Abstract PR03: Arginine deprivation as a potential targeted therapy for clear cell ovarian carcinoma
Bibliographic record
Abstract
Abstract In this study, we explored the metabolic pathways of clear cell ovarian carcinoma (CCOC) and the therapeutic importance of aberrant arginine metabolism in this cancer. In 2017, an estimated 22,440 women will be diagnosed with epithelial ovarian carcinoma (EOC) in the United States. EOC is divided into subtypes based on histology and prognosis. Among them, CCOC is truly a unique entity. Histologically, CCOC is characterized by clear cytoplasm, which stains PAS positive, indicating aberrant cellular glycogen storage. Genomic studies in CCOC have identified recurrent mutations in the ARID1A and PIK3CA genes, both encoding proteins with crucial roles in cellular metabolism, which further supports CCOC being a metabolism-dependent malignancy. At late stage, CCOC is more aggressive and refractory to conventional platinum-based therapy, compared to other EOC subtypes. Despite the lack of efficacy, platinum-based chemotherapy is still the gold standard for treating all EOC subtypes. The lack of targeted therapy for CCOC paints a grim picture for the patients as they inevitably relapse. Using a mass spectrometry-based study, we characterized the whole proteome of 17 formalin-fixed, paraffin-embedded (FFPE) patient CCOC tumors. The CCOC cases separated into 2 distinct subgroups based on unsupervised hierarchical clustering. We identified the top 250 most differentially expressed proteins between these 2 groups using Protein Expression Control Analysis (PECA) and subsequent pathway analysis through KEGG. Of these 250 proteins, 56 were metabolism-related, including Argininosuccinate Synthase 1 (ASS1). ASS1 is a crucial enzyme in the cellular synthesis of arginine; a deficiency in the enzyme makes cancer cells dependent on extracellular arginine for survival. In ASS-1 deficient sarcomas, targeted small-molecule therapy depriving extracellular arginine results in cell death and sensitization to conventional chemotherapy. In transcriptomic analysis of 55 patient CCOC tumors and cell lines, 13 cases had low ASS1 RNA expression compared to others. Subsequently, we collected 97 CCOC cases from a local tissue bank and studied ASS1 protein expression using immunohistochemistry. In these cases, ASS1 expression ranges from strong to diffusely weak to null, confirming the differential expression discovered in the proteomic and transcriptomic study. To this end, ASS1 levels were assessed in CCOC, endometrioid, and high-grade serous cell lines. ASS1 was not expressed in a subset of CCOC cell lines and was low in others. We further demonstrate that a subset of CCOC cell lines are sensitive to arginine deprivation, indicating that there may be some CCOC tumors that would benefit from combined arginine deprivation in conjunction with the gold standard platinum-based therapy. This abstract is also being presented as Poster A13. Citation Format: Jennifer Xiao Ye Ji, Dawn R. Cochrane, Basile Tessier-Cloutier, Lien N. Hoang, Yikan Wang, Angela Cheung, Christine Chow, Shane Colborne, Christopher Hughes, Gregg B. Morin, David G. Huntsman. Arginine deprivation as a potential targeted therapy for clear cell ovarian carcinoma. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr PR03.
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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) | 0.001 | 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 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".