The LSC17 Leukemic Stem Cell Signature Predicts Outcome in Pediatric Acute Myeloid Leukemia
Bibliographic record
Abstract
Abstract Introduction The model of the leukemic stem cell was proposed nearly 60 years ago as a mechanism of leukemic pathogenesis. Recently, a 17-gene leukemic stem cell (LSC) prognostic signature, termed the LSC17, was developed (Ng et al., 2016). The LSC17 score was shown to reliably differentiate adult acute myeloid leukemia (AML) patients into high and low risk groups. However, its applicability to pediatric AML remains unknown. Prognostic indicators are sensitive to differences in age. For example, NPM1 mutations, which are highly prognostic of favorable outcome, have no significance in patients older than 65 years (Ostronoff et al., 2015). Similarly, core-binding factor AML, also associated with favorable outcomes, loses prognostic value in older patients (Prebet et al., 2009). Given that age remained a significant prognostic factor in multivariate analyses with the LSC17, we evaluated the impact of the signature in older versus younger adults in the TCGA AML dataset and further, applied it to transcriptome sequence data from pediatric AML to assess the prognostic power in younger patients. Patients and Methods Pediatric AML biological samples were collected from patients enrolled in the Children's Oncology Group protocol AAML0531 and RNA sequencing was performed by the British Columbia Genome Sciences Center (Vancouver, BC). The Cancer Genome Atlas AML RNA sequencing data was downloaded from the Broad Institute Firehose (TCGA Network et al., 2013; Broad Institute TCGA Genome Data Analysis Center, 2016). LSC17 scores were calculated from log2 RPKM normalized gene expression as described by Ng et al . Results We initially verified that the LSC17 score is effective for risk stratification in adult AML by applying the method to the TCGA AML cohort, excluding PML-RARA, BCR-ABL1 fusions, and those who received no treatment (n = 155, range: 18 - 88 yrs). Patients with low LSC17 scores had significantly better overall survival (OS) compared to the high scoring group after 5 years (low-LSC17 OS: 38.0%, high-LSC17 OS: 15.2%, hazard ratio (HR) = 2.08, p We then evaluated the LSC17 signature to identify risk groups using RNA sequencing from our pediatric AML cohort (n = 446, range: 0.02 - 28 yrs). Univariate survival analysis revealed strong prognostic value for both overall survival (low-LSC17 OS at 5yrs: 72.8%, high-LSC17 OS at 5yrs: 51.4%, HR = 2.06, p Conclusion In this study, we validated that the LSC17 score can be used as an effective risk stratification tool in children and young adults. The LSC17 remains a strong independent prognostic factor after controlling for age, WBC, adverse FLT3-ITD mutations and favorable NPM1 mutations. While the LSC17 can be used as an informative indicator of long term survival, and event-free survival, we showed that this signature loses prognostic value in older AML patients. Download : Download high-res image (164KB) Download : Download full-size image Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 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) | 0.003 | 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".