Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene <i>ID4</i>
Bibliographic record
Abstract
The comprehensive characterization of a large number of cancer genomes will eventually lead to a compendium of genetic alterations in specific cancers. Unfortunately, the number and complexity of identified alterations complicate endeavors to identify biologically relevant mutations critical for tumor maintenance because many of these targets are not amenable to manipulation by small molecules or antibodies. RNA interference provides a direct way to study putative cancer targets; however, specific delivery of therapeutics to the tumor parenchyma remains an intractable problem. We describe a platform for the discovery and initial validation of cancer targets, composed of a systematic effort to identify amplified and essential genes in human cancer cell lines and tumors partnered with a novel modular delivery technology. We developed a tumor-penetrating nanocomplex (TPN) that comprised small interfering RNA (siRNA) complexed with a tandem tumor-penetrating and membrane-translocating peptide, which enabled the specific delivery of siRNA deep into the tumor parenchyma. We used TPN in vivo to evaluate inhibitor of DNA binding 4 (ID4) as a novel oncogene. Treatment of ovarian tumor-bearing mice with ID4-specific TPN suppressed growth of established tumors and significantly improved survival. These observations not only credential ID4 as an oncogene in 32% of high-grade ovarian cancers but also provide a framework for the identification, validation, and understanding of potential therapeutic cancer targets.
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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".