Biology subjects

Moffitt, R. A.

Publications and source records attributed to Moffitt, R. A..

5 recordsLinked to original sources

bioRxiv2025

Improving Differential Expression and Survival Analyses with Sample Specific Compartment Deconvolution.

MotivationStudies on bulk RNA-seq of tumor biopsies can yield incorrect results because varying proportions of non-tumor tissues in the samples obscure the true signal and impact the accuracy of survival and differential expression analyses. Single-cell sequencing avoids these problems, but is still too expensive in clinical settings. Other deconvolution algorithms extract tissue-specific gene expression profiles from bulk sequencing, but cannot do this on a per-sample basis. ResultsWe introduce SSCD - sample specific compartment deconvolution. SSCD extends non-negative matrix factorization with per-sample, per-gene constraint optimization. On simulated data, SSCD shows improvements in accuracy over existing methods. Using several real cancer datasets, we show that SSCD refines Differential Expression and survival analyses. AvailabilityCode and data are available at https://github.com/ayurovsky/SSCD.

cancer biology↗

bioRxiv2025

Enhancement of Prednisolone efficacy and safety in Duchenne muscular dystrophy via neutrophil elastase inhibition

Duchenne muscular dystrophy (DMD) is a genetic disorder characterized by muscle wasting, and leading to premature death. The pathogenesis of DMD is complex. It is triggered by loss of the cytoskeletal protein dystrophin, which causes muscle fiber instability and damage, followed by chronic inflammation and fibrotic replacement of damaged muscle tissue. Here we investigated the hypothesis that inhibition of neutrophil elastase, which is increased in dystrophic mice and impairs myogenesis, could provide a therapeutic effect in DMD. While neutrophil elastase inhibition, via the orally bioavailable compound Alvelestat, enhances myogenesis both in vitro and in vivo and reduces muscle damage and fibrosis, it does not produce a significant beneficial effect on the functional outcomes. However, when co- administered with the standard of care Prednisolone, Alvelestat dramatically enhances Prednisolone efficacy in a mouse model of DMD. In key functional tests, the performance of mice co-dosed with Alvelestat and Prednisolone is almost three times better than that of mice treated with Prednisolone alone. Additionally, co-administration of Alvelestat and Prednisolone reduces Prednisolones negative effects on muscle mass and resistance to fatigue. These findings are consistent with the finding that Prednisolone induces neutrophil elastase expression in patients affected by DMD. Our data support a beneficial role for neutrophil elastase inhibition in a mouse model of DMD that is also treated with Prednisolone, suggesting that a combinatorial therapy for DMD including a corticosteroid and a neutrophil elastase inhibitor could be trialed in humans. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=167 SRC="FIGDIR/small/640472v3_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f20853org.highwire.dtl.DTLVardef@b7fforg.highwire.dtl.DTLVardef@12fe312org.highwire.dtl.DTLVardef@e4cf0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2023

Clinically relevant orthotopic pancreatic cancer models for adoptive T cell therapy

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor. Prognosis is poor and survival is low in patients diagnosed with this disease; [~]12% at 5 years. Immunotherapy, including adoptive T cell transfer therapy, has not impacted outcomes in PDAC patients, due in part to the hostile tumor microenvironment (TME) which limits T cell trafficking and persistence. We posit that murine models serve as useful tools to study the fate of T cell therapy. Currently, genetically engineered mouse models (GEMM) for PDAC are considered a "gold-standard" as they recapitulate many aspects of human disease. However, these models have limitations, including marked tumor variability across individual mice and cost of colony maintenance. We characterized the immunologic features and trafficking patterns of adoptively transferred T cells in orthotopic PDAC models using two mouse cell lines, KPC-Luc and MT-5, isolated from KPC-GEMM mouse models (KrasLSL-G12D/+p53-/- and KrasLSL-G12D/+p53LSL-R172H/+, respectively). The MT-5 orthotopic model best recapitulates the cellular and stromal features of the TME in the PDAC GEMM. In contrast, far more host immune cells infiltrate KPC-Luc tumors, which have less stroma. Albeit CD4+ T cells were similarly detected in MT-5 tumors compared to KPC-GEMM in mice. Interestingly, we found that CAR T cells redirected to recognize mesothelin on these tumors that signal via CD3{sigma} and 41BB (Meso-41BB{sigma}-CAR T cells) post antigen recognition infiltrated the tumors of mice bearing stroma-devoid KPC-Luc orthotopic tumors, but not MT-5 tumors. Our data establish for the first time a reproducible and realistic clinical system useful for modeling stroma-rich and stroma-devoid PDAC tumors. These models shall serve in-depth study of how to overcome barriers that limit anti-tumor activity of adoptively transferred T cells.

cancer biology↗

bioRxiv2020

A Microglial Subset at the Tumor-Stroma Interface of Glioma

BackgroundMyeloid involvement in High Grade Gliomas, such as Glioblastoma, has become apparent and detrimental to disease outcomes. There is great interest in characterizing the HGG tumor microenvironment to understand how neoplastic lesions are supported, and to devise novel therapeutic targets. The tumor microenvironment of the central nervous system is unique as it contains neural and specialized glial cells, including the resident myeloid cells, microglia. Glioma-associated microglia and peripherally infiltrating monocytes/macrophages (GAM) accumulate within the neoplastic lesion where they facilitate tumor growth and drive immunosuppression. A longstanding limitation has been the ability to accurately differentiate microglia and macrophage roles in pathology, and identify the consequences of the spatial organization of these cells. ResultsHere we characterize the tumor-stroma border and identify peripheral glioma-associated microglia (PGAM) at the tumor leading edge as a unique subpopulation of GAM. Using data mining and analyses of samples derived from both murine and human sources, we show that PGAM exhibit a pro-inflammatory and chemotactic phenotype that is associated with peripheral monocyte recruitment, poorly enhancing radiomic features, and decreased overall survival. ConclusionsPGAM act as a unique subset of GAM, at the tumor-stroma interface, corresponding to disease outcomes. We propose the application of a novel gene signature to identify these cells, and suggest that PGAM constitute a cellular target of the TME. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/357459v1_ufig1.gif" ALT="Figure 1"> View larger version (76K): org.highwire.dtl.DTLVardef@6c08b6org.highwire.dtl.DTLVardef@b01421org.highwire.dtl.DTLVardef@1f99f9aorg.highwire.dtl.DTLVardef@15d5011_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2019

De novo compartment deconvolution and weight estimation of tumor samples (DECODER)

Tumors are mixtures of different compartments. While global gene expression analysis profiles the average expression of all compartments in a sample, identifying the specific contribution of each compartment remains a challenge. With the increasing recognition of the importance of non-neoplastic components, the ability to breakdown the gene expression contribution of each is critical. To this end, we developed DECODER, an integrated framework which performs de novo deconvolution, and compartment weight estimation for a single sample. We use DECODER to deconvolve 33 TCGA tumor RNA-seq datasets and show that it may be applied to other data types including ATAC-seq. We demonstrate that it can be utilized to reproducibly estimate cellular compartment weights in pancreatic cancer that are clinically meaningful. Application of DECODER across cancer types advances the capability of identifying cellular compartments in an unknown sample and may have implications for identifying the tumor of origin for cancers of unknown primary.

bioinformatics↗