Antisense oligonucleotide targeting of insulin‐like growth factor‐1 receptor (IGF‐1R) in prostate cancer
Bibliographic record
Abstract
OBJECTIVE: Altered expression of insulin-like growth factor receptor (IGF-1R) is associated with castrate-resistant prostate cancer (CRPC) progression. We hypothesize that increased expression and/or responsiveness of IGF-IR may promote disease progression. This study assesses ATL1101, a 2'-MOE-modified antisense oligonucleotide (ASO) targeting human IGF-IR, with regard to potency and anti-cancer activity in androgen-responsive (LNCaP) and -independent (PC3) prostate cancer cells in vitro and in vivo. METHODS: IGF-IR mRNA and protein expression was assessed in ATL1101- and control oligonucleotides (ODN)-treated prostate cancer cells by QT-PCR and immunoblotting. The effect of IGF-1R ASO on cell growth and apoptosis in vitro was examined by crystal violet assay, flow cytometry, and expression and activation state of downstream signaling targets was examined by immunoblotting. In vivo growth of subcutaneous xenografts was performed in nude mice treated with intraperitoneally administered ATL1101 or control ODN by measuring tumor volume of PC3 xenografts in intact mice, and tumor volume and serum prostate-specific antigen levels in castrated mice harboring LNCaP xenografts. RESULTS: We observed dose- and sequence-specific suppression of IGF-IR mRNA and protein expression in ATL1101-treated cells in vitro. Suppressed IGF-IR expression correlated with decreased proliferation and increased apoptosis of PC3 cells under standard culture conditions and of LNCaP cells under androgen-deprived culture conditions. ATL1101 suppressed PC3 tumor growth as a monotherapy and delayed CRPC progression of LNCaP xenografts. CONCLUSIONS: This study reports the first preclinical proof-of-principle data that this novel IGF-IR ASO selectively suppresses IGF-1R expression, suppresses growth of CRPC tumors, and delays CRPC progression in vitro and in vivo.
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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".