Biology subjects

Azimi, M.

Publications and source records attributed to Azimi, M..

8 recordsLinked to original sources

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

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

Caspase-Activated DNase Localizes to Cancer Causing Translocation Breakpoints During Cell Differentiation

Caspase activated DNase (CAD) induced DNA breaks promote cell differentiation and therapy-induced cancer cell resistance. CAD targeting activity is assumed to be unique to each condition, as differentiation and cancer genesis are divergent cell fates. Here, we made the surprising discovery that a subset of CAD-bound targets in differentiating muscle cells are the same genes involved in the genesis of cancer-causing translocations. In muscle cells, a prominent CAD-bound gene pair is Pax7 and Foxo1a, the mismatched reciprocal loci that give rise to alveolar rhabdomyosarcoma. We show that CAD-targeted breaks in the Pax7 gene are physiologic to reduce Pax7 expression, a prerequisite for muscle cell differentiation. A cohort of these CAD gene targets are also conserved in early differentiating T cells and include genes that spur leukemia/lymphoma translocations. Our results suggest the CAD targeting of translocation prone oncogenic genes is non-pathologic biology and aligns with initiation of cell fate transitions.

genomics↗

bioRxiv2024

Proteomic Profiling Reveals Age-Related Changes in Transporter Proteins in the Human Blood-Brain Barrier

The Blood-Brain Barrier (BBB) is a crucial, selective barrier that regulates the entry of molecules including nutrients, environmental toxins, and therapeutic medications into the brain. This function relies heavily on brain endothelial cell proteins, particularly transporters and tight junction proteins. The BBB continues to develop postnatally, adapting its selective barrier function across different developmental phases, and alters with aging and disease. Here we present a global proteomics analysis focused on the ontogeny and aging of proteins in human brain microvessels (BMVs), predominantly composed of brain endothelial cells. Our proteomic profiling quantified 6,223 proteins and revealed possible age-related alteration in BBB permeability due to basement membrane component changes through the early developmental stage and age-dependent changes in transporter expression. Notable changes in expression levels were observed with development and age in nutrient transporters and transporters that play critical roles in drug disposition. This research 1) provides important information on the mechanisms that drive changes in the metabolic content of the brain with age and 2) enables the creation of physiologically based pharmacokinetic models for CNS drug distribution across different life stages.

developmental 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↗

bioRxiv2018

Double Dissociation of Nicotinic alpha-7 and alpha-4/beta-2 Sub-receptor Agonists for Enhancing Learning and Attentional Filtering of Distraction

Nicotinic acetylcholine receptors (nAChR) modulate attention, memory, and higher executive functioning, but it has remained unclear whether nAChR sub-receptors tap into different neural mechanisms of these functions. We therefore set out to contrast the contributions of selective alpha-7 nAChR and alpha-4/beta-2 nAChR agonists in mediating value learning and attentional filtering of distractors in the nonhuman primate. We found that the alpha-7 nAChR agonist PHA-543613 selectively enhanced the learning speed of feature values but did not modulate how salient distracting information was filtered from ongoing choice processes. In contrast, the selective alpha-4/beta-2 nAChR agonist ABT-089 did not affect learning speed but reduced distractibility. This double dissociation was dose-dependent and evident in the absence of systematic changes in overall performance, reward intake, motivation to perform the task, perseveration tendencies, or reaction times. These results suggest nicotinic sub-receptor-specific mechanisms consistent with (1) alpha-4/beta-2 nAChR specific amplification of cholinergic transients in prefrontal cortex linked to enhanced cue detection in light of interferences, and (2) alpha-7 nAChR specific activation prolonging cholinergic transients, which could facilitate subjects to follow-through with newly established attentional strategies when outcome contingencies change. These insights will be critical for developing function-specific drugs alleviating attention and learning deficits in neuro-psychiatric diseases.

neuroscience↗

bioRxiv2018

Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Prediction errors signal unexpected outcomes indicating that expectations need to be adjusted. For adjusting expectations efficiently prediction errors need to be associated with the precise features that gave rise to the unexpected outcome. For many visual tasks this credit assignment proceeds in a multidimensional feature space that makes it ambiguous which object defining features are relevant. Here, we report of a potential solution by showing that neurons in all areas of the medial and lateral fronto-striatal networks encode prediction errors that are specific to separate features of attended multidimensional stimuli, with the most ubiquitous prediction error occurring for the reward relevant features. These feature specific prediction error signals (1) are different from a non-specific prediction error signal, (2) arise earliest in the anterior cingulate cortex and later in lateral prefrontal cortex, caudate and ventral striatum, and (3) contribute to feature-based stimulus selection after learning. These findings provide strong evidence for a widely-distributed feature-based eligibility trace that can be used to update synaptic weights for improved feature-based attention.\n\nHighlightsO_LINeural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network.\nC_LIO_LIFeature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex.\nC_LIO_LIVentral striatum neurons encode feature specific surprise strongest for the goal-relevant feature.\nC_LIO_LINeurons encoding feature-specific prediction errors contribute to attentional selection after learning.\nC_LI

neuroscience↗