147P - Non-small cell lung cancer: Artificial intelligence enables the identification of survival signatures complementary to an Immunologically active gene expression signature involving previous therapies

Background

Non-small cell lung cancer (NSCLC) is one of the most prevalent cancers worldwide. Centre Léon Bérard, Lyon, France has developed a 27-gene expression-based HOT score that predicts outcome in patients with advanced stage treated with immunotherapy. Yet, this signature could be complemented with additional models involving a smaller set of genes. In this study, we used KEM® (Knowledge Extraction and Management) explainable Artificial Intelligence platform, a tool that systematically extracts relations within the variables of a database, to refine the HOT score, shortening the signature and improving performances.

Methods

Starting from GEO warehouse (GSE161537), 2,568 variables were aggregated into a consolidated database with 82 NSCLC patients. Analysis focused on the associations between combinations of genes, previous treatment lines and overall survival (OS): KEM® extracted 194,349 relations that were filtered on the number of involved genes, statistical significance and specificity.

Results

We identified 5 genes, DKC1, HPGD, MLPH, ABCC4 and MVP, combined into two 4-gene signatures with similar performances that predicted survival with a significant interaction between previous treatment lines and gene expression: patients who carry these signatures and that had undergone at least two treatment lines before immunotherapy showed an improved OS in both models (hazard ratio = 0.45 and 0.36). Compared to the HOT score, the number of genes retained was reduced from 27 to 4, and performances improved: balanced accuracy increases from 0.62 (HOT score) to 0.78.

Conclusions

Our analysis enabled the preliminary identification of signatures that complement the HOT score: the impact of previous lines of therapy was included into the model, the number of markers was reduced and performances increased. These findings are currently being confirmed using Cancer Research Institute iAtlas, a database that contains gene expression for over 1,100 cancer patients across five different tissue types. Specific adjustments may be required to account for differences in gene expression measurements between the training and testing tests.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.