SHP2 Is Required for BCR-ABL1-Induced Hematologic Neoplasms
Bibliographic record
Abstract
Abstract BCR-ABL1-targeting tyrosine kinase inhibitors (TKIs) have revolutionized treatment of Philadelphia chromosome-positive (Ph + ) hematologic neoplasms. Nevertheless, acquired TKI resistance remains a major problem in chronic myeloid leukemia (CML), and TKIs are less effective against Ph + B-cell acute lymphoblastic leukemia (B-ALL). GAB2, a scaffolding adaptor that binds and activates SHP2, is essential for leukemogenesis by BCR-ABL1, and a GAB2 mutant lacking SHP2 binding cannot mediate leukemogenesis. Using a genetic loss-of-function approach and bone marrow transplantation (BMT) models for CML and BCR-ABL1 + B-ALL, we show that SHP2 is required for BCR-ABL1-evoked myeloid and lymphoid neoplasia. Ptpn11 deletion impairs initiation and maintenance of CML-like myeloproliferative neoplasm, and compromises induction of BCR-ABL1 + B-ALL. SHP2, and specifically, its SH2 domains, PTP activity and C-terminal tyrosines, is essential for BCR-ABL1 + , but not WT, pre-B cell proliferation. The MEK/ERK pathway is regulated by SHP2 in WT and BCR-ABL1 + pre-B cells, but is only required for the proliferation of BCR-ABL1 + cells. SHP2 is required for SRC family kinase (SFK) activation only in BCR-ABL1 + pre-B cells. RNAseq reveals distinct SHP2-dependent transcriptional programs in BCR-ABL1 + and WT pre-B cells. Our results suggest that SHP2, via SFKs and ERK, represses MXD3/4 to facilitate a MYC-dependent proliferation program in BCR-ABL1-transformed pre-B 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".