Replication of New Genomic Classification System in Acute Myeloid Leukemia with Normal Karyotype
Bibliographic record
Abstract
Abstract Introduction Acute myeloid leukemia (AML) is a genetically heterogeneous disease. A recent study (NEJM, 2016) classified 1540 patients into 14 subgroups using mutation information from targeted next generation sequencing data as well as cytogenetic information [1]. The classification criteria of 7 of these subgroups rely solely on mutation information. NK-AML is characterized by its lack of cytogenetic abnormalities. In this study, we attempted to replicate the prognostic stratification in an independent set of NK-AML patients using the NEJM study's genomic classification criteria. Patients and Methods This study included a total of 393 patients who met the following eligibility criteria: 1) age ≥ 15 years; 2) a diagnosis of NK-AML confirmed by conventional cytogenetic analysis; 3) treatment with induction chemotherapy using a standard protocol (a 3-day course of anthracycline with a 7-day course of cytosine arabinoside). The median follow-up duration was 55.1 months (range, 0.7-182.9). Analysis of genetic mutations were performed using targeted sequencing by Illumina Hiseq 2000 (Agilent custom probe set targeting entire exon regions of a myeloid panel consisting of 94 genes). Results We identified driver mutations across 28 genes or genomic regions, with 2 or more driver mutations identified in 15/393 patients (3.8%). Based on the genomic classification criteria, the patients were classified as follows: 136 patients (34.6%) with NPM1 mutations, 42 patients (10.7%) with mutated chromatin modifiers and/or RNA-splicing genes, 6 patients (1.5%) with TP53 mutations, 40 patients (10.2%) with biallelic CEBPA mutations, 8 patients (2.0%) with IDH2-R172 mutations and no other class-defining lesions, 108 patients (27.5%) with driver mutations but no detected class-defining lesions, 38 patients (9.7%) with no detected driver mutations, and 15 patients (3.8%) who met the criteria of more than one genomic subgroup. Of the 393 patients, 325 patients (82.7%) achieved complete remission (CR). CR rates vary depending on the genomic subgroup (75.9%-97.4%). The CR rate for each subgroup was as follows: 86.8% (118/136) of patients with NPM1 mutations61.9% (26/42) of patients with mutated chromatin and/or RNA-splicing genes83.3% (5/6) of patients with TP53 mutations97.5% (38/40) of patients with biallelic CEBPA mutations87.5% (7/8) of patients with IDH2-R172 mutations and no other class-defining lesions75.9% (82/108) of patients with driver mutations but no detected class-defining lesions97.3% (37/38) of patients with no detected driver mutations80.0% (12/15) of patients meeting criteria of more than one subgroup 5-year OS and 5-year relapse incidence (RI) for each subgroup was as follows: 49.3% (95% CI, 40.1-58.5) and 39.8% (95% CI, 30.1-49.2) of patients with NPM1 mutations11.6% (95% CI, 1.4-21.8) and 71.4% (95% CI, 45.7-86.5) of patients with mutated chromatin and/or RNA-splicing genes50.0% (95% CI, 10.0-90.0) and 20.0% (95% CI, 0.4-61.2) of patients with TP53 mutations68.3% (95% CI, 53.4-83.2) and 19.7% (95% CI, 8.5-34.4) of patients with biallelic CEBPA mutations56.3% (95% CI, 17.3-95.3) and 21.4% (95% CI, 0.3-67.3) of patients with IDH2-R172 mutations and no other class-defining lesions26.6% (95% CI, 17.4-35.8) and 53.2% (95% CI, 40.7-64.3) of patients with driver mutations but no detected class-defining lesions29.1% (95% CI, 14.2-44.0) and 43.8% (95% CI, 27.1-59.3) of patients with no detected driver mutations40.0% (95% CI, 15.3-64.7) and 33.3% (95% CI, 9.2-60.3) of patients that meet the criteria of more than one subgroup. The CR rates of the subgroup with mutated chromatin and/or RNA-splicing genes was significantly lower than the rest of the cohort (61.9% vs. 85.2%, p=0.00016). The 5-year OS and 5-year RI of the subgroup were also poorer than the others [61.9% vs. 85.2% in OS (p=0.00016), 71.4% vs. 40.1% in RI (p < 0.0001)]. Conclusion Our NK-AML cohort showed similar survival patterns to the cohort in Papaemmanuil et al (NEJM 2016). The subgroup in AML with mutated chromatin and/or RNA-Splicing genes had the poorest prognosis with respect to CR rate and overall survival. This analysis replicates the result of recently published genomic classification and supports its use for categorizing NK-AML patients. Reference [1] Genomic Classification and Prognosis in Acute Myeloid Leukemia. Papaemmanuil E et al. N Engl J Med, 2016 vol. 374 (23) pp. 2209-2221. Figure Figure. Disclosures Jang: Kyowa Hakko Kirin Co., Ltd.: Research Funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".