Clinical implementation of integrated whole-genome copy number and mutation profiling for glioblastoma

Ramkissoon, Shakti; Bi, Wenya Linda; Schumacher, Steven E.; Ramkissoon, Lori; Haidar, Sam; Knoff, David; Dubuc, Adrian M.; Brown, Loreal; Burns, Margot; Cryan, Jane; Abedalthagafi, Malak; Kang, Yun Jee; Schultz, Nikolaus; Reardon, David A.; Lee, Eudocia Q.; Rinne, Mikael L.; Norden, Andrew D.; Nayak, Lakshmi; Ruland, Sandra; Doherty, Lisa; LaFrankie, Debra C.; Horváth, Máté; Aizer, Ayal A.; Russo, Andrea; Arvold, Nils D.; Claus, Elizabeth B.; Al‐Mefty, Ossama; Johnson, Mark D.; Golby, Alexandra J.; Dunn, Ian F.; Chiocca, E. Antonio; Trippa, Lorenzo; Santagata, Sandro; Folkerth, Rebecca D.; Kantoff, Philip W.; Rollins, Barrett J.; Lindeman, Neal I.; Wen, Patrick Y.; Ligon, Azra H.; Beroukhim, Rameen; Alexander, Brian M.; Ligon, Keith L.

doi:10.1093/neuonc/nov015

Cited by 43 publications

(35 citation statements)

References 38 publications

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“…Contrary to adult oligodendrogliomas, the one anaplastic oligodendroglioma in our cohort did not harbor IDH1/2 mutations or 1p/19q co-deletion [19,22,26]. We detected KRAS G12V, EGFR R222C, and TERT promoter 2124C>T mutations, a Abbreviations: DIPG, diffuse intrinsic pontine glioma; EGFR, epidermal growth factor receptor; NOS, not otherwise specified; pHGG, pediatric high-grade glioma.…”

Section: Genomic Landscape Of Phggsmentioning

confidence: 51%

Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures

Johnson¹,

Severson²,

Vergilio³

et al. 2017

The Oncologist

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Section: Genomic Landscape Of Phggsmentioning

confidence: 51%

Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures

Johnson¹,

Severson²,

Vergilio³

et al. 2017

The Oncologist

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195

166

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“…Indeed, the unique biology, mutational landscape and predominant signaling pathways in HNC may explain the discrepancy in PD-L1 expression. It has been recently reported that PTEN loss-of-function mutations are frequent in glioblastoma (31.9% of specimens, TCGA data) (42). Furthermore, PD-L1 was upregulated after PTEN loss/PI3K activation in glioblastoma lines, suggesting that gliomas may rely more on this signaling pathway (17).…”

Section: Discussionmentioning

confidence: 99%

Identification of the Cell-Intrinsic and -Extrinsic Pathways Downstream of EGFR and IFNγ That Induce PD-L1 Expression in Head and Neck Cancer

et al. 2016

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Many cancer types, including head and neck cancers (HNC), express programmed death ligand 1 (PD-L1). Interaction between PD-L1 and its receptor, programmed death 1 (PD-1), inhibits the function of activated T cells and results in an immunosuppressive microenvironment, but the stimuli that induce PD-L1 expression are not well characterized. Interferon gamma (IFNγ) and the epidermal growth factor receptor (EGFR) utilize Janus kinase 2 (JAK2) as a common signaling node to transmit tumor cell-mediated extrinsic or intrinsic signals, respectively. In this study, we investigated the mechanism by which these factors upregulate PD-L1 expression in HNC cells in the context of JAK/STAT pathway activation, Th1 inflammation, and HPV status. We found that wild type, overexpressed EGFR significantly correlated with JAK2 and PD-L1 expression in a large cohort of HNC specimens. Furthermore, PD-L1 expression was induced in an EGFR- and JAK2/STAT1-dependent manner, and specific JAK2 inhibition prevented PD-L1 upregulation in tumor cells and enhanced their immunogenicity. Collectively, our findings suggest a novel role for JAK2/STAT1 in EGFR-mediated immune evasion, and therapies targeting this signaling axis may be beneficial to block PD-L1 upregulation found in a large subset of HNC tumors.

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“…IDH1/2 mutations were determined using immunohistochemistry, 22 mass spectrometry-based mutation genotyping (OncoMap), 22,23 or multiplex exome sequencing (OncoPanel) 8,24 depending on which genotyping technologies were available at the time of diagnosis. For this retrospective study, only gliomas with absence of IDH1/2 mutations as determined by full sequencing assay with OncoPanel, were included in our analyses as IDH-WT gliomas.…”

Section: Tissue Diagnosis and Genotypingmentioning

confidence: 99%

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

et al. 2016

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Background. High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. Methods. Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype. Results. Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features. Conclusion. Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.

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Clinical implementation of integrated whole-genome copy number and mutation profiling for glioblastoma

Cited by 43 publications

References 38 publications

Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures

Comprehensive Genomic Profiling of 282 Pediatric Low- and High-Grade Gliomas Reveals Genomic Drivers, Tumor Mutational Burden, and Hypermutation Signatures

Identification of the Cell-Intrinsic and -Extrinsic Pathways Downstream of EGFR and IFNγ That Induce PD-L1 Expression in Head and Neck Cancer

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

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