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

• Published in: Genome medicine Vol. 15; no. 1; pp. 16 - 15
• Main Authors: Nam, Yoonhee, Koo, Harim, Yang, Yingxi, Shin, Sang, Zhu, Zhihan, Kim, Donggeon, Cho, Hee Jin, Mu, Quanhua, Choi, Seung Won, Sa, Jason K, Seo, Yun Jee, Kim, Yejin, Lee, Kyoungmin, Oh, Jeong-Woo, Kwon, Yong-Jun, Park, Woong-Yang, Kong, Doo-Sik, Seol, Ho Jun, Lee, Jung-Il, Park, Chul-Kee, Lee, Hye Won, Yoon, Yeup, Wang, Jiguang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.03.2023; BioMed Central; BMC
• Subjects: Aminolevulinic acid; Brain cancer; Brain Neoplasms - drug therapy; Brain Neoplasms - genetics; Brain Neoplasms - metabolism; Brain tumors; Cancer; Cancer genomics; Cell cycle; Chemoresistance; Chemotherapy; DNA methylation; Drug resistance; Drug Resistance, Neoplasm - genetics; Early Growth Response Transcription Factors; EGR-4 protein; Epidermal growth factor; Gene expression; Genes; Genomes; Genomics; Glioblastoma; Glioblastoma - drug therapy; Glioblastoma - genetics; Glioblastoma - metabolism; Glioblastoma multiforme; Glioma - genetics; Glioma cells; Humans; Intra-tumoral heterogeneity; Isocitrate dehydrogenase; Machine learning; Molecular modelling; Mutation; Patients; Pharmacogenetics; Pharmacogenomics; Prediction models; Proteins; PTEN protein; Temozolomide; Temozolomide - pharmacology; Temozolomide - therapeutic use; Transcriptomics; United States > US; Intra-tumoral heterogeneity; Temozolomide; Cancer genomics; Glioblastoma; Machine learning; Pharmacogenomics
• ISSN: 1756-994X; 1756-994X
• DOI: 10.1186/s13073-023-01165-8

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. 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. 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 ). 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.