The evolutionary pattern of mutations in glioblastoma reveals therapy-mediated selection
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Andrea M. Muscat1,2, Nicholas C. Wong3,4, Katharine J. Drummond5,6, Elizabeth M. Algar7,8, Mustafa Khasraw1,2,9, Roel Verhaak10, Kathryn Field5,6, Mark A. Rosenthal11 and David M. Ashley1,2
1School of Medicine, Deakin University, Geelong, Victoria, Australia
2Cancer Services, Barwon Health, Geelong, Victoria, Australia
3Department of Pediatrics, The University of Melbourne, Parkville, Victoria, Australia
4Monash Bioinformatics Platform, Monash University, Clayton, Victoria, Australia
5Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, Victoria, Australia
6Department of Surgery, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
7Center for Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia
8Department of Molecular and Translational Science, Monash University, Clayton, Victoria, Australia
9NHMRC Clinical Trials Center, University of Sydney, Sydney, New South Wales, Australia
10The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
11Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
David M. Ashley, email: [email protected]
Keywords: glioblastoma, mutation profiling, neutral evolution, selection pressure, tumor heterogeneity
Received: August 02, 2017 Accepted: October 05, 2017 Published: December 15, 2017
Glioblastoma presents as a heterogeneous disease with poor prognosis despite the use of multimodal therapy. Analysis of genomic DNA changes between initial diagnosis and recurrence in response to standard treatment protocols would enhance understanding of disease progression and better inform new treatment strategies. A cohort of 21 patients with primary glioblastoma were examined between diagnosis and first recurrence. This study presented a rare opportunity to characterize molecular alterations in tumors observed in three patients who received no therapeutic intervention, other than surgery, offering a unique control. We focused this study by comparing the dynamic mutation profiles between the primary tumors and their matched recurrent counterparts. Molecular profiling of tumors was performed using multiplexed targeted deep sequencing of 409 well characterized cancer-associated genes, achieving a mean read depth of 1272 x. Three levels of evidence suggested an evolutionary pattern consistent with a response to therapy-mediated selection pressures exists in treated patients: 1) variant burden was reduced in recurrent tumors, 2) neutral evolutionary dynamics apparent in untreated tumors shifted toward a non-neutral mode of evolution in treated patients at recurrence, and 3) the recurrent tumor of one patient displayed an increased mutation rate attributable to a temozolomide-associated hypermutator phenotype. Our observations suggest that current treatment modalities are likely to fail in achieving long term remission with the majority of relapse samples containing distinct mutations when compared to primary diagnostic samples.
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