Abstract 2049: Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC)
Bibliographic record
Abstract
Abstract Background: Lung cancer is the leading cause of cancer death worldwide, with LUSC accounting for a third of cases. Stage I and II LUSC is typically treated with resection, and decisions related to adjuvant chemotherapy are related to recurrence risk, which is a function of stage. However, disease stage does not fully capture tumor biology, which may be a more important determinant of recurrence risk. Our objective was to identify biologically significant molecular subgroups of LUSC that more accurately reflect recurrence risk. Methods: Transcriptomic data for resected stage I/II LUSC were obtained from The Cancer Genome Atlas (TCGA). A training set consisting of patients who underwent resection without adjuvant chemotherapy or radiotherapy was used for discovery (N=161). A proprietary machine learning algorithm (HighLifeR™) was used to identify the genes most associated with disease-free survival (DFS). Molecular subgroups were identified by unsupervised clustering of prognostic genes. Functional differences between molecular subgroups were identified by gene set enrichment analysis (GSEA) and Ingenuity Pathway Analysis (IPA). Results: HighLifeR™ identified 60 highly prognostic genes. Two molecular subgroups were identified with significantly different median DFS: a high-risk group with 59.300 months and hazard ratio of 11.551, and a low-risk group that did not reach 50% DFS (log-rank p = 1.96 x 10-5). These subgroups did not differ in age, sex, race, smoking status, or overall stage (p > 0.05). Importantly, univariate and multivariate Cox regression analysis showed that the molecular subgroups outperformed clinical stage in predicting DFS. The molecular subgroups were biologically distinct. On GSEA, the high-risk group was significantly enriched in genes involved in mitotic spindle assembly (NES=1.94, p<0.001); TGFα signaling and PI3K/AKT/mTOR signaling were modestly enriched. IPA pathway analysis demonstrated significant enrichment of numerous pathways linked to neuronal growth and signaling (eg: CREB signaling, S100 signaling, pathways in myelination and synaptogenesis), as well as TGFα signaling and AMPK signaling (all p<0.001). Conclusions: Our prognostic transcriptomic signature identified two biologically distinct molecular subgroups of LUSC. Molecular subgroup classification was more predictive of DFS in stage I and II LUSC than any other clinical or pathological variable. If further validation confirms this, then this biomarker may form the basis of a diagnostic test that helps inform which patients could be considered for adjuvant chemotherapy. Citation Format: Ashar Siddiqui, Cynthia Stretch, Farshad Farshidfar, Oliver F. Bathe. Clinically and biologically distinct molecular subtypes of lung squamous cell carcinoma (LUSC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2049.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".