Prediction and prioritization of rare oncogenic mutations in the cancer Kinome using novel features and multiple classifiers

Abstract

Cancer is a genetic disease that develops through a series of somatic mutations, a subset of which drive cancer progression. Although cancer genome sequencing studies are beginning to reveal the mutational patterns of genes in various cancers, identifying the small subset of "causative" mutations from the large subset of "non-causative" mutations, which accumulate as a consequence of the disease, is a challenge. In this article, we present an effective machine learning approach for identifying cancer-associated mutations in human protein kinases, a class of signaling proteins known to be frequently mutated in human cancers. We evaluate the performance of 11 well known supervised learners and show that a multiple-classifier approach, which combines the performances of individual learners, significantly improves the classification of known cancer-associated mutations. We introduce several novel features related specifically to structural and functional characteristics of protein kinases and find that the level of conservation of the mutated residue at specific evolutionary depths is an important predictor of oncogenic effect. We consolidate the novel features and the multiple-classifier approach to prioritize and experimentally test a set of rare unconfirmed mutations in the epidermal growth factor receptor tyrosine kinase (EGFR). Our studies identify T725M and L861R as rare cancer-associated mutations inasmuch as these mutations increase EGFR activity in the absence of the activating EGF ligand in cell-based assays.

Figure 1. Structural location of selected EGFR mutation sites.

Protein crystal structure [PDB∶2JIU] shown as cartoon, with sites G724, T725, L858 and L861 shown as spheres. Structural regions highlighted in yellow are kinase subdomain I and the activation loop. The structure image was generated using PyMOL .

Figure 2. Auto-phosphorylation of wild-type and mutant EGFR and impact of mutations on downstream EGFR signaling.

The blot shows phosphorylation of the four C-terminal tail tyrosines (Y1086, Y1045, Y845, Y1173 and Y1068) in EGFR, and two downstream proteins, ERK1/2 and AKT, in the presence (+) and absence(−) of EGF. “Un” indicates untransfected CHO cells. Total levels of EGFR (GFP), ERK1/2, AKT and tubulin (control) are also shown.

Figure 3. Quantified tyrosine auto-phosphorylation levels of wild-type and mutant-type EGFR.

Quantified phosphorylation levels are shown in the form of histograms. Quantification was done using Image J.