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Replication of New Genomic Classification System in Acute Myeloid Leukemia with Normal Karyotype

2016· article· en· W2589908125 on OpenAlexaff
Tae‐Hyung Kim, Jae‐Sook Ahn, Marc S. Tyndel, Hyeoung‐Joon Kim, Yeo‐Kyeoung Kim, Seung-Shin Lee, Seo-Yeon Ahn, Sung‐Hoon Jung, Deok‐Hwan Yang, Je‐Jung Lee, Hee Jeong Park, Seung Hyun Choi, Chul Won Jung, Jun Ho Jang, Hee‐Je Kim, Joon Ho Moon, Sang Kyun Sohn, Jong-Ho Won, Sung‐Hyun Kim, Zhaolei Zhang, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsCEBPAMyeloid leukemiaNPM1BiologyOncologyMutationGeneticsCancer researchGeneInternal medicineKaryotypeMedicineChromosome

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.021
GPT teacher head0.269
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes1
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