Abstract 3844: Identification and validation of novel therapeutic targets driving clonal heterogeneity in treatment-refractory GBM
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common primary adult brain tumor, characterized by extensive cellular and genetic heterogeneity. Even with surgery, standard chemotherapy with temozolomide (TMZ), and radiation, tumor re-growth (or recurrence) and patient relapse are inevitable. Patients face a median survival of <15 months, with uniformly fatal outcomes upon disease progression post-therapy. Recent profiling of GBM-initiating genes has shown that evolution of cancer-driving clones or cell populations within a solid tumor may progress through (and possibly be driven by) cancer treatment, such that GBM recurrence may no longer resemble the genetic landscape of the original primary tumor. Understanding and mapping clonal evolution of the primary GBM through therapy and at recurrence will allow for the discovery of novel targets specific to treatment-refractory GBM. Here, we have developed early passage patient-derived brain tumor initiating cell (BTIC) lines that have been annotated by genomic deep-sequencing technologies to systematically characterize and describe the extent of intratumoral heterogeneity. Tagged with a red florescent protein, these BTIC lines were engrafted into immunocompromised NOD SCID mice. Following half-maximal tumor engraftment, tumor bearing mice underwent a clinically relevant chemoradiotherapy regimen, with 2 Gy gamma-irradiation on the first day and 66 mg/kg temozolomide for five consecutive days. Following therapy, mice were kept alive until tumor recurrence. Engrafted BTICs were harvested at initial tumor formation, minimal residual disease after chemoradiotherapy, and tumor recurrence. Samples were analyzed by RNA and genomic deep-sequencing technologies to map cancer progression and identify novel therapeutic targets in treatment-refractory GBM. Potential therapeutic targets were validated by their effect on self-renewal and proliferation of patient-derived BTIC lines of human GBM in vitro and in vivo. Using CRISPR Cas9, potential targets were knocked out in patient-derived BTIC lines of human GBM in order to characterize the effect on sphere formation and proliferation in vitro, and tumor formation in vivo. Following validation of new therapeutic targets of treatment-refractor GBM, we aim to build novel biotherapeutics against highly validated cell surface targets, and establish preclinical testing protocols using our novel patient-derived and therapy-adapted xenograft model of treatment-resistant GBM. Citation Format: Chirayu Chokshi, Nick Yelle, Parvez Vora, Chitra Venugopal, Maleeha Qazi, Mohini Singh, Minomi Subapanditha, Avrilynn Ding, Sheila K. Singh. Identification and validation of novel therapeutic targets driving clonal heterogeneity in treatment-refractory GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3844. doi:10.1158/1538-7445.AM2017-3844
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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) | 0.002 | 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".