Comparative Transcriptome Analysis of Different Subsets of CD34+ Normal and Chronic Myeloid Leukemia Cells Identifies Novel Perturbations in the CML Stem Cell Population.
Bibliographic record
Abstract
Abstract Chronic myeloid leukemia (CML) arises from a hematopoietic stem cell that acquires a BCR-ABL fusion gene. During the chronic phase of the disease, this cell produces an expanding multi-lineage clone that usually comes to dominate all terminal stages of myelopoiesis. However, eventually, further mutations are acquired which cause progression to a rapidly fatal acute leukemia. To obtain new insights into the molecular perturbations that cause CML stem cells to initiate chronic phase disease, we generated Long Serial Analysis of Gene Expression (LongSAGE) libraries (∼200,000 tags/library) from extracts of highly purified lin−CD34+CD45RA−CD36−CD71−CD7−CD38+ and lin−CD34+CD45RA−CD36−CD71−CD7−CD38− normal bone marrow (BM) and G-CSF-mobilized peripheral blood (G-mPB) cells as well as cells from 3 chronic phase CML patients with predominantly Ph+/BCR-ABL+ cells in both of these very primitive cell subsets. Long term culture-initiating cell (LTC-IC) and direct colony-forming cell (CFC) assays performed on an aliquot of each of these cell populations showed the frequency of LTC-IC were 4 to 130-fold higher in the 34+38− cells than in the matching 34+38+ cells with the opposite trend for the CFCs. Comparison of the tags present in the pooled CML and pooled normal BM and G-mPB libraries revealed many differentially expressed genes. Real-time RT-PCR analysis of lin−CD34+ cells from 14 chronic phase CML patients and 3 normal BMs confirmed the differential expression of 14 candidate transcripts identified by SAGE (changes ranging from 3-fold lower to 80-fold higher in the CML cells, p<0.05). The altered levels of expression of 5 of these genes (i.e., beta-catenin, MLLT3, IL1R1, LY6E and GAS2) were highly correlated with the relative levels of BCR-ABL transcripts in the same cells (r ≥0.6). 5 of the 14 genes (IL1R1, vWF, SOX4, SELL and RHOB) were found to be differentially expressed in the 3-day post-transduction progeny of CD34+ cord blood cells exposed to a BCR/ABL-IRES-GFP vs a control GFP- lentivirus preparation (n=2). 3 (GAS2, DUSP1 and TP53BP2) were upregulated (5 to 11-fold) in imatinib-treated K562 cells (as compared to untreated K562 cells) but their expression remained unchanged in similarly treated KG1 cells (a primitive BCR-ABL-negative human AML cell line) providing further evidence that their deregulated expression is secondary to the kinase activity of p210BCR-ABL. In addition, from a comparison of the meta-library for the 34+38− CML cells with the meta-libraries for both the normal 34+38− and 34+38+ cells, the meta-library for the 34+38+ CML cells and most publicly accessible SAGE data, we were able to identify 65 novel tags in the 34+38− CML cells. 32 of these unique tags were located within conserved genomic regions and >2 kb away from known transcripts, and of these 32, 3 were confirmed to represent novel transcripts using a PCR approach. These results illustrate the potential of SAGE to reveal novel as well as known components in the transcriptomes of rare normal and cancer stem cell populations. Investigation of their roles in primitive human cells transduced with BCR-ABL and BCR-ABL+ cell lines indicates the utility of these models for further delineation of the complex effects of BCR-ABL expression in chronic phase CML stem cells.
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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 | 0.000 | 0.000 |
| 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".