shRNA Kinome Screen Identifies TBK1 as a Therapeutic Target for HER2+ Breast Cancer
Bibliographic record
Abstract
HER2(+) breast cancer is currently treated with chemotherapy plus anti-HER2 inhibitors. Many patients do not respond or relapse with aggressive metastatic disease. Therefore, there is an urgent need for new therapeutics that can target HER2(+) breast cancer and potentiate the effect of anti-HER2 inhibitors, in particular those that can target tumor-initiating cells (TIC). Here, we show that MMTV-Her2/Neu mammary tumor cells cultured as nonadherent spheres or as adherent monolayer cells select for stabilizing mutations in p53 that "immortalize" the cultures and that, after serial passages, sphere conditions maintain TICs, whereas monolayer cells gradually lose these tumorigenic cells. Using tumorsphere formation as surrogate for TICs, we screened p53-mutant Her2/Neu(+) tumorsphere versus monolayer cells with a lentivirus short hairpin RNA kinome library. We identified kinases such as the mitogen-activated protein kinase and the TGFβR protein family, previously implicated in HER2(+) breast cancer, as well as autophagy factor ATG1/ULK1 and the noncanonical IκB kinase (IKK), TANK-binding kinase 1 (TBK1), which have not been previously linked to HER2(+) breast cancer. Knockdown of TBK1 or pharmacologic inhibition of TBK1 and the related protein, IKKε, suppressed growth of both mouse and human HER2(+) breast cancer cells. TBK1/IKKε inhibition promoted cellular senescence by suppressing p65-NF-κB and inducing p16(Ink4a). In addition, TBK1/IKKε inhibition cooperated with lapatinib, a HER2/EGFR1-targeted drug, to accelerate apoptosis and kill HER2(+) breast cancer cells both in culture and in xenografts. Our results suggest that patients with HER2(+) breast cancer may benefit from anti-TBK1/IKKε plus anti-HER2 combination therapies and establish conditions that can be used to screen for additional TIC-specific inhibitors of HER2(+) breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".