Biology subjects

Moch, H.

Publications and source records attributed to Moch, H..

18 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

RIPPLET: Mutation-Only Gene and Pathway Profiling for Precision Oncology

Clinical implementation of comprehensive genomic profiling, via whole-genome (WGS) or whole-exome sequencing (WES), is constrained by sparse mutation burdens and analytic pipelines reliant on matched transcriptomes. Currently, gene-centric analysis prevails, but overlooks the complex, multigene and pathway perturbations shaping tumor biology. We introduce RIPPLET, a DNA-only framework converting somatic variants into quantitative gene-impact scores and topology-aware pathway-perturbation profiles. By integrating tissue-specific protein-protein interaction networks with cohort-informed reweighting, RIPPLET prioritizes likely functionally relevant alterations. Applied across 33 TCGA cancer types, RIPPLET surpasses four state-of-the-art multi-omic driver-prioritization tools in recovering cancer type-specific drivers. In a cohort of metastatic cutaneous melanomas, it identifies pathway signatures that predict drug response, provide prognostic insight and distinguish immune-infiltration phenotypes without RNA data, independently validated on an in-house cohort. RIPPLET enables DNA-only inference of tumor-specific gene and pathway dysregulation, aligning with clinical sequencing workflows and offering a scalable precision-oncology strategy in transcriptome-limited settings.

bioinformatics↗

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↗

bioRxiv2024

Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids

Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternative splice isoforms. This complex phenomenon can be revealed by an in-depth understanding of cellular heterogeneity at the single-cell level. Recent advances in single-cell long- read sequencing technologies enable comprehensive transcriptome sequencing at the single-cell level. In this study, we have generated single-cell long-read sequencing of Patient-Derived Organoid (PDO) cells of clear-cell Renal Cell Carcinoma (ccRCC), an aggressive and lethal form of cancer that arises in kidney tubules. We have used the Multiplexed Arrays Sequencing (MAS-ISO-Seq) protocol of PacBio to sequence full-length transcripts exceptionally deep across 2,599 single cells to obtain the most comprehensive view of the alternative landscape of ccRCC to date. On average, we uncovered 86,182 transcripts across PDOs, of which 31,531 (36.6%) were previously uncharacterized. In contrast to known transcripts, many of these novel isoforms appear to exhibit cell-specific expression. Nonetheless, >50% of these novel transcripts were predicted to possess a complete protein-coding open reading frame. This finding suggests a biological role for these transcripts within kidney cells. Moreover, an analysis of the most dominant transcript switching events between ccRCC and non-ccRCC cells revealed that many switching events were cell and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our research elucidates the intricate transcriptomic architecture of ccRCC, potentially exposing the mechanisms underlying its aggressive phenotype and resistance to conventional cancer therapies.

cancer biology↗

bioRxiv2024

Comparison of Single-cell Long-read and Short-read Transcriptome Sequencing of Patient-derived Organoid Cells of ccRCC: Quality Evaluation of the MAS-ISO-seq Approach

Single-cell RNA sequencing is used in profiling gene expression differences between cells. Short-read sequencing platforms provide high throughput and high-quality information at the gene-level, but the technique is hindered by limited read length, failing in providing an understanding of the cell heterogeneity at the isoform level. This gap has recently been addressed by the long-read sequencing platforms that provide the opportunity to preserve full-length transcript information during sequencing. To objectively evaluate the information obtained from both methods, we sequenced four samples of patient-derived organoid cells of clear cell renal cell carcinoma and one healthy sample of kidney organoid cells on Illumina Novaseq 6000 and PacBio Sequel IIe. For both methods, for each sample, the cDNA was derived from the same 10x Genomics 3 single-cell gene expression cDNA library. Here we present the technical characteristics of both datasets and compare cell metrics and gene-level information. We show that the two methods largely overlap in the results but we also identify sources of variability which present a set of advantages and disadvantages to both methods.

genomics↗

bioRxiv2023

Compressed sensing expands the multiplexity of imaging mass cytometry

The multiplexity of current antibody-based imaging is limited by the number of reporters that can be detected simultaneously. Compressed sensing can be used to recover high-dimensional information from low-dimensional measurements when the data has a structure that allows sparse representation. Previously, in composite in situ imaging (CISI) of transcriptomic data, compressed sensing leveraged the gene co-regulation structure that allows sparse representation and recovered spatial expression of 37 RNA species with the measurement of 11 fluorescent channels. Here, we extended the compressed sensing framework to protein expression data measured by imaging mass cytometry (IMC). CISI-IMC accurately recovered spatial expression of 16 proteins from the images of 8 composite channels, which in effect expanded the current multiplexity limit of IMC by 8 channels. With this ratio, up to 80 protein markers could be compressed into currently available 40 isotope channels. Training the CISI-IMC framework using data collected on tissues from various locations in the human body enabled the decompression of composite data from a wide range of tissue types. Our work laid the foundation for much higher plex protein imaging by using CISI.

