Autocrine signaling can explain the emergence of Allee effects in cancer cell populations.

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.

Astrocytes enhance glioblastoma growth.

Glioblastoma (GBM) is a deadly disease with a need for deeper understanding and new therapeutic approaches. The microenvironment of glioblastoma has previously been shown to guide glioblastoma progression. In this study, astrocytes were investigated with regard to their effect on glioblastoma proliferation through correlative analyses of clinical samples and experimental in vitro and in vivo studies. Co-culture techniques were used to investigate the GBM growth enhancing potential of astrocytes. Cell sorting and RNA sequencing were used to generate a GBM-associated astrocyte signature and to investigate astrocyte-induced GBM genes. A NOD scid GBM mouse model was used for in vivo studies. A gene signature reflecting GBM-activated astrocytes was associated with poor prognosis in the TCGA GBM dataset. Two genes, periostin and serglycin, induced in GBM cells upon exposure to astrocytes were expressed at higher levels in cases with high "astrocyte signature score". Astrocytes were shown to enhance glioblastoma cell growth in cell lines and in a patient-derived culture, in a manner dependent on cell-cell contact and involving increased cell proliferation. Furthermore, co-injection of astrocytes with glioblastoma cells reduced survival in an orthotopic GBM model in NOD scid mice. In conclusion, this study suggests that astrocytes contribute to glioblastoma growth and implies this crosstalk as a candidate target for novel therapies.

Aberrant immunostaining pattern of the CD24 glycoprotein in clinical samples and experimental models of pediatric medulloblastomas.

The CD24 glycoprotein is a mediator of neuronal proliferation, differentiation and immune suppression in the normal CNS, and a proposed cancer biomarker in multiple peripheral tumor types. We performed a comparative analysis of CD24 gene expression in a large cohort of pediatric and adult brain tumors (n = 813), and further characterized protein expression in tissue sections (n = 39), primary brain tumor cultures (n = 12) and a novel orthotopic group 3 medulloblastoma xenograft model. Increased CD24 gene expression was demonstrated in ependymomas, medulloblastomas, anaplastic astrocytomas and glioblastomas, although medulloblastomas displayed higher expression than all other tumor entities. Preferential expression of CD24 in medulloblastomas was confirmed at protein level by immunostaining and computerized image analysis of cryosections. Morphologies and immunophenotyping of CD24(+) cells in tissue sections tentatively suggested disparate functions in different tumor subsets. Notably, protein staining of medulloblastoma cells was associated with prominent cytoplasmic and membranous granules, enabling rapid and robust identification of medulloblastoma cells in clinical tissue samples, as well as in experimental model systems. In conclusion, our results implicate CD24 as a clinically and experimentally useful medulloblastoma immunomarker. Although our results encourage further functional studies of CD24 as a potential molecular target in subsets of brain tumors, the promiscuous expression of CD24 in vivo highlights the importance of specificity in the future design of such targeted treatment.

ERBB3 is a marker of a ganglioneuroblastoma/ganglioneuroma-like expression profile in neuroblastic tumours.

BACKGROUND: Neuroblastoma (NB) tumours are commonly divided into three cytogenetic subgroups. However, by unsupervised principal components analysis of gene expression profiles we recently identified four distinct subgroups, r1-r4. In the current study we characterized these different subgroups in more detail, with a specific focus on the fourth divergent tumour subgroup (r4). METHODS: Expression microarray data from four international studies corresponding to 148 neuroblastic tumour cases were subject to division into four expression subgroups using a previously described 6-gene signature. Differentially expressed genes between groups were identified using Significance Analysis of Microarray (SAM). Next, gene expression network modelling was performed to map signalling pathways and cellular processes representing each subgroup. Findings were validated at the protein level by immunohistochemistry and immunoblot analyses. RESULTS: We identified several significantly up-regulated genes in the r4 subgroup of which the tyrosine kinase receptor ERBB3 was most prominent (fold change: 132-240). By gene set enrichment analysis (GSEA) the constructed gene network of ERBB3 (n = 38 network partners) was significantly enriched in the r4 subgroup in all four independent data sets. ERBB3 was also positively correlated to the ErbB family members EGFR and ERBB2 in all data sets, and a concurrent overexpression was seen in the r4 subgroup. Further studies of histopathology categories using a fifth data set of 110 neuroblastic tumours, showed a striking similarity between the expression profile of r4 to ganglioneuroblastoma (GNB) and ganglioneuroma (GN) tumours. In contrast, the NB histopathological subtype was dominated by mitotic regulating genes, characterizing unfavourable NB subgroups in particular. The high ErbB3 expression in GN tumour types was verified at the protein level, and showed mainly expression in the mature ganglion cells. CONCLUSIONS: Conclusively, this study demonstrates the importance of performing unsupervised clustering and subtype discovery of data sets prior to analyses to avoid a mixture of tumour subtypes, which may otherwise give distorted results and lead to incorrect conclusions. The current study identifies ERBB3 as a clear-cut marker of a GNB/GN-like expression profile, and we suggest a 7-gene expression signature (including ERBB3) as a complement to histopathology analysis of neuroblastic tumours. Further studies of ErbB3 and other ErbB family members and their role in neuroblastic differentiation and pathogenesis are warranted.

