Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.12.14.571722

Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly

Faisal, S. M.·Clewner, J. E.·Stack, B.·Varela, M. L.·Comba, A.·Motsch, S.·Castro, M. G.·Lowenstein, P. R.
View original source ↗Export metadata ↓

Abstract

Glioblastoma (GBM), known for its invasive nature, remains a challenge in clinical oncology due to its poor prognosis. Only 5% of patients live past 2 years. The extensive intra-tumoral heterogeneity, combined with aggressive infiltration into surrounding healthy brain tissue limits complete resection and reduces the efficiency of therapeutic interventions. In previous studies using ex-vivo 3D explants and in-vivo intravital imaging, we discovered the existence of oncostreams. Oncostreams are accumulations of nematically aligned elongated spindle-like cells constituted by both tumor and non-tumor cells. We observed a direct correlation between the density of oncostreams and glioma aggressiveness, in genetically engineered mouse glioma models, in high-grade human gliomas, and especially in gliosarcomas. Oncostreams play a pivotal role in the intra-tumoral distribution of both tumoral and non-tumoral cells, potentially facilitating collective invasion of neighboring healthy brain tissue. We further identified a unique molecular signature intrinsic to oncostreams, with a prominent overexpression of COL1A1, MMP9, ADAMts2, and ACTA2 - pivotal genes influencing gliomas mesenchymal transformation and potential determinants of tumor malignancy. COL1A1 inhibition in genetic mouse gliomas resulted in the elimination of oncostreams and induced significant changes in the tumor microenvironment, a reduction in mesenchymal-associated gene expression, and prolonged animal survival. Based on this foundation, we endeavored to model glioma oncostreams in vitro, evaluating the potential of various pharmacologic agents on the formation and organization of oncostreams. Using an optimized workflow, oncostreams were established using GFP+ NPA cells (NRAs\shP53\shATRX) derived from a genetically engineered mouse model utilizing the Sleeping Beauty transposon system. In-depth global and localized statistical analysis employing Julia programming and R Studio based in-house scripts provided insights into the behavior and organization of glioma cells. Our in vitro model led us to probe the impact of factors like cell density, cell morphology, collagen coating, exposure to neurotransmitter agonists, and changes in calcium levels. We also explored interventions targeting specific cytoskeleton structures like non-muscle myosin II B and C, myosin, actomyosin, and microtubules on oncostream formation and organization. In conclusion, our data provide novel information on patterns of glioma migration, which will inform mechanisms of glioma collective invasion in vivo. Through quantitative analysis of these pathologically aggressive and invasive structures, we highlight the importance of potential anti-invasion targets in improving outcomes for GBM patients. Integrating anti-invasive molecules with conventional treatments could significantly enhance clinical benefits. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/571722v1_ufig1.gif" ALT="Figure 1"> View larger version (95K): org.highwire.dtl.DTLVardef@31a40corg.highwire.dtl.DTLVardef@1093c09org.highwire.dtl.DTLVardef@1449e7corg.highwire.dtl.DTLVardef@10892_HPS_FORMAT_FIGEXP M_FIG Dynamics of oncostream structure and cellular motility modulation. This graphical abstract represents the intricacies of the oncostream structure, a proposed model for the collective migration of cancer cells. The central diagram illustrates the oncostream structure, delineated by various treatment conditions radiating outward. Each segment displays a fluorescent micrograph showing the effect of specific inhibitors and compounds on cellular oncostream structure. The array of compounds, including TC-I-15 (21 integrin inhibitor), Collagenase, p-nitro Blebbistatin, Cytochalasin-D, BAPTA-AM, Histamine, Glutamate, 4-Hydroxy acetophenone (4-HAP), Rho-Inhibitor, and Rho-Activator I, are marked on each corresponding segment. Quantitative measures of cellular migration speed, expressed in micrometers per hour (m/h) are noted for each treatment. Notably, the top half of the diagram reveals the oncostreams sensitivity to pharmacological drug treatments, whereas the bottom half shows resistance to these treated conditions. This representation emphasizes the selective effects of pharmacological agents on cancer cell motility within the oncostream framework. C_FIG

Explore related subjects

cancer biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Faisal, S. M., Clewner, J. E., Stack, B., Varela, M. L., Comba, A., Motsch, S., Castro, M. G., Lowenstein, P. R.. 2023-12-15. Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly. https://doi.org/10.1101/2023.12.14.571722

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

m6A-Driven Intratumoral Cholesterol Biosynthesis Fuels Castration-Resistant Prostate Cancer Progression

