Xenium segmentation. This substantial difference indicates that our method drastically reduces the number of unassigned transcripts and missed cells, providing a more complete profile of tissue heterogeneity. To assess the consistency of the identified cell boundaries, we treated both our unique cell assignments and the image-based segments as cell-level clustering. Their agreement yielded an Adjusted Rand Index (ARI) of 0.4653 and a Normalized Mutual Information (NMI) of 0.8939. These metrics show that our image-free method achieves a comparable level of agreement with the conventional image-based segmentation, while operating solely on transcript data.Overall, the results demonstrate that our method achieves a substantially higher transcript assignment rate and comparable segmentation quality to image-based methods, confirming its robustness and potential as an effective, image-free alternative for cell identification in spatial transcriptomics.DiscussionOur study demonstrates the feasibility of estimating cell positions and types solely from transcript point clouds. By combining spatial organization with characteristic expression profiles, the method enables reliable transcript-to-cell assignment without auxiliary imaging. Applied to lung cancer data, it achieved a higher transcript assignment rate and strong agreement with image-based segmentation, while also detecting cells and transcripts missed by conventional methods. Future directions include scaling the model to larger datasets, validating it across diverse tissue types, and automating processes such as parameter tuning.

Abstract A comparative examination of breast cancer survival between two data sets from the Kurdistan area of Iraq and a corresponding data set from Germany is the aim of this paper. Using breast cancer data from the Kurdistan area of Iraq, both censored and unfiltered, we developed a methodology in a previous publication (2016) for predicting survival probabilities and hazard functions in a health context when a significant fraction of participants are lost to the research. This study follows earlier research (2023) where we had to use unique estimation methods to address the two Iraqi datasets' filtering problems. In particular, the data from Nanakaly hospital in the city of Erbil and Hewa hospitals in the city of Sulamani involved problems with hidden censoring affecting the survival time, leading to significant biases in survival curves generated using standard methods, and we had developed new Markov chain-based methods for generating survival curves providing adjusted Kaplan Meier analyses. Due to the availability of a reliable survival function, we chose to work with a German data set from the W. Sauerbrei Institute for Medical Biometry and Informatics, University of Freiburg—Germany. Our data analysis leads us to the conclusion that younger German women had a higher breast cancer survival rate than patients from the Kurdistan Region of Iraq.

Oropharyngeal cancer (OPC) involves the base of tongue, palatine tonsils, lingual tonsil, and soft palate. Established risk factors for OPC include tobacco usage, alcohol usage, and human papillomavirus infection. Although white populations tend to have the highest risk of developing OPC, Black patients are more likely to experience distant stage disease. The aim of this study was to elucidate epidemiological factors that are associated with regional and distant stage disease at OPC diagnosis. We performed a retrospective cross-sectional analysis utilizing the Florida Cancer Data System (FCDS) from 2010 to 2017. Sociodemographic factors among Black and white patients were compared using chi-square analysis. Multivariable logistic regression analysis determined risk factors associated with distant stage diagnosis. Geographical mapping of census tract levels was performed to illustrate prevalence of distant stage disease at diagnosis in Florida. Among 8,908 OPC cases, 7,534 (84.6%) patients were white non-Hispanic, 834 (9.4%) were white Hispanic, and 540 (6.1%) were Black. Multivariable logistic regression revealed increased distant stage disease compared to local stage among those who were Black (compared to white non-Hispanic and white Hispanic; OR = 1.55 [95% CI:1.12-2.13], p = 0.007), separated/divorced/widowed (OR = 1.36 [95% CI:1.11-1.68], p = 0.004) (compared to married), and lack insurance (OR = 1.67 [95% CI:1.16-2.41], p = 0.006) or have public insurance (OR = 1.26 [95% CI:1.04-1.53], p = 0.017) (compared to those with private insurance). There was decreased regional stage disease compared to local stage among females (OR = 0.57 [95% CI:0.49-0.66], p < .001) and older patients (OR = 0.975, [95% CI:0.968-0.982], p < 0.001). Mapping revealed higher percent of distant stage diagnoses in census tracts with lower median income. Distant stage at OPC diagnosis is influenced by many risk factors, including race, sex, age, marital, and insurance status. Geographical mapping analysis can help direct screening efforts to high-risk communities.

