Novel potential oncogenic and druggable mutations of FGFRs recur in the kinase domain across cancer types

The D647N mutation of FGFR1 induces ligand-independent receptor activation

The activation loop (A-loop) of kinases, a key regulatory region, is recurrently mutated in several kinase proteins in cancer resulting in dysregulated kinase activity and response to kinase inhibitors. FGFR1 receptor tyrosine kinase represents an important oncogene and therapeutic target for solid and hematological tumors. Here we investigate the biochemical and molecular effects of D647N mutation lying in the A-loop of FGFR1. When expressed in normal and tumoral in vitro cell models, FGFR1D647N is phosphorylated also in the absence of ligands, and this is accompanied by the activation of intracellular signaling. The expression of FGFR1D647N significantly increases single and collective migration of cancer cells in vitro and in vivo, when compared to FGFR1WT. FGFR1D647N expression exacerbates the aggressiveness of cancer cells, increasing their invasiveness in vitro and augmenting their pro-angiogenic capacity in vivo. Remarkably, the D647N mutation significantly increases the sensitivity of FGFR1 to the ATP-competitive inhibitor Erdafitinib suggesting the possibility that this mutation could become a specific target for the development of new inhibitors. Although further efforts are warranted for an exhaustive description of the activation mechanisms, for the identification of more specific inhibitors and for confirming the clinical significance of mutated FGFR1D647N, overall our data demonstrate that the D647N substitution of FGFR1 is a novel pro-oncogenic activating mutation of the receptor that, when found in cancer patients, may anticipate good response to erdafitinib treatment.

Functional and embedding feature analysis for pan-cancer classification

With the increasing number of people suffering from cancer, this illness has become a major health problem worldwide. Exploring the biological functions and signaling pathways of carcinogenesis is essential for cancer detection and research. In this study, a mutation dataset for eleven cancer types was first obtained from a web-based resource called cBioPortal for Cancer Genomics, followed by extracting 21,049 features from three aspects: relationship to GO and KEGG (enrichment features), mutated genes learned by word2vec (text features), and protein-protein interaction network analyzed by node2vec (network features). Irrelevant features were then excluded using the Boruta feature filtering method, and the retained relevant features were ranked by four feature selection methods (least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Monte Carlo feature selection and light gradient boosting machine) to generate four feature-ranked lists. Incremental feature selection was used to determine the optimal number of features based on these feature lists to build the optimal classifiers and derive interpretable classification rules. The results of four feature-ranking methods were integrated to identify key functional pathways, such as olfactory transduction (hsa04740) and colorectal cancer (hsa05210), and the roles of these functional pathways in cancers were discussed in reference to literature. Overall, this machine learning-based study revealed the altered biological functions of cancers and provided a reference for the mechanisms of different cancers.