Utilisation of semiconductor sequencing for the detection of predictive biomarkers in glioblastoma

Abstract

The standard treatment for glioblastoma involves a combination of surgery, radiation and chemotherapy but have limited impact on survival. The exponential increase in targeted agents directed at pivotal oncogenic pathways now provide new therapeutic opportunities for this tumour type. However, lack of comprehensive precision oncology testing at diagnosis means such therapeutic opportunities are potentially overlooked. To investigate the role of semiconductor sequencing for detection of predictive biomarkers in routine glioblastoma samples we have undertaken analysis of test trending data generated by a clinically validated next generation sequencing platform designed to capture actionable genomic variants distributed across 505 genes. Analysis was performed across a cohort of 55 glioblastoma patients. Analysis of trending data has revealed a complex and rich actionable mutational landscape in which 166 actionable mutations were detected across 36 genes linked to 17 off label targeted therapy protocols and 111 clinical trials. The majority of patients harboured three or more actionable mutations affecting key cancer related regulatory networks including the PI3K/AKT/MTOR and RAS/RAF/MEK/MAPK signalling pathways, DNA-damage repair pathways and cell cycle checkpoints. Linkage with immunotherapy and PARP inhibitors was identified in 44% of glioblastoma patients as a consequence of alterations in DNA-damage repair genes. Taken together our data indicates that precision oncology testing utilising semiconductor sequencing can be used to identify a broad therapeutic armamentarium of targeted therapies and immunotherapies that can be potentially employed for the improved clinical management of glioblastoma patients.

Fig 1. The actionable genomic landscape in glioblastoma.

A) Pie chart showing frequency of altered genes in glioblastoma (n = 55). Segments not showing percentages have a frequency of <6%. B) Bar chart showing the ten most frequently genetically altered genes including variant type.

Fig 2. Frequency of actionable genetic alterations by variant type (inner ring) and by gene (outer ring) in all variants detected (n = 164).

Fig 3. Frequency of altered genes in each variant type.

A) Copy number amplifications; B) Copy number deletions; C) Multiple nucleotide variants (MNV); D) gene fusions and E) Single nucleotide variants (SNV).