Neoadjuvant Chemotherapy Increases Cytotoxic T Cell, Tissue Resident Memory T Cell, and B Cell Infiltration in Resectable NSCLC
Bibliographic record
Abstract
Introduction The combination of programmed cell death protein-1 or programmed death-ligand 1 immune checkpoint blockade and chemotherapy has revolutionized the treatment of advanced NSCLC, but the mechanisms underlying this synergy remain incompletely understood. In this study, we explored the relationships between neoadjuvant chemotherapy and the immune microenvironment (IME) of resectable NSCLC to identify novel mechanisms by which chemotherapy may enhance the effect of immune checkpoint blockade. Methods Genomic, transcriptomic, and immune profiling data of 511 patients treated with neoadjuvant chemotherapy followed by surgery (NCT) versus upfront surgery (US) were compared with determined differential characteristics of the IMEs derived from whole-exome sequencing (NCT = 18; US = 73), RNA microarray (NCT = 45; US = 202), flow cytometry (NCT = 17; US = 39), multiplex immunofluorescence (NCT = 10; US = 72), T-cell receptor sequencing (NCT = 16 and US = 63), and circulating cytokines (NCT = 18; US = 73). Results NCT was associated with increased infiltration of cytotoxic CD8 + T cells and CD20 + B cells. Moreover, NCT was associated with increases in CD8 + CD103 + and CD4 + CD103 + PD-1 + TIM3 − tissue resident memory T cells. Gene expression profiling supported memory function of CD8 + and CD4 + T cells. However, NCT did not affect T-cell receptor clonality, richness, or tumor mutational burden. Finally, NCT was associated with decreased plasma BDNF (TrkB) at baseline and week 4 after surgery. Conclusions Our study supports that, in the context of resectable NSCLC, neoadjuvant chemotherapy promotes antitumor immunity through T and B cell recruitment in the IME and through a phenotypic change toward cytotoxic and memory CD8 + and CD4 + memory helper T cells.
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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) | 0.002 | 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".