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.

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

“… 50 ), which has many cell surface receptors upstream and groups of transcriptional events (e.g., ERK transcriptional program) downstream. For MAPK-related cancers, biomarkers in the intermediate layer (RAS/RAF/MEK or P38) have proven to be most efficacious, as opposed to the biomarkers in the cell surface receptor (input) or downstream (output) layers 51 . This additionally supports our notion that the best biomarkers are to be discovered at the fulcrum points and layers of the bow-tie-structured biological networks.…”

“…Interestingly, in the RAS/RAF/MEK/MAPK signaling pathway, the best (so far) biomarkers have been genetic, found through non-flow means (see the study by Williams et al. 51 and references therein). This suggests that our bow-tie modeling approach can be generalized beyond the flow data and JAK-STAT system, which is a promising avenue for future research.…”

Biochemical Signposts: Navigating the Landscape of Early Cancer Diagnosis and Prognostic Insights

“…The combination of numerous biomarkers gives a more exhaustive comprehension of the sub-atomic scene, upgrading the potential for early malignant growth recognition [26]. The relationship between biomarker articulation and endurance rates gives significant prognostic experiences [30]. Risk proportions and p-values are determined to evaluate the impact of each biomarker on understanding results.…”