Machine Learning Based Combination of Multi-omics Data for Subgroup Identification in Non-small Cell Lung Cancer

NSCLC is a heterogeneous disease with a poor prognosis. Identifying novel subtypes can help classify patients with similar molecular and clinical phenotypes and treatment responses. This study uses a machine learning-based approach to compress the multi-omics (mRNA, miRNA, methylation, and protein expression) NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of different clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. A decision-level model was then built using these classifiers, which was used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics. This classification can prove to help decide the therapeutic option for NSCLC patients.