Biology subjects

Azimzade, Y.

Publications and source records attributed to Azimzade, Y..

4 recordsLinked to original sources

bioRxiv2024

Integrated multiomics analysis unveils how macrophages drive immune suppression in breast tumors and affect clinical outcomes

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression and patient prognosis are still poorly understood. Unraveling these effects is vital for identifying potential targets to improve treatment outcomes. In this study, we devised an explainable machine learning (XML) pipeline to scrutinize the associations between TME cellular constituents and relapse-free survival (RFS). By applying our pipeline to estimated cell fractions in the METABRIC and TCGA datasets and comparing these results with associations to pathological complete response (pCR) after neoadjuvant chemotherapy (NAC), we created a comprehensive catalog of the TMEs role based on 5000 patient samples. Our findings reveal an unexpected dichotomy in which macrophages correlate positively with pCR but negatively with RFS, particularly within estrogen receptor-positive (ER+) and Luminal A and B (LumA/B) cancer subtypes. We show that this pattern is driven by heterogeneity in breast tumors characterized by increasing levels of macrophage infiltration. Through imaging mass cytometry (IMC) analysis, we discovered that macrophages tend to accumulate in the vicinity of HLA-ABChi epithelial cells as their frequency increases in tumor tissues and also express elevated levels of HLA-ABC protein. Combining IMC with single-cell RNA sequencing (scRNA-seq) data, we uncovered a significant association between these HLA-ABChi macrophages and regulatory and exhausted T cells (TReg and TEx), suggesting their involvement in immune suppression, likely by creating a chronically activated immunosuppressive TME. Subsequent cell-cell communication analysis predicted interactions between HLA-ABChi macrophages and TEx cells via the ligands SIGLEC9, ALCAM, and CSF1, and with TReg cells through APP, ANGPTL4, and SIGLEC9 signaling. Considering the clinical relevance of macrophages in ER+ (LumA/B) subtypes, our research enhances the characterization of macrophage-driven immune suppression in these tumors and identifies potential targets for immunomodulatory strategies.

bioinformatics↗

bioRxiv2024

Loss of chromosome cytoband 13q14.2 orchestrates breast cancer pathogenesis and drug response

Breast cancer (BCa) is a major global health challenge, characterized by chromosomal instability (CIN) and subsequent acquisition of extensive somatic copy number alterations (CNAs). CNAs including amplifications and deletions, significantly influence intra-tumor heterogeneity and the tumor microenvironment (TME). Among these, the loss of chromosome 13q14.2 emerges as a considerable factor in BCa pathogenesis and treatment responses. We provide evidence that this genomic alteration is under positive selective pressure, correlating with poorer patient survival. Furthermore, through multi-omic and in vitro analyses, we uncover a dual role of 13q14.2 loss: it confers a survival advantage to tumor cells and modulate the cell cycle and pro-apoptotic pathways in cancer cells, affecting macrophages population in the TME, while paradoxically increasing tumor susceptibility to BCL2 inhibitors. These findings suggest that targeting 13q14.2 as a biomarker in BCa could enhance the efficacy of existing treatments and offer a new avenue for improving clinical outcomes in BCa. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/599481v1_fig1.gif" ALT="Figure 1"> View larger version (93K): org.highwire.dtl.DTLVardef@123f961org.highwire.dtl.DTLVardef@c952f0org.highwire.dtl.DTLVardef@16430eborg.highwire.dtl.DTLVardef@c7c954_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical abstract C_FIG

cancer biology↗

bioRxiv2023

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled the identification of phenotypic diversity within breast tumor tissues. However, the contribution of these cell phenotypes to tumor biology and treatment response has remained less understood. This is primarily due to the limited number of available samples and the inherent heterogeneity of breast tumors. To address this limitation, we leverage a state-of-the-art scRNA-seq atlas and employ CIBER-SORTx to estimate cell phenotype fractions by de-convolving bulk expression profiles in more than 2000 samples from patients who have undergone Neoad-juvant Chemotherapy (NAC). We introduce a pipeline based on explainable Machine Learning (XML) to robustly explore the associations between different cell phenotype fractions and the response to NAC in the general population as well as different subtypes of breast tumors. By comparing tumor subtypes, we observe that multiple cell types exhibit a distinct association with pCR within each subtype. Specifically, Dendritic cells (DCs) exhibit a negative association with pathological Complete Response (pCR) in Estrogen Receptor positive, ER+, (Luminal A/B) tumors, while showing a positive association with pCR in ER-(Basal-like/HER2-enriched) tumors. Analysis of new spatial cyclic immunoflu-orescence data and publicly available imaging mass cytometry data showed significant differences in the spatial distribution of DCs between ER subtypes. These variations underscore disparities in the engagement of DCs within the tumor microenvironment (TME), potentially driving their divergent associations with pCR across tumor subtypes. Overall, our findings on 28 different cell types provide a comprehensive understanding of the role played by cellular compo-nents of the TME in NAC outcomes. They also highlight directions for further experimental investigations at a mechanistic level.

bioinformatics↗

bioRxiv2020

A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in-silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting. Author summaryComputer simulation based on mathematical models provides a way to improve the understanding of complex processes in oncology. In this paper we develop a stochastic mesoscopic simulation approach that incorporates key cellular processes while keeping computational costs to a minimum. Our methodology captures the development of tumor heterogeneity and the underlying evolutionary dynamics. The physical scale considered allows microscopic information to be included, tracking the spatio-temporal evolution of tumor heterogeneity and reconstructing clinically sized tumors from high-resolution medical imaging data, with a low computational cost. We illustrate the functionality of the modeling approach for the case of glioblastoma, an epitome of heterogeneity in tumors.

systems biology↗