Performance of a prognostic genomic signature for early-stage NSCLC in matched fresh frozen and RNA-stabilized tissue.
Bibliographic record
Abstract
7532 Background: Recent clinical studies have demonstrated the benefit of adjuvant chemotherapy (ACT) in some early-stage non-small cell lung cancer (NSCLC) patients. A 15-gene signature, developed using fresh frozen (FF) tissue, has been shown to be an independent prognostic marker that identifies high risk patients who may benefit from ACT. Use of this signature in tissue preserved in an RNA stabilization reagent is desired for easier access to tumor tissue in the clinical setting. Methods: Matched FF and RNAlater-preserved (RNAL) tissues were obtained from 43 NSCLC patients. Patients provided written consent for the collection of tumor tissue at the time of surgery under an IRB approved protocol. Each tissue sample was split into 2 pieces, creating biological replicates for each tissue format. For each patient, RNA was extracted from 4 tissue pieces (2 FF, 2 RNAL), followed by microarray-based genomic profiling (Affymetrix U133 Plus 2.0). The 15-gene signature was applied to each profile, generating a numerical risk score and a risk category (high, low) using methods previously established (Zhu 2010 J Clin Oncol). The level of agreement was evaluated within biological replicates of each tissue format, as well as between the averaged biological replicates of matched FF and RNAL tissues. Results: The concordance in risk category between averaged biological replicates of matched FF and RNAL tissues was 84%, with a Pearson correlation of 0.74. This level of agreement is comparable to the inherent reproducibility of the assay observed within biological replicates of FF tissue, which demonstrated concordance of 79% and Pearson correlation of 0.83. In addition, a statistical in silico simulation was used to demonstrate that if the risk scores in this study had spanned the full dynamic range of the assay while maintaining the same level of inherent reproducibility observed in the current study, the level of concordance would be 89% with a Pearson correlation of 0.93. Conclusions: The level of agreement between matched FF and RNAL tissues is not inferior to that seen within FF biological replicates. Therefore, the 15-gene signature maintains its performance when used in RNAlater-preserved NSCLC tissues.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".