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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".