Kinome-wide screening of HER2+ breast cancer cells for molecules that mediate cell proliferation or sensitize cells to trastuzumab therapy
Bibliographic record
Abstract
Understanding the signaling differences that distinguish human HER2-amplified (HER2-positive (HER2+)) breast cancers from other breast cancer subtypes may help to identify protein drug targets for the specific treatment of HER2+ breast cancers. We performed two kinome-wide small interfering RNA (siRNA) screens on five HER2+ breast cancer cell lines, seven breast cancer cell lines in which HER2 was not amplified and two normal breast cell lines. To pinpoint the main kinases driving HER2 signaling, we performed a comprehensive siRNA screen that identified loss of the HER2/HER3 heterodimer as having the most prominent inhibitory effect on the growth of HER2+ breast cancer cells. In a second siRNA screen focused on identifying genes that could sensitize HER2+ cells to trastuzumab treatment, we found that loss of signaling members downstream of phosphatidylinositol 3 kinase (PI3K) potentiated the growth inhibitory effects of trastuzumab. Loss of HER2 and HER3, as well as proteins involved in mitogenic and environmental stress pathways inhibited the proliferation of HER2+ cells only in the absence of trastuzumab, suggesting that these pathways are inhibited by trastuzumab treatment. Loss of essential G2/M cell cycle mediators or proteins involved in vesicle organization exerted inhibitory effects on HER2+ cell growth that were unaffected by trastuzumab. Furthermore, the use of a sensitization index (SI) identified targeting the PI3K pathway to sensitize to trastuzumab treatment. Antagonism using the SI identified MYO3A, MYO3B and MPZL1 as antagonizers to trastuzumab treatment among HER2+ cell lines. Our results suggest that the dimerization partners of HER2 are important for determining the activation of downstream proliferation pathways. Understanding the complex layers of signaling triggered downstream of HER2 homodimers and heterodimers will facilitate the selection of better targets for combination therapies intended to treat HER2+ breast cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".