RNA Sequencing-Based Measurement of Fusion-Transcript for Minimal Residual Disease (MRD) Monitoring in Core-Binding Factor Acute Myeloid Leukemia (CBF-AML)
Bibliographic record
Abstract
Abstract Introduction Recent studies utilizing NGS demonstrated that residual allelic burden at complete remission (CR) is associated with worse overall survival (OS) and relapse incidence in AML. Quantitative PCR (qPCR) based disease monitoring is current practice in CBF-AML. However, qPCR requires standardization and the result for the same sample may vary depending on several factors. Also, other known prognostic factors such as cKIT mutation require an additional test. As RNA-seq can detect gene rearrangement as well as somatic mutations, we hypothesized that RNA-seq on samples taken at diagnosis and at remission can be used to monitor these genetic alterations simultaneously and can be utilized for minimal residual disease (MRD) monitoring in CBF-AML. Patients and Methods This study included 42 CBF-AML patients (23 RUNX1-RUNX1T1 and 19 CBFB-MYH11 AML). Overall, 84 bone marrow samples (42 diagnosis-CR pairs) were subjected to targeted RNA-seq using Illumina TruSight Pan-Cancer panel. After read mapping, gene count was measured using HTSeq followed by DEseq2 for gene expression quantification. Average number of sequenced reads was 3.5M reads with 87% overall mapping rate. Gene fusions in diagnostic samples were detected using EricScript. All 84 samples as well as 42 samples from T-cell fraction (CD3+, as a control) were also subjected to DNA sequencing, targeting a panel of 84 genes (Agilent SureSelect custom gene panel). Average on-target coverage was 1,606x. All other computational analyses were done using R and python. Results In diagnostic samples, class-defining gene fusion events were detected in all 42 patients. In CR samples, we tracked identical junctions identified in corresponding diagnostic samples. As expected, both CBFB-MYH11 and RUNX1-RUNX1T1 showed significant reduction in all CR samples compared to their corresponding diagnostic samples (p < 2.2e-13 and p < 6.3e-05, Fig A and B). CBFB-MYH11 was detectable in 6/19 CR samples (32%) and RUNX1-RUNX1T1 was detectable in 15/23 CR samples (65%). Reduction level of RUNX1-RUNX1T1 measured by RNA-seq showed positive correlation with the reduction level measured by qPCR (Pearson's Rho = 0.74, p < 5.4e-05, Fig C). As per mutational profile at diagnosis, we detected 74 mutations in 38 samples (n=38/42, 90%). NRAS (36%), KIT (36%), KRAS (17%) and, ASXL2 (17%) were commonly mutated. Survival analyses on each gene and each protein locus identified cKIT-D816 mutation as an adverse prognostic factor (HR = 3.57, [1.15 - 11.11], p = 0.028). We were able to detect all cKIT-D816 mutations in RNA-seq. Using information from NGS, we built a prognostic model for RUNX1-RUNX1T1 AML (n = 23). Decision tree analysis identified three distinct subgroups of RUNX1-RUNX1T1 AML on the basis of reduction level of RUNX1-RUNX1T1 and mutation profile (Fig D). Consistent with previous studies, 3-log or deeper reduction of RUNX1-RUNX1T1 transcript level was the most significant prognostic factor (low risk group). The algorithm further divided the patients who failed to achieve 3-log reduction according to the presence of cKIT-D816 mutation at diagnosis (intermediate and high risk group). For three defined groups, 2-year OS rates were 87%, 74%, and 33% (p = 0.08, Fig E) and 2-year relapse incidence rates were 13%, 42%, and 67% (p = 0.048, Fig F). Conclusion RNA-seq can be utilized to quantify RUNX1-RUNX1T1 and CBFB-MYH11 transcripts on diagnostic and CR samples in CBF-AML. We also showed that RNA-seq can stratify RUNX1-RUNX1T1 AML patients into three risk groups according to their long-term prognosis. Figure. Figure. 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 | 0.001 |
| 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) | 0.002 | 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".