p53 Drives Lung Cancer Regression through a TSC2/TFEB-dependent Senescence Program
Bibliographic record
Abstract
Pharmacologic restoration of p53 tumor suppressor function is a conceptually appealing therapeutic strategy for the many deadly cancers with compromised p53 activity, including lung adenocarcinoma. However, the p53 pathway has remained undruggable, partly because of insufficient understanding of how to drive effective therapeutic responses without toxicity. In this study, we use mouse and human models to deconstruct the transcriptional programs and sequelae underlying robust therapeutic responses in lung adenocarcinoma. We show that p53 drives potent tumor regression by direct Tsc2 transactivation, leading to mTORC1 inhibition and Transcription factor EB (TFEB) nuclear accumulation, which in turn triggers lysosomal gene expression programs, autophagy, and cellular senescence. Senescent lung adenocarcinoma cells secrete factors to recruit macrophages, precipitating cancer cell phagocytosis and tumor regression. Collectively, our analyses reveal a surprisingly complex cascade of events underlying a p53 therapeutic response in lung adenocarcinoma and illuminate targetable nodes for p53 combination therapies, thus establishing a critical framework for optimizing p53-based therapeutics. SIGNIFICANCE: Cancer therapies based on targeting the p53 pathway remain elusive. To address this gap, we unravel the detailed sequence of events governing p53-induced tumor regression in lung adenocarcinoma. These analyses reveal a TSC2-mTORC1-TFEB axis underlying p53-driven senescence and tumor regression, which suggests new strategies to perfect p53-based combination therapies for lung adenocarcinoma.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".