Acquisition of tumor cell phenotypic diversity along the EMT spectrum under hypoxic pressure: Consequences on susceptibility to cell-mediated cytotoxicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tumor escape to immunosurveillance and resistance to immune attacks present a major hurdle in cancer therapy, especially in the current era of new cancer immunotherapies. We report here that hypoxia, a hallmark of most solid tumors, orchestrates carcinoma cell heterogeneity through the induction of phenotypic diversity and the acquisition of distinct epithelial-mesenchymal transition (EMT) states. Using lung adenocarcinoma cells derived from a non-metastatic patient, we demonstrated that hypoxic stress induced phenotypic diversity along the EMT spectrum, with induction of EMT transcription factors (EMT-TFs) SNAI1, SNAI2, TWIST1, and ZEB2 in a hypoxia-inducible factor-1α (HIF1A)-dependent or -independent manner. Analysis of hypoxia-exposed tumor subclones, with pronounced epithelial or mesenchymal phenotypes, revealed that mesenchymal subclones exhibited an increased propensity to resist cytotoxic T lymphocytes (CTL), and natural killer (NK) cell-mediated lysis by a mechanism involving defective immune synapse signaling. Additionally, targeting EMT-TFs, or inhibition of TGF-β signaling, attenuated mesenchymal subclone susceptibility to immune attack. Together, these findings uncover hypoxia-induced EMT and heterogeneity as a novel driving escape mechanism to lymphocyte-mediated cytotoxicity, with the potential to provide new therapeutic opportunities for cancer patients.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it