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Record W2810783330 · doi:10.1158/1538-7445.am2018-140

Abstract 140: A novel signature of mesenchymal stromal cells in high-grade serous ovarian carcinoma

2018· article· en· W2810783330 on OpenAlexaff
Ali Hussain, Véronique Voisin, Stephanie Poon, Jalna Meens, Julia Dmytryshyn, Gary D. Bader, Benjamin G. Neel, Laurie Ailles

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsFibroblast activation protein, alphaStromal cellCancer-Associated FibroblastsSerous fluidMesenchymal stem cellCancer researchOvarian cancerTumor microenvironmentBiologyCell sortingExtracellular matrixFibroblastCancer cellOvarian carcinomaCellCell culturePathologyCancerMedicineCell biologyGeneticsTumor cells

Abstract

fetched live from OpenAlex

Abstract High grade serous ovarian cancer (HGSOC) is the most common and lethal subtype of ovarian cancer. Many cancers, including HGSOC, contain a “stromal” component comprised of cancer-associated fibroblasts (CAFs), immune cells, and blood vessels, as well as their secreted factors and extracellular matrix. This complex microenvironment plays a significant role in promoting tumor growth, therapy resistance, and invasion. The majority of studies of CAFs utilize cells cultured and passaged in vitro under crude conditions to maintain their viability. Such conditions alter the original phenotype of the cells and may select for specific subpopulations to grow out. To facilitate direct isolation of CAFs from primary tumor tissues, we screened a panel of antibodies to identify cell surface proteins that are uniquely expressed on CAFs. We discovered CD49e as a mesenchymal marker and then validated its specificity to CAFs through immunohistochemistry on tissue sections obtained from HGSOC patients. The discovery of CD49e as a CAF-specific cell surface marker facilitated fluorescence-activated cells sorting (FACS) to isolate CAFs directly from primary tumors, allowing us to avoid in vitro manipulation and to characterize their transcriptional and functional profiles in the primary setting. We next performed transcriptional profiling of primary CAFs isolated from 12 HGSOC patients and found that CAFs fall into two subgroups with unique gene expression signatures. A cell surface protein, fibroblast activation protein (FAP) is the defining marker for separating the two subgroups (FAP-Hi and FAP-Lo). The FAP-Hi subgroup possesses the classical gene signature of CAFs that is reported in the literature. When we isolated FAP-Hi and FAP-Lo cells and placed them into classical CAF growth conditions in vitro, both cell types had mesenchymal features, but FAP-Hi cells grow faster. Thus under the classical conditions for growing CAFs, FAP-Hi cells have a growth advantage, outcompeting FAP-Lo cells and becoming the dominant cells used for experimentation. Flow cytometry for FAP indicates that both CAF subtypes co-exist in every tumor, but their ratio varies from one patient to another. Patients whose tumors are dominated by FAP-Hi CAFs have worse clinical outcome than patients whose tumors are dominated by FAP-Lo CAFs. Thus, we have functionally characterized the role of FAP-Hi and FAP-Lo fibroblasts in the context of HGSOC. We have shown that FAP-Hi fibroblasts support tumor proliferation and invasion of ovarian cancer cells in vitro and in vivo. On the other hand, FAP-Lo cells suppress cancer cell proliferation and invasion in vitro and in vivo. Citation Format: Ali Hussain, Veronique Voisin, Stephanie Poon, Jalna Meens, Julia Dmytryshyn, Gary Bader, Benjamin Neel, Laurie Ailles. A novel signature of mesenchymal stromal cells in high-grade serous ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 140.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.067
GPT teacher head0.383
Teacher spread0.316 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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