Therapeutic Targeting of the Premetastatic Stage in Human Lung-to-Brain Metastasis
Bibliographic record
Abstract
Abstract Brain metastases (BM) result from the spread of primary tumors to the brain and are a leading cause of cancer mortality in adults. Secondary tissue colonization remains the main bottleneck in metastatic development, yet this “premetastatic” stage of the metastatic cascade, when primary tumor cells cross the blood–brain barrier and seed the brain before initiating a secondary tumor, remains poorly characterized. Current studies rely on specimens from fully developed macrometastases to identify therapeutic options in cancer treatment, overlooking the potentially more treatable “premetastatic” phase when colonizing cancer cells could be targeted before they initiate the secondary brain tumor. Here we use our established brain metastasis initiating cell (BMIC) models and gene expression analyses to characterize premetastasis in human lung-to-BM. Premetastatic BMIC engaged invasive and epithelial developmental mechanisms while simultaneously impeding proliferation and apoptosis. We identified the dopamine agonist apomorphine to be a potential premetastasis-targeting drug. In vivo treatment with apomorphine prevented BM formation, potentially by targeting premetastasis-associated genes KIF16B, SEPW1, and TESK2. Low expression of these genes was associated with poor survival of patients with lung adenocarcinoma. These results illuminate the cellular and molecular dynamics of premetastasis, which is subclinical and currently impossible to identify or interrogate in human patients with BM. These data present several novel therapeutic targets and associated pathways to prevent BM initiation. Significance: These findings unveil molecular features of the premetastatic stage of lung-to-brain metastases and offer a potential therapeutic strategy to prevent brain metastases. Cancer Res; 78(17); 5124–34. ©2018 AACR.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".