MétaCan
Menu
← Back to cohort

Abstract GMM-030: MODELS AND ANALYTIC TECHNIQUES OF MULLERIAN TISSUE-DERIVED ORGANOIDS

2019· article· en· W3172767700 on OpenAlexaff
Germain C. Ho, Dawn R. Cochrane, Evan Gibbard, Kieran R. Campbell, Basile Tessier‐Cloutier, Kendall Greening, Forouh Kalantari, Genny Trigo‐Gonzalez, Yemin Wang, Jessica N. McAlpine, Sohrab P. Shah, David G. Huntsman

Bibliographic record

VenueClinical Cancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrganoidMatrigelBiologyCarcinogenesisOvarian cancerCancer researchSerous fluidProgenitor cellStem cellCellular differentiationCell biologyCancerAngiogenesisGeneticsGene

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION: Ovarian cancer is the 5th deadliest cancer found in women and is the deadliest involving the gynecological tract. Most epithelial ovarian cancers have extra-ovarian origins and can be stratified into various histotypes: high and low-grade serous (HGS and LGS), endometrioid (ENOC), clear cell (CCOC), and mucinous – each of which are proposed to have distinct precursor lesions. We present organoids as a useful model to study precursor lesions and the process of tumorigenesis in epithelial ovarian carcinomas. Organoids recapitulate the in vivo growth microenvironment and are useful to study the mechanisms of tumorigenesis from healthy cells. We have previously proposed that ENOC arise from the secretory cell lineage, while CCOC originate from the ciliated cell lineage, and organoids are an ideal model to examine in greater depth the impact of mutation on specific cell populations, such as ciliated cells. METHODS: Surgical fallopian tube and endometrial tissues, removed for non-cancer reasons, were cultured in 2D followed by plating into Matrigel. Matrigel cultures were supplemented with media containing stem/progenitor differentiation factors promoting organoid growth. To study the effect of mutations often found in ovarian cancers on organoid growth and development, gene knockouts were produced using CRISPR lentiviruses on cells prior to Matrigel culture. Lentiviral transductions were optimized for organoid formation and for minimizing invasiveness accrued on cells. CRISPR gRNA constructs were validated by Western Blot and qPCR. Organoids containing knockouts of p53, BRCA1 and BRCA2 were used to model precursor lesions of HGS, whereas ARID1A knockouts and an inducible PIK3CA activating mutations were used to model CCOC. To gain further insight into ciliated cells of the endometrium, organoids were treated with the notch inhibitor-DBZ to drive differentiation of cells towards a ciliated cell lineage. We analyzed organoids by single-cell RNA sequencing (scRNA-seq), immunohistochemistry (IHC), and immunofluorescence staining (IF). Single cells were derived by purifying the organoids from Matrigel followed by a chemical and physical digestion. scRNA-seq was performed utilizing the 10X Genomics Platform and analyzed by in-house bioinformaticians. Bioinformatic analyses included stringent QC to remove low-quality and dead cells, before applying unsupervised learning algorithms like PCA and Gaussian mixture modeling as well as differential expression analysis to understand both how samples relate to each other and cell types discovered within each sample. RESULTS: We successfully recapitulated the histology observed in tissues by growing endometrial and fallopian tube organoids. The notch inhibitor, DBZ forced ciliated cell differentiation, as observed by IHC, IF and scRNA-seq. scRNA-seq clustering of DBZ-treated organoid cultures revealed a possible intermediary state between progenitor and ciliated cells. Initial IHC and IF analyses of CRISPR-mediated organoids reveal successful gene manipulation. CONCLUSIONS: Organoid cultures present as a powerful method for modelling precursor lesions; they can be readily manipulated genetically and with rapid turnaround compared to conventional mouse models. Organoids are also amenable to sequencing at single-cell resolution. The ability to model ovarian cancers with permanent knockouts in human tissue serves as a necessary link between animal models and human therapy. Citation Format: Germain C. Ho, Dawn R. Cochrane, Evan W. Gibbard, Kieran Campbell, Basile Tessier-Cloutier, Kendall Greening, Forouh Kalantari, Genny Trigo-Gonzalez, Yemin Wang, Jessica N. McAlpine, Sohrab P. Shah, David G. Huntsman. MODELS AND ANALYTIC TECHNIQUES OF MULLERIAN TISSUE-DERIVED ORGANOIDS [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-030.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.276
GPT teacher head0.547
Teacher spread0.271 · 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

Explore more

Same venueClinical Cancer Research→Same topicReproductive Biology and Fertility→French-language works237,207→