Biology subjects

Bayer, F. P.

Publications and source records attributed to Bayer, F. P..

9 recordsLinked to original sources

bioRxiv2026

TOPAS: phosphoproteome data analysis and decision support platform for molecular tumor boards

The molecular tumor board (MTB) is central to precision oncology, providing personalized treatment recommendations based on molecular profiles of patient tumors. Genomics is instrumental for MTBs but often fails to identify clinically actionable targets, a gap that phosphoproteomics can fill. We present the tumor proteome activity status (TOPAS) platform, an end-to-end analysis pipeline that converts terabytes of phosphoproteomic data into patient-specific reports for MTB discussions, focusing on clinically relevant signaling linked to oncogenic mechanisms and therapeutic targets. Designed to scale with growing cohorts, the platform integrates data from 1,998 tumor samples to support patient- and cohort-level hypothesis generation. A web portal handles quality control, calculates TOPAS scores, identifies tumor antigens and immune checkpoints, and offers interactive analyses of differential protein abundance and outlier detection. The TOPAS platform is open source, addresses a critical unmet need and facilitates broader adoption of phosphoproteomics in precision oncology in the future.

bioinformatics↗

bioRxiv2026

Prospective pan-cancer phosphoproteomics at clinical scale extends therapeutic options in precision oncology

Genomics-guided precision oncology has improved survival in cancer entities with actionable mutations but cannot capture oncogenic signaling that manifests at the protein level. Here, we report a prospective, real-world pan-cancer study profiling proteomes and phosphoproteomes of 1,998 tumor samples from adults and children with rare or advanced cancers enrolled in the German precision oncology programs DKFZ/NCT/DKTK MASTER, CATCH and INFORM and their molecular tumor boards (MTBs). We developed tumor proteome activity status (TOPAS) scores for 46 clinically relevant kinases, an immune activity score capturing antigen presentation and T-cell activation and identified therapeutically targetable cell-surface proteins for 94% of patients. These readouts enhance MTB recommendations by exposing actionable non-genomic kinase activity, refining interpretation of oncogenic genome alterations, and highlighting cell-surface treatment options. Three proof-of-concept analyses indicate clinical utility including kinase activity-stratified pazopanib response in sarcoma, immune activity score-tracked checkpoint-inhibitor outcomes pan-cancer, and a phosphoproteomic biomarker distinguishing EGFR-inhibitor response in chordoma.

cancer biology↗

bioRxiv2026

High-Speed Mass Spectrometers diminish the difference between Data-Dependent and Data-Independent Acquisition Proteomics

Data-dependent acquisition mass spectrometry (DDA-MS) and data-independent acquisition mass spectrometry (DIA-MS) have historically offered complementary strengths in bottom-up proteomics, with DDA providing high-selectivity spectra for post-translational modification (PTM) analysis and DIA enabling more systematic peptide sampling. Here, we asked if this is still the case for the Orbitrap Astral platform that offers high-speed DDA and (ultra-) narrow-window DIA (nDIA) capabilities across proteome and phosphoproteome applications. When DDA and DIA measurements were parameter-matched (to the extent possible), the differences in analytical performance diminished markedly. Across extensive replicate analyses, both methods continued to identify new peptides and proteins without reaching saturation, indicating that the molecular complexity of biological samples still overwhelms even the fastest liquid chromatography-MS (LC-MS) methods. Incomplete sampling also contributed to substantial peptide-level non-overlap between DDA and nDIA and data completeness was only modestly better for nDIA than DDA across many replicates. Quantitatively, DDA and nDIA showed broadly similar precision and accuracy, with nDIA offering slightly higher precision and DDA slightly better accuracy in controlled mixture experiments. MS1-based quantification outperformed MS2-based quantification, particularly for short gradients, supporting MS1 quantification as a robust and general strategy for high-throughput proteomics. In phosphoproteomic samples, DDA and nDIA identified similar numbers of phosphopeptides, but DDA retained a small edge for phosphorylation site localisation. Together, the results show that advances in acquisition speed and sensitivity are narrowing the historical gap between DDA and DIA, while also revealing that current LC-MS workflows remain far from providing comprehensive proteome coverage. Going forward, further gains in dynamic range, scan speed, sensitivity, and transparent software tools will be required to reach systematic, comprehensive and reliable measurements of complex proteomes in a single shot.

biochemistry↗

bioRxiv2025

Chemical proteomics decrypts the kinases that shape the dynamic human phosphoproteome

Mass-spectrometry-based phosphoproteomics enables the analysis of thousands of protein phosphorylation events across the human proteome. However, there is a lack of scalable, hypothesis-free, and statistically sound approaches for discovering, evaluating, and falsifying kinase::substrate relationships (KSRs). Here, we developed a new concept termed potency-coherence analysis. By measuring and integrating 17 million peptidoform-specific dose-response curves for 133 kinase inhibitors with known targets and affinities, we could critically re-evaluate published KSRs and discover thousands of potency-coherent and motif-plausible new KSRs for 96 human kinases. Application of these high-confidence KSRs enabled the estimation of kinase and signaling pathway activities in cancer patient biopsies. This unified and extendable framework has been implemented in ProteomicsDB to aid researchers in understanding the human phosphoproteome in health and disease.

