Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma.

BACKGROUND: Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. METHODS: We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. RESULTS: In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e-4 for progression-free survival (PFS) and 3.63e-4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher's exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e-4 for PFS and 3.66e-4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). CONCLUSIONS: We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.

DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening.

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image; this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method's accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R2 correlation.

High-Content Analysis-Based Sensitivity Prediction and Novel Therapeutics Screening for c-Met-Addicted Glioblastoma.

(1) Background: Recent advances in precision oncology research rely on indicating specific genetic alterations associated with treatment sensitivity. Developing ex vivo systems to identify cancer patients who will respond to a specific drug remains important. (2) Methods: cells from 12 patients with glioblastoma were isolated, cultured, and subjected to high-content screening. Multi-parameter analyses assessed the c-Met level, cell viability, apoptosis, cell motility, and migration. A drug repurposing screen and large-scale drug sensitivity screening data across 59 cancer cell lines and patient-derived cells were obtained from 125 glioblastoma samples. (3) Results: High-content analysis of patient-derived cells provided robust and accurate drug responses to c-Met-targeted agents. Only the cells of one glioblastoma patient (PDC6) showed elevated c-Met level and high susceptibility to the c-Met inhibitors. Multi-parameter image analysis also reflected a decreased c-Met expression and reduced cell growth and motility by a c-Met-targeting antibody. In addition, a drug repurposing screen identified Abemaciclib as a distinct CDK4/6 inhibitor with a potent c-Met-inhibitory function. Consistent with this, we present large-scale drug sensitivity screening data showing that the Abemaciclib response correlates with the response to c-Met inhibitors. (4) Conclusions: Our study provides a new insight into high-content screening platforms supporting drug sensitivity prediction and novel therapeutics screening.

Secretome analysis of patient-derived GBM tumor spheres identifies midkine as a potent therapeutic target.

Glioblastoma (GBM) is the most lethal primary brain tumor with few treatment options. The survival of glioma-initiating cells (GICs) is one of the major factors contributing to treatment failure. GICs frequently produce and respond to their own growth factors that support cell proliferation and survival. In this study, we aimed to identify critical autocrine factors mediating GIC survival and to evaluate the anti-GBM effect of antagonizing these factors. Proteomic analysis was performed using conditioned media from two different patient-derived GBM tumor spheres under a growth factor-depleted status. Then, the antitumor effects of inhibiting an identified autocrine factor were evaluated by bioinformatic analysis and molecular validation. Proteins secreted by sphere-forming GICs promote cell proliferation/survival and detoxify reactive oxygen species (ROS). Among these proteins, we focused on midkine (MDK) as a clinically significant and pathologically relevant autocrine factor. Antagonizing MDK reduced the survival of GBM tumor spheres through the promotion of cell cycle arrest and the consequent apoptotic cell death caused by oxidative stress-induced DNA damage. We also identified PCBP4, a novel molecular predictor of resistance to anti-MDK treatment. Collectively, our results indicate that MDK inhibition is an important therapeutic option by suppressing GIC survival through the induction of ROS-mediated cell cycle arrest and apoptosis.

The Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling.

Glioblastoma is a highly aggressive and lethal brain tumor, with limited treatment options. Abnormal activation of the neddylation pathway is observed in glioblastoma, and the NEDD8-activating enzyme (NAE) inhibitor, MLN4924, was previously shown to be effective in glioblastoma cell line models. However, its effect has not been tested in patient-derived glioblastoma stem cells. We first analyzed public data to determine whether NEDD8 pathway proteins are important in glioblastoma development and patient survival. NAE1 and UBA3 levels increased in glioblastoma patients; high NEDD8 levels were associated with poor clinical outcomes. Immunohistochemistry results also supported this result. The effects of MLN4924 were evaluated in 4 glioblastoma cell lines and 15 patient-derived glioblastoma stem cells using high content analysis. Glioblastoma cell lines and patient-derived stem cells were highly susceptible to MLN4924, while normal human astrocytes were resistant. In addition, there were various responses in 15 patient-derived glioblastoma stem cells upon MLN4924 treatment. Genomic analyses indicated that MLN4924 sensitive cells exhibited enrichment of Extracellular Signal Regulated Kinase (ERK) and Protein kinase B (AKT, also known as PKB) signaling. We verified that MLN4924 inhibits ERK and AKT phosphorylation in MLN4924 sensitive cells. Our findings suggest that patient-derived glioblastoma stem cells in the context of ERK and AKT activation are sensitive and highly regulated by neddylation inhibition.

Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers.

BACKGROUND: Gynecologic malignancy is one of the leading causes of mortality in female adults worldwide. Comprehensive genomic analysis has revealed a list of molecular aberrations that are essential to tumorigenesis, progression, and metastasis of gynecologic tumors. However, targeting such alterations has frequently led to treatment failures due to underlying genomic complexity and simultaneous activation of various tumor cell survival pathway molecules. A compilation of molecular characterization of tumors with pharmacological drug response is the next step toward clinical application of patient-tailored treatment regimens. RESULTS: Toward this goal, we establish a library of 139 gynecologic tumors including epithelial ovarian cancers (EOCs), cervical, endometrial tumors, and uterine sarcomas that are genomically and/or pharmacologically annotated and explore dynamic pharmacogenomic associations against 37 molecularly targeted drugs. We discover lineage-specific drug sensitivities based on subcategorization of gynecologic tumors and identify TP53 mutation as a molecular determinant that elicits therapeutic response to poly (ADP-Ribose) polymerase (PARP) inhibitor. We further identify transcriptome expression of inhibitor of DNA biding 2 (ID2) as a potential predictive biomarker for treatment response to olaparib. CONCLUSIONS: Together, our results demonstrate the potential utility of rapid drug screening combined with genomic profiling for precision treatment of gynecologic cancers.

