Divergent Clinical and Immunologic Outcomes Based on <i>STK11</i> Co-mutation Status in Resectable <i>KRAS-</i> Mutant Lung Cancers Following Neoadjuvant Immune Checkpoint Blockade
Bibliographic record
Abstract
PURPOSE: Co-mutations of the Kirsten rat sarcoma virus (KRAS) and serine/threonine kinase 11 (STK11) genes in advanced non-small cell lung cancer (NSCLC) are associated with immune checkpoint blockade (ICB) resistance. Although neoadjuvant chemoimmunotherapy is now a standard-of-care treatment for resectable NSCLC, the clinical and immunologic impacts of KRAS and STK11 co-mutations in this setting are unknown. EXPERIMENTAL DESIGN: We evaluated and compared recurrence-free survival of resectable KRAS-mutated NSCLC tumors, with or without co-occurring STK11 mutations, treated with neoadjuvant ICB. Single-cell transcriptomics was performed on tumor-infiltrating T cells from seven KRASmut/STK11wt tumors and six KRAS and STK11 co-mutated (KRASmut/STK11mut) tumors. RESULTS: Relative to KRASmut/STK11wt tumors, KRASmut/STK11mut exhibited significantly higher recurrence risk. Single-cell transcriptomics showed enhanced oxidative phosphorylation with evidence of decreased prostaglandin E2 signaling and increased IL-2 signaling in CD8+ tumor-infiltrating lymphocytes (TIL) from KRASmut/STK11mut tumors, a finding that was mirrored in KRASwt tumors that relapsed. TILs from KRASmut/STK11mut tumors expressed high levels of molecules associated with tumor residence, including CD39 and ZNF683 (HOBIT). CONCLUSIONS: These divergent T-cell transcriptional fates suggest that T-cell maintenance and residence may be detrimental to antitumor immunity in the context of neoadjuvant ICB for resectable NSCLC, regardless of KRAS mutation status. Our work provides a basis for future investigations into the mechanisms underpinning prostaglandin E2 signaling and IL-2 signaling as they relate to T-cell immunity to cancer and to divergent clinical outcomes in KRASmut/STK11mut NSCLC treated with neoadjuvant ICB.
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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".