Abstract IA022: Arginine, serine, xCT and ME, the therapeutic metabolism of different sarcomas
Bibliographic record
Abstract
Abstract The absolute diversity of the biology of the sarcomas makes therapeutic development challenging. As most of tumor metabolism is composed of transporters and enzymes, finding metabolic dependencies should allow for small molecule targeting. Due to the rapid metabolic evolution that tumors undergo in response to the targeting of any one metabolic pathway, a deep understanding of sarcoma metabolism is needed. First, the most common metabolic adaptation that sarcomas undergo is loss of expression of argininosuccinate synthetase 1 (ASS1), which is silenced in ~90% of cases by methylation. This results in sarcomas being arginine auxotrophic and sensitive to arginine starvation therapies, such as with arginine deiminase (ADI-PEG20). Not only does arginine starvation alter the Warburgian biology of sarcomas making them dependent of glutamine, but it can be used to upregulate the expression of cell surface transports such as hENT, which allows to gemcitabine internalization. In addition, mouse modeling has demonstrated that vesicular trafficking is key to overcoming initial arginine starvation in vivo, a process that can be blocked with chloroquine. This arginine dependency is likely related to the mesenchymal origin of sarcomas, which would explain its high recurrence rate of ASS1 silencing across histologies. Next, due to the upregulation of 3-phosphoglycerate dehydrogenase (PHGDH), osteosarcoma preferentially utilizes glucose to make the serine that enters the folate cycle, as opposed to completing lower glycolysis. In addition, as osteosarcoma does not seem to depend on extracellular serine import, inhibition of PHGDH leads to pro-survival compensation by the mTORC pathway. This can be targeted on both the AMP Kinase and the AKT dependent parts of the MTORC pathway that converge at FOXO3. When both are inhibited approach demonstrates unique triple synergy and therapeutic strategy for osteosarcoma due to its unique serine biology. Finally, synovial sarcoma lack the expression of malic enzyme 1 (ME1), also likely due to its cell of origin. This leads to reduced glucose oxidation, enhanced glycolysis, and compensatory increased flux through the pentose phosphate pathway for cytoplasmic NADPH production. Additionally, absence of ME1 in SS results in significant reductions in the GSH/GSSG ratio as well as a reduced glutathione synthesis. Sensitivity to GSH pathway inhibition is also reduced while sensitivity to inhibition of the thioredoxin system is significantly increased. ME1 absence results in increases in the labile iron pool that sensitizes synovial sarcoma to ferroptosis. Therefore, ME1 null SS is exquisitely sensitive to induction of ferroptosis with xCT inhibition. This can be accomplished by erastin analogs in vitro and ACXT-3102 (a tumor targeted erastin) in vivo. As we learn more about each sarcoma, we must understand the underlying metabolism of each subtype and their cell of origin. Given the targetable nature of metabolic enzymes and transporters, an ever deeper understanding of sarcoma metabolism is warranted. Citation Format: Brian A. Van Tine. Arginine, serine, xCT and ME, the therapeutic metabolism of different sarcomas [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA022.
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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 | 0.001 |
| 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) | 0.005 | 0.002 |
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".