Abstract 570: A glioblastoma translational pipeline: discovery of novel tumor antigens that drive GBM recurrence
Bibliographic record
Abstract
Abstract Background: Glioblastoma (GBM) is the most common malignant primary adult brain tumor, characterized by extensive cellular and genetic heterogeneity. Even with surgery, temozolomide chemotherapy and radiation, tumor re-growth and patient relapse are inevitable, with a median survivorship of just 15 months. Genomic profiling studies have shown that clonal evolution within GBM may be driven by cancer treatment, such that the recurrence may no longer resemble the genetic landscape of the original primary tumor. Furthermore, intratumoral heterogeneity associated with clonal evolution complicates biomarker discovery and treatment personalization and underlies treatment failure. Thus, modeling clonal heterogeneity and evolution to understand cancer progression is critical for the development of effective therapeutic approaches. We aim to identify new therapeutic targets that drive clonal evolution in treatment-refractory GBM and develop novel and empirical therapeutic paradigms targeting recurrent GBM. Experimental Procedure: We employed a transcriptomic, proteomic and functional genomics approach to discover and validate genes that drive GBM recurrence. Using a therapy-adapted patient-derived xenograft (PDX) model of treatment-refractory GBM, we profiled the transcriptomic and proteomic landscape of treatment-naïve primary GBM through conventional chemotherapy and radiation therapy, and into recurrence. To complement the transcriptomic data, we used an unbiased genome-wide CRISPR-Cas9 screening platform to identify genes essential for self-renewal in recurrent GBM, as well as to identify novel sensitizers and suppressors of conventional therapy. Furthermore, we coupled cellular DNA barcoding technology with our PDX model to profile the clonal evolution of tumor cells through therapy. Results: Integrative analysis of deep sequencing and surface proteomics of tumor cells harvested at tumor formation, minimal residual disease after chemoradiotherapy, and tumor recurrence from the PDX model resulted in the identification of novel therapeutic targets in treatment-refractory GBM. Using CRISPR, potential targets were knocked out in patient-derived GBMs in order to characterize the effect on self-renewal and tumor formation. We report the successful barcoding of patient-derived primary, treatment-naïve GSCs at a single cell resolution that were expanded into clonal populations, intracranially engrafted in immunodeficient mice and treated with SoC therapy. Conclusion: We have generated a translational pipeline from initial target discovery, through target validation, to building new biotherapeutics against novel targets, and preclinical testing in our PDX model of treatment-resistant GBM. A promising lead panel of biotherapeutic modalities is being translated into early clinical development, generating targeted therapies and hope for future GBM patients. Note: This abstract was not presented at the meeting. Citation Format: Parvez Vora, Chitra Venugopal, Chirayu Chokshi, Maleeha Qazi, Nazanin Tatari, Kevin Brown, Nicholas Yelle, Jarrett Adams, David Tieu, Mathieu Seyfrid, Mohini Singh, Neil Savage, Minomi Subapanditha, David Bakhshinyan, Laura Kuhlmann, Thomas Kislinger, Sachdev Sidhu, Jason Moffat, Sheila Kumari Singh. A glioblastoma translational pipeline: discovery of novel tumor antigens that drive GBM recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 570.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".