Inference of glioblastoma migration and proliferation rates using single time-point images.

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.

Real-time evaluation of glioblastoma growth in patient-specific zebrafish xenografts.

BACKGROUND: Patient-derived xenograft (PDX) models of glioblastoma (GBM) are a central tool for neuro-oncology research and drug development, enabling the detection of patient-specific differences in growth, and in vivo drug response. However, existing PDX models are not well suited for large-scale or automated studies. Thus, here, we investigate if a fast zebrafish-based PDX model, supported by longitudinal, AI-driven image analysis, can recapitulate key aspects of glioblastoma growth and enable case-comparative drug testing. METHODS: We engrafted 11 GFP-tagged patient-derived GBM IDH wild-type cell cultures (PDCs) into 1-day-old zebrafish embryos, and monitored fish with 96-well live microscopy and convolutional neural network analysis. Using light-sheet imaging of whole embryos, we analyzed further the invasive growth of tumor cells. RESULTS: Our pipeline enables automatic and robust longitudinal observation of tumor growth and survival of individual fish. The 11 PDCs expressed growth, invasion and survival heterogeneity, and tumor initiation correlated strongly with matched mouse PDX counterparts (Spearman R = 0.89, p < 0.001). Three PDCs showed a high degree of association between grafted tumor cells and host blood vessels, suggesting a perivascular invasion phenotype. In vivo evaluation of the drug marizomib, currently in clinical trials for GBM, showed an effect on fish survival corresponding to PDC in vitro and in vivo marizomib sensitivity. CONCLUSIONS: Zebrafish xenografts of GBM, monitored by AI methods in an automated process, present a scalable alternative to mouse xenograft models for the study of glioblastoma tumor initiation, growth, and invasion, applicable to patient-specific drug evaluation.

MYCMI-7: A Small MYC-Binding Compound that Inhibits MYC: MAX Interaction and Tumor Growth in a MYC-Dependent Manner.

Deregulated expression of MYC family oncogenes occurs frequently in human cancer and is often associated with aggressive disease and poor prognosis. While MYC is a highly warranted target, it has been considered "undruggable," and no specific anti-MYC drugs are available in the clinic. We recently identified molecules named MYCMIs that inhibit the interaction between MYC and its essential partner MAX. Here we show that one of these molecules, MYCMI-7, efficiently and selectively inhibits MYC:MAX and MYCN:MAX interactions in cells, binds directly to recombinant MYC, and reduces MYC-driven transcription. In addition, MYCMI-7 induces degradation of MYC and MYCN proteins. MYCMI-7 potently induces growth arrest/apoptosis in tumor cells in a MYC/MYCN-dependent manner and downregulates the MYC pathway on a global level as determined by RNA sequencing. Sensitivity to MYCMI-7 correlates with MYC expression in a panel of 60 tumor cell lines and MYCMI-7 shows high efficacy toward a collection of patient-derived primary glioblastoma and acute myeloid leukemia (AML) ex vivo cultures. Importantly, a variety of normal cells become G1 arrested without signs of apoptosis upon MYCMI-7 treatment. Finally, in mouse tumor models of MYC-driven AML, breast cancer, and MYCN-amplified neuroblastoma, treatment with MYCMI-7 downregulates MYC/MYCN, inhibits tumor growth, and prolongs survival through apoptosis with few side effects. In conclusion, MYCMI-7 is a potent and selective MYC inhibitor that is highly relevant for the development into clinically useful drugs for the treatment of MYC-driven cancer. Significance: Our findings demonstrate that the small-molecule MYCMI-7 binds MYC and inhibits interaction between MYC and MAX, thereby hampering MYC-driven tumor cell growth in culture and in vivo while sparing normal cells.

p53-Mediated Radiosensitization of 177Lu-DOTATATE in Neuroblastoma Tumor Spheroids.

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.

Integrative discovery of treatments for high-risk neuroblastoma.

Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.

A Patient-Derived Cell Atlas Informs Precision Targeting of Glioblastoma.

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents with a complex spectrum of genomic aberrations, but the pharmacological consequences of these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis of 100 patient-derived GBM cell cultures from the human glioma cell culture (HGCC) cohort. Exploring 1,544 drugs, we find that GBM has two main pharmacological subgroups, marked by differential response to proteasome inhibitors and mutually exclusive aberrations in TP53 and CDKN2A/B. We confirm this trend in cell and in xenotransplantation models, and identify both Bcl-2 family inhibitors and p53 activators as potentiators of proteasome inhibitors in GBM cells. We can further predict the responses of individual cell cultures to several existing drug classes, presenting opportunities for drug repurposing and design of stratified trials. Our functionally profiled biobank provides a valuable resource for the discovery of new treatments for GBM.