Discovery and validation of a cross-platform 21-gene prognostic signature in neuroblastoma.
Bibliographic record
Abstract
10035 Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite the development of risk stratification tools to improve prognostication, prediction of patient survival outcomes in NB remains poor. In this study we used an unbiased machine-learning algorithm to develop and validate a transcriptomic signature capable of predicting 5-year overall (OS) and event-free survival (EFS) in these patients. Methods: The TARGET-Neuroblastoma dataset (n = 243) was used as the training set. Two independent NB cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 266) were used as validation sets. Elastic net regression was employed to identify transcripts associated with EFS. Association of the developed signature with EFS and OS was evaluated using univariate Cox proportional hazards (CoxPH), Kaplan-Meier, and 5-year receiver-operator characteristic curves in validation cohorts. Further, the independent prognostic value of the signature was assessed using multivariate CoxPH models with relevant clinicopathologic variables including age, INSS stage, and N-Myc amplification status in both validation sets. Finally, a nomogram was developed to integrate the signature with prognostic clinicopathologic variables to evaluate their combined efficacy for prediction of 5-year EFS and OS. Results: We identified a 21-gene signature that demonstrates significant association with EFS and OS in both E-MTAB-178 and GSE49710 validation cohorts. Moreover, the signature is independent of clinicopathological variables and can be effectively incorporated into a risk model, improving the prognostic performance. Several genes within the signature have been previously implicated in NB, including ECEL1, HOXC9 and ARAF1. Conclusions: To the best of our knowledge, we are the first to use an unbiased machine learning approach to generate a transcriptomic gene signature for neuroblastoma prognosis externally validated in multiple cohorts across platforms. This 21-gene transcriptomic signature significantly associated with EFS and OS in this disease. Combining this signature with current prognostic clinicopathologic variables will improve risk stratification of affected patients and may inform effective clinical decision-making.[Table: see text]
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".