Suppression of protein tyrosine phosphatase N23 predisposes to breast tumorigenesis via activation of FYN kinase
Bibliographic record
Abstract
Disruption of the balanced modulation of reversible tyrosine phosphorylation has been implicated in the etiology of various human cancers, including breast cancer. Protein Tyrosine Phosphatase N23 (PTPN23) resides in chromosomal region 3p21.3, which is hemizygously or homozygously lost in some breast cancer patients. In a loss-of-function PTPome screen, our laboratory identified PTPN23 as a suppressor of cell motility and invasion in mammary epithelial and breast cancer cells. Now, our TCGA (The Cancer Genome Atlas) database analyses illustrate a correlation between low PTPN23 expression and poor survival in breast cancers of various subtypes. Therefore, we investigated the tumor-suppressive function of PTPN23 in an orthotopic transplantation mouse model. Suppression of PTPN23 in Comma 1Dβ cells induced breast tumors within 56 wk. In PTPN23-depleted tumors, we detected hyperphosphorylation of the autophosphorylation site tyrosine in the SRC family kinase (SFK) FYN as well as Tyr142 in β-catenin. We validated the underlying mechanism of PTPN23 function in breast tumorigenesis as that of a key phosphatase that normally suppresses the activity of FYN in two different models. We demonstrated that tumor outgrowth from PTPN23-deficient BT474 cells was suppressed in a xenograft model in vivo upon treatment with AZD0530, an SFK inhibitor. Furthermore, double knockout of FYN and PTPN23 via CRISPR/CAS9 also attenuated tumor outgrowth from PTPN23 knockout Cal51 cells. Overall, this mechanistic analysis of the tumor-suppressive function of PTPN23 in breast cancer supports the identification of FYN as a therapeutic target for breast tumors with heterozygous or homozygous loss of PTPN23.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".