Novel Semi-Replicative Retroviral Vector Mediated Double Suicide Gene Transfer Enhances Antitumor Effects in Patient-Derived Glioblastoma Models.

As glioblastomas are mostly localized infiltrative lesions, gene therapy based on the retroviral replicating vector (RRV) system is considered an attractive strategy. Combinations of multiple suicide genes can circumvent the limitations associated with each gene, achieving direct and synergistic cytotoxic effects, along with bystander cell killing. In this study, we constructed a semi-and pseudotyped-RRV (sp-RRV) system harboring two suicide genes-herpes simplex virus type 1 thymidine kinase (TK) and yeast cytosine deaminase (CD)-to verify the dissemination and antitumor efficacy of our sp-RRV system (spRRVe-sEF1α-TK/sRRVgp-sEF1α-CD) in seven patient-derived glioblastoma stem-like cells (GSCs). Flow cytometry and high-content analysis revealed a wide range of transduction efficiency and good correlation between the delivery of therapeutic genes and susceptibility to the prodrugs ganciclovir and 5-fluorocytosine in patient-derived GSCs in vitro. Intra-tumoral delivery of spRRVe-sEF1α-TK/sRRVgp-sEF1α-CD, combined with prodrug treatment, synergistically inhibited cell proliferation and angiogenesis while increasing apoptosis and the depletion of tumor-associated macrophages in orthotopic glioblastoma xenografts. Genomic profiling of patient-derived GSCs revealed that the key genes preventing sp-RRV infection and transmission were associated with cell adhesion, migration, development, differentiation, and proliferation. This is the first report demonstrating that a novel sp-RRV-mediated TK/CD double suicide gene transfer system has high oncolytic power against extremely heterogeneous and treatment-refractory glioblastomas.

Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy.

Outcomes of anticancer therapy vary dramatically among patients due to diverse genetic and molecular backgrounds, highlighting extensive intertumoral heterogeneity. The fundamental tenet of precision oncology defines molecular characterization of tumors to guide optimal patient-tailored therapy. Towards this goal, we have established a compilation of pharmacological landscapes of 462 patient-derived tumor cells (PDCs) across 14 cancer types, together with genomic and transcriptomic profiling in 385 of these tumors. Compared with the traditional long-term cultured cancer cell line models, PDCs recapitulate the molecular properties and biology of the diseases more precisely. Here, we provide insights into dynamic pharmacogenomic associations, including molecular determinants that elicit therapeutic resistance to EGFR inhibitors, and the potential repurposing of ibrutinib (currently used in hematological malignancies) for EGFR-specific therapy in gliomas. Lastly, we present a potential implementation of PDC-derived drug sensitivities for the prediction of clinical response to targeted therapeutics using retrospective clinical studies.

Potent effect of the MDM2 inhibitor AMG232 on suppression of glioblastoma stem cells.

Testing new ways to identify untapped opportunities for glioblastoma therapies remains highly significant. Amplification and overexpression of MDM2 gene is frequent in glioblastoma and disrupting the MDM2-p53 interaction is a promising strategy to treat the cancer. RG7112 is the first-in class inhibitor and recently discovered AMG232 is the most potent MDM2 inhibitor known to date. Here, we compared the effects of these two clinical MDM2 inhibitors in six glioblastoma cell lines and ten patient-derived glioblastoma stem cells. Targeted sequencing of the TP53, MDM2 genes and whole transcriptome analysis were conducted to verify genetic status associated with sensitivity and resistance to the drugs. Although TP53 wild-type glioblastoma cell lines are similarly sensitive to AMG232 and RG7112, we found that four TP53 wild-type out of ten patient-derived glioblastoma cells are much more sensitive to AMG232 than RG7112 (average IC50 of 76 nM vs. 720 nM). Among these, 464T stem cells containing MDM2 gene amplification were most sensitive to AMG232 with IC50 of 5.3 nM. Moreover, AMG232 exhibited higher selectivity against p53 wild-type cells over p53 mutant stem cells compared to RG7112 (average selectivity of 512-fold vs. 16.5-fold). Importantly, we also found that AMG232 is highly efficacious in three-dimensional (3D) tumor spheroids growth and effectively inhibits the stemness-related factors, Nestin and ZEB1. Our data provide new evidence that glioblastoma stem cells have high susceptibility to AMG232 suggesting the potential clinical implications of MDM2 inhibition for glioblastoma treatment. These will facilitate additional preclinical and clinical studies evaluating MDM2 inhibitors in glioblastoma and direct further efforts towards developing better MDM2-targeted therapeutics.