Abstract LB263: A transcriptomic signature predicts mortality risk and immune checkpoint inhibitor response beyond molecular and morphological features in lung adenocarcinoma from never smokers
Bibliographic record
Abstract
Abstract Lung adenocarcinoma in never smokers (NS-LUAD) has a high mortality rate. Compared to LUAD from patients with smoking history, NS-LUAD are less sensitive to immune checkpoint blockade (ICB) potentially due to differences in tumor mutational burden and immune microenvironment. Knowledge of NS-LUAD phenotypic plasticity and cell composition can provide guidance for prognosis and treatment. However, previous studies of LUAD gene expression landscape were predominantly conducted in smokers and full transcriptomic sequencing (RNA-seq) was only examined in a few dozens of NS-LUAD. By investigating cell dynamics through RNA-seq data from 684 NS-LUAD, we identified three gene expression-based subtypes that encapsulate tumors’ clinical, morphological, and genomic features. The ‘steady’ subtype features low proliferation markers and high fraction of alveolar cells, while it is depleted of TP53 mutations and ALK fusions. With its moderate-to-well differentiated morphological features, this subtype is associated with prolonged overall survival and predicted favorable response to immune checkpoint inhibitors, independent of the PD1/PD-L1 expression levels. The ‘proliferative’ subtype features increased proliferation, and enrichment of TP53 mutations and ALK and other gene fusions. The ‘chaotic’ subtype is characterized by elevated levels of mesenchymal cell state, increased proportions of cancer associated fibroblasts and histologic features of mixed-lineage, and is associated with worst overall survival, even within stage I tumors. We derived a signature of 60 genes’ expression sufficient to recapitulate the transcriptomic classification and validated it in an independent NS-LUAD dataset. This 60-gene signature strongly predicted survival even within subgroups based on tumor stage and beyond known molecular or morphological features, confirming its importance for NS-LUAD prognostication in clinical settings. Citation Format: Wei Zhao, Tongwu Zhang, Xing Hua, Phuc H. Hoang, Mona Miraftab, Monjoy Saha, John P. McElderry, Jian Sang, Olivia Lee, Caleb Hartman, Azhar Khandekar, Sunandini Sharma, Frank J. Colón-Matos, Samuel Anyaso-Samuel, Defei Wang, Kristine Jones, Amy Hutchinson, Belynda Hicks, Jennifer Rosenbaum, Xiaoming Zhong, Yang Yang, Angela Pesatori, Dario Consonni, Karun Mutreja, Scott Lawrence, Nathaniel Rothman, Ludmil B. Alexandrov, Charles Leduc, Marina K. Baine, Philippe Joubert, Lynette M. Sholl, William D. Travis, Robert Homer, Qing Lan, Stephen J. Chanock, Lixing Yang, Soo-Ryum Yang, Jianxin Shi, Maria Teresa Landi. A transcriptomic signature predicts mortality risk and immune checkpoint inhibitor response beyond molecular and morphological features in lung adenocarcinoma from never smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB263.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".