Biology subjects

Rieder, D.

Publications and source records attributed to Rieder, D..

10 recordsLinked to original sources

bioRxiv2025

IBDome: An integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases

Multi-omic and multimodal datasets with detailed clinical annotations offer significant potential to advance our understanding of inflammatory bowel diseases (IBD), refine diagnostics, and enable personalized therapeutic strategies. In this multi-cohort study, we performed an extensive multi-omic and multimodal analysis of 1,002 clinically annotated patients with IBD and non-IBD controls, incorporating whole-exome and RNA sequencing of normal and inflamed gut tissues, serum proteomics, and histopathological assessments from images of H&E-stained tissue sections. Transcriptomic profiles of normal and inflamed tissues revealed distinct site-specific inflammatory signatures in Crohns disease (CD) and ulcerative colitis (UC). Leveraging serum proteomics, we developed an inflammatory protein severity signature that reflects underlying intestinal molecular inflammation. Furthermore, foundation model-based deep learning accurately predicted histologic disease activity scores from images of H&E-stained intestinal tissue sections, offering a robust tool for clinical evaluation. Our integrative analysis highlights the potential of combining multi-omics and advanced computational approaches to improve our understanding and management of IBD.

molecular biology↗

bioRxiv2024

NovumRNA: accurate prediction of non-canonical tumor antigens from RNA sequencing data

Non-canonical tumor-specific antigens (ncTSAs) can expand the pool of targets for cancer immunotherapy, but require robust and comprehensive computational pipelines for their prediction. Here, we present NovumRNA, a fully-automated Nextflow pipeline for predicting different classes of ncTSAs from patients RNA sequencing data. We extensively validated NovumRNA using publicly-available and newly-generated datasets, demonstrating the robustness of its analytical modules and predictions. NovumRNA analysis of colorectal cancer organoid data revealed comparable ncTSA potential for microsatellite stable and unstable tumors and candidate therapeutic targets for patients with low tumor mutational burden. Finally, our investigation of glioblastoma cell lines demonstrated increased ncTSAs burden upon indisulam treatment, and detection by NovumRNA of therapy-induced ncTSAs, which we could validate experimentally. These findings underscore the potential of NovumRNA for identifying synergistic drugs and novel therapeutic targets for immunotherapy, which could ultimately extend its benefit to a broader patient population.

bioinformatics↗

bioRxiv2024

Dissecting heterogeneity of tumor microenvironment in colorectal cancer using high-resolution single-cell atlas

The immune composition of the tumor microenvironment (TME) has a major impact on the therapeutic response and clinical outcome in patients with colorectal cancer (CRC). Here, we comprehensively characterize the TME at the single-cell level by first building a large-scale atlas that integrates 4.27 million single cells from 1,670 patient samples. We then complemented the atlas with single-cell profiles from four CRC cohorts with 266 patients, including cells with low mRNA content, spatial transcriptional profiles from 3.7 million cells, and protein profiles from 0.7 million cells. The analysis of the atlas allows refined tumor classification into four immune phenotypes: immune desert, B cell enriched, T cell enriched, and myeloid cell enriched subtypes. Within the myeloid compartment we uncover distinct subpopulations of neutrophils that acquire new functional properties in blood and in the TME, including anti-tumorigenic capabilities. Further, spatial multimodal single-cell profiling reveals that neutrophils are organized in clusters within distinct functional niches. Finally, using an orthotopic mouse model we show that cancer-derived systemic signals modify neutrophil production in the bone marrow, providing evidence for tumor-induced granulopoiesis. Our study provides a big data resource for the CRC and suggests novel therapeutic strategies targeting neutrophils.

bioinformatics↗

bioRxiv2024

Conventional therapy induces tumor immunoediting and modulates the immune contexture in colorectal cancer