Both nuclear pore complexes (NPCs) and RNA N6-methyladenosine (m6A) machinery are indispensable for proper cellular function. Although their collaborative roles in the nuclear export of messenger RNAs (mRNAs) have been reported, it remains ambiguous whether and how this collaboration may contribute to cancer progression. Here we identify a functional cooperation between NPCs and m6A signaling that promotes the development of castration-resistant prostate cancer (CRPC). We showed that nuclear export of m6A-modified mRNAs, mediated by the interaction between RNA methyltransferase METTL3 and the nucleoporin NUP93, is functionally coupled to cholesterol biosynthesis. Given that cholesterol-fueled intratumoral androgen production is one of the mechanisms driving CRPC, we demonstrated that overexpression of the wild-type METTL3 or NUP93, but neither the enzymatically dead METTL3 nor the mutant NUP93 that loses METTL3-interacting capability, elevates intracellular levels of androgens, activates AR signaling under castrate condition, and promotes androgen-independent growth of prostate cancer cells both in vitro and in vivo. Importantly, pharmacological inhibition of METTL3 or targeted demethylation on mRNAs encoding key cholesterol biosynthesis enzymes effectively suppressed CRPC malignancy. Together, these findings uncover a therapeutically targetable m6A-METTL3-NUP93 axis that links nuclear mRNA export and metabolic reprogramming to fuel CRPC progression, providing a conceptually new strategy for the treatment of this lethal disease.

cancer biology↗
bioRxiv2026

ST6Gal2 promotes α2,6-sialylation and aggressive phenotypes in neuroblastoma cells

Neuroblastoma is the most common extracranial solid tumor of childhood. Its clinical behavior ranges from spontaneous regression to lethal, treatment-refractory disease. Aberrant 2,6-sialylation contributes to aggressive phenotypes in many cancers, but the role of ST6Gal2, a neural-enriched 2,6-sialyltransferase, in neuroblastoma is largely unexplored. Here, we examine the clinical and functional significance of ST6Gal2 in neuroblastoma. In two independent public cohorts (SEQC, n=498; Kocak, n=649), high ST6GAL2 expression was associated with significantly worse overall and event-free survival. In the SEQC cohort, ST6GAL2 expression was higher in high-risk and MYCN-amplified tumors, varied across International Neuroblastoma Staging System stages, and correlated positively with a mesenchymal transcriptional signature (Spearman {rho}=0.181). The mesenchymal correlation was reproduced in the Kocak cohort ({rho}=0.204). Stable shRNA-mediated knockdown of ST6GAL2 in SK-N-AS and SK-N-BE(2) cells reduced proliferation and viability, impaired wound closure, and decreased migration and invasion. In preliminary experiments in SK-N-AS cells, ST6GAL2 knockdown reduced binding of Sambucus nigra agglutinin, consistent with a role for ST6Gal2 in 2,6-sialylation. Together, these findings link ST6Gal2 expression to aggressive clinical and transcriptional features and pro-tumorigenic phenotypes in neuroblastoma and nominate ST6Gal2-mediated sialylation as a candidate pathway for mechanistic study.

cancer biology↗
bioRxiv2026

Unsupervised transcriptomic analysis of paired pre- and post-treatment specimens reveals divergent chemoimmunomodulatory induction trajectories in breast cancer

The immunomodulatory effects of chemotherapy (chemoimmunomodulation; CIM) are clinically consequential and heterogeneous, yet no systematic framework exists for classifying the immunomodulatory trajectory a tumor follows in response to treatment (CIM trajectory). Here, we present the CIM Induction Classifier (CIMIC), an unsupervised clustering pipeline leveraging delta gene expression across 3,189 CIM-related genes to classify specimens chemoimmunomodulatory trajectory. Applied to two pre- and post-chemotherapy breast cancer (BC) datasets (NKI/SMC, N = 36; NEO, N = 19) and nine epirubicin-perturbed triple-negative BC (TNBC) cell lines, CIMIC identified two divergent CIM trajectories: a functional CIM (Fun-CIM) trajectory, broadly conserved across tumors and cell lines and characterized by induction of inflammatory cell death, antigen presentation, viral mimicry, and adaptive immune activation programs, and a dysfunctional CIM (Dys-CIM) trajectory, characterized by induction of proteostatic and metabolic stress-adaptation programs, reduced immune cell abundances and cytotoxic activity, and enrichment of aggressive BC subtypes. Using survival and longitudinal transcriptomic data in NKI/SMC (N = 20), treatment-induced increases in Fun-CIM-associated genes and ssGSEA scores were associated with reduced recurrence, whereas Dys-CIM-associated genes and scores were associated with increased recurrence. In multivariable analyses within independent chemotherapy-treated BC cohorts (METABRIC, N = 412; SCAN-B, N = 2,462), higher baseline Fun-CIM ssGSEA scores were associated with better outcomes, whereas higher baseline Dys-CIM ssGSEA scores were associated with worse outcomes. These findings establish CIM as a dynamic, trajectory-level process and position CIMIC as a framework for defining CIM trajectories and supporting future efforts to identify predictors, mechanisms, and therapeutic strategies that maximize beneficial CIM.

cancer biology↗