Germinal Centers Determine the Prognostic Relevance of Tertiary Lymphoid Structures and Are Impaired by Corticosteroids in Lung Squamous Cell Carcinoma
Bibliographic record
Abstract
Abstract In solid tumors, the presence of lymph node–like structures called tertiary lymphoid structures (TLS) is associated with improved patient survival. However, little is known about how TLS develop in cancer, how their function affects survival, and whether they are affected by cancer therapy. In this study, we used multispectral microscopy, quantitative pathology, and gene expression profiling to analyze TLS formation in human lung squamous cell carcinoma (LSCC) and in an experimental model of lung TLS induction. We identified a niche of CXCL13+ perivascular and CXCL12+LTB+ and PD-L1+ epithelial cells supporting TLS formation. We also characterized sequential stages of TLS maturation in LSCC culminating in the formation of germinal centers (GC). In untreated patients, TLS density was the strongest independent prognostic marker. Furthermore, TLS density correlated with GC formation and expression of adaptive immune response–related genes. In patients treated with neoadjuvant chemotherapy, TLS density was similar, but GC formation was impaired and the prognostic value of TLS density was lost. Corticosteroids are coadministered with chemotherapy to manage side effects in LSCC patients, so we evaluated whether they impaired TLS development independently of chemotherapy. TLS density and GC formation were each reduced in chemotherapy-naïve LSCC patients treated with corticosteroids before surgery, compared with untreated patients, a finding that we confirmed in the experimental model of lung TLS induction. Overall, our results highlight the importance of GC formation in TLS during tumor development and treatment. Significance: Corticosteroid treatment during chemotherapy negatively affects the development of tertiary lymphoid structures and abrogates their prognostic value in patients with lung cancer. Cancer Res; 78(5); 1308–20. ©2018 AACR.
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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".