Abstract 1751: The influence of sex on prognostic markers for non-small cell lung cancer
Bibliographic record
Abstract
Abstract There are significant differences between the sexes in the incidence, presentation, and response to therapy of non-small cell lung cancer (NSCLC). This study evaluated the effects of these differences on mRNA-based prognostic markers. Several prognostic markers have been developed over the past decade by various groups, generally from microarray datasets. These markers look at the mRNA levels of specific genes and use these to determine whether a patient is likely to good or poor outcome. If such markers were successful at complementing traditional staging criteria (i.e. molecular sub-staging) then patients predicted to have poor outcome could receive additional or more intensive therapies. Surprisingly, to date, the effect of sex on such markers has not been evaluated, despite major differences in disease between males and females. To evaluate this issue, I compiled the data from nine transcriptomic studies of primary NSCLC. These studies were integrated using a novel normalization approach and then subject to meta-analysis. For each gene present in the analysis (16,391 in total), the univariate prognostic capacity was calculated separately for men and women using median dichotomization and Cox proportional hazards modeling. Of the top 100 genes in each sex, only 12 were overlapping. Novel sex-specific prognostic signatures were then derived, and shown to out-perform sex-independent signatures by a statistically significant (p < 0.01) margin. In summary, sex plays a major role in NSCLC etiology and response to therapy. I have demonstrate here that it also greatly effects the efficacy of transcriptomic prognostic markers. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1751.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".