Back

KRAS inhibition drives adaptive surfaceome remodeling and reveals ALPP/ALPPL2 as a targetable vulnerability in pancreatic cancer

DeLiberty, J. M.; Kong, F. Y.; Yu, Q.; Sindoni, N.; Lin, J.; Kang, K.; Kim, E. Y.; Chen, S.; Padhye, A.; Coker, O.; Choe, J. H.; Paulo, J. A.; Shin, M.; Hendrianto, L.; Takahashi, K.; Varga-Wiles, J.; Parent, B. D.; Li, Z.; Shubitidze, S.; Oni, T. E.; Peng, D. H.; Heffernan, T. P.; Liu, S.; Yeung, B. K. S.; Zou, B.; Stalnecker, C. A.; Aguirre, A. J.; Mancias, J. D.

2026-10-02 cancer biology
10.64898/2026.10.01.756025 bioRxiv
Show 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.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.