bioinformatics↗

bioRxiv2023

Multi-resolution deep learning characterizestertiary lymphoid structures in solid tumors

Tertiary lymphoid structures (TLSs) are dense accumulations of lym-phocytes in inflamed peripheral tissues, including cancer, and are associated with improved survival and response to immunotherapy in various solid tumors. Histological TLS quantification has been pro-posed as a novel predictive and prognostic biomarker, but lack of standardized methods of TLS characterization hampers assessment of TLS densities across different patients, diseases, and clinical centers. We introduce a novel approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hema-toxylin and eosin stained digital pathology slides. We developed a HookNet-TLS model using n=1019 manually annotated TCGA slides from clear cell renal cell carcinoma, muscle-invasive blad-der cancer, and lung squamous cell carcinoma. We show that HookNet-TLS automates TLS quantification with a human-level performance and demonstrates prognostic associations similar to visual assessment. We made HookNet-TLS publicly available to aid the adoption of objective TLS assessment in routine pathology.

pathology↗

bioRxiv2022

Comparison of calling pipelines for whole genome sequencing: an empirical study demonstrating the importance of mapping and alignment

Rapid advances in high-throughput DNA sequencing technologies have enabled the conduct of whole genome sequencing (WGS) studies, and several bioinformatics pipelines have become available. The aim of this study was the comparison of 6 WGS data pre-processing pipelines, involving two mapping and alignment approaches (GATK utilizing BWA-MEM2 2.2.1, and DRAGEN 3.8.4) and three variant calling pipelines (GATK 4.2.4.1, DRAGEN 3.8.4 and DeepVariant 1.1.0). We sequenced one genome in a bottle (GIAB) sample 70 times in different runs, and one GIAB trio in triplicate. The truth set of the GIABs was used for comparison, and performance was assessed by computation time, F1 score, precision, and recall. In the mapping and alignment step, the DRAGEN pipeline was faster than the GATK with BWA-MEM2 pipeline. DRAGEN showed systematically higher F1 score, precision, and recall values than GATK for single nucleotide variations (SNVs) and Indels in simple-to-map, complex-to-map, coding and non-coding regions. In the variant calling step, DRAGEN was fastest. In terms of accuracy, DRAGEN and DeepVariant performed similarly and both superior to GATK, with slight advantages for DRAGEN for Indels and for DeepVariant for SNVs. The DRAGEN pipeline showed the lowest Mendelian inheritance error fraction for the GIAB trios. Mapping and alignment played a key role in variant calling of WGS, with the DRAGEN outperforming GATK.

bioinformatics↗

bioRxiv2021

CanIsoNet: A Database to Study the Functional Impact of Isoform Switching Events in Cancer

MotivationAlternative splicing, as an essential regulatory mechanism in normal mammalian cells, is frequently disturbed in cancer and other diseases. Switches in the expression of most dominant alternative isoforms can alter protein interaction networks of associated genes giving rise to a disease or disease progression. Here, we present CanIsoNet (disease-specific isoform interaction network), a database to view, browse and search these isoform switching events. CanIsoNet is the first webserver that incorporates isoform expression data with STRING interaction networks and ClinVar annotations to predict the pathogenic impact of isoform switching events in various diseases. ResultsData in CanIsoNet can be browsed by disease or searched by genes or isoforms in annotation-rich data tables. Various annotations for 11,072 isoforms and 13,531 unique isoform switching events across 28 different tissue types are provided. The network density score for each disease-specific isoform, PFAM domain IDs of disrupted interactions, domain structure visualization of transcripts and expression data of switched isoforms for each sample are given. Additionally, the genes annotated in ClinVar are highlighted in the interactive interaction network. AvailabilityCanIsoNet is freely available at https://caniso.net. The source codes can be found under a Creative Common License at https://github.com/kahramanlab/CanIsoNet_Web. Supplementary InformationSupplementary data are available at Bioinformatics online.

