Single-cell molecular profiling using ex vivo functional readouts fuels precision oncology in glioblastoma
Panovska, D.; Nazari, P.; Cole, B.; Creemers, P.-J.; Derweduwe, M.; Solie, L.; Van Gassen, S.; Claeys, A.; Verbeke, T.; Saeys, Y.; Van Der Planken, D.; Bosisio, F. M.; Put, E.; Bamps, S.; Clement, P.; Verfaillie, M.; Sciot, R.; Ligon, K. L.; De Vleeschouwer, S.; Martinez, A.; De Smet, F.
Show abstract
BackgroundFunctional profiling of freshly isolated glioblastoma cells is being evaluated as a next-generation method for precision oncology. While promising, its success largely depends on the method to evaluate treatment activity which requires sufficient resolution and specificity. MethodsHere, we describe the precision oncology by single-cell profiling using ex vivo readouts of functionality (PROSPERO) assay to evaluate the intrinsic susceptibility of high- grade brain tumor cells to respond to therapy. Different from other assays, PROSPERO extends beyond life/death screening by rapidly evaluating acute molecular drug responses at single-cell resolution. ResultsThe PROSPERO assay was developed by correlating short-term single-cell molecular signatures using CyTOF to long-term cytotoxicity readouts in representative patient- derived glioblastoma cell cultures (n=14) that were exposed to radiotherapy and the small- molecule p53/MDM2 inhibitor AMG232. The predictive model was subsequently projected to evaluate drug activity in freshly resected GBM samples from patients (n=34). Here, PROSPERO revealed an overall limited capacity of tumor cells to respond to therapy, as reflected by the inability to induce key molecular markers upon ex vivo treatment exposure, while retaining proliferative capacity, insights that were validated in PDX models. This approach also allowed the investigation of cellular plasticity, which in PDCLs highlighted therapy-induced proneural-to-mesenchymal transitions, while in patients samples this was more heterogeneous. ConclusionPROSPERO provides a precise way to evaluate therapy efficacy by measuring molecular drug responses using specific biomarker changes in freshly resected brain tumor samples, in addition to providing key functional insights in cellular behavior, which may ultimately complement standard, clinical biomarker evaluations.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images 93%
- Multiplexed drug testing of tumor slices using a microfluidic platform 92%
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma 92%
Similar papers in this journal
- Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns 95%
- Spatial transcriptomic analysis of Sonic Hedgehog Medulloblastoma identifies that the loss of heterogeneity and promotion of differentiation underlies the response to CDK4/6 inhibition 95%
- Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts 94%
Similar papers in this journal
- Pyruvate carboxylation identifies Glioblastoma Stem-like Cells opening new metabolic strategy to prevent tumor recurrence 95%
- Spatial profiling of longitudinal glioblastoma reveals consistent changes in cellular architecture, post-treatment 94%
- Preclinical efficacy of combinatorial B7-H3 CAR T cells and ONC206 against diffuse intrinsic pontine glioma 94%
Similar papers in this journal
- Chemoresistome Mapping in Individual Breast Cancer Patients Unravels Diversity in Dynamic Transcriptional Adaptation 93%
- Prognostic association of immunoproteasome expression in solid tumours is governed by the immediate immune environment 91%
- Transcriptome-wide analysis of circRNA and RBP profiles and their molecular and clinical relevance for GBM 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.