Transcription factors and their target genes form regulatory modules known as regulons, which exhibit significant specificity across various cell types. The integration of single-cell transcriptome data, transcription factor motif data, and ChIP-seq data presents a challenging task in identifying cell-type-specific regulons and examining their activities. In response, this study presents a Deep Structured Semantic Model for inferring and prioritizing cell-type-specific Regulons (DSSMReg). This approach utilizes single-cell transcriptome and transcription factor motif data to map transcription factors and target genes into a low-dimensional semantic space, resulting in the generation of feature vectors. The model then computes the cosine similarity between transcription factors and target genes to evaluate their regulatory strength and subsequently infers cell-type-specific regulons based on this assessment. Moreover, DSSMReg employs the AUCell algorithm to rank the importance of regulons for each cell type. We compared DSSMReg against five representative gene regulatory inference algorithms using scRNA-seq data from five cell lines, with DSSMReg achieving the highest evaluation metrics for both AUROC and AUPRC. Furthermore, we applied DSSMReg to infer cell-type-specific regulons from scRNA-seq data of triple-negative breast cancer and human bone marrow hematopoietic stem cells. Our results indicated that regulons with high AUCell scores possess significant biological relevance. The source code of DSSMReg is freely available at https://github.com/YaxinF/DSSMReg.

Cancer research has seen transformative advances with single-cell RNA sequencing, yet challenges persist in accurately classifying cancers with similar gene expression profiles. Addressing this, we developed an integrated computational framework leveraging single-cell RNA sequencing data from 10x Genomics Datasets, mathematical analysis, and Seurat-based clustering to classify and predict cancer types. A key methodological innovation involves the application of a mathematical approach that employs the Hausdorff distance matrix and norm analysis across a range of gene correlation thresholds. By generating stacked line plot patterns of the computed norms, the method captures distinct trends that differentiate between similar and dissimilar cancer types, thereby enabling effective classification and prediction. Key findings include robust classification accuracy for breast and lung cancers, derived from dynamic gene network analyses, while colorectal and ovarian cancers presented challenges linked to higher intratumoral heterogeneity. Our results revealed unique norm patterns reflective of distinct transcriptional architectures, including the dynamic immune landscapes in breast cancer and linear transcriptional progression in lung cancer. Validation on independent datasets underscored the method's reliability in categorizing unseen cancer data, providing statistical confidence for breast and lung cancer classifications. Beyond classification, the study advances understanding of cancer gene correlation networks, offering novel insights into transcriptional diversity and tumor microenvironment interactions. This framework bridges gaps in current methodologies, combining precision with scalability for diverse datasets. By integrating mathematical tools with single-cell RNA sequencing data, this study establishes a foundation for transformative applications in cancer diagnostics and treatment.

In this manuscript, we develop a unified joint modelling and estimation framework for zero-inflated count and longitudinal semi-continuous data, with a focus on models structured around the exponential family and two-part hurdle formulations. We first review and synthesize existing longitudinal hurdle models, identifying a common structure across diverse approaches. Motivated by this foundation, we introduce novel joint models that integrate semi-continuous longitudinal outcomes with time-to-event data, and propose new methods for dynamic prediction in the presence of semi-continuous outcomes. To facilitate flexible estimation and inference across this class of models, we propose a Bayesian estimation strategy based on a Markov Chain Monte Carlo (MCMC) algorithm. We have implemented these methods in the R package UHJM (available at https://github.com/tbaghfalaki/UHJM), providing accessible tools for parameter estimation and risk prediction. The utility of our framework is demonstrated through simulation studies and two real-world applications characterized by excess zeros.

Spatiotemporal patterns in malignant brain and central nervous system cancer incidence and mortality in the United States.

