Abstract PO052: Uncovering the evolution of Glioblastoma proteome landscape from primary to the recurrent stage for development of novel diagnostic and predictive biomarkers
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is characterized by extensive cellular and genetic heterogeneity. A wealth of literature describes the biology of primary GBM (p-GBM), but we currently lack an understanding of how GBM evolves through therapy to become a very different tumor at recurrence, which may explain why therapies against p-GBM fail to work in recurrent GBM (r-GBM). Therefore, to understand the evolution of r-GBM, we aimed to characterize patient-matched p-GBM and r-GBM proteome and identify potential therapeutic targets for r-GBM. We collected one of the world’s largest patient-matched p-GBM and r-GBM samples from the Hamilton Health Sciences for gene expression profiling, proteomic analyses and tissue microarray (TMA) construction. Nano-String analysis was performed for GBM subtype identification. Furthermore, patient demographics was generated for survival analysis. The top potential therapeutic targets for r-GBM were identified by proteomic analysis and were validated on TMA using immunohistochemistry. The essentiality of each protein in r-GBM were assessed using CRISPR KO studies and the top hit were selected for pre-clinical testing. 6798 proteins were detected by shotgun, label-free proteomic analyses. Differential expression analysis on the surface proteins revealed a distinct set of proteins overexpressed in r-GBM among which 7 proteins were selected as top potential therapeutic targets for r-GBM. Besides, the patients were grouped based on survival rate and the differential expression analysis revealed significantly enriched proteins and pathways in short-term survivors which cause aggressive phenotypes in GBM. In addition, consensus clustering identified five protein clusters which show distinction between primary vs recurrent tumors. Our data also strongly supports a preponderance of immune regulatory/suppressive genes as important drivers of r-GBM. This study resulted in identification of diagnostic and predictive biomarkers which is extremely complementary and instructive for the development of new poly-therapeutic paradigms for GBM patients at the recurrent level and will lead to improvement of patient’s survival. Citation Format: Nazanin Tatari, Shahbaz Khan, Julie Livingstone, Chitra Venugopal, Jennifer Chan, Cynthia Hawkins, John Provias, Jian-Qiang Lu, Kjetil Ask, Thomas Kislinger, Sheila Singh. Uncovering the evolution of Glioblastoma proteome landscape from primary to the recurrent stage for development of novel diagnostic and predictive biomarkers [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO052.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".