Biology subjects

Kisakol, B.

Publications and source records attributed to Kisakol, B..

6 recordsLinked to original sources

bioRxiv2026

Spatiotemporal dynamics of Mcl-1 abundance and its influence on apoptosis susceptibility

The Bcl-2 protein family defines cellular competence for mitochondrial outer membrane permeabilization (MOMP) and apoptotic cell death. In proliferating cells, the Bcl-2 family member Mcl-1 accumulates across the cell cycle and confers trans-mitotic resistance to extrinsic apoptosis. We show here that Mcl-1, but not Bcl-xL, additionally undergoes a coordinated redistribution from the cytosol to mitochondria, concomitant with its over-proportional accumulation late in the cell cycle. Live-cell monitoring of Mcl-1 dynamics at single-cell resolution, combined with mathematical modelling, enabled us to quantify that Mcl-1 redistribution substantially contributes to elevating MOMP thresholds. Furthermore, we found that Mcl-1 accumulation and redistribution act concomitantly but independently to increase MOMP thresholds as cells approach mitosis and this elevated resistance is reset in daughter cells after division. Notably, heterogeneities in Mcl-1 abundance and subcellular distribution are pronounced even among isogenic cells within the same cell-cycle phase, and thus contribute to substantial cell-to-cell variability in MOMP susceptibility. Analysis of colorectal cancer tissue samples showed that variability in Mcl-1 expression and distribution is likewise prominent between cells in patient tumors and were predicted to drive intra-tumour heterogeneity in responses to treatments that induce MOMP. Overall, we demonstrate how changes in Mcl-1 amounts and localisation integrate with cell-cycle progression to modulate apoptotic susceptibility, thereby shaping cell-fate outcomes and contributing to cell-to-cell heterogeneities in death decision making.

cell biology↗

bioRxiv2026

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

BackgroundStage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guarantee prolonged disease-free survival. ObjectiveThe spatial, quantitative, and qualitative characteristics of various cell types within tumour tissues could be key to developing accurate prognostic AI models. DesignTissue microarrays created from primary tumour tissues collected during surgical resection from a cohort of 493 stage III colorectal cancer (CRC) patients were analysed for 61 protein markers at the single-cell level using multiplexed immunofluorescence imaging via the Cell DIVE platform. Subsequent cell-type classification enabled quantitative cell-type analyses, co-localisation neighbourhood assessments, and cell-type-specific protein signature discoveries that distinguish between early and late/non-recurring patient samples. ResultsThis study identifies a stem cell protein profile that drives tumour relapse. A deep neural network (DNN) model, based on a stem cell protein signature composed of BAX, MLKL, FLIP, GLUT1, and CDX2, provided accurate prognosis for stage III CRC patients in both discovery and validation cohorts and in an independent validation cohort. Nodal count-based metric further increased prognosis accuracy. Our study also revealed distinct spatial arrangements of immune, endothelial, and stem cells that were linked to early tumour recurrence. ConclusionOur findings propose a clinically promising prognostic tool based on a five-protein stem cell signature. These markers not only predict chemotherapy resistance in cancer stem cells but also suggest potential therapeutic strategies such as combinatorial treatments incorporating small molecule inhibitors targeting FLIP and GLUT1. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIMore than 20% of stage III colorectal cancer patients will experience early tumour recurrence within the first 3 years post treatment that includes surgery and adjuvant 5-FU based chemotherapy treatment. C_LIO_LISeveral studies pointed towards involvements of number of cell type specific spatial neighbourhoods in tumour progression where some immune tumour microenvironment promoting angiogenesis and intravasation events, some may provide immunosuppression. C_LIO_LICancer stem cells could be responsible for metastatic tumour spread, early recurrence and chemoresistance. C_LI What this study addsO_LISpatial single cell quantitative multiplex profiling of 45 cancer hallmark proteins and 15 cell identity markers in 493 stage III CRC patients tissue samples demonstrated significant differences in cellular proximity neighbourhoods, cell type specific abundance and expression between the early and late recurrence samples. C_LIO_LIWe discover that macrophages show spatial association with the blood vessels in early recurrence samples. Moreover, we observed conglomeration of B cells and macrophages with Tregulatory, Thelper and Tcytotoxic cells in association with early recurrences. C_LIO_LIWe showed that stromal abundance of Tregulatory, Thelper, Tcytotoxic cells and monocytes are significantly in late, and no recurrence samples compared to early recurrence samples. C_LIO_LIThe most differential expression profile that differentiates late and no recurrence samples from the early recurrence samples is related to the stem cell population. Particularly, we found overexpression of GLUT1, FLIP and downregulation of BAX, BAK, MLKL and CDX2 proteins in the cancer stem cell of early recurrence samples. C_LIO_LIWe built a neural network based on the cancer stem cell protein signature (BAX, MLKL, FLIP, GLUT1 and CDX2 proteins) that delivers a high-performance prognostic classifier. C_LI How this study might affect research, practice or policyO_LIOur results propose a clinically promising prognostic tool based on a five-protein stem cell signature that outperforms existing clinical and proposed transcriptomic based signatures for separation between risk groups. C_LIO_LIMoreover, our five-protein signature markers not only predict stem cell chemotherapy resistance and therefore tumour recurrence but also suggest potential therapeutic strategies. For instance, this approach could guide combinatorial treatments at high risk of chemoresistance, such as incorporating small molecule inhibitors targeting FLIP (currently in discovery phase) and GLUT1 (already under preclinical trial evaluation). C_LI

