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Record W4405050250 · doi:10.1182/blood-2024-211936

Can Optical Genome Mapping Replace Bone Marrow Biopsies in Acute Myeloid Leukemia?

2024· article· en· W4405050250 on OpenAlexaff
Logan Hahn, John F. DeCoteau, Karen Mochoruk, Gabriela Tanumihardja, Holly Giasson, Ronald Geyer, Waleed Sabry, Mohamed Elemary, Julie Stakiw, Rebecca Ellyn MacKay, Mark Bosch

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSaskatchewan Cancer AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsBone marrowMyeloid leukemiaLeukemiaMedicineCancer researchPathologyBiologyImmunology

Abstract

fetched live from OpenAlex

Background Identifying structural variants (SVs) is essential for targeted treatment and prognosis in acute myeloid leukemia (AML) patients. Current cytogenetic techniques like karyotyping and fluorescence in situ hybridization (FISH) face challenges, leading to test failure rates of over 20% (Pullarkat 2008; Grimwade 1998). Additionally, conventional cytogenetic analysis yields normal or non-specific profiles for over one third of AML patients with analyzable karyotypes (Mawad 2012). Optical genome mapping (OGM) poses a potential solution to these limitations. OGM involves the identification of SVs through analysis of ultra-high molecular weight DNA and does not require dividing cells for analysis. OGM has much higher sensitivity than karyotyping and is typically at the 5% variant allele fraction level. Given the resolution of this technique, we hypothesize that OGM results from peripheral blood (PB) and bone marrow (BM) specimens are highly concordant, thereby obviating the need for diagnostic BM biopsies in many AML patients. In the present study, we determine the concordance rate of results obtained through OGM on PB, OGM on BM and conventional cytogenetics in a small cohort of patients with AML. Methods Fourteen consecutive adult patients with AML and greater than 20% blasts in PB, BM, or both were included in the analysis. Conventional cytogenetics (karyotype and FISH) and OGM were performed on BM or PB samples. Twelve patients had OGM performed on both PB and BM specimens. Additionally, 14 BM and 8 PB samples were analyzed by next generation sequencing (NGS) using the Oncomine Myeloid Research Assay from ThermoFisher, which includes a 40-gene DNA panel and an RNA panel covering 29 fusion drivers, in order to compare findings based on European LeukemiaNet (ELN) 2022 risk categories. Results The mean age at diagnosis was 62 years (range: 20-79) The sample consisted of 8 women and 6 men. The mean percentage of PB blasts at diagnosis was 28% (range: 0.1-88), whereas the mean percentage of BM blasts was 55% (range: 20-88%). Two patients had less than 5% blasts in the PB. By using conventional cytogenetics, 11 patients were categorized as having intermediate risk, while 3 were in the adverse risk category. When comparing conventional cytogenetics and OGM performed on either BM or PB specimens, there was agreement in all 14 cases in terms of ELN risk category. In the patients (n=12) who had OGM performed on both their PB and BM samples, there were no discrepancies detected. In the 8 patients who had OGM and NGS performed on both PB and BM, there was also complete agreement in terms of ELN risk stratification. Conclusions In this ongoing study, OGM completed on BM or PB demonstrated all the same SVs detected by conventional cytogenetics. Additionally, there was total agreement between OGM results obtained on PB or BM, even in patients with low levels of circulating blasts. These results indicate that utilizing OGM could enable clinicians to perform necessary testing for AML patients through a more sensitive and less invasive method, potentially enhancing outcomes.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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
Published2024
Admission routes1
Has abstractyes

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