IGF-IR tyrosine kinase interacts with NPM-ALK oncogene to induce survival of T-cell ALK+ anaplastic large-cell lymphoma cells
Bibliographic record
Abstract
Type I insulin-like growth factor receptor (IGF-IR) tyrosine kinase plays important roles in the pathogenesis of several malignancies. Although it promotes the growth of stimulated hematopoietic cells, a direct role of IGF-IR in malignant lymphoma has not been identified. Anaplastic lymphoma kinase-positive anaplastic large-cell lymphoma (ALK(+) ALCL) is a unique type of T-cell lymphoma. Approximately 85% of ALK(+) ALCL cases harbor the translocation t(2;5)(p23;q35), which generates the chimeric oncogene NPM-ALK. In the present study, we explored a possible role of IGF-IR in ALK(+) ALCL. Our results demonstrate that IGF-IR and IGF-I are widely expressed in ALK(+) ALCL cell lines and primary tumors. Importantly, we identified novel reciprocal functional interactions between IGF-IR and NPM-ALK. Antagonism of IGF-IR decreased the viability, induced apoptosis and cell-cycle arrest, and decreased proliferation and colony formation of ALK(+) ALCL cell lines. These effects could be explained by alterations of cell survival regulatory proteins downstream of IGF-IR signaling. Our findings improve current understanding of the biology of IGF-IR and NPM-ALK and have significant therapeutic implications as they identify IGF-IR signaling as a potential therapeutic target in ALK(+) ALCL and possibly other types of malignant lymphoma.
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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 | 0.001 |
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".