bioRxivCancer BiologyPreprint
Multi-omic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation
E. Erickson, I. You, G. Perry, A. Dugourd, K. Donovan, C. Crafter, J. Johannes, S. Williamson, J. Moss, S. Ros, R. Ziegler, S. Barry, E. Fischer, N. Gray, R. Madsen, A. Toker
Abstract
Over 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, there remains scope to improve upon the therapeutic window of the current generation of AKT inhibitors. Here we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-driven phenotypes in breast cancer cell lines. A systematic growth inhibition screen across 288 cancer cell lines confirmed a substantially higher potency for INY-05-040 (median GI50adj = 1.1 {micro}M) compared to our first-generation AKT degrader (INY-03-041; median GI50adj = 3.1 {micro}M), with both compounds outperforming catalytic AKT inhibition with GDC-0068 (median GI50adj > 10 {micro}M). Using multi-omic profiling and causal network integration in breast cancer cells, we demonstrate that the enhanced efficacy of INY-05-040 is associated with sustained suppression of AKT signaling, followed by a potent induction of the stress mitogen activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low baseline JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Collectively, our study presents a systematic framework for mapping the network-wide signaling effects of therapeutically relevant compounds, and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.
- organism: human
- assay: proteomics
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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