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Ojala, V. K.

Publications and source records attributed to Ojala, V. K..

2 recordsLinked to original sources

Recurrent cancer-associated ERBB4 mutations are transforming and confer resistance to targeted therapies

Receptor tyrosine kinase ERBB4 (HER4) is frequently mutated in human cancer, and ERBB4 mutations have been identified in patients relapsing on targeted therapy. Here, we addressed the functional consequences of recurrent cancer-associated ERBB4 mutations that are located at regions important for dimer interactions and/or are paralogous to known oncogenic hotspot mutations in other ERBB genes. Eleven out of 18 analyzed mutations were transforming in cell models, thus suggesting oncogenic potential for more than half of the recurrent ERBB4 mutations. More detailed analyses of the most potent mutations, S303F, E452K and L798R, showed that they are activating, can co-operate with other ERBB receptors and are targetable with clinically available second-generation pan-ERBB inhibitors neratinib, afatinib and dacomitinib. Furthermore, the S303F mutation, together with a previously identified activating ERBB4 mutation, E715K, promoted resistance to third-generation EGFR inhibitor osimertinib in EGFR-mutant lung cancer model in vitro and in vivo. Together, these results are expected to facilitate clinical interpretation of the most recurrent cancer-associated ERBB4 mutations. The findings provide rationale for testing the efficacy of clinically used pan-ERBB inhibitors in patients harboring driver ERBB4 mutations both in the treatment-naive setting, and upon development of resistance to targeted agents.

cancer biology↗

An unbiased pathway analysis (UPA) designed for multi-omics inference of cell signaling pathways

New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into regulatory complexes and pathways. DMPA was validated with publicly available omics data and was found accurate in discovering protein-protein interactions, kinase substrate phosphosite relationships, transcription factor target gene relationships, metabolic reactions, epigenetic trait associations and signaling pathways. DMPA was benchmarked against existing module and network discovery and multi-omics integration methods and outperformed previous methods in module and signaling pathway discovery especially when applied to datasets with low sample sizes and zero-inflated data. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome regulatory complexes and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected both the predicted function and the direction of the predicted function in validation experiments.

systems biology↗