RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma

SIMPLE SUMMARY: Glioblastoma (GBM) is a type of primary brain cancer that is extremely aggressive and almost always fatal. To examine the response to treatment and classify GBM non-invasively, researchers are turning to proteome analysis to identify protein biomarkers associated with interventions. However, interpreting large proteomic panels can be challenging, requiring computational analysis to identify trends. In this study, we aimed to select the most informative proteomic features that define proteomic alteration resulting from concurrent chemoirradiation (CRT) treatment which is standard of care for GBM following maximal surgical resection. We developed a novel rank-based feature weighting method called RadWise, which uses two popular feature selection methods, the least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR), to identify relevant proteomic parameters. The computational analysis showed that RadWise outperformed other methods that did not employ a feature selection process, achieving high accuracy rates with very few selected proteomic features. ABSTRACT: Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 7 selected proteomic features with an accuracy rate value of 93.921%, comparing favorably with techniques that do not employ feature selection.