TMET-24. MULTI-OMIC CHARACTERIZATION OF THE TEMPORAL METABOLIC ADAPTATIONS UNDERPINNING THERAPY RESISTANCE AND TUMOR RECURRENCE IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Therapy resistance and tumor recurrence are barriers to achieving long-term survival in cancer patients, particularly evident in glioblastoma (GBM), known for its dismal 5-year survival rate below 10%. Despite aggressive treatment regimens, including maximal resection, alkylating chemotherapy, and radiation, the majority of GBM patients experience relapse within 7-9 months post-initial intervention. Recurrent GBM poses surgical challenges and resists subsequent treatments, leading to an almost universally fatal outcome and an average survival of 12-18 months. Metabolic networks can serve as ancient stress-protection mechanisms that operate independently of genomic changes, offering an initial line of defense against exogenous pressures. Although the role of metabolism in cancer has gained prominence, our understanding of the metabolic adaptations following chemoradiotherapy and their contributions to therapy resistance and tumor recurrence in highly aggressive GBM tumors remains incomplete. We postulate that metabolic adaptations play a pivotal role in facilitating therapy resistance and GBM recurrence. Exploring the metabolic dependencies of these persistent cells may unveil vulnerabilities for therapeutic exploitation to counteract chemoradiotherapy resistance. We established clinically relevant patient-derived in vitro and in vivo models mirroring the standard GBM treatment protocol. Using state-of-the-art single-cell and bulk multi-omics technologies, we profiled the temporal transcriptomic and metabolomic adaptations from therapy-naïve primary GBM to chemoradiotherapy-resistant recurrent tumors. Our findings revealed unique metabolic transitions that occurred during chemoradiotherapy treatment in GBM cells that were conserved in vitro and in vivo across ten genetically diverse GBM patients. In a pre-clinical animal trial, a novel metabolism-targeting combinatorial treatment regimen prevented the emergence of recurrent tumors and significantly prolonged survival compared to chemoradiotherapy alone. Altogether, our results uncover distinct metabolic adaptations mediating therapy resistance and tumor recurrence in GBM. These findings emphasize the need to consider metabolic flexibility and dependencies for effective therapeutic strategies, offering potential avenues to overcome chemoresistance in recurrent GBM tumors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".