Personalized siRNA-Nanoparticle Systemic Therapy using Metastatic Lymph Node Specimens Obtained with EBUS-TBNA in Lung Cancer
Bibliographic record
Abstract
Abstract Inhibiting specific gene expression with siRNA provides a new therapeutic strategy to tackle many diseases at the molecular level. Recent strategies called high-density lipoprotein (HDL)-mimicking peptide-phospholipid nanoscaffold (HPPS) nanoparticles have been used to induce siRNAs-targeted delivery to scavenger receptor class B type I receptor (SCARB1)-expressing cancer cells with high efficiency. Here, eight ideal therapeutic target genes were identified for advanced lung cancer throughout the screenings using endobronchial ultrasonography–guided transbronchial needle aspiration (EBUS-TBNA) and the establishment of a personalized siRNA-nanoparticle therapy. The relevance of these genes was evaluated by means of siRNA experiments in cancer cell growth. To establish a therapeutic model, kinesin family member-11 (KIF11) was selected as a target gene. A total of 356 lung cancers were analyzed immunohistochemically for its clinicopathologic significance. The antitumor effect of HPPS-conjugated siRNA was evaluated in vivo using xenograft tumor models. Inhibition of gene expression for these targets effectively suppressed lung cancer cell growth. SCARB1 was highly expressed in a subset of tumors from the lung large-cell carcinoma (LCC) and small-cell lung cancer (SCLC) patients. High-level KIF11 expression was identified as an independent prognostic factor in LCC and squamous cell carcinoma (SqCC) patients. Finally, a conjugate of siRNA against KIF11 and HPPS nanoparticles induced downregulation of KIF11 expression and mediated dramatic inhibition of tumor growth in vivo. Implications: This approach showed delivering personalized cancer-specific siRNAs via the appropriate nanocarrier may be a novel therapeutic option for patients with advanced lung cancer. Mol Cancer Res; 16(1); 47–57. ©2017 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".