Glioblastoma (GBM) is the most common primary adult brain tumor. Despite extensive efforts, the median survival for GBM patients is approximately 14 months. GBM therapy could benefit greatly from patient-specific targeted therapies that maximize treatment efficacy. Here we report a platform termed SynergySeq to identify drug combinations for the treatment of GBM by integrating information from The Cancer Genome Atlas (TCGA) and the Library of Integrated Network-Based Cellular Signatures (LINCS). We identify differentially expressed genes in GBM samples and devise a consensus gene expression signature for each compound using LINCS L1000 transcriptional profiling data. The SynergySeq platform computes disease discordance and drug concordance to identify combinations of FDA-approved drugs that induce a synergistic response in GBM. Collectively, our studies demonstrate that combining disease-specific gene expression signatures with LINCS small molecule perturbagen-response signatures can identify preclinical combinations for GBM, which can potentially be tested in humans.

Human endogenous retroviruses (HERVs) are ancestral viral relics that constitute nearly 8% of the human genome. Although normally silenced, the most recently integrated provirus HERV-K (HML-2) can be reactivated in certain cancers. Here, we report pathological expression of HML-2 in malignant gliomas in both cerebrospinal fluid and tumor tissue that was associated with a cancer stem cell phenotype and poor outcomes. Using single-cell RNA-Seq, we identified glioblastoma cellular populations with elevated HML-2 transcripts in neural progenitor–like cells (NPC-like) that drive cellular plasticity. Using CRISPR interference, we demonstrate that HML-2 critically maintained glioblastoma stemness and tumorigenesis in both glioblastoma neurospheres and intracranial orthotopic murine models. Additionally, we demonstrate that HML-2 critically regulated embryonic stem cell programs in NPC-derived astroglia and altered their 3D cellular morphology by activating the nuclear transcription factor OCT4, which binds to an HML-2–specific long-terminal repeat (LTR5Hs). Moreover, we discovered that some glioblastoma cells formed immature retroviral virions, and inhibiting HML-2 expression with antiretroviral drugs reduced reverse transcriptase activity in the extracellular compartment, tumor viability, and pluripotency. Our results suggest that HML-2 fundamentally contributes to the glioblastoma stem cell niche. Because persistence of glioblastoma stem cells is considered responsible for treatment resistance and recurrence, HML-2 may serve as a unique therapeutic target.

Most children with medulloblastoma (MB) achieve remission, but some face very aggressive metastatic tumors. Their dismal outcome highlights the critical need to advance therapeutic approaches that benefit such high-risk patients. Minnelide, a clinically relevant analog of the natural product triptolide, has oncostatic activity in both preclinical and early clinical settings. Despite its efficacy and tolerable toxicity, this compound has not been evaluated in MB. Utilizing a bioinformatic data set that integrates cellular drug response data with gene expression, we predicted that Group 3 (G3) MB, which has a poor 5-year survival, would be sensitive to triptolide/Minnelide. We subsequently showed that both triptolide and Minnelide attenuate the viability of G3 MB cells ex vivo. Transcriptomic analyses identified MYC signaling, a pathologically relevant driver of G3 MB, as a downstream target of this class of drugs. We validated this MYC dependency in G3 MB cells and showed that triptolide exerts its efficacy by reducing both MYC transcription and MYC protein stability. Importantly, Minnelide acted on MYC to reduce tumor growth and leptomeningeal spread, which resulted in improved survival of G3 MB animal models. Moreover, Minnelide improved the efficacy of adjuvant chemotherapy, further highlighting its potential for the treatment of MYC-driven G3 MB.

Glioblastoma (GBM) remains the most common and lethal adult malignant primary brain cancer with few treatment options. A significant issue hindering GBM therapeutic development is intratumor heterogeneity and plasticity. GBM tumors contain neoplastic cells within a fluid spectrum of diverse transcriptional states. Identifying effective therapeutics requires a platform that predicts the differential sensitivity and resistance of these states to various treatments. Here, we develop scFOCAL ( S ingle- C ell F ramework for - O mics C onnectivity and A nalysis via L 1000), to quantify the cellular drug sensitivity and resistance landscape. Using single-cell RNA sequencing of newly diagnosed and recurrent GBM tumors, we identify compounds from the LINCS L1000 database with transcriptional response signatures selectively discordant with distinct GBM cell states, and leverage this capability to predict combination synergy. We validate the significance of these findings in vitro, ex vivo, and in vivo, and identify a combination of an OLIG2 inhibitor and Depatux-M for the treatment of GBM. Our studies suggest that scFOCAL identifies cell states that are sensitive and resistant to targeted therapies in GBM using a measure of cell and drug connectivity, which can be applied to identify new synergistic combinations.

Circulating tumor cells (CTCs), a population of cancer cells that represent the seeds of metastatic nodules, are a promising model system for studying metastasis. However, the expansion of patient-derived CTCs ex vivo is challenging and dependent on the collection of high numbers of CTCs, which are ultra-rare. Here we report the development of a combined CTC and cultured CTC-derived xenograft (CDX) platform for expanding and studying patient-derived CTCs from metastatic colon, lung, and pancreatic cancers. The propagated CTCs yielded a highly aggressive population of cells that could be used to routinely and robustly establish primary tumors and metastatic lesions in CDXs. Differential gene analysis of the resultant CTC models emphasized a role for NF-κB, EMT, and TGFβ signaling as pan-cancer signaling pathways involved in metastasis. Furthermore, metastatic CTCs were identified through a prospective five-gene signature (BCAR1, COL1A1, IGSF3, RRAD, and TFPI2). Whole-exome sequencing of CDX models and metastases further identified mutations in constitutive photomorphogenesis protein 1 (COP1) as a potential driver of metastasis. These findings illustrate the utility of the combined patient-derived CTC model and provide a glimpse of the promise of CTCs in identifying drivers of cancer metastasis.