Antisense Treatment in Human Prostate Cancer and Melanoma
Bibliographic record
Abstract
Antisense reagents and technology have developed as extraordinarily useful tools for analysis of gene function. The capacity of antisense to reduce expression of RNA (including protein-encoding mRNA and non-coding RNA) important in a multitude of diseases (including cancer) has led to the concept of using antisense molecules as drugs to treat those diseases. Both antisense RNA (RNAi) and antisense oligonucleotides (ASOs) are being developed for this purpose, with ASOs currently the most advanced in clinical testing. ASOs inhibit translation or induce degradation of complementary target RNA, and both Phase I and Phase II trials are either completed or in progress for a number of diseases. In this review, we focus on antisense approaches to treatment of two cancers (melanoma and hormone-resistant prostate cancer) where the early application of ASOs has provided important information revealing both potential for success and lessons for future preclinical and clinical investigation of ASOs as anti-cancer drugs. The progress of clinical application of two ASOs showing promise in treatment of human cancers--Oblimersen (G3139), targeting BCL2 for the treatment of metastatic melanoma, and Custirsen (OGX-11), targeting clusterin for the treatment of hormone refractory prostate cancer (HRPC)--is examined.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".