New, effective treatment strategies for glioblastomas (GBMs), the most malignant and invasive brain tumors in adults, are highly needed. In this study, we investigated the potential of integrin α10β1 as a therapeutic target in GBMs. Expression levels and the role of integrin α10β1 were studied in patient-derived GBM tissues and cell lines. The effect of an antibody–drug conjugate (ADC), an integrin α10 antibody conjugated to saporin, on GBM cells and in a xenograft mouse model was studied. We found that integrin α10β1 was strongly expressed in both GBM tissues and cells, whereas morphologically unaffected brain tissues showed only minor expression. Partial or no overlap was seen with integrins α3, α6, and α7, known to be expressed in GBM. Further analysis of a subpopulation of GBM cells selected for high integrin α10 expression demonstrated increased proliferation and sphere formation. Additionally, siRNA-mediated knockdown of integrin α10 in GBM cells led to decreased migration and increased cell death. Furthermore, the ADC reduced viability and sphere formation of GBM cells and induced cell death both in vitro and in vivo. Our results demonstrate that integrin α10β1 has a functional role in GBM cells and is a novel, potential therapeutic target for the treatment of GBM.

In many human cancers, the rate of cell growth depends crucially on the size of the tumor cell population. Low, zero, or negative growth at low population densities is known as the Allee effect; this effect has been studied extensively in ecology, but so far lacks a good explanation in the cancer setting. Here, we formulate and analyze an individual-based model of cancer, in which cell division rates are increased by the local concentration of an autocrine growth factor produced by the cancer cells themselves. We show, analytically and by simulation, that autocrine signaling suffices to cause both strong and weak Allee effects. Whether low cell densities lead to negative (strong effect) or reduced (weak effect) growth rate depends directly on the ratio of cell death to proliferation, and indirectly on cellular dispersal. Our model is consistent with experimental observations from three patient-derived brain tumor cell lines grown at different densities. We propose that further studying and quantifying population-wide feedback, impacting cell growth, will be central for advancing our understanding of cancer dynamics and treatment, potentially exploiting Allee effects for therapy.

P53 is involved in DNA damage response and is an exciting target for radiosensitization in cancer. Targeted radionuclide therapy against somatostatin receptors with ¹⁷⁷Lu-DOTATATE is currently being explored as a treatment for neuroblastoma. The aim of this study was to investigate the novel p53-stabilizing peptide VIP116 in neuroblastoma, both as monotherapy and together with ¹⁷⁷Lu-DOTATATE. Five neuroblastoma cell lines, including two patient-derived xenograft (PDX) lines, were characterized in monolayer cultures. Four out of five were positive for ¹⁷⁷Lu-DOTATATE uptake. IC₅₀ values after VIP116 treatments correlated with p53 status, ranging between 2.8–238.2 μM. IMR-32 and PDX lines LU-NB-1 and LU-NB-2 were then cultured as multicellular tumor spheroids and treated with ¹⁷⁷Lu-DOTATATE and/or VIP116. Spheroid growth was inhibited in all spheroid models for all treatment modalities. The most pronounced effects were observed for combination treatments, mediating synergistic effects in the IMR-32 model. VIP116 and combination treatment increased p53 levels with subsequent induction of p21, Bax and cleaved caspase 3. Combination treatment resulted in a 14-fold and 1.6-fold induction of MDM2 in LU-NB-2 and IMR-32 spheroids, respectively. This, together with differential MYCN signaling, may explain the varying degree of synergy. In conclusion, VIP116 inhibited neuroblastoma cell growth, potentiated ¹⁷⁷Lu-DOTATATE treatment and could, therefore, be a feasible treatment option for neuroblastoma.

Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs.

In the study of brain tumors, patient-derived three-dimensional sphere cultures provide an important tool for studying emerging treatments. The growth of such spheroids depends on the combined effects of proliferation and migration of cells, but it is challenging to make accurate distinctions between increase in cell number versus the radial movement of cells. To address this, we formulate a novel model in the form of a system of two partial differential equations (PDEs) incorporating both migration and growth terms, and show that it more accurately fits our data compared to simpler PDE models. We show that traveling-wave speeds are strongly associated with population heterogeneity. Having fitted the model to our dataset we show that a subset of the cell lines are best described by a “Go-or-Grow”-type model, which constitutes a special case of our model. Finally, we investigate whether our fitted model parameters are correlated with patient age and survival.

Glioblastoma (GBM), the most common primary brain cancer in adults, is characterized by rapid local invasion along diverse routes, such as infiltration of white matter tracts and penetration of perivascular spaces. We investigate the hypothesis that GBM invasion routes correlate with the transcriptional states of individual cells and identify regulators of route-specific invasion. Utilizing patient-derived GBM xenograft models, we integrate single-cell transcriptomics and spatial proteomics, revealing that mesenchymal and oligodendrocyte progenitor-like GBM cells migrate perivascularly, while neural progenitor and astrocyte-like GBM cells invade diffusely. Computational reconstruction identifies ANXA1 as a perivascular invasion driver and lineage-restricted transcription factors RFX4 and HOPX as drivers of diffuse invasion, predictive of patient survival. Genetic ablation of these genes alters invasion phenotypes and extends survival in xenografted mice, clarifying the role of cell states in GBM invasion, and highlighting potential therapeutic targets for selective invasion route targeting in GBM patients.