Fig. 1: Overview of the ASTUTE framework.

In A–C we illustrate how our framework can efficiently integrate mutations with gene expression data to perform the extraction of dysregulated genes associated with KEAP1 or NFE2L2 mutations in distinct NSCLC datasets. In D we highlight the consistent association of the identified genes with the NRF2 pathway. In E we show that ASTUTE can stratify patients based on the identified expression signatures, thus enhancing the prognostic insights returned by the approach. Finally, in F we showcase that ASTUTE could determine a set of genes consistently dysregulated across in NSCLC andother cancer types, emphasizing their role in prognosis at the pan-cancer level. ASTUTE’s multidimensional analysis enables a deeper understanding of genotype-phenotype associations in cancer.