There is a demand for population level research on the potential genetic-basis of mesothelioma (e.g. BRCA1-associated protein-1 [BAP1]) independent of other risk factors, such as amphibole asbestos exposure. By surrogate, another primary cancer history can be used to explore this issue, including in the USA, where the incidence rates (IRs) in men, but not women, are temporally aligned with historical asbestos consumption. We computed age-adjusted IRs of mesothelioma in females and males stratified by other primary cancer history using publicly available U.S. cancer data from 1975 to 2021. To facilitate comparison with other cancers associated with BAP1, we calculated age-adjusted IRs for female breast cancer and melanoma. Similar to breast cancer and melanoma, ~ 25% of females with mesothelioma had a history of at least one other primary cancer. While IRs of mesothelioma in males without a history of other primary cancers were temporally aligned with historical asbestos consumption trends in the USA, IRs of mesothelioma among males with other primary cancer histories showed no relationship with asbestos consumption trends. Our findings suggest that a genetic predisposition for malignancy contributes to U.S. mesothelioma rates and is a distinct risk factor independent of asbestos exposure.

Data Sources for Clinical T1 Renal Masses and the Potential for Bias.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention.

Use of population-based cancer registries for cancer surveillance and control in Latin America.

A hybrid framework integrating serum biochemical markers and FTIR spectroscopy with machine learning for early cancer screening.

Sensitivity and specificity are foundational metrics for cancer detection tools. However, most machine learning algorithms prioritize overall accuracy during optimization, which fails to align with clinical priorities of early detection. We aim to develop a feature selection machine learning algorithm while maximizing sensitivity at a given specificity. We developed SMAGS-LASSO, a machine learning algorithm that combines our developed Sensitivity Maximization at a Given Specificity (SMAGS) framework with L1 regularization for feature selection. This approach simultaneously optimizes sensitivity at user-defined specificity thresholds while performing feature selection. SMAGS-LASSO utilizes a custom loss function with L1 regularization and multiple parallel optimization techniques. We used train-test splits and cross-validation, comparing against LASSO and random forest using sensitivity and AUC metrics. We evaluated our method on synthetic datasets and real-world protein colorectal cancer biomarker data. In synthetic datasets designed to contain strong signals for both sensitivity and specificity, SMAGS-LASSO significantly outperformed standard LASSO, achieving sensitivity of 1.00 (95% confidence interval, 0.98-1.00) compared with 0.19 (95% confidence interval, 0.13-0.23) for LASSO at 99.9% specificity. In colorectal cancer data, SMAGS-LASSO demonstrated 21.8% improvement over LASSO (P value = 2.24E-04) and 38.5% over random forest (P value = 4.62E-08) at 98.5% specificity while selecting the same number of biomarkers. SMAGS-LASSO enables the development of minimal biomarker panels that maintain high sensitivity at predefined specificity thresholds, offering superior performance for early cancer detection. This method provides a promising approach for early cancer detection and other medical diagnostics requiring sensitivity-specificity optimization.

Abstract OP 32: Health Status 1, B210 (FCSH), September 5, 2025, 09:00 - 10:00 Aim The WHO Action Plan for Refugee and Migrant Health highlights the need for strengthened migration health governance and data-driven policymaking. However, the absence of systematically collected and comparable health data among migrants remains a critical barrier. Approximately 12% of the European population (87 million people) has a migration background, and the risk of cancer among migrants can differ significantly from both their country of birth and their host country. Cancer RADAR aims to address this knowledge gap by providing a Europe-wide quantification of infection-related and screening-detectable cancer risks, stratified by migration background. We present the feasibility of such systematic data collection. Methods Cancer RADAR explores the feasibility and methodology of mapping infection-related (liver, stomach, and cervical) and screening-detectable (cervical, breast, colorectal, and lung) cancer risks among individuals with a migration background across Europe. In collaboration with pilot cancer registries, we co-created a protocol to systematically collect cancer data stratified by birth country, serving as a proxy for first-generation migration background. Results Cancer data stratified by birth country is available from 44 cancer registries and through data linkage from 8 cancer registries representing 20 European countries. Barriers to data collection include time constraints, limited infrastructure, financial resources, and the need for ethical approvals in the case of data linkage. Facilitators include the opportunity to contribute to decreasing inequalities in cancer outcomes and increase the visibility of a registry. Data from pilot cancer registries confirm increased risks for infection-related cancers and a similar or decreased risks for colon and breast cancer among individuals with a migration background, compared to the host population. Conclusion We present the feasibility of quantifying and monitoring cancer risks among migrants, with the goal to provide actionable evidence to inform data-driven policymaking aimed at reducing health disparities.

Bridging data gaps: Methodological advances in extracting and analyzing genetic information from unstructured clinical records in hereditary cancer.