cancer biology↗

bioRxiv2021

A statistical test for clonal exclusivity in tumour evolution

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Pairs of mutations that are overrepresented in a clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical test, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our test for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 6 clonally exclusive and 2 clonally co-occurring gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

cancer biology↗

bioRxiv2021

Blunted Fas signaling favors RIPK1-driven neutrophil necroptosis in critically ill COVID-19 patients

Critically ill COVID-19 patients are characterized by a severely dysregulated cytokine profile and elevated neutrophil counts, which are thought to contribute to disease severity. However, to date it remains unclear how neutrophils contribute to pathophysiology during COVID-19. Here, we assessed the impact of the dysregulated cytokine profile on the tightly regulated cell death program of neutrophils. We show that in a subpopulation of neutrophils, canonical apoptosis was skewed towards rapidly occurring necroptosis. This phenotype was characterized by abrogated caspase-8 activity and increased RIPK1 levels, favoring execution of necroptosis via the RIPK1-RIPK3-MLKL axis, as further confirmed in COVID-19 biopsies. Moreover, reduction of sFas-L levels in COVID-19 patients and hence decreased signaling to Fas directly increased RIPK1 levels and correlated with disease severity. Our results suggest an important role for Fas signaling in the regulation of cell death program ambiguity via the ripoptosome in neutrophils during COVID-19 and a potential therapeutic target to curb inflammation and thus influence disease severity and outcome.

immunology↗

bioRxiv2020

SF3B1 promotes tumor malignancy through splicing-independent co-activation of HIF1α

Mutations in the splicing factor SF3B1 are frequently occurring in various cancers and drive tumor progression through the activation of cryptic splice sites in multiple genes. Recent studies also demonstrate a positive correlation between expression levels of wildtype SF3B1 and tumor malignancy, but underlying mechanisms remain elusive. Here, we report that SF3B1 acts as an activator of HIF signaling through a splicing-independent mechanism. We demonstrate that SF3B1 forms a heterotrimer with HIF1 and HIF1{beta}, facilitating binding of the HIF1 complex to hypoxia response elements (HREs) to activate target gene expression. We further validate the relevance of this mechanism for tumor progression. Monoallelic deletion of Sf3b1 impedes formation and progression of hypoxic pancreatic cancer via impaired HIF signaling, but is well tolerated in normoxic chromophobe renal cell carcinoma. Our work uncovers an essential role of SF3B1 in HIF1 signaling, providing a causal link between high SF3B1 expression and aggressiveness of solid tumors.

cell biology↗

bioRxiv2020

SAMHD1 mutations in mantle cell lymphoma: identification as disease driver conferring in vitro resistance to nucleoside analogues

The genomic landscape of mantle cell lymphoma (MCL) includes frequent alterations of TP53, ATM, CCND1 and KMT2D. Thus far, the mutational landscape provides little information for treatment stratification. We characterized a cohort of MCL by targeted next generation sequencing and discovered SAMHD1 as a novel recurrently mutated gene (8.5% of investigated cases, 4/47 samples). Furthermore, we provide evidence of in vitro resistance of SAMHD1 mutated patient-derived MCL cells to cytarabine and fludarabine.

cancer biology↗

bioRxiv2020

CD39+PD-1+CD8+ T cells mediate metastatic dormancy in breast cancer

Some breast tumors metastasize aggressively whereas others remain in a state of metastatic dormancy for months or even years. The mechanism governing such metastatic dormancy remains largely unknown. Through high-parametric single-cell mapping in mice, we identified a discrete population of CD39+PD-1+CD8+ T cells present both in primary tumors and in dormant metastasis, which was hardly found in aggressively metastasizing tumors. Using blocking antibodies, we found that dormancy depended on TNF and IFN{gamma}. Of note, immunotherapy reduced the number of dormant cancer cells in the lungs. Adoptive transfer of purified CD39+PD-1+CD8+ T cells prevented metastatic outgrowth. In human breast cancer, the frequency of CD39+PD-1+CD8+ but not of total CD8+ T cells correlated with delayed metastatic relapse after resection (disease-free survival), thus underlining the biological relevance of CD39+PD-1+CD8+ T cells for controlling experimental and human breast cancer. Furthermore, density of CD39+PD-1+CD8+ T cells may serve as a novel biomarker and may serve as a potential immunotherapy target. Thus, we discovered that a primary breast tumor primes a systemic, CD39+PD-1+CD8+ T cell response that is essential for metastatic dormancy in the lungs.

cancer biology↗

bioRxiv2020

Highly multiplexed molecular and cellular mapping of breast cancer tissue in three dimensions using mass tomography.

A holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.

cell biology↗

bioRxiv2020

The Genomic Landscape of Prostate Cancer Brain Metastases

Lethal prostate cancer commonly metastasizes to bone, lymph nodes, and visceral organs but with more effective therapies, there is an increased frequency of metastases to the brain. Little is known about the genomic drivers of prostate cancer brain metastases (PCBM). To address this, we conducted a comprehensive multi-regional, genomic, and targeted transcriptomic analysis of PCBM from 28 patients. We compared whole-exome and targeted RNA sequencing with matched primary tumors when available (n = 10) and with publicly available genomic data from non-brain prostate cancer metastases (n = 416). In addition to common alterations in TP53, AR, RB1, and PTEN, we identified highly significant enrichment of mutations in NF1 (25% cases (6/28), q = 0.049, 95% CI = 2.38 - 26.52, OR = 8.37) and RICTOR (17.9% cases (5/28), q = 0.01, 95% CI = 6.74 - 480.15, OR = 43.7) in PCBM compared to non-brain prostate cancer metastases, suggesting possible activation of the druggable pathways RAS/RAF/MEK/ERK and PI3K/AKT/mTOR, respectively. Compared to non-brain prostate cancer metastases, PCBM were almost three times as likely to harbor DNA homologous repair (HR) alterations (42.9% cases (12/28), p =0.016, 95% CI = 1.17 - 6.64, OR = 2.8). When considering the combination of somatic mutations, copy number alteration, and Large-scale State Transitions, 64.3% of patients (18/28) were affected. HR alterations may be critical drivers of brain metastasis that potentially provide cancer cells a survival advantage during re-establishment in a special microenvironment. We demonstrate that PCBM have genomic dependencies that may be exploitable through clinical interventions including PARP inhibition.

cancer biology↗

bioRxiv2019

The HLA Ligand Atlas. A resource of natural HLA ligands presented on benign tissues.

The human leukocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Here, we describe the HLA Ligand Atlas, an extensive collection of mostly matched HLA-I and -II ligandomes from 225 benign samples (29 tissues, 21 subjects). The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 89,853 HLA-I- and 140,861 HLA-II ligands. We observe that the immunopeptidomes differ considerably between tissues and individuals on both source protein and HLA-ligand level. 1,407 HLA-I ligands stem from non-canonical genomic regions. We highlight the importance of comparatively analyzing both benign and malignant tissues to inform tumor association, based on a case study in three glioblastoma patients. The resource provides insights into applied and basic immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy, and autoimmunity. It is publicly available at www.hla-ligand-atlas.org.

immunology↗

bioRxiv2018

Intra-tumor heterogeneity and clonal exclusivity in renal cell carcinoma

Intra-tumour heterogeneity is the molecular hallmark of renal cancer, and the molecular tumour composition determines the treatment outcome of renal cancer patients. In renal cancer tumourigenesis, in general, different tumour clones evolve over time. We analysed intra-tumour heterogeneity and subclonal mutation patterns in 178 tumour samples obtained from 89 clear cell renal cell carcinoma patients. In an initial discovery phase, whole-exome and transcriptome sequencing data from paired tumour biopsies from 16 ccRCC patients were used to design a gene panel for follow-up analysis. In this second phase, 826 selected genes were targeted at deep coverage in an extended cohort of 89 patients for a detailed analysis of tumour heterogeneity. On average, we found 22 mutations per patient. Pairwise comparison of the two biopsies from the same tumour revealed that on average 62% of the mutations in a patient were detected in one of the two samples. In addition to commonly mutated genes (VHL, PBRM1, SETD2 and BAP1), frequent subclonal mutations with low variant allele frequency (<10%) were observed in TP53 and in mucin coding genes MUC6, MUC16, and MUC3A. Of the 89 ccRCC tumours, 87 (~98%) harboured private mutations, occurring in only one of the paired tumour samples. Clonally exclusive pathway pairs were identified using the WES data set from 16 ccRCC patients. Our findings imply that shared and private mutations significantly contribute to the complexity of differential gene expression and pathway interaction, and might explain clonal evolution of different molecular renal cancer subgroups. Multi-regional sequencing is central for the identification of subclones within ccRCC.

cancer biology↗