Transcriptome Analysis Reveals Distinct Patterns Between the Invasive and Noninvasive Pituitary Neuroendocrine Tumors

Although most pituitary neuroendocrine tumors (PitNETs)/pituitary adenomas remain intrasellar, a significant proportion of tumors show parasellar invasive growth and 6% to 8% infiltrate the bone structures, thus affecting the prognosis. There is an unmet need to identify novel markers that can predict the parasellar growth of PitNETs. Furthermore, mechanisms that regulate bone invasiveness of PitNETs and factors related to tumor vascularization are largely unknown. We used genome-wide mRNA analysis in a cohort of 77 patients with PitNETs of different types to explore the differences in gene expression patterns between invasive and noninvasive tumors with respect to the parasellar growth and regarding the rare phenomenon of bone invasiveness. Additionally, we studied the genes correlated to the contrast enhancement quotient, a novel radiological parameter of tumor vascularization. Most of the genes differentially expressed related to the parasellar growth were genes involved in tumor invasiveness. Differentially expressed genes associated with bone invasiveness are involved in NF-κB pathway and antitumoral immune response. Lack of clear clustering regarding the parasellar and bone invasiveness may be explained by the influence of the cell lineage-related genes in this heterogeneous cohort of PitNETs. Our transcriptomics analysis revealed differences in the molecular fingerprints between invasive, including bone invasive, and noninvasive PitNETs, although without clear clustering. The contrast enhancement quotient emerged as a radiological parameter of tumor vascularization, correlating with several angiogenesis-related genes. Several of the top genes related to the PitNET invasiveness and vascularization have potential prognostic and therapeutic application requiring further research.

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.

Annotation of pituitary neuroendocrine tumors with genome-wide expression analysis

Pituitary neuroendocrine tumors (PitNETs) are common, generally benign tumors with complex clinical characteristics related to hormone hypersecretion and/or growing sellar tumor mass. PitNETs can be classified based on the expression pattern of anterior pituitary hormones and three main transcriptions factors (TF), SF1, PIT1 and TPIT that regulate differentiation of adenohypophysial cells. Here, we have extended this classification based on the global transcriptomics landscape using tumor tissue from a well-defined cohort comprising 51 PitNETs of different clinical and histological types. The molecular profiles were compared with current classification schemes based on immunohistochemistry. Our results identified three main clusters of PitNETs that were aligned with the main pituitary TFs expression patterns. Our analyses enabled further identification of specific genes and expression patterns, including both known and unknown genes, that could distinguish the three different classes of PitNETs. We conclude that the current classification of PitNETs based on the expression of SF1, PIT1 and TPIT reflects three distinct subtypes of PitNETs with different underlying biology and partly independent from the expression of corresponding hormones. The transcriptomic analysis reveals several potentially targetable tumor-driving genes with previously unknown role in pituitary tumorigenesis.

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.

We lack effective treatment for half of children with high-risk neuroblastoma. Here, the authors introduce an algorithm that can predict the effect of interventions on gene expression signatures associated with high disease processes and risk, and identify and validate promising drug targets.

PDTM-43. THERAPEUTIC TARGETS FROM BIG DATA: INTEGRATIVE DISCOVERY OF TREATMENTS FOR HIGH-RISK NEUROBLASTOMA

Despite major advances in the molecular exploration of pediatric cancers, approximately 50 % of children with high-risk neuroblastoma lack effective treatment. To identify new therapeutic options for this group of high-risk patients, we have combined integrative data analysis with experimental evaluation in patient-derived xenograft cells. We propose a new algorithm, TargetTranslator, which combines data from tumor biobanks, pharmacological databases, and cellular networks, to predict how particular targeted interventions will affect mRNA signatures associated with high patient risk. We find more than 80 known and novel targets to be associated with neuroblastoma risk and differentiation signatures. To evaluate these predictions, we performed RNA sequencing of drug-treated cell lines derived from high-risk patients and show that predicted compounds suppress risk signatures and malignant phenotypes. Using a xenograft model, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method (made available as a public tool, targettranslator.org) will enhance and expedite the discovery of risk-associated targets for pediatric and adult cancers.

Abstract 3395: TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma

Despite the many advances in the molecular characterization of neuroblastoma, effective treatments for high-risk patients are currently lacking. Using publically available data, integrated computational analysis offers new opportunities to uncover druggable subgroups. In the TargetTranslator project, we have combined state-of-the-art Big Data techniques to identify new targets in high-risk subgroups of neuroblastoma. Starting with clinical or genomic risk factors, TargetTranslator builds a consensus molecular signature across cohorts. This signature is scored against a massive amount of data from (i) childhood tumor biobanks (TARGET, R2), (ii) drug profiling data from cancer cell models (NIH-LINCS), and (iii) drug targets and pathways (STITCH, STRING, MSIGDB). As output, the system provides ranked lists of compounds, molecular targets and pathways for each risk factor, as well as an aggregated probability score. In a proof-of-concept study, we used TargetTranslator to recommend treatments for high-risk neuroblastoma subgroups, including COG high-risk, MYCN amplification, 11q deletion, and signatures of differentiation and ALK activation. Depending on risk group stratification, we detected between 21 and 680 substances (p&lt;0.001), most of which were strongly associated to approximately 10 key targets. In addition to confirming a key role for the PI3K/mTOR and MAPK pathways, we detected high scoring compounds in non-canonical pathways, including neurotransmission, GLI/SMO, ROCK and PKA. Experimental validation of 11 predicted drugs in patient-derived neuroblastoma lines confirmed our signatures and reduced viability. We also identified four new compounds that significantly (p&lt;0.05) suppressed MYCN protein levels (ROCK inhibitor fasudil, CNR2 agonist GW405833, CDK inhibitor AZD5438, lovastatin) at concentrations non-toxic in zebrafish embryos. By integrating data from patients, drugs, and drug-protein networks, we establish a new computational pipeline that predicts protein targets in specific patient subgroups. The TargetTranslator introduces a way to bridge drug effects with patient data and will be available as a user-friendly web tool on www.targettranslator.org in 2018.

Citation Format: Elin Almstedt, Caroline Wärn, Ramy Elgendy, Neda Hekmati, Emil Rosén, Ida Larsson, Rebecka Jörnsten, Cecilia Crona, Sven Nelander. TargetTranslator: Big data identifies non-canonical targets for high risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3395.