FKBPL-based peptide, ALM201, targets angiogenesis and cancer stem cells in ovarian cancer
Bibliographic record
Abstract
BACKGROUND: ALM201 is a therapeutic peptide derived from FKBPL that has previously undergone preclinical and clinical development for oncology indications and has completed a Phase 1a clinical trial in ovarian cancer patients and other advanced solid tumours. METHODS: In vitro, cancer stem cell (CSC) assays in a range of HGSOC cell lines and patient samples, and in vivo tumour initiation, growth delay and limiting dilution assays, were utilised. Mechanisms were determined by using immunohistochemistry, ELISA, qRT-PCR, RNAseq and western blotting. Endogenous FKBPL protein levels were evaluated using tissue microarrays (TMA). RESULTS: ALM201 reduced CSCs in cell lines and primary samples by inducing differentiation. ALM201 treatment of highly vascularised Kuramochi xenografts resulted in tumour growth delay by disruption of angiogenesis and a ten-fold decrease in the CSC population. In contrast, ALM201 failed to elicit a strong antitumour response in non-vascularised OVCAR3 xenografts, due to high levels of IL-6 and vasculogenic mimicry. High endogenous tumour expression of FKBPL was associated with an increased progression-free interval, supporting the protective role of FKBPL in HGSOC. CONCLUSION: FKBPL-based therapy can (i) dually target angiogenesis and CSCs, (ii) target the CD44/STAT3 pathway in tumours and (iii) is effective in highly vascularised HGSOC tumours with low levels of IL-6.
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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 | 0.000 |
| 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".