Biology subjects

kolch, W.

Publications and source records attributed to kolch, W..

7 recordsLinked to original sources

bioRxiv2026

SMAD3-driven cell state transitions underpin immunoresistance in bladder cancer

Despite the clinical success of anti-PD-1/PD-L1 immunotherapy in a subset of patients with high-risk bladder cancer (BLCA), most tumors remain refractory to treatment. We hypothesized that immunotherapy resistance is encoded within the intrinsic signaling state of BLCA cells and arises through specific cell-state transitions that precede therapeutic exposure. To test this hypothesis, we applied a cell State Transition Assessment and Regulation (cSTAR) framework to identify key signaling modules and aberrant causal network connections that (i) drive oncogenic progression and acquisition of aggressive phenotypes and (ii) govern BLCA cell susceptibility to T cell-mediated cytotoxicity. cSTAR analysis identified TGF{beta}/SMAD3 signaling as the principal driver of both oncogenic transformation and immunoresistance in BLCA cells. Inhibiting SMAD3 sensitized BLCA cells to PD-1-expressing Jurkat cells and primary human CD4+ T cell-mediated cytotoxicity through suppression of autophagy and activation of multiple cell death pathways. By integrating cell state transition analysis with functional immune co-culture assays, our work demonstrates that tumor cell state transitions control sensitivity to T cell killing, complementing established immune-intrinsic mechanisms of immunotherapy response.

systems biology↗

bioRxiv2026

T cell-Macrophage Interactions Potentially Influence Chemotherapeutic Response in Ovarian Cancer Patients.

Tumor development and progression involve complex cell-cell interactions and dynamic co-evolution between cancer cells, immune cells and stromal cells in the tumour microenvironment, and this may influence therapeutic resistance. A large proportion of this network relies on direct physical interactions between cells, particularly T-cell mediated interactions. Cell-cell communication inference has now become routine in downstream scRNAseq analysis, but this mostly fails to capture physical cell-cell interactions due to tissue dissociation. Doublets occur naturally in scRNA-seq and are usually excluded from analysis. However, they may represent directly interacting cells that remain undissociated during library preparation. In the present study, we uncover the physical interaction landscape of the ovarian tumour microenvironment using the scRNAseq datasets from 13 treatment-naive ovarian cancer patients. Focusing on T-cell-Macrophage (T-Mac) interaction doublet, we reveal the modulatory effect of macrophages on T cells and the potential influence of this interaction on therapeutic response. Our findings show that T-Macs from resistant patients are functionally polarized to the M2 phenotype and engage T cells to induce T-cell exhaustion. Whereas, T-Macs from sensitive patients are predominantly of the M1 polarized phenotype, physically engaging T cells that lack exhaustion signatures. We also demonstrate that T cells and macrophages in T-Mac doublet are interacting primarily for the purpose of antigen presentation, with the enrichment of several ligand-receptor pairs involved in TCR-MHC interactions and immune synapse formations. We partly validated some of these findings from a spatial transcriptomics dataset of ovarian cancer patients from a separate cohort.

bioinformatics↗

bioRxiv2025

Inferring physical cell-cell communication networks from scRNAseq data using univariate linear models.

Cells in tissues interact by direct physical contact or over short and long distances via secreted mediators. Cell-cell communication inference has now become routine in downstream scRNAseq analysis but this mostly fails to capture physical cell-cell interactions due to tissue dissociation. Multiplets (mostly doublets) in scRNAseq may represent undissociated physically attached cells that become sequenced together. Hence, identifying multiplets may serve as a good starting point to harness scRNAseq data for physical cell-cell interaction inference. In this study, we develop a computational method which utilizes univariate linear models (ULM) to identify multiplets in scRNAseq datasets, predict their cellular compositions, and infer physical cell-cell interaction networks. Indeed, our method showed good sensitivity ([~]56%) with excellent precision ([~]99%) in predicting FACS sorted doublet cell pairs with known constituents (ground truth), recording comparable or superior performance over two other existing methods. Also, applying it to scRNAseq data of partially dissociated tissues containing real multiplets unraveled physical networks which recapitulated the microanatomical structures of the tested tissues. This further underscores the accuracy in our predictions to capture biologically meaningful interactions. Finally, we tested our method on classical scRNAseq datasets and obtained biologically reasonable results. For example, when tested on classical cancer scRNAseq datasets, we recovered important interactions which followed biologically plausible cell interactions, validated by cell-cell colocalization in matched spatial transcriptomics datasets. This reassured the accuracy of our method in depicting physical interactions only between cells that were truly in close proximity in tissues.

