Identification of New microRNA Biomarkers and Candidate Target Genes in Primitive CML Cells Using Global Comparative RNA analyses
Bibliographic record
Abstract
Abstract Imatinib mesylate (IM) and other ABL tyrosine kinase inhibitors (TKIs) have had a major impact on early phase CML patient outcomes. However, they are rarely curative and initial and acquired TKI resistance remain challenges. This is attributed to the finding that chronic phase CML stem cells are innately more resistant to IM and other TKIs than the bulk of the more mature cells they generate. To identify differentially expressed and new miRNAs in CD34+ CML stem/progenitor cells that could be potential biomarkers and therapeutic targets, we used Illumina Deep Sequencing to obtain absolute miRNA expression profiles of highly purified CD34+ cells obtained at diagnosis from three CML patients who were classified retrospectively, after IM therapy, as IM-responders and three as IM-nonresponders. CD34+ cells isolated from five normal bone marrow (NBM) samples were similarly analyzed as controls. Bioconductor DESeq analysis revealed 63 differentially expressed miRNAs in the CD34+ cells from CML and NBM samples (P<0.05). Interestingly, 12 of these were differentially expressed in CD34+ cells from the IM-responders and nonresponders. Most of the 63 differentially expressed miRNAs identified were present at reduced levels in the CD34+ CML cells as compared to NBM, but 17 were increased. In addition, 34 novel miRNAs were identified in the CD34+ CML stem/progenitor cells. We next validated sequencing data in CD34+ cells from IM-responders (n=12), IM-nonresponders (n=10) and normal individuals (n=11) using a high-throughput quantitative microfluidic device. These studies confirmed the differential expression in CD34+ CML cells of 32 of the 63 identified miRNAs (P<0.05), including an increased level of oncomirs miR-155 and miR-17-92, and a decreased level of the tumor suppressors, miR-145, miR-151, and miR-452. Importantly, we detected significant changes in some of these miRNAs in CD34+ cells from CML patients after three months of nilotinib (NL) treatment (23 normalized after three months of NL treatment, whereas 10 showed little change). To further correlate miRNA profiles with corresponding mRNA expression changes, and to identify potential target genes, RNA-seq was performed on the same RNA samples. Bioconductor RmiR analysis was performed to match miRNA target genes whose expression was inversely correlated with the expression of the deregulated miRNAs based on three of six prediction algorithms (mirBase, TargetScan, miRanda, tarBase, mirTarget2, and PicTar). We identified 1,210 differentially expressed mRNAs that are predicted targets of the deregulated miRNAs in the comparison of CML and NBM data. Strikingly, only seven differentially expressed mRNAs were predicted targets of the deregulated miRNAs identified from a comparison of the IM-responders and nonresponders. Most of these are predicted to have roles in regulation of the cell cycle, MAPK signaling and TGF-beta signaling pathways by DAVID Bioinformatics Resources, which clusters predicted target genes to known KEGG pathways. Thus, aberrant, differentially expressed miRNAs and target genes identified in primitive CML stem/progenitor cells may serve as useful biomarkers to predict clinical response of CML patients to TKI therapy, and may ultimately lead to identification of potential therapeutic targets for improved treatment of CML patients. 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 | 0.000 | 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) | 0.001 | 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 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".