Acquisition of tumor cell phenotypic diversity along the EMT spectrum under hypoxic pressure: Consequences on susceptibility to cell-mediated cytotoxicity
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.
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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) | 0.000 | 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 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".