Abstract AP17: MOLECULAR AND FUNCTIONAL HETEROGENEITY OF CANCER ASSOCIATED FIBROBLASTS IN HIGH-GRADE SEROUS OVARIAN CANCER
Bibliographic record
Abstract
Abstract High grade serous ovarian cancer (HGSC) is typically diagnosed at an advanced stage and the vast majority of patients relapse and die within 5 years of diagnosis. Significant clinical problems in HGSC include wide-spread abdominal dissemination of disease and chemotherapy resistance. Cancer-associated fibroblasts (CAFs) have been shown to play a role in promotion of cancer cell proliferation and invasion, and mediation of chemotherapy resistance. To interrogate the molecular properties of CAFs in HGSC we used fluorescence activated cell sorting to isolate CAFs directly from primary tumor samples and performed gene expression profiling. We found that patients stratify into two classes based on their CAF gene signatures: One with high expression of Fibroblast Activation Protein (FAP-High; FH) and one with low expression of FAP (FAP-Low; FL). FH CAFs express classical CAF genes whereas FL CAFs possesses a preadipocyte-like molecular signature. The FL phenotype has remained largely unnoticed as it is generally out-competed in vitro by FH cells when grown under classical CAF culture conditions. Patients from The Cancer Genome Atlas (TCGA), as well as from our own institute, can be stratified into FH and FL subtypes; in both cohorts patients with FH CAFs have a significantly shorter disease-free and overall survival. In vitro and in vivo functional assays performed with isolated CAFs of both types indicate that FH CAFs aggressively promote proliferation, invasion and therapy resistance of cancer cells, whereas FL CAFs do not. Finally, we identified TCF21, a transcriptional repressor, as a FL-specific transcription factor. Analysis of published TCF21 ChIP-Seq data indicates that TCF21 targets a large number of genes specific to FH CAFs. Overexpression of TCF21 in FH CAFs partially reversed their ability to promote cancer cell invasion and tumor growth. Our discovery of CAF heterogeneity in HGSC highlights the need to personalize patient treatment with respect to both cancer and stromal phenotypes. FH patients may benefit from inhibition of cancer-stroma interactions or from epigenetic modulators that reprogram cancer-promoting FH CAFs into the non-supportive FL state. Citation Format: Ali Hussain, Veronique Voisin, Stephanie Poon, Jalna Meens, Julia Dmytryshyn, Josh Paterson, Marcus Bernardini3, Gary Bader, Benjamin G Neel, Laurie E Ailles. MOLECULAR AND FUNCTIONAL HETEROGENEITY OF CANCER ASSOCIATED FIBROBLASTS IN HIGH-GRADE SEROUS OVARIAN CANCER [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 AP17.
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 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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".