Biology subjects

Benary, M.

Publications and source records attributed to Benary, M..

3 recordsLinked to original sources

bioRxiv2026

Copy number signatures in targeted gene panels associate with patient outcomes in routine clinical data

Targeted gene panels (TGPs) dominate clinical sequencing, yet most copy-number (CN) signature studies rely on genome-wide assays. Whether signatures can be recovered from TGPs and retain clinical relevance remains unclear. We analyzed two real-world TGP cohorts comprising 1,726 patients across 62 tumor types, including 825 with clinical annotations, and made the underlying data publicly available. TGP-derived signatures recapitulated established biological associations, including links to homologous recombination deficiency and TP53 alterations, concordant with published genome-wide assay-derived CN signatures. Using our detailed clinical data, we found TGP-derived CN signatures associated with overall survival under standard therapies in ovarian, pancreatic, and colorectal cancer. In ovarian cancer, CN2 was associated with CCNE1 amplification and shorter survival under paclitaxel/carboplatin, with the survival association validated in an independent SNP-array cohort. These findings demonstrate that routine TGPs yield biologically and clinically relevant CN signatures with potential as biomarkers for therapy stratification.

genetics↗

bioRxiv2026

Translating multi-omics complexity into sparse prognostic biomarkers for multiple myeloma

Multiple myeloma (MM) exhibits profound molecular heterogeneity, yet current risk stratification relies on cytogenetics or single-omics signatures that often fail to capture cross-layer regulatory complexity. We re-analyzed a multi-omics dataset integrating copy-number, transcriptomic, proteomic, and phosphoproteomic data to dissect how common genomic driver alterations propagate through the molecular cascade. Supervised classification demonstrated that downstream layers, particularly the proteome and phosphoproteome, classify genomic events more accurately than primary genomic or transcriptomic data. Intriguingly, trans-acting features alone were sufficient for classification, indicating that while direct dosage effects manifest at the RNA level, downstream network responses dominate the proteomic state. Multi-omics factor analysis (MOFA2) identified a continuous latent axis predicting progression-free and overall survival independent of R-ISS. This factor captured a gain(1q)/del(13q) axis modulated by immune infiltration and NSD2 expression, integrating variance across all four modalities. To enable clinical translation, we derived sparse, single-modality proxies using elastic net regression. An RNA proxy faithfully recapitulated the multi-omic factor and validated independently in published microarray and RNAseq cohorts, demonstrating robust prognostic utility across treatment eras. These findings reveal that multi-omics integration uncovers hidden prognostic axes obscured by single-omics analyses, and that sparse proxies can bridge the gap between complex discovery and clinical implementation.

cancer biology↗

bioRxiv2023

preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology

MotivationIn precision oncology, clinicians are aiming to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the annotation and evaluation of individual variants, for which usually a range of knowledge bases are manually screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonization are necessary. Summarypreon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. Availability and Implementationpreon is implemented in Python and freely available via the PyPI repository. Source code and gold standard data sets are available at https://github.com/ermshaua/preon/. Contactmanuela.benary@bih-charite.de Supplementary informationSupplementary data are available online.

bioinformatics↗