Abstract 171: Pre-miR-518b and pre-miR-598, novel serum biomarkers of de novo chemoresistance in advanced or unresectable NSCLC
Bibliographic record
Abstract
Abstract Background: Dysregulation of microRNAs (miRNAs) has been widely implicated in a variety of cancers and have been recognized as prognostic and/or predictive biomarkers. Lack of a standard method for stratifying advanced-stage non-small cell lung cancer (NSCLC) patients receiving platinum combination therapy often results in a number of patients that do not derive benefit yet are still exposed to treatment toxicity. We hypothesized that miRNAs in pre-treatment serum and/or plasma could be used to differentiate NSCLC patients who would have disease progression (PD) to first-line carboplatin and gemcitabine chemotherapy at first response assessment. Methods: miRNA array profiling containing probes in triplicate for 900 mature miRNAs (Sanger miRBASE v13.0 released in March 2009) and 450 precursor (pre-) miRNAs along with positive and negative control probes was performed on total RNA isolated from the pre-treatment serum and plasma of 24 advanced or unresectable NSCLC patients who went on to receive first-line carboplatin and gemcitabine chemotherapy. SAM data analysis was applied to find significantly differentially-expressed miRNAs in one condition (PD at first radiologic response) in contrast to another (those without disease progression [nonPD]). Data was normalized to U6 small nuclear RNA and the top differentially-expressed miRNAs were identified based on fold change, p-values, q-values, and false discovery rates. Differentially-expressed miRNAs were validated by quantitative PCR. Single validated candidates or combinations thereof were selected based on specificity and sensitivity to segregate patients with PD vs. nonPD. Correlations for clinical parameters with candidate miRNA were also assessed. Results: Four miRNAs were identified using miRNA microarray as potential candidate qualifiers. Two of them, pre-miR-518b and pre-miR-598, were validated in a qPCR assay and were shown to be significantly over-expressed in serum of PD patients. ROC curves plotting a combination of these two pre-miRNAs were able to discriminate between PD vs. nonPD patients with 83% sensitivity and 72% specificity. No significant correlation of these two pre-miRNAs was observed with clinical parameters such as age, gender, histology, smoking status, or overall survival. No significant plasma miRNA candidates were identified. Conclusion: Serum miRNAs may serve as a proof-of-concept screening tool in predicting chemoresistance to platinum-based combination chemotherapy. This is an important step towards personalized medicine and could enable stratification of patients to treatment options thus reducing exposure to specific toxic chemotherapy agents that in the end result would not benefit the patient. 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 171. doi:10.1158/1538-7445.AM2011-171
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".