Different Characteristics Identified by Single Nucleotide Polymorphism Array Analsysis in Leukemia Suggest the Need for Different Application Strategics Depending On Disease Category.
Bibliographic record
Abstract
Abstract Abstract 2490 Background: High resolution SNP-A (single nucleotide polymorphism array) based karyotyping has become one of the most useful tools to investigate complex genomic lesions in leukemic cells. SNP-A has the potential to be applied as a diagnostic tool and as a surrogate for prognostic stratification in leukemias. As such, SNP-A should be a valuable asset in the diagnostic flow in clinical cytogenetic setting. However, due to limited laboratory and health care resources, its application needs to be decided carefully with a reasonable guideline. The present study investigated the value of SNP-A as an array based karyotyping tool in 469 cases with leukemias/MDS. The detection rate of SNP-A based karyotyping in combination with metaphase cytogenetics (MC) was assessed, with the goal of proposing a practical approach for clinical applications of SNP-A in leukemias/MDS. Material and Methods: Four hundred sixty nine patients with diverse subtypes of leukemia were included; 133 AML with normal karyotype (AML-NK), 98 AML with core binding factor rearrangement (CBF-AML), 61 MDS including chronic myelomonocytic leukemia, 118 CML in chronic phase (CML-CP), and 59 ALL (B-lineage). The Genome-Wide Human SNP 6.0 Array (Affymetrix, Santa Clara, CA, USA) was performed. Results: In the 469 patients, the detection rate was 20%(96/469) by MC alone, 33%(156/469) by SNP-A alone, and 39%(184/469) by combined use of SNP-A and MC. According to subtype of leukemias/MDS, the detection rate identified by SNP-A was different and the incidence of CN-LOH varied. In addition, each leukemia subtype showed a different pattern in terms of their types, sizes, and locations of copy number alterations and CN-LOH lesions. SNP-A detected chromosomal lesions not identified by MC, especially in AML-NK with a detection rate of 32%(43/133). In CBF-AML, the detection rates by MC alone and combined use of MC and SNP-A were 29%(28/98) and 40%(39/98), respectively. AML-NK showed more frequent CN-LOH (23%,31/133) than CBF-AML (3%,3/98). In AML-NK, loss of lesions occurred predominantly on 17p, 17q, or 7q. In AML-CBF, 8q or 9q was the common site of loss lesions. Trisomy 8 and CN-LOH of 13q were common lesions in both AML-NK and AML-CBF. As for MDS, t he detection was improved to 54%(33/61) by combined SNP-A and MC, compared with 39%(24/61) using MC alone. MDS showed frequent CN-LOH (25%,15/61) on 11q or 14q. Consistent with MC, loss lesions were frequently found on 5q, 7q, or 20q. In one patient with del(9q) as a sole abnormality, CN-LOH of 7q (26 Mb) was found by SNP-A. In ALL, detection was improved to 88%(51/59) by using combined SNP-A and MC, compared with 63%(37/59) by MC alone and CN-LOH was found in 20%(12/59) of patients. Concerning the size of abnormal lesions, the median size of loss in ALL was smaller than those of other leukemia subtypes. The size of focal deletions of IKZF 1, ETV 6, and CDKN 2A was 106–282 kb, 370 kb-4.5 Mb, and 115 kb-2.1 Mb, respectively. Furthermore, in ALL, aneuploidy abnormalities were accurately identified by SNP-A. ALL case with normal karyotype by MC showed gain or loss of several whole chromosomes by SNP-A, suggesting presence of hyperdiploidy clones or hypodiploidy clones. In particular, in a case showing chromosome numbers of 46–60 by MC, SNP-A revealed two kinds of abnormal lesions; CN-LOH and gain (tetrasomy) on several whole chromosomes, consistent with doubling of a near-haploid clone. In CML, t he detection rates of MC alone and combined use of MC and SNP-A was 3%(4/118) and 24%(28/118), respectively. Among abnormal lesions identified by SNP-A, 90% (35/39) were losses; 83%(29/35) were submicroscopic 9q34 or/and 22q11.2 deletions adjacent to the t(9;22) breakpoint. CN-LOH was found only in 2%(2/118) in CML-CP. Conclusion: The present study shows that SNP-A is useful to improve the detection rate of genomic lesions in diverse leukemia subtypes. The decision to perform SNP-A in addition to MC and/or FISH should be made according to the disease category of each leukemia subtype. In AML, it would be better that SNP-A is used depending on the karyotypic results by MC, while routine SNP-A appears to be an efficient screening method in MDS and ALL and the benefit is minimal in CML A consensus needs to be reached between laboratories as to the practical strategies for the clinical application of SNP-A. 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".