Comparative Analysis of Downstream Genetic Targets of the Variant Acute Promyelocytic Leukemia Fusion Proteins NPM-RARA and NuMA-RARA.
Bibliographic record
Abstract
Abstract Acute promyelocytic leukemia (APL) is characterized by accumulation of abnormal promyelocytes in the bone marrow and peripheral blood, and sensitivity to treatment with all-trans retinoic acid. APL cases have a balanced chromosomal translocation involving retinoic acid receptor alpha (RARA) on chromosome 17. The resulting fusion proteins (X-RARA) are aberrant transcription factors and block ATRA-induced neutrophil differentiation. Loss of RARA signalling impairs granulopoiesis, but is not sufficient to cause a leukemic phenotype. We elucidated the identities of additional signalling pathways, which can potentially cooperate with X-RARA in APL, that are commonly modulated by multiple X-RARA. We used the U-937 hematopoetic cell line retrovirally transduced with NPM-RARA (Kamel-Reid et al, 2003), and NuMA-RARA, in addition to NB4 cells (expressing PML-RARA) to determine the common genes and pathways deregulated in APL. Gene expression analysis was carried out on RNA harvested in triplicate from control and X-RARA expressing cell lines, using the Affymetrix U133Plus2 array platform. Gene expression and pathways analysis of array data was carried out using a suite of analysis tools. Array data were validated in an independent sample set by real-time quantitative PCR. We observed a total of 311 genes deregulated at least 2-fold by NuMA-RARA (192 up-regulated, 119 down-regulated), 393 genes deregulated by NPM-RARA (292 up-regulated, 101 down-regulated), and 2056 genes deregulated by PML-RARA (1097 up-regulated, 959 down-regulated). A total of 65 genes, in 5 major interaction networks, were commonly deregulated by all three X-RARA (42/65 up-regulated, 23/65 down-regulated). The majority of these genes are involved in cellular signalling (14 genes, p-value 1.57E-07–7.77E-3), transcription (13 genes, p-value 5.68E-7–3.91E-3), cell proliferation (25 genes, p-value 9.73E-7–7.77E-3), apoptosis (26 genes, p-value 9.96E-7–7.71E-3), and cell movement (17 genes, p-value 1.43E-6–7.60E-3). Genes involved in the CEBPA interaction network (GFI1, TRIB2, ELA2), as well as other genes that we anticipated to be deregulated in APL including ID1, MMP9, and JUN were found through this analysis. NF-kB (p-value 2.10E-3), AHR (p-value 2.12E-3), IL-6 (p-value 5.40E-3), and G-protein coupled receptor (p-value 7.45E-3) signalling were among the top canonical pathways determined to be altered by X-RARA. Over-expression of a number of NF-kB downstream transcriptional targets, including VEGF, IL8, MMP9, cIAP2, and TNFAIP3, were also observed in multiple X-RARA expressing cell lines. In addition, in vitro results were compared to NuMA-RARA gene targets identified in primary bone marrow cultures derived from the hCG-NuMA-RARA transgenic mouse model (Sukhai et al, 2004). We observed that pathways involved in cell signalling, cell death, gene expression, proliferation, and cell cycle were significantly deregulated in both mouse and human datasets, indicating that these pathways may be important cooperating events in APL. Our data represent the first comparison of the genetic profiles of the variant fusion proteins NPM-RARA and NuMA-RARA in a haematopoietic cell system. Our studies are a significant step in identifying key targets that cooperate with X-RARA in the development of APL.
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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) | 0.002 | 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".