surviBALL: exploring lncRNA expression at diagnosis for 5-year EFS risk stratification in pediatric B-ALL—a proof of concept
Bibliographic record
Abstract
Abstract Background B-cell Acute Lymphoblastic Leukemia (B-ALL) remains an important cause of cancer-related death in children. Therefore, accurate identification at diagnosis of patients at high risk of relapse is crucial. In this context, long non-coding RNAs (lncRNAs) could be novel candidates with great potential. Hence, the aim of this study was to identify new prognostic biomarkers in pediatric B-ALL through an RNA sequencing (RNA-seq) approach that allows the detailed exploration of a wide range of lncRNAs. Methods Total RNA from two cohorts of B-ALL patients (C1 with 50 Spanish patients, and C2 with 72 Canadian patients) was sequenced with a depth of approximately 150 million paired-reads using Illumina technology. All protein coding and non-coding genes included in lncRNAKB annotation were studied to develop a gene expression-based 5-year Event Free Survival (EFS) prediction model. Results First, univariate Cox proportional hazards analyses identified 48 genes significantly associated with higher EFS risk in both cohorts. From these, ALASSO regression selected five genes, all of which are lncRNAs, as the most informative to develop the prediction model, which we have called surviBALL. Stratification of patients into three risk groups according to the surviBALL model revealed significantly poorer EFS in high-risk patients across C1, C2, and the integrated C1 + C2 cohort ( P < 0.001). Validation in an independent cohort of 177 publicly available B-ALL samples confirmed surviBALL’s prediction capacity ( P = 2.80 × 10 − 4 ) and its independence of both subtype and MRD. Conclusions These findings suggest that surviBALL has the potential to complement current risk stratification approaches, particularly by identifying patients at high risk of relapse at diagnosis. As a hypothesis-generating proof of concept, this study highlights the promise of more personalized treatment strategies and warrants further validation in independent cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".