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M. Marczyk

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Open access Aug 2026

Time-dependent prognostic impact of tumor grade in stage I-III breast cancers

Background Breast cancer (BC) mortality risk extends over decades; yet, the temporal prognostic patterns of histological grade across subtypes and stages remain unclear. Patients and methods Women with stage I-III BC diagnosed between 2000 and 2018 were identified from Surveillance, Epidemiology, and End Results registries (N = 767 218). Breast cancer-specific mortality (BCSM), as the primary endpoint, was analyzed using cumulative incidence functions including competing risk and annual hazard rates stratified by grade, estrogen receptor (ER) status, stage, and adjuvant chemotherapy. Restricted mean survival time differences were calculated to quantify absolute survival differences across follow-up intervals. Landmark survival analyses were carried out for 0 to <60, 60 to <120, and 120 to <180 months from diagnosis. Time-varying effects of grade were assessed through interval-specific modeling to evaluate nonproportional hazards. Results Overall, 23%, 44%, and 33% of cancers were grades 1, 2, and 3, respectively, accounting for 8%, 35%, and 57% of BC deaths. In each consecutive follow-up period, the contribution of grade 1 cancers to BCSM increased, and the contribution of grade 3 cancers decreased. Overall, 59% and 30% of deaths from grades 1 and 3 cancers, respectively, occurred after 5 years. In ER-positive disease, grade 3 tumors showed peak annual hazards between years 3 and 5, whereas grade 1 tumors had lower but sustained hazards extending beyond 10 years; the hazard curves converged between years 10 and 12. In ER-negative disease, early hazards were higher, and convergence also started earlier at years 5-8. Absolute risk was influenced by nodal status and tumor stage within each grade category and follow-up interval. Conclusion Grade 1 cancers show sustained risk beyond 10 years, whereas grade 3 cancers exhibit front-loaded risk. These temporal risk patterns, together with other clinical and genomic data, could inform extended endocrine therapy decisions and surveillance strategies.

M. Mariani, M. Ochocki, G. Bianchini et al. · 0 citations
Open access Aug 2026

A Practical Workflow for Correcting Kit-Specific Effects in Whole-Exome Sequencing Data

Large-scale, multi-center projects have become common in the era of rapid technological development, but protocol standardization remains challenging. In whole-exome sequencing (WES), various exome enrichment kits exhibit variable efficiency across genomic regions, leading to systematic, non-biological batch effects, much stronger than other technical factors. We propose a workflow to minimize the effect of WES capture inconsistencies in single-nucleotide variation (SNV) data. The pipeline consists of quality control, mapping to the genome, SNV calling, joint genotyping, and imputing genotypes using reference haplotypes. SNVs are then aggregated into gene-level features measuring the burden of deleterious variants. Finally, a gene-level imputation is performed using a customized algorithm. Namely, if the detection rate of a gene is low in samples enriched with a given capture kit but high in samples enriched with other kits, missing values in the former group are imputed, as such differences are unlikely to reflect true biology. As a benchmark, we conducted a study on over a thousand breast cancer cases across 11 cohorts, using eight exome capture kits. We demonstrated that the proposed pipeline leads to a considerable decrease in the batch effect signal, potentially increasing the likelihood of finding true biological signals.

Laura Jarosz, M. Ochocki, Julia Merta et al. · 0 citations

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