ArticleNeuro-oncology2019
Xenograft-based, platform-independent gene signatures to predict response to alkylating chemotherapy, radiation, and combination therapy for glioblastoma.
Article in Neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- GTP Signaling Links Metabolism, DNA Repair, and Responses to Genotoxic Stress.Cancer discovery · 2024Article
- Identification of glioblastoma stem cell-associated lncRNAs using single-cell RNA sequencing datasets.Stem cell reports · 2023Article
- Machine Learning & Molecular Radiation Tumor Biomarkers.Seminars in radiation oncology · 2023Review
- Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma.Neoplasia (New York, N.Y.) · 2023Article
- Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.NPJ genomic medicine · 2021Article
- The Role of Network Science in Glioblastoma.Cancers · 2021Review
- Expression of the Androgen Receptor Governs Radiation Resistance in a Subset of Glioblastomas Vulnerable to Antiandrogen Therapy.Molecular cancer therapeutics · 2020Article
- Establishment and Characterisation of Heterotopic Patient-Derived Xenografts for Glioblastoma.Cancers · 2020Article
- Effects of Brain Irradiation in Immune-Competent and Immune-Compromised Mouse Models.Radiation research · 2020Article
- Discovery of predictive biomarkers in malignant gliomas.Neuro-oncology · 2019Article
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12 authors at 4 institutions in 1 country.
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Abstract
backgroundPredictive molecular biomarkers to select optimal treatment for patients with glioblastoma and other cancers are lacking. New strategies are needed when large randomized trials with correlative molecular data are not feasible.
methodsGene signatures (GS) were developed from 31 orthotopic glioblastoma patient-derived xenografts (PDXs), treated with standard therapies, to predict benefit from radiotherapy (RT-GS), temozolomide (Chemo-GS), or the combination (ChemoRT-GS). Independent validation was performed in a heterogeneously treated clinical cohort of 502 glioblastoma patients with overall survival as the primary endpoint. Multivariate Cox analysis was used to adjust for confounding variables and evaluate interactions between signatures and treatment.
resultsPDX models recapitulated the clinical heterogeneity of glioblastoma patients. RT-GS, Chemo-GS, and ChemoRT-GS were correlated with benefit from treatment in the PDX models. In independent clinical validation, higher RT-GS scores were associated with increased survival only in patients receiving RT (P = 0.0031, hazard ratio [HR] = 0.78 [0.66-0.92]), higher Chemo-GS scores were associated with increased survival only in patients receiving chemotherapy (P < 0.0001, HR = 0.66 [0.55-0.8]), and higher ChemoRT-GS scores were associated with increased survival only in patients receiving ChemoRT (P = 0.0001, HR = 0.54 [0.4-0.74]). RT-GS and ChemoRT-GS had significant interactions with treatment on multivariate analysis (P = 0.0009 and 0.02, respectively), indicating that they are bona fide predictive biomarkers.
conclusionsUsing a novel PDX-driven methodology, we developed and validated 3 platform-independent molecular signatures that predict benefit from standard of care therapies for glioblastoma. These signatures may be useful to personalize glioblastoma treatment in the clinic and this approach may be a generalizable method to identify predictive biomarkers without resource-intensive randomized trials.
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