Combination drug screen identifies synergistic drug interaction of BCL-XL and class I histone deacetylase inhibitors in MYC-amplified medulloblastoma cells.

PURPOSE: Patients with MYC-amplified Group 3 medulloblastoma (MB) (subtype II) show poor progression-free survival rates. Class I histone deacetylase inhibitors (HDACi) are highly effective for the treatment of MYC-amplified MB in vitro and in vivo. Drug combination regimens including class I HDACi may represent an urgently needed novel treatment approach for this high risk disease. METHODS: A medium-throughput in vitro combination drug screen was performed in three MYC-amplified and one non-MYC-amplified MB cell line testing 75 clinically relevant drugs alone and in combination with entinostat. The drug sensitivity score (DSS) was calculated based on metabolic inhibition quantified by CellTiter-Glo. The six top synergistic combination hits were evaluated in a 5 × 5 combination matrix and a seven-ray design. Synergy was validated and characterized by cell counts, caspase-3-like-activity and poly-(ADP-ribose)-polymerase-(PARP)-cleavage. On-target activity of drugs was validated by immunoprecipitation and western blot. BCL-XL dependency of the observed effect was explored with siRNA mediated knockdown of BCL2L1, and selective inhibition with targeted compounds (A-1331852, A-1155463). RESULTS: 20/75 drugs effectively reduced metabolic activity in combination with entinostat in all three MYC-amplified cell lines (DSS ≥ 10). The combination entinostat and navitoclax showed the strongest synergistic interaction across all MYC-amplified cell lines. siRNA mediated knockdown of BCL2L1, as well as targeted inhibition with selective inhibitors showed BCL-XL dependency of the observed effect. Increased cell death was associated with increased caspase-3-like-activity. CONCLUSION: Our study identifies the combination of class I HDACi and BCL-XL inhibitors as a potential new approach for the treatment of MYC-amplified MB cells.

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data.

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://itrex.kitz-heidelberg.de.

Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids.

Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify image-based drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patient-derived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screen -specific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patient-derived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drug-sensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results ( R ≈ 0.7 ) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, combining drug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.

Functional Therapeutic Target Validation Using Pediatric Zebrafish Xenograft Models.

The survival rate among children with relapsed tumors remains poor, due to tumor heterogeneity, lack of directly actionable tumor drivers and multidrug resistance. Novel personalized medicine approaches tailored to each tumor are urgently needed to improve cancer treatment. Current pediatric precision oncology platforms, such as the INFORM (INdividualized Therapy FOr Relapsed Malignancies in Childhood) study, reveal that molecular profiling of tumor tissue identifies targets associated with clinical benefit in a subgroup of patients only and should be complemented with functional drug testing. In such an approach, patient-derived tumor cells are exposed to a library of approved oncological drugs in a physiological setting, e.g., in the form of animal avatars injected with patient tumor cells. We used molecularly fully characterized tumor samples from the INFORM study to compare drug screen results of individual patient-derived cell models in functional assays: (i) patient-derived spheroid cultures within a few days after tumor dissociation; (ii) tumor cells reisolated from the corresponding mouse PDX; (iii) corresponding long-term organoid-like cultures and (iv) drug evaluation with the corresponding zebrafish PDX (zPDX) model. Each model had its advantage and complemented the others for drug hit and drug combination selection. Our results provide evidence that in vivo zPDX drug screening is a promising add-on to current functional drug screening in precision medicine platforms.

Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM.

The international precision oncology program INFORM enrolls relapsed/refractory pediatric cancer patients for comprehensive molecular analysis. We report a two-year pilot study implementing ex vivo drug sensitivity profiling (DSP) using a library of 75-78 clinically relevant drugs. We included 132 viable tumor samples from 35 pediatric oncology centers in seven countries. DSP was conducted on multicellular fresh tumor tissue spheroid cultures in 384-well plates with an overall mean processing time of three weeks. In 89 cases (67%), sufficient viable tissue was received; 69 (78%) passed internal quality controls. The DSP results matched the identified molecular targets, including BRAF, ALK, MET, and TP53 status. Drug vulnerabilities were identified in 80% of cases lacking actionable (very) high-evidence molecular events, adding value to the molecular data. Striking parallels between clinical courses and the DSP results were observed in selected patients. Overall, DSP in clinical real-time is feasible in international multicenter precision oncology programs.

A Pharmacogenetic Study of VDR fok1 and TYMS Polymorphisms and Their Association With Glucocorticoid-Induced Osteonecrosis in Egyptian Children With Acute Lymphoblastic Leukemia.

Purpose: Osteonecrosis is a significant toxicity resulting from the treatment of pediatric Acute Lymphoblastic Leukemia (ALL). This study aimed to investigate the relationship between vitamin D receptor fok1 (VDR fok1) and thymidylate synthase (TYMS) gene polymorphisms with the glucocorticoid (GC) induced osteonecrosis (ON) in Egyptian pediatric ALL patients. In addition, to identify the possible association of genetic polymorphisms with other factors such as gender and ALL subtypes. Patients and Methods: A retrospective case-control study was conducted on 102 pediatric ALL patients under the age of 18 who were treated at Children Cancer Hospital Egypt according to St Jude SR/HR total XV protocol. The recruited patients were composed of 51 cases who developed GC-induced osteonecrosis and 51 age- and gender-matched patients who received glucocorticoids but remained osteonecrosis-free (controls). Genotyping of the VDR fok1 and TYMS genes was performed using restriction fragment length polymorphism (RFLP) and conventional PCR, respectively. Results: For the total 102 studied patients, the VDR fok1 single nucleotide polymorphisms (SNPs) frequency distribution were TT (8.8%), CT (34.3%), and CC (56.9%), while the TYMS tandem repeat gene variations were reported as 2R2R (20.6%), 2R3R (45.1%), and 3R3R (34.3%). VDR fok1 and TYMS polymorphic variants showed no association neither with gender; P-values 0.3808 and 0.1503, respectively, nor with ALL subtypes; P-values 0.9396 and 0.6596, respectively. The VDR fok1 polymorphisms showed a significant association with the development of ON; P-value = 0.003, on the other hand, TYMS tandem repeats did not show significant impact on osteonecrosis development; P-value = 0.411. Conclusion: This study showed a significant association between the VDR fok1 polymorphism and osteonecrosis. Such clinical pharmacogenetics results would be promising to discuss the possibility of dose adjustments aiming a regimen with the highest efficacy and least toxicity.