Clinical and Genetic Relevance of Somatic Mutations in Core Binding Factor (CBF) Acute Myeloid Leukemia (AML) Patients Using Serial Sequencing
Bibliographic record
Abstract
Abstract Objectives To investigate the clinical relevance of somatic mutations in core binding factor (CBF) acute myeloid leukemia (AML) patients at diagnosis and relapse and hierarchy of mutation acquisition using serial sequencing. Patients and Methods Eighty seven patients (pts) diagnosed with CBF AML were enrolled in this study (62 pts carrying RUNX1-RUNX1T1 /t(8; 21) and 25 pts carrying CBFB-MYH11 /inv(16). Median follow-up duration among survivors was 56.9 months (range 0-155.3). Using a 84 gene panel, we performed targeted deep sequencing in 357 samples using Illumina Hiseq 2500. Sequenced samples include bone-marrow/peripheral blood taken at initial diagnosis (n=87) and T-cell (CD3+, n=68), CD34+/CD38-(n=66) and CD34+/CD38+ (n=68) fractions. In addition, samples taken at complete remission (n=53), and relapse (n=15) were sequenced. Average of on-target coverage was 1657.8x. Results We detected 166 mutations in 79/87 pts at time of diagnosis (90.8%, median 2 mutations/pts, range 0-7). At diagnosis, KIT (39%), NRAS (33%), ASXL2 (14%), KRAS (13%), RAD21 (7%), and FLT3 (6%) were commonly mutated. When grouped by biological pathway, frequencies of mutations in RAS (KRAS or NRAS ), chromatin modifiers and cohesin complex were significantly different among two sub-populations (p = 0.002, 0.01, and 0.02, respectively, Figure A). Survival analyses show that KIT -D816mut is an adverse prognostic factor for overall survival (hazard ratio (HR) 2.31, 95% CI [1.04-5.14], p=0.04) and relapse (HR 2.76 [1.06-7.16], p =0.04). Mutation in RAS was a favorable factor for relapse (HR 0.13 [0.03-0.56], p=0.001, Figure B). Multivariate analysis confirmed that only RAS mutation was a significant favorable factor for the incidence of relapse (HR 0.20 [0.05-0.86], p=0.03). Taking advantage of serial sequencing and real-time polymerase chain reaction (PCR) data (RUNX1-RUNX1T1 ), we inferred mutation dynamics and clonal hierarchies. Mean allelic burden at diagnosis and relapse were comparable (mean variant allele frequency (VAF) = 22.17% and 22.22%, respectively), whereas mutations were nearly cleared at complete remission (mean VAF = 0.25%) (Figure C). With initial genetic rearrangement detected at both diagnosis and relapse, 14/15 pts carried additional 39 mutations throughout the course of the disease. Fifteen mutations were stable, whereas 9 and 15 mutations were cleared/decreased and acquired/selected at relapse. Inference based on mutation dynamics revealed stable mutations are more likely to be earlier events than mutations that are cleared at relapse (p = 0.01, Figure D). Furthermore, integrated analyses of reduction rate of the RUNX1-RUNX1T1 and mutation dynamics showed RUNX1-RUNX1T1 is no later event than all targeted mutations in our cohort, further explaining clonal hierarchies of CBF AML with RUNX1-RUNX1T1 (Figure E) . Conclusion and Summary Current study provided distinct mutation patterns between two sub-population of CBF AML. Multivariate analyses showed that presence of mutation in KRAS or NRAS was a favorable prognostic factor. In addition, results from longitudinal sequencing combined with RUNX1-RUNX1T1 level provides further insights on the order of genetic mutation acquisition during leukemogenesis. Download : Download high-res image (235KB) Download : Download full-size image 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".