Assessment of a new genomic classification system in acute myeloid leukemia with a normal karyotype
Bibliographic record
Abstract
// Jae-Sook Ahn 1 , Hyeoung-Joon Kim 1 , Yeo-Kyeoung Kim 1 , Seung-Shin Lee 1 , Seo-Yeon Ahn 1 , Sung-Hoon Jung 1 , Deok-Hwan Yang 1 , Je-Jung Lee 1 , Hee Jeong Park 2 , Ja-Yeon Lee 2 , Seung Hyun Choi 2 , Chul Won Jung 3 , Jun-Ho Jang 3 , Hee Je Kim 4 , Joon Ho Moon 5 , Sang Kyun Sohn 5 , Yoo Jin Lee 5 , Jong-Ho Won 6 , Sung-Hyun Kim 7 , Zhaolei Zhang 8, 9, 10 , TaeHyung Kim 8, 9 and Dennis Dong Hwan Kim 11 1 Hematology-Oncology, Chonnam National University Hwasun Hospital, Jeollanam-do, Korea 2 Genomic Research Center for Hematopoietic Diseases, Chonnam National University Hwasun Hospital, Jeollanam-do, Korea 3 Division of Hematology-Oncology, Samsung Medical Center, Seoul, Korea 4 Department of Hematology, The Catholic University of Korea, Seoul, Korea 5 Department of Hematology-Oncology, Kyungpook National University Hospital, Seoul, Korea 6 Department of Hematology-Oncology, Soon Chun Hyang University Hospital, Seoul, Korea 7 Department of Hematology-Oncology, Dong-A University College of Medicine, Busan, Korea 8 Department of Computer Science, University of Toronto, Toronto, ON, Canada 9 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada 10 Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada 11 Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada Correspondence to: Hyeoung-Joon Kim, email: hjoonk@chonnam.ac.kr Keywords: genomic classification; AML; next generation sequencing; normal karyotype; prognosis Received: April 28, 2017     Accepted: December 04, 2017     Published: December 22, 2017 ABSTRACT This study was performed to assess if a recently recommended genomic classification is predictive in patients with normal-karyotype (NK) acute myeloid leukemia (AML). A total of 393 patients were included. Analysis of genetic mutations was performed using targeted resequencing with an Illumina Hiseq 2000. We identified driver mutations across 40 genes, with one or more driver mutations identified in 95.7% of patients. The molecular subclassification was as follows: 34.6% patients (n = 136) with AML with the NPM1 mutation, 10.7% (n = 42) with AML with mutated chromatin or RNA-splicing genes or both, 1.5% (n = 6) with AML with TP53 mutations, 13.5% (n = 53) with AML with biallelic CEBPA mutations, 2.0% (n = 8) with AML with IDH2-R1 72 mutations and no other class-defining lesion, 29.5% (n = 116) with AML with driver mutations but no detected class-defining lesion, 4.3% (n = 17) with AML with no detected driver mutation, and 3.8% (n = 15) patients with AML who met the criteria for ≥2 genomic subgroups. The 5-year overall survival and relapse rate of subgroup in AML with mutated chromatin, RNA-splicing genes, or both was 11.6% (95% CI = 1.4–21.8%) and 71.4% (95% CI = 45.7–86.5%), respectively. This study suggests that the recently recommended genomic classification is an appropriate and replicable categorization system in the NK AML population. The subgroup of AML with mutated chromatin, RNA-splicing genes, or both showed extremely poor survival in NK-AML; thus, a novel approach is needed to improve their prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".