Biology subjects

Morrissy, S.

Publications and source records attributed to Morrissy, S..

8 recordsLinked to original sources

bioRxiv2026

Microbiome-derived hydroxyphenyl propanoates enhance antitumour immunity by potentiating gasdermin D activity in tumour-associated myeloid cells

The microbiome plays a critical role in immune health and response to immune-stimulating treatments, including cancer immunotherapy. However, the molecular mechanisms by which commensal microbes influence cancer immunology remain poorly understood. Here, we report a class of microbiome-derived metabolites called hydroxyphenyl propanoates (HPP) that enhance tumour immune surveillance and response to immune checkpoint blockade (ICB) therapy in mice. HPPs function as potentiators of innate immune signalling in tumour-associated myeloid cells by promoting cleavage of the pore-forming protein gasdermin D (GSDMD), a critical effector of inflammasome function. This enhances the secretion of proinflammatory cytokines, such as IL-1 family members, while simultaneously protecting against pyroptosis. Secreted IL-1 family cytokines in turn elicit autocrine and paracrine inflammatory signaling in tumour-infiltrating leukocytes. HPP supplementation increases anticancer T cell function, improves disease control and extends overall survival (OS) in tumour-bearing mice harboring complex microbiomes or those treated with broad-spectrum antibiotics. In humans, GSDMD activation is associated with a favourable response to PD-1 blockade therapy in patients with advanced stage melanoma. Our study uncovers a molecular mechanism of regulation over host antitumour immunity that is modifiable and can be harnessed for improved cancer immunotherapy in a broad spectrum of patients.

cancer biology↗

bioRxiv2025

Compound Delivery of eVLPs Enhances Prime Editing for Targeted Genome Engineering and High-Throughput Screening

Engineered virus-like particles (eVLPs) enable transgene-free ribonucleoprotein delivery for genome editing applications, yet optimized delivery strategies for high-throughput applications remain unexplored. Prime editing enables precise genomic modifications but suffers from limited efficiency that constrains its widespread adoption. Here, we present PRIME-VLP (Progressive Repeated Infections for Maximized Editing via Virus-Like Particles), a delivery strategy that enhances prime editing efficiency for both targeted genome engineering and high-throughput prime editing screening. PRIME-VLP leverages the temporal dynamics of eVLP-mediated editing through multiple sequential transductions with sub-saturating eVLP doses delivered at optimal intervals. This approach achieves 1.5 to 2.8-fold improvements in editing efficiency across diverse genomic targets and cell types. PRIME-VLP maintains high specificity without increasing off-target effects, compromising cellular viability or causing transcriptional perturbations. By decoupling pegRNA and editor delivery through pegRNA-free eVLPs, PRIME-VLP enables pooled prime editing screens, circumventing transgene silencing limitations of conventional lentiviral-based screens. Using a 6,000-pegRNA library targeting TP53, PRIME-VLP achieved 2.8-fold higher editing efficiency and improved reproducibility compared to conventional lentiviral delivery. An eVLP-based screen identified functional TP53 loss-of-function variants that confer resistance to MDM2 inhibition by Nutlin-3. This work expands the versatility of eVLPs beyond their current in vivo therapeutic applications, demonstrating their promise for high-throughput functional genomics by overcoming the delivery limitations of lentiviral systems.

genomics↗

bioRxiv2023

Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma

Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.

cancer biology↗

bioRxiv2023

mosaicMPI: a framework for modular data integration across cohorts and -omics modalities

Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.

bioinformatics↗

bioRxiv2020

Copy-scAT: An R package for detection of large-scale and focal copy number alterations in single-cell chromatin accessibility datasets