bioinformatics↗

bioRxiv2024

A TEMPORAL MODEL OF TUMOR-IMMUNE DYNAMICS IN HIGH GRADE SEROUS OVARIAN CANCER

Patients with high-grade serous ovarian cancer (HGSOC) typically present with widespread metastasis, obscuring a temporal understanding of tumor-immune dynamics. We performed multi-site global proteomics and matched immunohistochemistry (IHC) of CD4+ and CD8+ tumor infiltrating lymphocytes (TILs) in patient samples. The protein expression profiles were ordered by pseudotime, recapitulating metastatic progression observed in the clinic, and providing a framework to explore tumor-immune dynamics from localized to metastatic disease. Metastatic progression correlated with immune cell infiltration and the recruitment of Tregs to counterbalance the effect of {gamma}{delta} and CD4+ T cells. Whilst later-stage metastases recruited Tregs via chemokines induced by SNX8, early-stage tumors relied more on antigen presentation. The exclusion of CD4+ TILs from the epithelium was correlated with metastatic progression, whereas CD8+ TILs were not, likely due to a predominance of exhausted CD8+ T cells. In early-stage tumors, keratin-expressing cancer cells recruited Tregs via MHC class II, resulting in an inflammatory phenotype characterized by limited IFN{gamma} production and non-clonally expanded T cells. Additionally, IFI44L expression on macrophages caused immune exclusion by downregulating CD53 on T cells. Our findings reveal novel mechanisms of immune escape associated with localized disease and metastatic progression in HGSOC, highlighting potential targets to improve the efficacy of immunotherapy.

cancer biology↗

bioRxiv2022

Analysis of alternative mRNA splicing in vemuraf-enib-resistant melanoma cells

Alternative mRNA splicing is common in cancers. In BRAF V600E mutated malignant melanoma a frequent mechanism of acquired resistance to BRAF inhibitors involves alternative splicing (AS) of BRAF. The resulting shortened BRAF protein constitutively dimerizes and conveys drug resistance. Here, we have analysed AS in SKMEL-239 melanoma cells and a BRAF inhibitor (vemurafenib) resistant derivative that expresses an AS, shortened BRAF V600E transcript. Transcriptome analysis showed differential expression of spliceosome components between the two cell lines. As there is no consensus approach to analysing AS events, we used and compared four common AS softwares based on different principles, DEXSeq, rMATS, ASpli, and LeafCutter. Two of them correctly identified the BRAF V600E AS in the vemurafenib resistant cells. Only 12 AS events were identified by all four softwares. Testing the AS predictions experimentally showed that these overlapping predictions are highly accurate. Interestingly, they identified AS caused alterations in the expression of melanin synthesis and cell migration genes in the vemurafenib resistant cells. This analysis shows that combining different AS analysis approaches produce reliable results and meaningful, biologically testable hypotheses. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/484656v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@9f4920org.highwire.dtl.DTLVardef@179e9bcorg.highwire.dtl.DTLVardef@e05632org.highwire.dtl.DTLVardef@3b1c41_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

KBoost: a new method to infer gene regulatory networks from gene expression data

Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24000 genes in less than 2 hours on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost.

bioinformatics↗

bioRxiv2019

Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is timeconsuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder). Author SummaryLinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches. Phosphorylation networks govern virtually all fundamental biochemical processes in cells, and thus have moved into the centre of interest in biology, medicine and drug development. Fundamentally different from current approaches, LinkPhinder is inherently network-based and makes use of the most recent AI de-velopments. We represent existing phosphorylation data as knowledge graphs, a format for large-scale and robust knowledge representation. Training a link prediction model on such a structure leads to novel, biologically valid phosphorylation network predictions that cannot be made with competing tools. Thus our new conceptual approach can lead to establishing a new niche of AI applications in computational biology.

bioinformatics↗