Jubenville, Tyler

Background Genes that enhance T-cell function represent promising targets for improving engineered T-cell therapies for cancer. While extensive CRISPR knockout screens have identified key genes enhancing T-cell persistence, employing Sleeping Beauty ( SB ) insertional mutagenesis, which induces both gain-of-function (GOF) and loss-of-function (LOF) mutations via the generation of fusion transcripts with endogenous genes, may uncover additional critical factors that previous approaches have overlooked. Methods We developed transgenic mice carrying D oxycycline (Dox)- i nducible SB mutag e nesis s y stem (DiSBey) in primary T cells. Using DiSBey, we conducted screens for genetic alterations enhancing T-cell persistence under chronic antigen exposure. Specifically, CD8 + T cells from Dox-fed DiSBey mice were subjected to repeated anti-CD3 stimulation over 18 days to mimic chronic antigenic stimulation. We then identified SB transposon genomic insertion sites and corresponding fusion transcripts from the persistent DiSBey CD8 + T cells using enhanced-specificity tagmentation sequencing and RNA sequencing, respectively. Results Under chronic stimulation, SB -mutagenized CD8 + T cells exhibited improved persistence and reduced terminal exhaustion phenotype. Across six independent screens, we identified 38 genes that were recurrently targeted by the SB transposon T2/Onc2 and differentially expressed under chronic anti-CD3 stimulation. T2/Onc2 insertions into Bach2 and Elmo1 were recurrently identified at the genomic level and were associated with altered nascent transcript expression. Bach2 , known as a key regulator of T-cell memory formation and resistance to chronic viral infection-induced exhaustion but less characterized in engineered T cells for cancer therapy, was found to counteract exhaustion in vitro and enhance in vivo tumor persistence in the B16-Ova tumor model. Further, we showed that ectopic Bach2 expression levels influence engineered T-cell differentiation lineage, as low Bach2 overexpression retained more functional progenitor exhausted T cells and exhibited improved therapeutic efficacy. Finally, in human CART19-28ζ cells, BACH2 overexpression enhanced cytotoxicity and tumor control following chronic cancer stimulation. Conclusions Controllable SB mutagenesis using DiSBey mice provides a novel platform for functional screening of genes that improve T-cell phenotypes important for their use as therapies. Our findings highlight a dose-dependent role of BACH2 in enhancing the function of engineered T cells under conditions of chronic antigenic stimulation.

Abstract Background Malignant peripheral nerve sheath tumors (MPNSTs) can arise from atypical neurofibromas (ANF). Loss of the polycomb repressor complex 2 (PRC2) is a common event. Previous studies on PRC2-regulated genes in MPNST used genetic add-back experiments in highly aneuploid MPNST cell lines which may miss PRC2-regulated genes in NF1-mutant ANF-like precursor cells. A set of PRC2-regulated genes in human Schwann cells (SCs) has not been defined. We hypothesized that PRC2 loss has direct and indirect effects on gene expression resulting in MPNST, so we sought to identify PRC2-regulated genes in immortalized human Schwann cells (iHSCs). Methods We engineered NF1-deficient iHSCs with loss of function SUZ12 or EED mutations. RNA sequencing revealed 1327 differentially expressed genes to define PRC2-regulated genes. To investigate MPNST pathogenesis, we compared genes in iHSCs to consistent gene expression differences between ANF and MPNSTs. Chromatin immunoprecipitation sequencing was used to further define targets. Methylome and proteomic analyses were performed to further identify enriched pathways. Results We identified potential PRC2-regulated drivers of MPNST progression. Pathway analysis indicates many upregulated cancer-related pathways. We found transcriptional evidence for activated Notch and Sonic Hedgehog (SHH) signaling in PRC2-deficient iHSCs. Functional studies confirm that Notch signaling is active in MPNST cell lines, patient-derived xenografts, and transient cell models of PRC2 deficiency. A combination of MEK and γ-secretase inhibition shows synergy in MPNST cell lines. Conclusions We identified PRC2-regulated genes and potential drivers of MPNSTs. Our findings support the Notch pathway as a druggable target in MPNSTs. Our identification of PRC2-regulated genes and pathways could result in more novel therapeutic approaches.

Abstract Background Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. The aim of this study was to develop a physics-based framework connecting to transcriptomics to predict patient-specific glioblastoma cell migration. Methods and Results We applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3 principal physical parameters that govern cell migration: motor number—describing myosin II activity, clutch number—describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) cell lines across mesenchymal (MES), proneural, and classical subtypes and 2 institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3 kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found that glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 18 genes that correlated with the physical parameters, suggesting transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration. Conclusions We describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data that can potentially be used to develop patient-specific anti-migratory therapeutic strategies.

Abstract Glioblastoma is the most aggressive malignant brain tumor with the poor survival due to its invasive nature driven by cell migration. There is still no effective treatment targeting glioma cell migration based on their transcriptomic information, due to the lack of understanding in the links to the fundamental mechanics of cell migration. In the previous studies, we developed a physics-based motor-clutch model, the cell migration simulator (CMS), to identify the basic mechanisms of cell migration in various cell types. In this study, we found that glioblastoma patient-derived (xenograft) (PD(X)) cell lines across mesenchymal (MES), proneural (PN), classical (CL) subtypes and two institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. We applied the CMS as a consistent framework to describe the physical parameters of glioblastoma patient cell migration. Using single cell migration and force generation data on compliant 2D surfaces, we found all glioma PD(X) lines (11 patients) had balanced myosin/adhesion ratios resulting in effective migration. Mesenchymal (MES) lines had enhanced F-actin polymerization resulting in higher motility. Adherent PDX lines had increased myosin-adhesion amount resulting in higher traction force. The CMS also predicted the differential cell migration under cytoskeletal drugs between glioma subtypes. Finally, we found 18 genes in actin cytoskeleton and focal adhesion pathways significantly correlated with the glioma physical parameters, which were being used to estimate the physical parameters of a larger cohort (66 patients) and to strategize targeting their cell migration. Overall, we described a consistent physics-based framework for parameterizing individual glioblastoma patients, connecting to clinical transcriptomic data, and predicting drug effects, that can potentially be used to develop subtype and patient-specific anti-migratory therapeutic strategies.