Abstract 1911: Uncovering novel targets of recurrent glioblastoma using transcriptomic profiling in a patient-derived xenograft model
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common and aggressive adult primary brain tumor feared for its near uniformly fatal prognosis despite advances in multimodal therapy including surgical resection, chemotherapy and radiation. Poor patient survival due to tumor relapse is thought to be linked to intratumoral heterogeneity (ITH), driven by various environmental cues including chemotherapy and radiation treatment. ITH can be explained at the cellular level by the existence of multiple populations of cancer cells, including cancer stem cells (CSCs), which have acquired stemness properties like self-renewal, proliferation, and multilineage differentiation. In brain tumors, CSCs or brain tumor initiating cells (BTICs), have been shown to be resistant to both chemotherapy and radiation treatment, allowing them to escape therapy and allowing for tumor recurrence. To profile ITH as it evolves through therapy delivery, we have developed a novel and dynamic BTIC patient-derived xenograft (PDX) model of treatment-refractory human GBM, allowing for multimodal profiling of GBM BTICs through tumor engraftment, remission, and recurrence. In this study, we present the transcriptomic profiling at each stage, and novel target selection and validation through CRISPR/Cas9 knockouts, well-established in vitro stem cell assays, and in vivo characterization of their tumor initiation, development, and maintenance properties. Despite the fact that the BTIC population is responsible for GBM recurrence and thus patient demise, it remains a largely unknown landscape. Consequently, therapies that focus on targeting the BTIC compartment within the bulk tumor would provide better treatment and prognosis for patients with brain tumors. The study we present provides a unique therapeutic window into the recurrence of GBM, which drives patient mortality, yet is profiled far less than primary treatment-naïve GBM. Citation Format: Nicolas Yelle, Chirayu Chokshi, Parvez Vora, Kevin R. Brown, Maleeha A. Qazi, Mohini Singh, Jarrett J. Adams, Chitra Venugopal, Sachdev Sidhu, Jason Moffat, Sheila K. Singh. Uncovering novel targets of recurrent glioblastoma using transcriptomic profiling in a patient-derived xenograft model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1911.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".