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

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Title: Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma
Authors: Nam, Yoonhee, Koo, Harim, Yang, Yingxi, Shin, Sang, Zhu, Zhihan, Kim, Donggeon, Cho, Hee J., Mu, Quanhua, Choi, Seung W., Sa, Jason K., Seo, Yun J., Kim, Yejin, Lee, Kyoungmin, Oh, Jeong-Woo, Kwon, Yong-Jun, Park, Woong-Yang, Kong, Doo-Sik, Seol, Ho J., Lee, Jung-Il, Park, Chul-Kee, Lee, Hye W., Yoon, Yeup, Wang, Jiguang
Publication Year: 2023
Collection: Columbia University: Academic Commons
Subject Terms: Glioblastoma multiforme, Brain--Cancer--Chemotherapy, Drug resistance in cancer cells, Machine learning--Industrial applications, Cancer--Genetic aspects
Description: 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 ...
Document Type: article in journal/newspaper
Language: English
DOI: 10.7916/mqt4-pw13
Availability: https://doi.org/10.7916/mqt4-pw13
Accession Number: edsbas.50A706D2
Database: BASE
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  Data: Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma
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  Data: <searchLink fieldCode="AR" term="%22Nam%2C+Yoonhee%22">Nam, Yoonhee</searchLink><br /><searchLink fieldCode="AR" term="%22Koo%2C+Harim%22">Koo, Harim</searchLink><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yingxi%22">Yang, Yingxi</searchLink><br /><searchLink fieldCode="AR" term="%22Shin%2C+Sang%22">Shin, Sang</searchLink><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Zhihan%22">Zhu, Zhihan</searchLink><br /><searchLink fieldCode="AR" term="%22Kim%2C+Donggeon%22">Kim, Donggeon</searchLink><br /><searchLink fieldCode="AR" term="%22Cho%2C+Hee+J%2E%22">Cho, Hee J.</searchLink><br /><searchLink fieldCode="AR" term="%22Mu%2C+Quanhua%22">Mu, Quanhua</searchLink><br /><searchLink fieldCode="AR" term="%22Choi%2C+Seung+W%2E%22">Choi, Seung W.</searchLink><br /><searchLink fieldCode="AR" term="%22Sa%2C+Jason+K%2E%22">Sa, Jason K.</searchLink><br /><searchLink fieldCode="AR" term="%22Seo%2C+Yun+J%2E%22">Seo, Yun J.</searchLink><br /><searchLink fieldCode="AR" term="%22Kim%2C+Yejin%22">Kim, Yejin</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+Kyoungmin%22">Lee, Kyoungmin</searchLink><br /><searchLink fieldCode="AR" term="%22Oh%2C+Jeong-Woo%22">Oh, Jeong-Woo</searchLink><br /><searchLink fieldCode="AR" term="%22Kwon%2C+Yong-Jun%22">Kwon, Yong-Jun</searchLink><br /><searchLink fieldCode="AR" term="%22Park%2C+Woong-Yang%22">Park, Woong-Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Kong%2C+Doo-Sik%22">Kong, Doo-Sik</searchLink><br /><searchLink fieldCode="AR" term="%22Seol%2C+Ho+J%2E%22">Seol, Ho J.</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+Jung-Il%22">Lee, Jung-Il</searchLink><br /><searchLink fieldCode="AR" term="%22Park%2C+Chul-Kee%22">Park, Chul-Kee</searchLink><br /><searchLink fieldCode="AR" term="%22Lee%2C+Hye+W%2E%22">Lee, Hye W.</searchLink><br /><searchLink fieldCode="AR" term="%22Yoon%2C+Yeup%22">Yoon, Yeup</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiguang%22">Wang, Jiguang</searchLink>
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  Data: 2023
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  Data: Columbia University: Academic Commons
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  Data: <searchLink fieldCode="DE" term="%22Glioblastoma+multiforme%22">Glioblastoma multiforme</searchLink><br /><searchLink fieldCode="DE" term="%22Brain--Cancer--Chemotherapy%22">Brain--Cancer--Chemotherapy</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+resistance+in+cancer+cells%22">Drug resistance in cancer cells</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning--Industrial+applications%22">Machine learning--Industrial applications</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer--Genetic+aspects%22">Cancer--Genetic aspects</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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 ...
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  Data: 10.7916/mqt4-pw13
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