Abstract 4949: microRNA (miR) analysis as a tool for discovering platinum resistance mechanisms in non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
Abstract Background: NSCLC is the most common cause of cancer-related death in the world. Platinum-based chemotherapy is the mainstay of treatment both in the adjuvant and the metastatic settings, but platinum resistance is common. A variety of mechanisms have been reported to underlie platinum-resistance. miRs are short non-coding RNA molecules, each regulating a large number of genes. We hypothesized that in platinum-resistant NSCLC cells, a small number of differentially expressed miRs modulating an abundance of gene products, activate in concert a large number of platinum-resistance mechanisms. Aim: To uncover mechanisms of platinum-resistance in NSCLC, by indentifying miRs differentially expressed in platinum resistant cells. Methods: Platinum-resistant cell lines were created by cisplatin selection of Calu6 and NCI-H23, NSCLC cell lines. RNA was hybridized to Affymetrix miR arrays, followed by TargetScan algorithm analysis. Levels of specific miRs were quantified by RT-PCR (ABI) and were modulated by miR inhibitors (Ambion). Results: Two sets of sensitive (S) and platinum-resistant (R) cell lines were created, each set consisting of a parental S line and several R sub-lines. miR array analysis was performed on the S and R lines. miRs that were significantly more abundant in each of four R lines compared to the corresponding S lines were compiled. Genes predicted to be targeted by each of those miRs with a p value < .05 were tabulated. Surprisingly, only 16 genes were common to those four gene lists, including CDK6, an important G1-S cell cycle regulator. mRNA and protein levels of CDK6 were higher in S compared to R lines, and accordingly, growth rate of R lines was slower. miR145, one of the miRs upregulated in the R lines, is predicted to target CDK6. Transfection of a miR145 inhibitor resulted in a mild increase in mRNA levels of CDK6, supporting its role as a regulator of CDK6. Conclusions: Utilizing a number of biological systems (sets of cells lines) that model the same phenotype (platinum resistance) allows for a robust analysis of the involved molecules. miR profiling of multiple systems can identify genes that are commonly regulated in different systems by different miRs. Using this approach we discovered upregulation of miRs, including miR145, resulting in downregulation of CDK6, as a potential mechanism of platinum resistance in NSCLC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4949. doi:10.1158/1538-7445.AM2011-4949
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".