Hypoxia is linked to acquired resistance to immune checkpoint inhibitors in lung cancer
Bibliographic record
Abstract
Despite the established use of immune checkpoint inhibitors (ICIs) to treat non-small cell lung cancer (NSCLC), only a subset of patients benefit from treatment and ∼50% of patients whose tumors respond eventually develop acquired resistance (AR). To identify novel drivers of AR, we generated murine Msh2 knock-out (KO) lung tumors that initially responded but eventually developed AR to anti-PD-1, alone or in combination with anti-CTLA-4. Resistant tumors harbored decreased infiltrating T cells and reduced cancer cell-intrinsic MHC-I and MHC-II levels, yet remained responsive to IFNγ. Resistant tumors contained extensive regions of hypoxia, and a hypoxia signature derived from single-cell transcriptional profiling of resistant cancer cells was associated with decreased progression-free survival in a cohort of NSCLC patients treated with anti-PD-1/PD-L1 therapy. Targeting hypoxic tumor regions using a hypoxia-activated pro-drug delayed AR to ICIs in murine Msh2 KO tumors. Thus, this work provides a rationale for targeting tumor metabolic features, such as hypoxia, in combination with immune checkpoint inhibition.
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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) | 0.001 | 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 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".