BackgroundCancer immunotherapies for patients with colorectal cancer (CRC) continue to lag behind other solid cancer types with the exception of 4% of patients with microsatellite-instable tumors. Thus, there is an urgent need to broaden the clinical benefit of checkpoint blockers to CRC by combining conventional therapies to sensitise tumors to immunotherapy. However, the impact of conventional drugs on immunoediting, potentially promoting the positive selection of less immunogenic variants, and on the tumor immune contexture in CRC, remain elusive. MethodsWe performed comprehensive multimodal profiling using longitudinal samples from metastatic CRC patients undergoing neoadjuvant therapy with mFOLFOX6 and Bevacizumab. Exome-sequencing, RNA-sequencing and multiplexed immunofluorescence imaging was carried out on tumor samples obtained before and after therapy and the data was analysed using established methods. The results of the analysis were extrapolated to publicly available datasets (TCGA and CPTAC). In order to identify a surrogate marker, an explainable artificial intelligence method was developed using a transformer-based analytical pipeline for the identification of features in Hematoxylin and Eosin (H&E) images associated with specific biological processes, followed by manual evaluation of highly informative tiles by a pathologist. ResultsMutational profiles were highly modified and the level of genetic intertumoral heterogeneity between patients varied following treatment. Evolutionary analysis indicated eradication of some clones and dominant clonal prevalence of others, supporting the notion of pharmacologically-induced cancer immunoeditin. Post treatment samples showed upregulation of HLA class II genes, activation of differentiation and stemness pathways, and changes in the consensus molecular subtypes. The tumor immune contexture was characterised by increased densities of CD8+ and CD4+ T cells, but reduced T cell-tumor cell interactions (and increased T cell exhaustion. The AI-guided analyses of the H&E images pinpointed extracellular mucin deposits associated with stemness genes, suggesting a surrogate marker for routine pathological evaluation. ConclusionsConventional therapy induces immunoediting and modulates the immune contexture in metastatic CRC patients.

cancer biology↗

bioRxiv2023

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomic data. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through the analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability that explains the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME, and ultimately to determine its unfolding and fate during immunotherapy with immune checkpoint blockers.

bioinformatics↗

bioRxiv2022

Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer

BackgroundDespite major advances in the development of targeted therapies, precision (immuno)oncology approaches for patients with colorectal cancer continue to lag behind other solid cancers. Functional precision oncology - a strategy that is based on perturbing primary tumor cells from cancer patients with drugs - could provide an alternate road forward to personalize treatment. MethodsWe extend here the functional precision oncology paradigm to measuring phosphoproteome landscapes using patient-derived organoids (PDOs). We first employed steady-state multi-omics (exome sequencing, RNA sequencing, and proteomics) and single-cell characterization of the PDOs. The PDOs were then perturbed with kinase inhibitors (MEKi, PI3Ki, mTORi, TBKi, BRAFi, and TAKi), and large-scale phosphoproteomics profiling using data-independent acquisition was carried out. Further, we used imaging mass-cytometry-based single-cell proteomic profiling of the primary tumors to characterize cellular composition of the tumor-microenvironment (TME) and to quantify heterocellular signaling crosstalk. ResultsWe show that kinase inhibitors induce profound off-target effects resulting in a crosstalk with oncogenic and immune-related pathways. Reconstruction of the topologies of the kinase networks revealed that the patient-specific rewiring of the central EGFR-RAS-MAPK network is unaffected by mutations. Moreover, we show non-genetic heterogeneity of the PDOs and patient- and inhibitor-specific upregulation of stemness and differentiation genes by kinase inhibitors. We complemented our functional profiling by spatial proteomics profiling of the primary tumors using imaging mass cytometry. We quantify spatial heterocellular crosstalk and tumor-immune cell interactions, showing an avoidance of PD1+ immune cells and PD-L1+ tumor cells. ConclusionsCollectively, we provide a multi-modal framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno)-oncology in colorectal cancer.

