Comparison of the Transcriptomic Signature of Pediatric Vs. Adult CML and Normal Bone Marrow Stem Cells
Bibliographic record
Abstract
Abstract Introduction Pediatric chronic myeloid leukemia (CML) accounts for 10 to 15% of children with myeloid leukemia and 2 to 9% of all pediatric leukemias. Prior to the discovery of tyrosine kinase inhibitors (TKI) such as imatinib, stem cell transplantation was the only curative treatment for both adults and children with CML. However, due to the small numbers of patients, standardized treatment approaches for pediatric CML have not been established. There are several unique characteristics of CML diagnosed in children and adolescents, and young adults (AYA; 16-29 years), compared to adults. Children and AYA with CML present with a higher white blood count and have larger spleens, higher peripheral blast counts, and lower hemoglobin levels, suggesting that the biology of pediatric CML is different than adult CML. In addition, potential side effects of TKIs unique to pediatric CML patients include impaired bone growth, fertility and immune function, however none have been extensively studied. We hypothesize that the differences in clinical presentation of pediatric CML patients are due to unique molecular characteristics that are absent in adult CML patients. To test this hypothesis, we studied the transcriptomic signature of pediatric CD34+ CML cells compared to adult CML and normal age-matched bone marrow CD34+ cells. Methods CD34+ cells were isolated from pediatric CML (n=7), adult CML (n=8), pediatric normal (n=2) and adult normal (n=3) bone marrow samples. Total RNA was isolated from cells, and then cDNA libraries were generated. Prepared libraries were sequenced on the Illumina HiSeq 4000 instrument. We aligned reads using the HISAT2 alignment software, and mapped to genes with HT-Seq. We removed genes that had zero reads across all the samples, resulting in a set of 4,696 genes that were detected in one or more samples. In case of technical replicates, we used mean of replicates. We performed three differential expression comparisons with edgeR: (1) Pediatric CML vs Adult CML, (2) Adult CML vs Adult Normal, and (3) Pediatric CML vs Pediatric Normal. We used a False Discovery Rate (FDR) of £ 20% and absolute log2 fold-change ³ 1 for selecting differentially expressed genes in each comparison. We used Fisher's exact test to identify significant KEGG pathways for the differentially expressed genes in each comparison. Results Pediatric CML vs Adult CML We found 24 differentially expressed genes (15 over- and 9 under-expressed). Though no pathway was found to be significant at the false discovery rate (FDR) £ 20%, we identified a number of sub-pathways that are relevant. For example, the Chemokine Signaling pathway shows at the top of the list (ordered by raw p-value) because of two genes, XCR1 and HCK, associated with VEGF and MAPK pathways involved in cell proliferation, angiogenesis, DNA repair, and cancer pathogenesis. Adult CML vs Adult Normal We found 60 genes (30 over- and 30 under-expressed) differentially expressed when comparing adult CML patients to normal adults. Ten genes overlapped with 24 genes we identified when comparing pediatric and adult CML patients. We found 11 pathways as significant at FDR £ 10%. Multiple pathways, including Cell adhesion, allograft rejection, Graft versus Host Disease, and Type I diabetes pathways, showed downregulation of MHC, with subsequent downstream reduction in expression of apoptosis-related genes. The IL-17 pathway makes sense, as MAPK, well-known to be associated with various cancers, is down-regulated. Lastly, in the NK pathway the gene DAP12 is up-regulated. This gene is known as a tyrosine kinase binding protein, and although tyrosine kinase inhibitors are the standard treatment for CML, the role of DAP12 in relation to leukemia has not yet been described. Pediatric CML vs Pediatric Normal We found 509 genes (350 over- and 159 under-expressed) differentially expressed in pediatric CML patients compared to normal. Interestingly, transcriptional regulators are differentially enriched in the hematopoietic stem cell differentiation function group including GATA1, GATA2, KLF1 and KLF2. RFC is down-regulated. RFC is a mismatch repair gene known to be involved in colorectal cancer. Many of the significant pathways are involved in glucose and fatty acid metabolism. Our pilot study identified novel molecular features of pediatric CML bone marrow stem cells, providing new insights into the novel biomarkers and pathogenesis of pediatric CML. Disclosures Gotlib: Blueprint Medicines: Consultancy, Honoraria, Research Funding; Promedior: Research Funding; Deciphera: Consultancy, Honoraria, Research Funding; Incyte: Consultancy, Honoraria, Research Funding; Kartos: Consultancy; Celgene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Research Funding.
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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 | 0.001 | 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 | 0.000 |
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".