Presence of Recurrent Somatic Mutations in Mesenchymal Stromal Cell Fractions Isolated from Acute Myeloid Leukemia As an Evidence of Clonality
Bibliographic record
Abstract
Introduction: Acute myeloid leukemia (AML) involves somatic mutations in hematopoietic stem cells (HSCs). Recent evidence suggests the bone marrow microenvironment, particularly mesenchymal stromal cells (MSCs), also influences AML development. Abnormalities in MSCs from AML patients have been observed (von der Heide et al., 2016), potentially impacting leukemia progression. This study aims to identify MSC somatic mutations and their consequences, further exploring their role in AML. Understanding MSCs' impact on the leukemic microenvironment may shed light on AML pathophysiology and guide targeted therapies, particularly benefiting patients with adverse risk disease. Methods: A total of 28 bone marrow cases were collected from AML patients with adverse risk, including those with complex karyotype, monosomy karyotype, chromosome 5/7/17 abnormalities, TP53 mutation, or relapsed/refractory AML. From these cases, 14 paired MSC and leukemic cell (LC) fractions were analyzed and are presented here. Magnetic bead sorting was used to isolate CD33+ LCs, while the plastic adherence method was employed for MSC culture. Following magnetic isolation, CD33- cells were plated in DMEM-LG with FBS and pen/strep, and MSC colonies formed within 48 hours. Contaminating cells were removed through media changes, and passaging was performed at 70-80% confluency. Flow cytometry at passage 3 characterized MSCs based on cell surface expression of CD105, CD73, and CD90, in accordance with the ISCT guideline. DNA and RNA were extracted from MSCs at passage 3 and subjected to whole exome sequencing (WES) using the Illumina platform, along with the LC fractions. WES aimed for 200X depth for the paired DNA samples. A standard bioinformatics pipeline was used for sequence alignment and variant calling, with variants requiring sufficient read depth (≥30x) and variant allele frequency in the case (>5%) and in the control (<5%). Synonymous variants were filtered out, and gene ontology and pathway enrichment analyses explored the biological significance and potential involvement in leukemogenesis of the identified mutations. Whole transcriptomics for gene expression analyses was also conducted for both MSC and LC fractions. Results: In 14 analyzed cases, a total of 548 somatic variants were identified in the MSC fraction, affecting 480 genes, including 518 nonsynonymous single nucleotide variants (SNVs), 7 stop-gain SNVs, 1 stoploss, 1 non-frameshift insertion, 2 frameshift deletions, 1 frameshift insertion, and 2 non-frameshift deletions, with 16 unknown variants. The average read depth was 75 (range: 15 - 887), and the average variant allele frequency was 50% (range: 11% - 90%). On average, 38 genes were mutated per MSC case, with 8 genes detected in at least 3 cases, 25 in exactly 2 cases, and 447 genes found in only one case. In the leukemic fraction, 686 somatic variants in 595 genes were identified, with an average read depth of approximately 75 (range: 10 - 2596) and an average variant allele frequency of 47% (range: 8% - 85%). On average, 48 mutations were detected per leukemic case, with 12 mutations found in at least 3 cases, 40 in 2 cases, and 543 in only one case. Among the 14 cases, 10 cases (71%) had mutations in known driver genes for AML ( FLT3, NPM1, IDH1, IDH2, DNMT3A, RUNX1, TP53) exclusively in the leukemic fraction. As seen in Figure 1., of the 480 genes mutated in the MSC fraction, 69 overlapped with the 595 genes mutated in the leukemic fraction, while 411 genes were uniquely mutated in the MSC fraction. Further analysis is ongoing, and pathway analysis will be conducted once the remaining 14 cases are processed. Conclusions: This study identifies distinct somatic mutations in AML patients' MSC and leukemic cell fractions, revealing genomic complexity and crosstalk impacting leukemia progression. Understanding the functional implications of these mutations is crucial for unraveling their roles in leukemogenesis and developing personalized therapeutic interventions targeting MSC somatic mutations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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 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".