cancer biology↗

bioRxiv2022

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to both, therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient and tumor subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,212,463 single-cells from 538 samples and 309 patients across 29 datasets, including our own dataset capturing cells with low mRNA content. Based on the cellular composition we stratified patients into immune deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identified specific cellular components associated with tumor histology and genotypes. Analysis of cells with low mRNA content uncovered distinct subpopulations of tissue-resident neutrophils (TRNs) that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. TRN-derived gene signature was associated with anti-PD-L1 treatment failure in a large NSCLC cohort. In briefSalcher, Sturm, Horvath et al. integrate single-cell datasets to generate the largest transcriptome atlas in NSCLC, refining patient stratification based on tumor immune phenotypes, and revealing associations of histological subtypes and genotypes with specific cellular composition patterns. Coverage of cells with low mRNA content by single-cell sequencing identifies distinct tissue-resident neutrophil subpopulations, which acquire new properties within the tumor microenvironment. Gene signature from tissue-resident neutrophils is associated with immune checkpoint inhibitor treatment failure. The integrated atlas is publicly available online (https://luca.icbi.at), allowing the dissection of tumor-immune cell interactions in NSCLC. HighlightsO_LIHigh-resolution single-cell atlas of the tumor microenvironment (TME) in NSCLC. C_LIO_LIHistological tumor subtypes and driver genes imprint specific cellular TME patterns. C_LIO_LIscRNA-seq of cells with low transcript count identifies distinct tissue-resident neutrophil (TRN) subpopulations and non-canonical functional properties in the TME niche. C_LIO_LITRN gene signature identifies patients who are refractory to treatment with PD-L1 inhibitors. C_LI O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/491204v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1050b6aorg.highwire.dtl.DTLVardef@3088c8org.highwire.dtl.DTLVardef@6408b8org.highwire.dtl.DTLVardef@1788c22_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data

SummaryAdvances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses in cancer, but also in infectious diseases like COVID-19. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Here we propose Scirpy, a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementationScirpy source code and documentation are available at https://github.com/icbi-lab/scirpy.

bioinformatics↗

bioRxiv2017

quanTIseq: quantifying immune contexture of human tumors

The immune contexture has a prognostic value in several cancers and the study of its pharmacological modulation could identify drugs acting synergistically with immune checkpoint blockers. However, the quantification of the immune contexture is hampered by the lack of simple and efficient methods. We developed quanTIseq, a deconvolution method that quantifies the densities of ten immune cell types from bulk RNA sequencing data and tissue imaging data. We performed extensive validation using simulated data, flow cytometry data, and immunohistochemistry data from three cancer cohorts.\n\nAnalysis of 8,000 samples showed that the activation of the CXCR3/CXCL9 axis, rather than the mutational load is associated with cytotoxic T cell infiltration. We also show the prognostic value of deconvolution-based immunoscore and T cell/B cell score in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture, and we suggest that it might have predictive value for immunotherapy.

bioinformatics↗

bioRxiv2017

Targeting the PD-1/PD-L1 pathway potentiates immunoediting to counterbalance neutral evolution in a mouse model of colorectal cancer

The cancer immunoediting hypothesis postulates a dual role of the immune system: protecting the host by eliminating tumor cells, and shaping the tumor by editing the genome. However, to what extent immunoediting is shaping the cancer genome is still a matter of debate. Moreover, the impact of cancer immunotherapy with checkpoint blockers on modulating immunoediting remains largely unexplored. Here we elucidated the impact of evolutionary and immune-related forces on editing the tumor in a mouse model of colorectal cancer (CRC). We first show that MC38 cell line is a valid model for hypermutated and microsatellite-unstable (MSI) CRC. Analyses of longitudinal samples of wild type and immunodeficient RAG1 knockout mice transplanted with MC38 cells revealed that upregulation of checkpoint molecules and infiltration of Tregs are the major tumor escape mechanisms. Strikingly, the impact of neutral evolution on sculpting the tumor outweighed immunoediting. Targeting the PD-1/PD-L1 pathway potentiated immunoediting and rendered tumors more homogeneous in the MC38 model. The immunoediting effects were less pronounced in a nonhypermutated/MSI- model CT26. Our study demonstrates that neutral evolution is the major force that sculpts the tumor, and that checkpoint blockade effectively enforces T cell dependent immunoselective pressure in a hypermutated/MSI model of CRC.

genomics↗