Abstract 2205: Exploring the presence and role of causative viruses in glioblastoma using a multi-omics approach
Bibliographic record
Abstract
Abstract Glioblastoma (GB) is the most aggressive brain cancer with a poor survival rate. While molecular markers have been established to improve treatment response with modest outcomes, the cause of this cancer remains unknown. Viruses were proposed as potential contributing factors to glioblastoma, particularly given that approximately 20% of all human cancers are virus-induced. While previous studies have detected viral presence in glioblastoma tissues, these investigations were limited by small sample sizes and often focused exclusively on either viral DNA or proteins, restricting the scope of their findings. To address these shortcomings, we explored the role of viruses in glioblastoma by utilising advanced analytical chemistry and high-throughput technologies to study viral proteins and genes and their interaction with tumour signalling pathway(s). We analysed 233 publicly accessible mass spectrometry metaproteome datasets encompassing 196 glioblastoma, 21 adjacent, and 16 normal brain tissues (PMID: 31331834, 31154438, 36720864), using the Trans-Proteomic Pipeline (PMID: 36648445) which converts raw data files, identifies, validates and quantifies peptides, and protein inference. To expand our analysis, we incorporated metaproteomic data from 100 samples (52 glioblastoma, 48 normal) and metagenomic data from seven glioblastoma tumours sourced from Hunter Cancer Biobank. Our comprehensive analysis, integrating meta-proteomics and meta-genomics, revealed a higher prevalence of multiple herpesvirus species, including human Herpes Simplex Virus-1, Herpes Simplex Virus-2, and Herpesvirus 8 within tumour tissues compared to control and adjacent tissues. To further elucidate the role of viral infections in tumour progression, we are currently investigating meta-transcriptomic data from 15 tumours.We are also investigating human proteins within a cohort of 100 samples to identify potential associations with our detected viruses. We aim to explore whether viral infections may influence human proteins involved in critical biological processes in glioblastoma. In a preliminary analysis, we quantified 3, 991 human proteins, of which 1, 155 were found to be deregulated. Gene enrichment analysis revealed up-regulated gene ontology biological processes associated with interspecies interactions and defence responses to other organisms. This suggests that these genes may potentially play a role in the host's reaction to viral infections. Additionally, Ingenuity Pathway Analysis identified 182 deregulated pathways, including several that may be implicated in viral responses, warranting further investigation.Collectively, our findings indicate a potential role of viruses in tumour development, possibly having the capacity to redefine the stratification of tumour types in GB patients. Citation Format: Bavani Gunasegaran, Shamini Ayyadhury, Shivani Krishnamurthy, Seong Beom Ahn, Benjamin Heng. Exploring the presence and role of causative viruses in glioblastoma using a multi-omics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2205.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".