A Novel Micro RNA Signature Identifies a Patient Subset with Poor Prognosis in Core Binding Factor AML
Bibliographic record
Abstract
Abstract Disruption of core binding factor (CBF) in acute myeloid leukemia (AML) has been identified as a favourable prognostic biomarker in AML. Consequently, CBF AML patients are typically treated less intensively than AML patients in most other prognostic groups. Nevertheless, a subset of CBF AML patients fail initial therapy and treatment of these patients after relapse is challenging. Few prognostic markers are available to stratify risk in CBF AML other than t(8;21) vs. inv(16) where t(8;21) is the unfavourable marker and KIT mutation status in t(8;21). In this study, we aimed to utilize miRNA expression profiles in CBF AML to identify patients with favourable vs. unfavourable prognoses. Patients and Methods We analyzed small RNA sequencing data from a discovery cohort of 188 de novo AML patients, of which 19 were CBF AML patients, from The Cancer Genome Atlas study (NEJM, 2013). We selected miRNAs with expression that satisfied a mean of greater than 10 read/106 miRNA mapped and a coefficient of variance greater than 2. Forty-eight miRNAs met these criteria and were further used for model-based clustering. As a validation cohort, we enrolled 38 CBF AML patients diagnosed from 1998 to 2014 at the Prince Margaret Cancer Center (PMCC). Diagnosis of CBF AML was confirmed with conventional cytogenetic analyses at a clinical genetics laboratory at the PMCC. PerfeCTa microRNA assays were applied to quantify the expression of miRNA normalized to RNU6. Results Discovery of the poor prognosis group in CBF AML patients Model-based clustering with 48 miRNAs from TCGA AML data set identified 4 distinct patient clusters (Figure A). Cluster 2 (C2) contained exclusively acute promyelocytic leukemia (APL) patients with high expression of all miRNAs and was excluded from further analysis. Notably, Cluster 1 (C1) was characterized by low expression of most of the 48 miRNAs of the list (Figure A). CBF AML patients were present in C1 (C1-CBF group; n=10) and C3 (C3-CBF group; n=9) only, with none in cluster 4. Figures B and C illustrate survival analyses of C1-CBF and C3-CBF patients compared to several other groups. Notably, C1-CBF patients had significantly worse overall survival (OS) (p=0.001) and disease free survival (DFS) (p=0.005) compared to C3-CBF, with survival statistics comparable to intermediate or poor risk AML subgroups, as measured by Kaplan-Meier analysis. Validation of C1-CBF and C3-CBF group in PMCC patients We identified 3 miRNAs, miR-127, miR-494, and miR-495, from the original 48, that were sufficient to perfectly reproduce the C1 and C3 subgroupings when clustering on them alone. We performed real time qPCR to measure the expression of these 3 miRNAs in the 38 CBF AML patients from the PMCC cohort and assigned them to the clusters. The PMCC C1-CBF (n=13) patients had significantly worse 3-yr OS and 3-yr DFS than the C3-CBF (n=25) patients (OS: C1-CBF, 23.1±11.7% vs C3-CBF, 72±9%; p=0.0062 and DFS: C1-CBF,27.8±13.6% vs C3-CBF 79.1± 8.3%; p=0.0092, Figures D & E). Using multivariate analysis with covariates including age, KIT mutation and translocation status, we demonstrated that these 3 miRNAs form an independent prognostic biomarker in CBF-AML for both OS and DFS (Table). Conclusion We demonstrate that 3 miRNAs (miR-127, miR-494, miR-495) can be used to stratify CBF-AML patients into favourable and unfavourable prognostic subgroups. We show that expression levels of this trio of miRNAs can be used as a clinical tool and we propose that CBF AML patients in the unfavourable miRNA group, C1-CBF, should no longer be treated as favourable risk patients. Figure Figure. Table Table. 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".