Genomic Profile of Chronic Lymphocytic Leukemia in Korea Identified by Targeted Sequencing
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) is extremely rare in Asian countries and there has been one report on genetic changes for 5 genes (TP53, SF3B1, NOTCH1, MYD88, and BIRC3) by Sanger sequencing in Chinese CLL. Yet studies of CLL in Asian countries using Next generation sequencing have not been reported. We aimed to characterize the genomic profiles of Korean CLL and to find out ethnic differences in somatic mutations with prognostic implications. We performed targeted sequencing for 87 gene panel using next-generation sequencing along with G-banding and fluorescent in situ hybridization (FISH) for chromosome 12, 13q14.3 deletion, 17p13 deletion, and 11q22 deletion. Overall, 36 out of 48 patients (75%) harbored at least one mutation and mean number of mutation per patient was 1.6 (range 0-6). Aberrant karyotypes were observed in 30.4% by G-banding and 66.7% by FISH. Most recurrent mutation (>10% frequency) was ATM (20.8%) followed by TP53 (14.6%), SF3B1 (10.4%), KLHL6 (8.3%), and BCOR (6.25%). Mutations of MYD88 was associated with moderate adverse prognosis by multiple comparisons (P = 0.055). Mutation frequencies of MYD88, SAMHD1, EGR2, DDX3X, ZMYM3, and MED12 showed similar incidence with Caucasians, while mutation frequencies of ATM, TP53, KLHL6, BCOR and CDKN2A tend to be higher in Koreans than in Caucasians. Especially, ATM mutation showed 1.5 fold higher incidence than Caucasians, while mutation frequencies of SF3B1, NOTCH1, CHD2 and POT1 tend to be lower in Koreans than in Caucasians. However, mutation frequencies between Caucasians and Koreans were not significantly different statistically, probably due to low number of patients. Collectively, mutational profile and adverse prognostic genes in Korean CLL were different from those of Caucasians, suggesting an ethnic difference, while profile of cytogenetic aberrations was similar to those of Caucasians.
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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) | 0.001 | 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 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".