Arginine Depletion Therapy with ADI-PEG20 Limits Tumor Growth in Argininosuccinate Synthase–Deficient Ovarian Cancer, Including Small-Cell Carcinoma of the Ovary, Hypercalcemic Type
Bibliographic record
Abstract
Abstract Purpose: Many rare ovarian cancer subtypes, such as small-cell carcinoma of the ovary, hypercalcemic type (SCCOHT), have poor prognosis due to their aggressive nature and resistance to standard platinum- and taxane-based chemotherapy. The development of effective therapeutics has been hindered by the rarity of such tumors. We sought to identify targetable vulnerabilities in rare ovarian cancer subtypes. Experimental Design: We compared the global proteomic landscape of six cases each of endometrioid ovarian cancer (ENOC), clear cell ovarian cancer (CCOC), and SCCOHT to the most common subtype, high-grade serous ovarian cancer (HGSC), to identify potential therapeutic targets. IHC of tissue microarrays was used as validation of arginosuccinate synthase (ASS1) deficiency. The efficacy of arginine-depriving therapeutic ADI-PEG20 was assessed in vitro using cell lines and patient-derived xenograft mouse models representing SCCOHT. Results: Global proteomic analysis identified low ASS1 expression in ENOC, CCOC, and SCCOHT compared with HGSC. Low ASS1 levels were validated through IHC in large patient cohorts. The lowest levels of ASS1 were observed in SCCOHT, where ASS1 was absent in 12 of 31 cases, and expressed in less than 5% of the tumor cells in 9 of 31 cases. ASS1-deficient ovarian cancer cells were sensitive to ADI-PEG20 treatment regardless of subtype in vitro. Furthermore, in two cell line mouse xenograft models and one patient-derived mouse xenograft model of SCCOHT, once-a-week treatment with ADI-PEG20 (30 mg/kg and 15 mg/kg) inhibited tumor growth in vivo. Conclusions: Preclinical in vitro and in vivo studies identified ADI-PEG20 as a potential therapy for patients with rare ovarian cancers, including SCCOHT.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".