Ectopic Expression of the Proto-oncogene Mer in Pediatric T-Cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
PURPOSE: The Mer receptor tyrosine kinase, cloned from a B-lymphoblastoid library, is the mammalian orthologue of the chicken retroviral oncogene v-eyk and sends antiapoptotic and transforming signals when activated. To determine if Mer expression is ectopic in T-cell acute lymphoblastic leukemia (ALL) and potentially important in leukemogenesis, we analyzed Mer expression in normal human thymocytes and lymphocytes and in pediatric ALL patient samples. EXPERIMENTAL DESIGN: Reverse transcription-PCR, flow cytometry, and immunohistochemistry were used to determine expression of Mer in sorted human thymocyte populations, lymphocytes, and lymphocytes activated by phytohemagglutinin or phorbol 12-myristate 13-acetate/ionophore. Mer expression in 34 T-cell ALL (T-ALL) patient samples was evaluated by reverse transcription-PCR, and Mer protein expression in a separate cohort of 16 patient samples was assayed by flow cytometry and Western blot. RESULTS: Mer expression was absent in normal thymocytes or lymphocytes, and in T cells activated with phytohemagglutinin or phorbol 12-myristate 13-acetate/ionophore. In contrast, Jurkat cells and T-ALL patient samples expressed unique 180 to 185 kDa Mer protein glycoforms. Substantial Mer RNA levels were principally observed in a subset of T-ALL patient samples that expressed B220 (P = 0.004) but lacked surface expression of CD3 (P = 0.02) and CD4 (P = 0.006), a phenotypic profile consistent with immature lymphoblasts. In addition, 8 of 16 T-ALL patient samples had Mer protein detected by flow cytometry and Western blot. CONCLUSIONS: Transforming Mer signals may contribute to T-cell leukemogenesis, and abnormal Mer expression may be a novel therapeutic target in pediatric ALL therapy.
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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".