FOLR1 as a therapeutic target in platinum-resistant ovarian carcinoma: unique expression patterns across ovarian carcinoma histotypes and molecular subtypes of low-grade serous carcinoma
Bibliographic record
Abstract
OBJECTIVE: With the development of novel antibody-drug conjugates (ADCs), folate receptor alpha (FOLR1) is a promising therapeutic target for the treatment of platinum-resistant tubo-ovarian carcinomas. The main aims of this study were to assess FOLR1 protein expression in a large cohort of ovarian carcinoma histotypes. To inform future clinical trial design we identified molecular correlates of FOLR1 expression in low-grade serous carcinoma (LGSC). METHODS: One thousand five hundred forty-seven ovarian carcinoma samples from 5 different Canadian cohorts were successfully evaluated by immunohistochemistry for FOLR1 expression using the PS2+ system. Statistical analyses with clinicopathological parameters, LGSC molecular subtypes, and overall survival (OS) were performed. RESULTS: 0.02), and p16 loss (48% p16 absent vs. 26% normal; p=0.03). Canonical MAPK mutation status and PR expression remained significant on multivariable analysis. No significant associations between OS and FOLR1 expression were observed. CONCLUSION: A significant proportion of LGSC express high FOLR1 levels supporting the development of clinical trials to investigate ADCs targeting FOLR1 as novel agents for treating this disease. In LGSC, high FOLR1 expression was associated with fewer MAPK pathway alterations, low PR expression, and p16 loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".