cancer biology↗

bioRxiv2025

Humanized glioblastoma patient-derived orthotopic xenografts recreate a locally immunosuppressed human immune ecosystem amenable to immunotherapeutic modulation

Immune-based strategies have so far failed to demonstrate clinical benefit in glioblastoma (GBM), largely due to the profound immunosuppressive tumor microenvironment (TME). To achieve more predictive preclinical insights, advanced in vivo models that faithfully recapitulate the human brain immune landscape are urgently needed. Here, we established GBM patient-derived orthotopic xenografts (PDOXs) across diverse mouse strains, including humanized models. Humanization was achieved through transplantation of CD34+ hematopoietic stem cells (HU-CD34+) or peripheral blood mononuclear cells (HU-PBMC). Both models successfully reconstituted human T-cells systemically, with stronger engraftment in HU-CD34+ mice. We observed selective infiltration and spatial organization to intracranial GBM tumors, including exhausted, memory-like, and regulatory CD4+ T-cell phenotypes, TIM-3+ immunosuppressive-like myeloid cells and intratumoral B cells. Mouse microglia-derived tumor-associated macrophages (TAMs) remained the dominant immunosuppressive immune population. Anti-PD-1 therapy, but not anti-GITR, modestly modulated the infiltration dynamics, demonstrating the susceptibility of the reconstructed adaptive immunity to immunotherapeutic intervention. These findings position humanized GBM PDOXs as a relevant preclinical platform to interrogate tumor-immune interactions and evaluate immunotherapeutic strategies in a human context. Key pointsO_LIGBM PDOXs developed in HU-CD34+ and HU-PBMC mice faithfully reconstitute systemic and local human adaptive immunity. C_LIO_LIHuman immune components undergo selective infiltration, spatial organization and transition towards exhausted CD4+ T-cells and immunosuppressive CD11c+ myeloid cells. C_LIO_LIAnti-PD-1, but not anti-GITR, locally promote human immune infiltration into intracranial GBM tumors, while sparing systemic compartments. C_LIO_LIHumanized GBM PDOXs provide a powerful preclinical platform to test novel immunotherapeutic strategies. C_LI Study importanceImmune checkpoint blockade has shown limited efficacy in GBM, reflecting the highly immunosuppressive and lymphocyte-poor nature of the TME. Conventional syngeneic and GEMM models fail to recapitulate these features, contributing to the translational disconnect between preclinical success and clinical failure. Humanized mice provide a solution to interrogate human-specific immunity in vivo, but their use in GBM has remained limited. Here, we provide the first comparison of GBM PDOX modeling in two complementary modes of humanization based on CD34+ HSCs and PBMCs. We systematically profile systemic and intratumoral compartments, showing that these models faithfully reconstitute human adaptive immunity and capture the interplay with the murine brain TME. Furthermore, we demonstrate clinically-relevant responses upon treatment with checkpoint antibodies targeting PD-1 and GITR, showing modulation of human immune subsets without altering murine TAM immunosuppression, underscoring the translational value of the system. This study establishes humanized GBM PDOXs as a versatile platform for dissecting tumor-immune interactions in the brain and for preclinical evaluation and development of novel immunotherapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/689484v2_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@1b684e1org.highwire.dtl.DTLVardef@1cdfbbdorg.highwire.dtl.DTLVardef@4a570borg.highwire.dtl.DTLVardef@98b112_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Multiplex analysis of colorectal cancer tissue describes the composition, cell biology and spatial effects of cell-in-cell events and identifies a T cell-dependent prognostic signature