Single-cell epigenomic assays have tremendous potential to illuminate mechanisms of transcriptional control in functionally diverse cancer cell populations. However, application of these techniques to clinical tumor specimens has been hampered by the current inability to distinguish malignant from non-malignant cells, which potently confounds data analysis and interpretation. Here we describe Copy-scAT, an R package that uses single-cell epigenomic data to infer copy number variants (CNVs) that define cancer cells. Copy-scAT enables studies of subclonal chromatin dynamics in complex tumors like glioblastoma. By deploying Copy-scAT, we uncovered potent influences of genetics on chromatin accessibility profiles in individual subclones. Consequently, some genetic subclones were predisposed to acquire stem-like or more differentiated molecular phenotypes, reminiscent of developmental paradigms. Copy-scAT is ideal for studies of the relationships between genetics and epigenetics in malignancies with high levels of intratumoral heterogeneity and to investigate how cancer cells interface with their microenvironment.

bioinformatics↗

bioRxiv2020

De novo reconstruction of microbial haplotypes by integrating statistical and physical linkage

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or haplotypes. However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis.

bioinformatics↗

bioRxiv2018

High-resolution structural genomics reveals new therapeutic vulnerabilities in glioblastoma

We investigated the role of 3D genome architecture in instructing functional properties of glioblastoma stem cells (GSCs) by generating the highest-resolution 3D genome maps to-date for this cancer. Integration of DNA contact maps with chromatin and transcriptional profiles identified specific mechanisms of gene regulation, including individual physical interactions between regulatory regions and their target genes. Residing in structurally conserved regions in GSCs was CD276, a gene known to play a role in immuno-modulation. We show that, unexpectedly, CD276 is part of a stemness network in GSCs and can be targeted with an antibody-drug conjugate to curb self-renewal, a key stemness property. Our results demonstrate that integrated structural genomics datasets can be employed to rationally identify therapeutic vulnerabilities in self-renewing cells.\n\nSIGNIFICANCEIn adult GBM, GSCs act as therapy-resistant reservoirs to nucleate tumor recurrence. New therapeutic approaches that target these cell populations hold the potential of significantly improving patient care and overall prognosis for this always-lethal cancer. Our work describes new links between 3D genome architecture and stemness properties in GSCs. In particular, through integration of multiple genomics and structural genomics datasets, we found an unexpected connection between immune-related genes and self-renewal programs in GBM. Among these, we show that targeting CD276 with knockdown strategies or specific antibody-drug conjugates achieve suppression of self-renewal. Strategies to target CD276+ cells are currently in clinical trials for solid tumors. Our results indicate that CD276-targeting agents could be deployed in GBM to specifically target GSC populations.\n\nHIGHLIGHTSO_LIWe generated high (sub-5 kb) resolution Hi-C maps for stem-like cells from GBM patients.\nC_LIO_LIIntegration of Hi-C and genomics datasets dissects mechanisms of gene regulation.\nC_LIO_LI3D genomes poise immune-related genes, including CD276, for expression.\nC_LIO_LITargeting CD276 curbs self-renewal properties of GBM cells.\nC_LI

cancer biology↗

bioRxiv2018

Subclonal architecture, evolutionary trajectories and patterns of inheritance of germline variants in pediatric glioblastoma

Pediatric glioblastoma (pGBM) is a lethal cancer with no effective therapies. Intratumoral genetic heterogeneity and mode of tumor evolution have not been systematically addressed for this cancer. Whole-genome sequencing of germline-tumor pairs showed that pGBM is characterized by intratumoral genetic heterogeneity and consequent subclonal architecture. We found that pGBM undergoes extreme evolutionary trajectories, with primary and recurrent tumors having different subclonal compositions. Analysis of variant allele frequencies supported a model of tumor growth involving slow-cycling cancer stem cells that give rise to fast-proliferating progenitor-like cells and to non-dividing cells. pGBM patients germlines had subclonal structural variants, some of which underwent dynamic frequency fluctuations during tumor evolution. By sequencing germlines of mother-father-patient trios, we found that inheritance of deleterious germline variants from healthy parents cooperate with de novo germline and somatic events to the tumorigenic process. Our studies therefore challenge the current notion that pGBM is a relatively homogeneous molecular entity.

cancer biology↗