systems biology↗

bioRxiv2025

Learning the Unseen: Data-Augmented Deep Learning for PTM Discovery with Prosit-PTM

Post-translational modifications (PTMs) are critical regulators of protein function, yet confidently identifying and localizing PTM sites across proteomes remains a challenging task. Integrating peptide property predictions into spectrum interpretation improves identification performance, but training data enabling zero-shot prediction across diverse PTMs are scarce. Here, we present a major expansion of the ProteomeTools dataset, comprising over 977,000 synthetic peptides, covering 22 PTM-residue combinations. Furthermore we developed Prosit-PTM, a model with chemically-informed encoding and amino acid substitution-based augmentation trained with our novel ground-truth dataset, that achieves accurate zero-shot predictions. Applied to modified peptides, Prosit-PTM enhances PTM-site localization in phosphoproteomics, increases identification of multiply modified peptides in histones, and enables data-driven rescoring for unseen modifications such as HLA peptides. Furthermore, the learned embeddings of amino acids and modifications capture physicochemical relationships underlying PTM-driven HLA presentation. Prosit-PTM is integrated into multiple open-source tools enabling PTM-aware rescoring, site localization, spectral library generation, and beyond.

bioinformatics↗

bioRxiv2025

Illuminating oncogenic KRAS signaling by multi-dimensional chemical proteomics

Mutated KRAS is among the most frequent activating genetic alterations in cancer and drug discovery efforts have led to inhibitors that block its activity. To better understand oncogenic KRAS signaling and the cytostatic effects of drugs, we performed comprehensive dose-dependent proteome-wide target deconvolution, pathway engagement and protein expression characterization of KRAS, MEK, ERK, SHP2 and SOS1 inhibitors in pancreatic (KRAS G12C, G12D) and lung cancer (KRAS G12C) cells. Analysis of the resulting 687,954 dose-response curves available online revealed both common and cell line-specific signaling networks dominated by oncogenic KRAS activity. Time-dose experiments separated early KRAS-MEK-ERK from CDK-mediated signaling that cause cells to exit from the cell cycle. This transition to a quiescent state occurred without substantial proteome re-modelling but extensive changes of protein phosphorylation and ubiquitylation. The collective data highlights the complexity of KRAS signaling in cancer and places a large number of new proteins into this functional context.

cell biology↗

bioRxiv2024

Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response

The DNA-damaging agent gemcitabine (GEM) is a first-line treatment for pancreatic cancer but chemoresistance is frequently observed. Several clinical trials investigate the efficacy of GEM in combination with targeted drugs including kinase inhibitors but the experimental evidence for such rational is often unclear. Here, we phenotypically screened 13 human pancreatic adenocarcinoma (PDAC) cell lines against GEM in combination with 140 clinical kinase inhibitors and observed strong synergy for the ATR inhibitor Elimusertib in most cell lines. Dose-dependent phosphoproteome profiling of four ATR inhibitors following DNA damage induction by GEM revealed a strong block of the DNA damage response pathway including phosphorylated pS468 of CHEK1 as the underlying mechanism of drug synergy. The current work provides a strong rationale for why the combination of GEM and ATR inhibition may be useful for the treatment of PDAC patients and constitutes a rich phenotypic and molecular resource for further investigating effective drug combinations.

cancer biology↗

bioRxiv2023

PTMNavigator: Interactive Visualization of Differentially Regulated Post-Translational Modifications in Cellular Signaling Pathways

Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform, which offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams. PTMNavigator provides [~]3000 canonical pathways from manually curated databases and further enables users to modify and create custom diagrams, tailored to their data. Additionally, PTMNavigator automatically runs multiple kinase and pathway enrichment algorithms whose results are directly integrated into the visualization. This offers a comprehensive view of the intricate relationship between PTMs and signaling pathways. To demonstrate the utility of PTMNavigator, we applied it to two phosphoproteomics perturbation datasets. First, PTMNavigator enhanced pathway enrichment analysis by showing how the regulated peptides and proteins are distributed in the pathways with high enrichment scores. Second, it visualized how drug treatments result in a discernable flow of PTM-driven signaling within pathways. Third, PTMNavigator aided in proposing extensions to an existing pathway by suggesting putative new links between both PTMs and pathway components. By enhancing our understanding of cellular signaling dynamics and facilitating the discovery of novel PTM-pathway interactions, PTMNavigator advances our knowledge of PTM biology and its implications in health and disease.

systems biology↗

bioRxiv2023

CurveCurator: A recalibrated F-statistic to assess, classify, and explore significance of dose-response curves

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software and interactive dashboard that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a novel thresholding procedure. Application of CurveCurator to large-scale data sets demonstrates its scalable utility across several application areas.

bioinformatics↗