The proteomic landscape of glioblastoma recurrence reveals novel and targetable immunoregulatory drivers

Sep 30, 2022·
Nazanin Tatari
,
Shahbaz Khan
,
Julie Livingstone
,
Kui Zhai
,
Dillon Mckenna
,
Vladimir Ignatchenko
,
Chirayu Chokshi
,
William D. Gwynne
,
Manoj Singh
,
Spencer Revill
,
Nicholas Mikolajewicz
Chenghao Zhu
Chenghao Zhu
,
Jennifer Chan
,
Cynthia Hawkins
,
Jian-Qiang Lu
,
John P. Provias
,
Kjetil Ask
,
Sorana Morrissy
,
Samuel Brown
,
Tobias Weiss
,
Michael Weller
,
Hong Han
,
Jeffrey N. Greenspoon
,
Jason Moffat
,
Chitra Venugopal
,
Paul C. Boutros
,
Sheila K. Singh
,
Thomas Kislinger
· 0 min read
Abstract
Glioblastoma (GBM) is characterized by extensive cellular and genetic heterogeneity. Its initial presentation as primary disease (pGBM) has been subject to exhaustive molecular and cellular profiling. By contrast, our understanding of how GBM evolves to evade the selective pressure of therapy is starkly limited. The proteomic landscape of recurrent GBM (rGBM), which is refractory to most treatments used for pGBM, are poorly known. We, therefore, quantified the transcriptome and proteome of 134 patient-derived pGBM and rGBM samples, including 40 matched pGBM–rGBM pairs. GBM subtypes transition from pGBM to rGBM towards a preferentially mesenchymal state at recurrence, consistent with the increasingly invasive nature of rGBM. We identified immune regulatory/suppressive genes as important drivers of rGBM and in particular 2–5-oligoadenylate synthase 2 (OAS2) as an essential gene in recurrent disease. Our data identify a new class of therapeutic targets that emerge from the adaptive response of pGBM to therapy, emerging specifically in recurrent disease and may provide new therapeutic opportunities absent at pGBM diagnosis.
Type
Publication
Acta Neuropathologica
publications
Chenghao Zhu
Authors
Research Assistant Professor
Chenghao Zhu is a Research Assistant Professor in the NCI-designated Cancer Center at Sanford Burnham Prebys. His research focuses on developing computational methods and software for proteogenomics and applying proteogenomics to cancer diagnosis, prognosis, and clinico-epidemiologic questions.