Abstract A58: Temporal dynamics underpin metabolism-driven cancer therapy cross-resistance
Bibliographic record
Abstract
Abstract The acquisition of resistance to chemotherapy is the underlying cause of relapse leading to metastasis and mortality. It is clear that the metabolic states of cancer cells contribute to cancer therapy failure. However, the inherent plasticity of cancer cells and the complexity of intratumoral heterogeneity have obscured a clear understanding for the role of metabolism in the development of resistance. Here, we used a computational biology approach in-tandem with functional assays at the single cell level to build a mechanistic understanding of an adaptive, metabolic cell-state-switch which leads to cross-therapy resistance of chemotherapies in cancer. We show that cytotoxic chemotherapies induce a temporally-dependent cell behavior via 1. induction of cell surface scaffold-kinase interactions 2. mitochondrial-induced reactive oxygen species (ROS) which are then requisite to drive 3. redox stress-mediated glucose uptake. Interestingly, we identified that the early-enhanced mitochondrial ROS promotes a delayed glucose shunt towards the pentose phosphate pathway (PPP) which was mediating cross-drug resistance. Using pharmacologic inhibitors of proteins in the PPP as well as inhibitors of upstream glycolysis intermediates, we were able to restore cancer cell sensitivity to combination therapies producing robust tumor responses in otherwise resistant cancer cells. These findings unveil a systems analysis of metabolic plasticity leading to therapy failure and provide novel strategies for treatment. Citation Format: Aaron Goldman, Andrew Dhawan, Ragini Medhi, Mohammad Kohandel, Shiladitya Sengupta. Temporal dynamics underpin metabolism-driven cancer therapy cross-resistance. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr A58.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".