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Record W2987487791 · doi:10.1101/845586

Arginine depletion through ADI-PEG20 to treat argininosuccinate synthase deficient ovarian cancer, including small cell carcinoma of the ovary, hypercalcemic type

2019· preprint· en· W2987487791 on OpenAlexafffund
Jennifer X. Ji, Dawn R. Cochrane, Basile Tessier‐Cloutier, Shary Chen, Germain Ho, Khyatiben V. Pathak, Isabel N. Alcazar, David Farnell, Samuel Leung, Angela Cheng, Christine Chow, Shane Colborne, Gian Luca Negri, F Kommoss, Anthony N. Karnezis, Gregg B. Morin, Jessica N. McAlpine, C. Blake Gilks, Bernard E. Weissman, Jeffrey M. Trent, Lynn Hoang, Patrick Pirrotte, Yemin Wang, David G. Huntsman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsCanada's Michael Smith Genome Sciences CentreCentre for Advancing Health OutcomesBC Cancer AgencyUniversity of British Columbia
FundersCanadian Cancer Society Research InstituteNational Cancer InstituteNational Institutes of HealthBC Cancer FoundationTerry Fox Research InstituteBC Cancer AgencyCanada Research Chairs
KeywordsOvarian cancerCancer researchCancerSerous carcinomaBiologyOvarian carcinomaOvaryTissue microarrayClear cell carcinomaMedicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

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. Immunohistochemistry of tissue microarrays were used as validation of 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 to HGSC. Low ASS1 levels were validated through IHC in a large patient cohort. The lowest levels of ASS1 were observed in SCCOHT, where ASS1 was absent in 2/15 cases, and expressed in less than 5% of the tumor cells in 8/15 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 of ADI-PEG20 (30mg/kg and 15mg/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. Translational relevance Many rare ovarian cancers lack effective management strategies and are resistant to the standard platinum- and taxane-based chemotherapy. Thus, for a rare ovarian cancer subtype like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) - an aggressive malignancy affecting young women in their twenties, effective targeted therapeutics are urgently needed. We used global proteomics to identify a deficiency in arginosuccinate synthase (ASS1) as a common feature among some rare ovarian cancer subtypes. Using in-vitro and in-vivo models, we demonstrated that the arginine-depriving investigational agent ADI-PEG20 effectively inhibited cell growth in ASS1 deficient ovarian cancers including SCCOHT, establishing it as a potential therapeutic agent for rare ovarian cancer subtypes deficient in ASS1. Further clinical investigation is warranted.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.243
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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