Small patient datasets reveal genetic drivers of non-small cell lung cancer subtypes using machine learning for hypothesis generation

Abstract

Aim: Many small datasets of significant value exist in the medical space that are being underutilized. Due to the heterogeneity of complex disorders found in oncology, systems capable of discovering patient subpopulations while elucidating etiologies are of great value as they can indicate leads for innovative drug discovery and development. Methods: Two small non-small cell lung cancer (NSCLC) datasets (GSE18842 and GSE10245) consisting of 58 samples of adenocarcinoma (ADC) and 45 samples of squamous cell carcinoma (SCC) were used in a machine intelligence framework to identify genetic biomarkers differentiating these two subtypes. Utilizing a set of standard machine learning (ML) methods, subpopulations of ADC and SCC were uncovered while simultaneously extracting which genes, in combination, were significantly involved in defining the subpopulations. A previously described interactive hypothesis-generating method designed to work with ML methods was employed to provide an alternative way of extracting the most important combination of variables to construct a new data set. Results: Several genes were uncovered that were previously implicated by other methods. This framework accurately discovered known subpopulations, such as genetic drivers associated with differing levels of aggressiveness within the SCC and ADC subtypes. Furthermore, phyosphatidylinositol glycan anchor biosynthesis, class X (PIGX) was a novel gene implicated in this study that warrants further investigation due to its role in breast cancer proliferation. Conclusions: The ability to learn from small datasets was highlighted and revealed well-established properties of NSCLC. This showcases the utility of ML techniques to reveal potential genes of interest, even from small datasets, shedding light on novel driving factors behind subpopulations of patients.