bioRxivCancer BiologyPreprint
Imatinib overrides taxane resistance by selective inhibition of novel CLIP1 variant obstructing the microtubule pore
K. Kita, P. V. Thakkar, G. Galletti, N. Madhukar, E. V. Navarro, I. Barasoain, H. V. Goodson, D. Sackett, J. F. Diaz, O. Elemento, M. A. Shah, P. Giannakakou
Abstract
Despite its widespread use, the majority of patients with gastric cancer (GC) will not respond to taxane chemotherapy due to resistance mechanisms. Here, we report the discovery of a novel truncated variant of the microtubule plus-end binding protein (+TIP) CLIP-170, hereafter CLIP-170S, whose expression is enriched in taxane resistant cell lines and patients with GC. To establish causation, we knocked-down (KD) CLIP-170S which completely reversed taxane resistance. Mass-spec proteomics and 5-RACE further showed that CLIP-170S lacked the first 150 amino acids, including the Cap-Gly motif required for microtubule (MT) plus-end localization. Mechanistically, we show that CLIP-170S was mislocalized from the MT plus-end to the MT lattice obstructing the MT pore surface site required for taxane entry into the MT lumen. Computational analysis of RNA-seq data from taxane-sensitive and resistant GC cell lines, predicted imatinib as the top candidate drug to overcome drug resistance. Imatinib treatment completely reversed taxane resistance, as predicted, and did so unexpectedly by selective depletion of CLIP-170S. Importantly, CLIP170S was found to be highly prevalent in tumor biopsies from patients with GC. Taken together, these data identify CLIP-170S as a novel, clinically prevalent +TIP variant that obstructs the MT pore and confers taxane resistance. The discovery of this previously unrecognized variant together with the computational discovery of Imatinib as a selective CLIP-170S inhibitor, implicate the MT pore in clinical taxane resistance and provide new therapeutic opportunities for treatment of GC and beyond.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: statistical model
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