Biology subjects

Brennan, D.

Publications and source records attributed to Brennan, D..

4 recordsLinked to original sources

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↗

bioRxiv2024

Physical cell-cell interactions regulate transcriptional programmes that control the responses of high grade serous ovarian cancer patients to therapy.

The tumour microenvironment is composed of a complex cellular network involving cancer, stromal and immune cells in dynamic interactions. A large proportion of this network relies on direct physical interactions between cells, which may impact patient responses to clinical therapy. Doublets in scRNA-seq are usually excluded from analysis. However, they may represent directly interacting cells. To decipher the physical interaction landscape in relation to clinical prognosis, we inferred a physical cell-cell interaction (PCI) network from biological doublets in a scRNA-seq dataset of approximately 18,000 cells, obtained from 7 treatment-naive ovarian cancer patients. Focusing on cancer-stromal PCIs, we uncovered molecular interaction networks and transcriptional landscapes that stratified patients in respect to their clinical responses to standard therapy. Good responders featured PCIs involving immune cells interacting with other cell types including cancer cells. Poor responders lacked immune cell interactions, but showed a high enrichment of cancer-stromal PCIs. To explore the molecular differences between cancer-stromal PCIs between responders and non-responders, we identified correlating gene signatures. We constructed ligand-receptor interaction networks and identified associated downstream pathways. The reconstruction of gene regulatory networks and trajectory analysis revealed distinct transcription factor (TF) clusters and gene modules that separated doublet cells by clinical outcomes. Our results indicate (i) that transcriptional changes resulting from PCIs predict the response of ovarian cancer patients to standard therapy, (ii) that immune reactivity of the host against the tumour enhances the efficacy of therapy, and (iii) that cancer-stromal cell interaction can have a dual effect either supporting or inhibiting therapy responses.

bioinformatics↗

bioRxiv2022

Small gene networks can delineate immune cell states and characterize immunotherapy response in melanoma

BackgroundSingle-cell sequencing studies have elucidated some of the underlying mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are difficult to implement as a general strategy or in a clinical diagnostic setting. In contrast, bulk RNAseq is now routine for both research and clinical applications. Therefore, our analysis extracts small transcription factor-directed co-expression networks (regulons) from single-cell RNA-seq data and uses them to deconvolute immune functional states from bulk RNA-seq data to characterize patient responses. MethodsRegulons were inferred in pre-treatment CD45+ cells from metastatic melanoma samples (n=19) treated with first-line ICI therapy (discovery dataset). A logistic regression-based classifier identified immune cell states associated with response, which were characterized according to differentially active, cell-state specific regulons. The complexity of these regulons was reduced and scored in bulk RNAseq melanoma samples from four independent studies (n=209, validation dataset). Patients were clustered according to their regulon scores, and the associations between cluster assignment, response, and survival were determined. Intercellular communication analysis of cell states was performed, and the resulting effector genes were analyzed by trajectory inference. ResultsRegulons preserved the information of gene expression data and accurately delineated immune cell phenotypes, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells, were associated with therapeutic responses in the discovery dataset. The cell states were characterized by seven differentially active and specific regulons that showed low specificity in non-immune cells. Four clusters with significantly different response outcomes (P <0.001) were identified in the bulk RNAseq validation cohort. An intercellular link between exhausted T cells and monocyte lineage cells was established, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. Analysis of ligand - receptor expression suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. ConclusionsRegulon-based characterization of cell states provides robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

immunology↗

bioRxiv2020

Morphometrical brain markers of sex difference

Many major neuropsychiatric pathologies, some of which appear in adolescence, show differentiated prevalence, onset, and symptomatology across the biological sexes. Therefore, mapping differences in brain structure between males and females during this critical developmental period may provide information about the neural mechanisms underlying the dimorphism of these pathologies. Utilizing a large dataset collected through the Adolescent Brain Cognitive Development study, we investigated the differences of adolescent (9-10 years old) male and female brains (n = 8325) by using a linear Support-Vector Machine Classifier to predict sex based on morphometry and image intensity values of structural brain imaging data. The classifier correctly classified the sex of 86% individuals with the insula, the precentral and postcentral gyri, and the pericallosal sulcus as the most discernable features. The role of these significant dimorphic features in psychopathology was explored by testing them as mediators between sex and clinical symptomology. The results demonstrate the existence of morphometrical brain markers of sex difference. Significance StatementMany psychiatric pathologies express differently across the sexes. Therefore, an understanding of the differences in brain structure between males and females during the critical developmental period of adolescence may provide the insights about the dimorphism of clinical symptomology and the general functions of the dimorphic brain structures. Using machine learning, we successfully classified males and females with a high accuracy based on morphometry and image intensity data extracted from structural MRI scans. The features which significantly contributed to classification were examined to determine brain regions which are dimorphic during adolescence. The relevance of these brain regions to the expression of psychopathology symptoms was also explored.

neuroscience↗