Cell competition is an emerging mechanism in which mammalian tissues maintain homeostasis by eliminating less fit (loser) cells through direct interactions with fitter (winner) neighbouring cells. In cancer, these competitive interactions may drive tumour evolution; however, spatial organisation and clinical relevance of these events remain poorly understood. One mechanism by which winner cells eliminate loser cells is engulfment, resulting in cell-in-cell (CIC) formation. Although CICs have been observed in many tumour types for over a century, their cellular composition, spatial context, interactions with the tumour microenvironment, and biological significance in human cancers remain unclear. Here, we systematically characterised the cellular identity and functional states of CICs in situ, examined their spatial interactions within the tumour microenvironment, and assessed their clinical relevance using spatially resolved single-cell data from a large cohort of colorectal cancer patients. We demonstrate that CICs occur predominantly between cancer cells but also involve cancer stem cell (CSC)-like populations and cytotoxic T cells. Engulfed (inner) cancer and CSC-like cells display molecular features consistent with a loser-cell phenotype, including increased apoptosis and reduced proliferation, whereas outer cancer cells exhibit winner-cell features such as upregulated glycolysis. Live-cell time-lapse experiments demonstrate that glucose accumulates in inner cells during lysosomal degradation following cell engulfment. Spatial analysis further revealed distinct CIC neighbourhoods, which we defined based on proximity to engulfment events. Cells within these regions, particularly CSC-like cells and cytotoxic T cells, exhibit increased metabolic stress, suggesting local competition for nutrients. Importantly, the presence of cytotoxic T cells within CIC neighbourhoods and spatial co-occurrence patterns between cancer cells and CSC-like populations are associated with improved patient outcomes. Together, our findings demonstrate that cell engulfment defines spatially organised competitive niches and may reflect cell competition within complex tumour microenvironments.

cancer biology↗

bioRxiv2025

High-Resolution Spatial Proteomics Characterises Colorectal Cancer Consensus Molecular Subtypes

BackgroundIdentification of the consensus molecular subtypes (CMS) opened significant potential for understanding the tumor biology and intertumoral heterogeneity of colorectal cancer (CRC). However, molecular subtyping in CRC traditionally relies on bulk transcriptomics, therefore, lacks spatial and single-cell level aspect. MethodsWe constructed tissue microarrays using tumor cores from 222 CRC patients. Arrays were stained and imaged using 54 cell identity and cancer hallmark markers, delivering spatially resolved protein profiles of >2 million cells. RNA sequencing data and CMS classification were also available for these patients. After segmentation of cancer, stromal and immune cells, we investigated intratumoral heterogeneity within CMS subtypes using spatially resolved single-cell protein profiling (>2 million cells). We compared cell types, their spatial organization and their expression of cancer hallmark-related proteins in CMS 1-4 subtypes. ResultsWe revealed tissue atlases illustrating the cell types/states, spatial heterogeneity, cellular neighborhoods, cellular network, and single-cell protein profiles of CMS tumors. CMS1 tumors had more CD3+, CD8+, and PD1+ immune cells that were found in the epithelial layer frequently. CMS1 was also associated with higher levels of metabolic reprogramming markers such as upregulated glycolysis. CMS2 showed immune segregation, reactive stroma patterns and higher levels of apoptotic and proliferative signaling proteins. CMS3 exhibited clustered cancer cells with high RIP3 levels, suggesting a pro-inflammatory microenvironment. CMS4 displayed stromal-centric and immune-evasive tumors characterized by decreased HLA-1 levels and regulatory T-cell exclusion from epithelium. ConclusionWe present a spatial protein atlas of CRC at single-cell resolution and demonstrate novel aspects of CMS tumour structures.

cancer biology↗

bioRxiv2024

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell state protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

cancer biology↗