Biology subjects

Rutishauser, D.

Publications and source records attributed to Rutishauser, D..

5 recordsLinked to original sources

bioRxiv2026

Decoding the Plasma Proteomic Landscape of Clear Cell Renal Cell Carcinoma Reveals Diagnostic and Prognostic Liquid Biopsy Biomarkers

Clear cell renal cell carcinoma (ccRCC) remains the most lethal urological malignancy, with high metastatic rates, both at initial diagnosis and during disease progression, contributing to poor survival outcomes. Current diagnostic and prognostic approaches rely primarily on histopathology, limiting early detection of localized disease and relevant intervention for metastatic patients. Here, we performed the most extensive to-date mass spectrometry-based discovery profiling of longitudinal plasma samples collected across multiple clinical follow-up points spanning up to five years post-diagnosis., to characterize the circulating plasma proteome and identify biomarkers for localized and metastatic disease. Network analysis identified protein modules enriched in pathways involved in matrix remodeling and metabolic deregulation, perpetuating the ccRCC phenotype. A five-protein signature, comprising PRL, THBS1, ANGPT1, IGFBP1, and SRGN, demonstrated high diagnostic performance for localized ccRCC. Notably, PRL appeared as a promising stand-alone biomarker (AUC = 0.812), with independent validation confirming its utility as a diagnostic biomarker. Importantly, a six-protein signature (AMBP, C1S, C2, IGFBP3, RASGRP2, TFRC) stringently distinguished metastatic from high-grade non-metastatic ccRCC cases. Further validation of these signatures could inform clinical decision-making, enabling early detection of metastasis and minimal residual disease and real-time longitudinal monitoring for ccRCC patients. Statement of SignificanceThis study presents the most comprehensive longitudinal plasma proteomic dataset for ccRCC to date, defining robust circulating protein biomarker signatures for both localized and metastatic disease, and establishing a proteomic landscape for minimally invasive, real-time monitoring and improved clinical management of ccRCC patients. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/742031v1_ufig1.gif" ALT="Figure 1000"> View larger version (35K): org.highwire.dtl.DTLVardef@196d9eorg.highwire.dtl.DTLVardef@16bbee5org.highwire.dtl.DTLVardef@c1c1a1org.highwire.dtl.DTLVardef@8f62a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2020

Proteogenomic heterogeneity of localized human prostate cancer progression

BackgroundTumor-specific genomic aberrations are routinely determined by high throughput genomic measurements. It remains unclear though, how complex genome alterations affect molecular networks through changing protein levels, and consequently biochemical states of tumor tissues. ResultsHere, we investigated the propagation of genomic effects along the axis of gene expression during prostate cancer progression. For that, we quantified genomic, transcriptomic and proteomic alterations based on 105 prostate samples, consisting of benign prostatic hyperplasia regions and malignant tumors, from 39 prostate cancer patients. Our analysis revealed convergent effects of distinct copy number alterations impacting on common downstream proteins, which are important for establishing the tumor phenotype. We devised a network-based approach that integrates perturbations across different molecular layers, which identified a sub-network consisting of nine genes whose joint activity positively correlated with increasingly aggressive tumor phenotypes and was predictive of recurrence-free survival. Further, our data revealed a wide spectrum of intra-patient network effects, ranging from similar to very distinct alterations on different molecular layers. ConclusionsThis study uncovered molecular networks with remarkably convergent alterations across tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single sub-network that was perturbed in all high-grade tumor regions.

systems biology↗

bioRxiv2019

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

biochemistry↗