A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.

We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.

Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling?

Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.

Clinical trial links oncolytic immunoactivation to survival in glioblastoma.

Immunotherapy failures can result from the highly suppressive tumour microenvironment that characterizes aggressive forms of cancer such as recurrent glioblastoma (rGBM)1,2. Here we report the results of a first-in-human phase I trial in 41 patients with rGBM who were injected with CAN-3110-an oncolytic herpes virus (oHSV)3. In contrast to other clinical oHSVs, CAN-3110 retains the viral neurovirulence ICP34.5 gene transcribed by a nestin promoter; nestin is overexpressed in GBM and other invasive tumours, but not in the adult brain or healthy differentiated tissue4. These modifications confer CAN-3110 with preferential tumour replication. No dose-limiting toxicities were encountered. Positive HSV1 serology was significantly associated with both improved survival and clearance of CAN-3110 from injected tumours. Survival after treatment, particularly in individuals seropositive for HSV1, was significantly associated with (1) changes in tumour/PBMC T cell counts and clonal diversity, (2) peripheral expansion/contraction of specific T cell clonotypes; and (3) tumour transcriptomic signatures of immune activation. These results provide human validation that intralesional oHSV treatment enhances anticancer immune responses even in immunosuppressive tumour microenvironments, particularly in individuals with cognate serology to the injected virus. This provides a biological rationale for use of this oncolytic modality in cancers that are otherwise unresponsive to immunotherapy (ClinicalTrials.gov: NCT03152318 ).

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling.

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Clinical Importance of the lncRNA NEAT1 in Cancer Patients Treated with Immune Checkpoint Inhibitors.

PURPOSE: mAbs targeting the PD-1/PD-L1 immune checkpoint are powerful tools to improve the survival of patients with cancer. Understanding the molecular basis of clinical response to these treatments is critical to identify patients who can benefit from this immunotherapy. In this study, we investigated long noncoding RNA (lncRNA) expression in patients with cancer treated with anti-PD-1/PD-L1 immunotherapy. EXPERIMENTAL DESIGN: lncRNA expression profile was analyzed in one cohort of patients with melanoma and two independent cohorts of patients with glioblastoma (GBM) undergoing anti-PD-1/PD-L1 immunotherapy. Single-cell RNA-sequencing analyses were performed to evaluate lncRNA expression in tumor cells and tumor-infiltrating immune cells. RESULTS: We identified the lncRNA NEAT1 as commonly upregulated between patients with melanoma with complete therapeutic response and patients with GBM with longer survival following anti-PD-1/PD-L1 treatment. Gene set enrichment analyses revealed that NEAT1 expression was strongly associated with the IFNγ pathways, along with downregulation of cell-cycle-related genes. Single-cell RNA-sequencing analyses revealed NEAT1 expression across multiple cell types within the GBM microenvironment, including tumor cells, macrophages, and T cells. High NEAT1 expression levels in tumor cells correlated with increased infiltrating macrophages and microglia. In these tumor-infiltrating myeloid cells, we found that NEAT1 expression was linked to enrichment in TNFα/NFκB signaling pathway genes. Silencing NEAT1 suppressed M1 macrophage polarization and reduced the expression of TNFα and other inflammatory cytokines. CONCLUSIONS: These findings suggest an association between NEAT1 expression and patient response to anti-PD-1/PD-L1 therapy in melanoma and GBM and have important implications for the role of lncRNAs in the tumor microenvironment.

PPRX-1701, a nanoparticle formulation of 6'-bromoindirubin acetoxime, improves delivery and shows efficacy in preclinical GBM models.

Derivatives of the Chinese traditional medicine indirubin have shown potential for the treatment of cancer through a range of mechanisms. This study investigates the impact of 6'-bromoindirubin-3'-acetoxime (BiA) on immunosuppressive mechanisms in glioblastoma (GBM) and evaluates the efficacy of a BiA nanoparticle formulation, PPRX-1701, in immunocompetent mouse GBM models. Transcriptomic studies reveal that BiA downregulates immune-related genes, including indoleamine 2,3-dioxygenase 1 (IDO1), a critical enzyme in the tryptophan-kynurenine-aryl hydrocarbon receptor (Trp-Kyn-AhR) immunosuppressive pathway in tumor cells. BiA blocks interferon-γ (IFNγ)-induced IDO1 protein expression in vitro and enhances T cell-mediated tumor cell killing in GBM stem-like cell co-culture models. PPRX-1701 reaches intracranial murine GBM and significantly improves survival in immunocompetent GBM models in vivo. Our results indicate that BiA improves survival in murine GBM models via effects on important immunotherapeutic targets in GBM and that it can be delivered efficiently via PPRX-1701, a nanoparticle injectable formulation of BiA.

Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.