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Abstract GMM-020: CELL OF ORIGIN, MUTATION AND MICROENVIRONMENT: MODELING EARLY EVENTS OF ENDOMETRIOSIS ASSOCIATED CANCERS

2019· article· en· W3166273369 on OpenAlexaff
Dawn R. Cochrane, Basile Tessier‐Cloutier, Germain Ho, Kieran R. Campbell, Evan Gibbard, Katherine M. Lawrence, Tayyebeh M. Nazeran, Anthony N. Karnezis, Clara Salamanca, Angela Cheng, Jessica N. McAlpine, Sohrab Shah, Lien Hoang, C. Blake Gilks, David G. Huntsman

Bibliographic record

VenueClinical Cancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsBiologyClear cellHistogenesisOvarian cancerCell of originPathologyCancer researchCellClear cell carcinomaCarcinomaCancerImmunohistochemistryMedicineImmunologyGenetics

Abstract

fetched live from OpenAlex

Abstract Both clear cell ovarian carcinoma (CCOC) and endometrioid ovarian carcinoma (ENOC) are associated with ovarian endometriotic cysts, which is believed to be their precursor lesion. However, genomic evidence is lacking which could explain how these two clinically distinct histotypes of ovarian cancer arise from the same precursor lesion. We therefore hypothesized that these cancers arise from distinct cells of origin within endometrial tissue. Global proteomic analysis of ovarian cancer histotypes identified CTH as a marker for CCOC. We further found that CTH is highly expressed in the ciliated cells of endometrium (both ectopic endometrium and endometriosis), and of the fallopian tube, with little expression in the secretory cells. We also find that other ciliated cell markers are expressed in CCOC, whereas endometrial secretory cell markers are expressed in ENOC. We propose a new model of CCOC and ENOC histogenesis wherein ENOC is derived from cells of secretory cell lineage whereas CCOC is derived from cells of ciliated cell lineage. However, it remains unclear how external factors in the endometriotic cyst cooperate with cell of origin and mutation to promote cancer formation. To study normal tissue biology, we are using organoid cultures of normal endometrium. As ciliated cells of the endometrium are rare, and we have a particular interest in determining whether they have other features that may link them to CCOC, we used a Notch inhibitor, DBZ, to force ciliated cell differentiation in the organoids. We observed a dramatic shift in the cellular content with DBZ treamtment towards ciliated cells. We performed single cell RNA sequencing (scRNAseq) on these endometrial organoids. In the normal endometrial organoids, cells were predominantly a secretory phenotype, characterized by high ESR1 expression, with a minor ciliated cell population. The ciliated cell population expressed several known ciliated markers (FOXJ1 and DNAH12). Upon treatment with DBZ, the number of secretory cells decreases dramatically and two populations of cells emerge which have ciliated cell markers. The larger ciliated cell population is similar to the ciliated cells in the untreated organoids. The smaller ciliated cell population in the DBZ treated organoids express some ciliated cell markers, but clusters separately from normal ciliated cells. We believe this population may reprepsent an intermediary population, which has not fully differentiated. Interestingly, this population expresses the cytokine IL6, while the normal ciliated cell population does not. This is of note because CCOCs express more IL6 compared to the other histotypes. Therefore, we can speculate that this intermediary ciliated cell population may represent cells from which CCOC arise, however more testing is needed. In the future, the scRNAseq data from organoids will be compared to CCOC and ENOC tumors to determine whether the tumors resemble more closely one population of normal cells. We will use viral transduction to introduce mutations into the organoid cultures to determine whether specific mutation leads to transformation towards a CCOC or ENOC-like phenotype. These studies will enable us to tease apart the relative contribution of mutation, microenvironment and the cell of origin to promote tumor formation. Citation Format: Dawn R Cochrane, Basile Tessier-Cloutier, Germain Ho, Kieran Campbell, Evan Gibbard, Katherine M Lawrence, Tayyebeh Nazeran, Anthony N. Karnezis, Clara Salamanca, Angela S Cheng, Jessica N McAlpine, Sohrab Shah, Lien N Hoang, C Blake Gilks and David G Huntsman. CELL OF ORIGIN, MUTATION AND MICROENVIRONMENT: MODELING EARLY EVENTS OF ENDOMETRIOSIS ASSOCIATED CANCERS [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr GMM-020.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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)0.0010.000

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 head0.157
GPT teacher head0.478
Teacher spread0.321 · 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 designSimulation or modeling
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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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