Intratumoral heterogeneity of MYC drives medulloblastoma metastasis and angiogenesis.

BACKGROUND: Intratumoral heterogeneity is crucially involved in metastasis, resistance to therapy, and cancer relapse. Amplifications of the proto-oncogene MYC display notable heterogeneity at the single-cell level and are associated with a particularly dismal prognosis in high-risk medulloblastomas (MBs). The aim of this study was to establish the relevance of interclonal cross-talk between MYC-driven and non-MYC-driven MB cells. METHODS: We used fluorescence in situ hybridization, single-cell transcriptomics, and immunohistochemistry, in vitro isogenic cell models, non-targeted proteomics, mass spectrometry-based metabolite quantification, HUVECs tube formation assay, and orthotopic in vivo experiments to investigate interclonal cross-talk in MB. RESULTS: We found that the release of lactate dehydrogenase A (LDHA) from MYC-driven cells facilitates metastatic seeding and outgrowth, while secretion of dickkopf WNT signaling pathway inhibitor 3 from non-MYC-driven cells promotes tumor angiogenesis. This tumor-supporting interaction between both subclones was abrogated by targeting the secretome through pharmacological and genetic inhibition of LDHA, which significantly suppressed tumor cell migration. CONCLUSION: Our study reveals the functional relevance of clonal diversity and highlights the therapeutic potential of targeting the secretome to interrupt interclonal communication and progression in high-risk MB.

Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro.

In cancer pharmacology, a drug candidate's therapeutic potential is typically expressed as its ability to suppress cell growth. Different methods in assessing the cell phenotype and calculating the drug effect have been established. However, inconsistencies in drug response outcomes have been reported, and it is still unclear whether and to what extent the choice of data post-processing methods is responsible for that. Studies that systematically examine these questions are rare. Here, we compare three established calculation methods on a collection of nine in vitro models of glioblastoma, exposed to a library of 231 clinical drugs. The therapeutic potential of the drugs is determined on the growth curves, using growth inhibition 50% (GI50) and point-of-departure (PoD) as the criteria. An effect is detected on 36% of the drugs when relying on GI50 and on 27% when using PoD. For the area under the curve (AUC), a threshold of 9.5 or 10 could be set to discriminate between the drugs with and without an effect. GI50, PoD, and AUC are highly correlated. The ranking of substances by different criteria varies somewhat, but the group of the top 20 substances according to one criterion typically includes 17-19 top candidates according to another. In addition to generating preclinical values with high clinical potential, we present off-target appreciation of top substance predictions by interrogating the drug response data of non-cancer cells in our calculation technology.