bioRxivCancer BiologyPreprint
KRAS inhibition drives adaptive surfaceome remodeling and reveals ALPP/ALPPL2 as a targetable vulnerability in pancreatic cancer
Jonathan M. DeLibertyiD, Fan Yi Kong, Qijia Yu, Nicole SindoniiD, Johnson Lin, Kalisa KangiD, Elizabeth Y. KimiD, Sabrina CheniD, Aparna PadhyeiD, Oluwadara CokeriD, Joshua H. ChoeiD, Joao A. Paulo, Myungsun ShiniD, Lewis Hendrianto, Kazuki Takahashi, Jaclyn Varga-Wiles, Brendan D. ParentiD, Ziyue LiiD, Salome Shubitidze, Tobiloba E. OniiD, David H. PengiD, Timothy Paul HeffernaniD, Shaojun Liu, Bryan K. S. YeungiD, Bin Zou, , Joseph Douglas ManciasiD
Abstract
KRAS is mutated in over 90% of pancreatic ductal adenocarcinoma (PDAC) and is required for tumor initiation and maintenance. While clinical trials testing mutant-selective and pan-RAS inhibitors (KRASi) have demonstrated significant clinical benefit, genetic and non-genetic mechanisms of resistance limit the long-term efficacy of monotherapy strategies. Here, we used quantitative temporal analysis of the global, phosphorylation, and surface proteome to comprehensively identify non-genetic adaptive pathways and cell surface biomarkers in response to acute and long-term KRAS inhibition in PDAC. Through integrative machine learning analysis of the KRASi-proteome and publicly available datasets, we discovered top priority cell surface targets for combination therapy with KRASi. We identified Placental Alkaline Phosphatase (ALPP) as upregulated in response to KRASi through compensatory JAK2/STAT3 signaling. We developed a novel clinical grade ALPP/ALPPL2 (ALPG)-targeting antibody drug conjugate (ADC) with site-specific MMAE conjugation and demonstrated single agent efficacy in PDAC models, as well as enhanced internalization and anti-tumor efficacy in combination with KRASi. Together, these studies comprehensively define the KRASi-induced proteome and identify ALPP/ALPPL2 as a promising cell surface target for combination with KRASi to enhance response and forestall KRASi resistance.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 0
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