Biology subjects

The, M.

Publications and source records attributed to The, M..

12 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↗

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

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

Towards routine proteome profiling of FFPE tissue: Insights from a 1,200 case pan-cancer study

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. Here, we did so by analyzing 1,220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4,000 proteins and discovering that current software fails to process such large data sets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community by way of a web resource. It enables a range of analysis including quantitative comparisons of proteins between patients or cohorts or the discovery of protein fingerprints representing the tissue of origin, or proteins enriched in certain cancer entities.

pathology↗

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↗

bioRxiv2023

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

Substantial efforts are underway that aim to deepen our understanding of human brain morphology, structure and function using high-resolution imaging as well has high-content molecular profiling technologies. The current work adds to these efforts by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 10,000 proteins. This was enabled by the optimization, characterization and implementation of a high-sensitivity and high-throughput micro-flow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing >2,000 consecutive samples prepared from formalin fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted e.g. brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual protein markers for specific brain regions. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built Shiny App (https://brain-region-atlas.proteomics.ls.tum.de).

neuroscience↗

bioRxiv2020

Integrating identification and quantification uncertainty for differential protein abundance analysis with Triqler

Protein quantification for shotgun proteomics is a complicated process where errors can be introduced in each of the steps. Triqler is a Python package that estimates and integrates errors of the different parts of the label-free protein quantification pipeline into a single Bayesian model. Specifically, it weighs the quantitative values by the confidence we have in the correctness of the corresponding PSM. Furthermore, it treats missing values in a way that reflects their uncertainty relative to observed values. Finally, it combines these error estimates in a single differential abundance FDR that not only reflects the errors and uncertainties in quantification but also in identification. In this tutorial, we show how to (1) generate input data for Triqler from quantification packages such as MaxQuant and Quandenser, (2) run Triqler and what the different options are, (3) interpret the results, (4) investigate the posterior distributions of a protein of interest in detail and (5) verify that the hyperparameter estimations are sensible.

bioinformatics↗

bioRxiv2018

Focus on the spectra that matter by clustering of quantification data in shotgun proteomics

In shotgun proteomics, the information extractable from label-free quantification experiments is typically limited by the identification rate and the noise level in the quantitative data. This generally causes a low sensitivity in differential expression analysis on protein level. Here, we propose a quantification-first approach for peptides that reverses the classical identification-first workflow. This prevents valuable information from being discarded prematurely in the identification stage and allows us to spend more effort on the identification process. Specifically, we introduce a method, Quandenser, that applies unsupervised clustering on both MS1 and MS2 level to summarize all analytes of interest without assigning identities. Not only does this eliminate the need for redoing the quantification for each new set of search parameters and engines, but it also reduces search time due to the data reduction by MS2 clustering. For a dataset of partially known composition, we could now employ open modification and de novo searches to identify analytes of interest that would have gone unnoticed in traditional pipelines. Moreover, Quandenser reports error rates for feature matching, which we integrated into our probabilistic protein quantification method, Triqler. This propagates error probabilities from feature to protein level and appropriately deals with the noise in quantitative signals caused by false positives and missing values. Quandenser+Triqler outperformed the state-of-the-art method MaxQuant+Perseus, consistently reporting more differentially abundant proteins at 5% FDR: 123 vs. 117 true positives with 2 vs. 25 false positives in a dataset of partially known composition; 62 vs. 3 proteins in a bladder cancer set; 8 vs. 0 proteins in a hepatic fibrosis set; and 872 vs. 661 proteins in a nanoscale type 1 diabetes set. Compellingly, in all three clinical datasets investigated, the differentially abundant proteins showed enrichment for functional annotation terms. The source code and binary packages for all major operating systems are available from https://github.com/statisticalbiotechnology/quandenser, under Apache 2.0 license.

systems biology↗

bioRxiv2018

Integrated identification and quantification error probabilities for shotgun proteomics

Protein quantification by label-free shotgun proteomics experiments is plagued by a multitude of error sources. Typical pipelines for identifying differentially expressed proteins use intermediate filters in an attempt to control the error rate. However, they often ignore certain error sources and, moreover, regard filtered lists as completely correct in subsequent steps. These two indiscretions can easily lead to a loss of control of the false discovery rate (FDR). We propose a probabilistic graphical model, Triqler, that propagates error information through all steps, employing distributions in favor of point estimates, most notably for missing value imputation. The model outputs posterior probabilities for fold changes between treatment groups, highlighting uncertainty rather than hiding it. We analyzed 3 engineered datasets and achieved FDR control and high sensitivity, even for truly absent proteins. In a bladder cancer clinical dataset we discovered 35 proteins at 5% FDR, whereas the original study discovered 1 and MaxQuant/Perseus 4 proteins at this threshold. Compellingly, these 35 proteins showed enrichment for functional annotation terms, whereas the top ranked proteins reported by MaxQuant/Perseus showed no enrichment. The model executes in minutes and is freely available at https://pypi.org/project/triqler/.

bioinformatics↗

bioRxiv2017

A protein standard that emulates homology for the characterization of protein inference algorithms

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known content, and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, one of the inherent problems of interpreting data is that the measured analytes are peptides and not the actual proteins themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. For a realistic benchmark of protein inference procedures, there is, therefore, a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the usage of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

bioinformatics↗