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Substrate-biased activity-based probes identify proteases that cleave receptor CDCP1

Abstract

CUB domain-containing protein 1 (CDCP1) is an oncogenic orphan transmembrane receptor and a promising target for the detection and treatment of cancer. Extracellular proteolysis of CDCP1 by poorly defined mechanisms induces pro-metastatic signaling. We describe a new approach for the rapid identification of proteases responsible for key proteolytic events using a substrate-biased activity-based probe (sbABP) that incorporates a substrate cleavage motif grafted onto a peptidyl diphenyl phosphonate warhead for specific target protease capture, isolation and identification. Using a CDCP1-biased probe, we identify urokinase (uPA) as the master regulator of CDCP1 proteolysis, which acts both by directly cleaving CDCP1 and by activating CDCP1-cleaving plasmin. We show that coexpression of uPA and CDCP1 is strongly predictive of poor disease outcome across multiple cancers and demonstrate that uPA-mediated CDCP1 proteolysis promotes metastasis in disease-relevant preclinical in vivo models. These results highlight CDCP1 cleavage as a potential target to disrupt cancer and establish sbABP technology as a new approach to identify disease-relevant proteases.

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Fig. 1: Design of an sbABP targeting CDCP1-extracellular domain (ECD) cleavage sites.
Fig. 2: ABPs targeting CDCP1-ECD cleavage sites inhibit CDCP1 proteolysis.
Fig. 3: Identification of proteases reacting with sbABPs targeting CDCP1-ECD cleavage sites.
Fig. 4: uPA controls CDCP1 proteolysis in cancer cells.
Fig. 5: uPA-mediated CDCP1 proteolysis modulates aggressiveness of pancreatic cancer cells.

Data availability

The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information and source data files. Specialized reagents and MS data are available from J.D.H. Gene expression data from TCGA and ICGC cohorts are available online at https://xenabrowser.net/ and https://icgc.org/. Source data are provided with this paper.

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Acknowledgements

This work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC; grant APP1121970), the Avner Pancreatic Cancer Foundation (grant R3 JH 2.1) and the Cancer Council Queensland (grant APP1082040) to J.D.H., grants from the NHMRC, a Cancer Institute Career Development Fellowship and a Philip Hemstritch Fellowship in Pancreatic Cancer to M.P. and a joint grant from the German Academic Exchange Service (DAAD; German-Australian Network on Personalized Medicine to B.S.H., T.D., V.M. and J.D.H.). H.K. is supported by the Sigrid Juselius Foundation. J.D.H. is supported by the Mater Foundation. S.L. is supported by the EPSRC Centre for Doctoral Training in Physical Sciences Innovation in Chemical Biology for Bioindustry and Healthcare (grant EP/LO15498/1). We acknowledge the Translational Research Institute, which receives support from the Australia Federal Government, for providing the core facilities that enabled this research, particularly the Biological Resources Facility and Preclinical Imaging Facility.

Author information

Authors and Affiliations

Authors

Contributions

The study was conceived and designed by T. Kryza, T. Khan, S.L., E.W.T. and J.D.H.. Methodology was developed by T. Kryza, T. Khan and S.L. and data were acquired by T. Kryza, T. Khan, S.L., Y.H. and J.Y. Data analysis and interpretation (for example, statistical analysis, biostatistics and computational analysis) was performed by T. Kryza, T. Khan, S.L., Y.H. and J.Y.; M.P., B.S.H., J.Y., S.P., H.K., J.K.R., T.D., V.M., U.R., Y.H., E.W.T. and J.D.H. were all involved in administrative, technical or material support (that is, reporting or organizing data and constructing databases). All authors were involved in writing, reviewing and revising the manuscript. The study was supervised by J.D.H.

Corresponding author

Correspondence to John D. Hooper.

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Competing interests

E.W.T. is a founder, Director and shareholder in Myricx Pharma Ltd (UK). No potential conflicts of interest were disclosed by the other authors.

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Extended data

Extended Data Fig. 1 Structures of CDCP1-sbABPs.

Chemical structure of CDCP1-sbABP-1 to 4.

Extended Data Fig. 2 uPA and CDCP1 expression in uPA knock-down cells.

Gene expression analysis of uPA and CDCP1 by RT-qPCR on cDNA samples from TKCC05, PC3 and CAOV3 cells stably expressing control shRNA (shNT) or uPA-targeting ShRNA (shuPA#1 and shuPA#2). Results are expressed as mean of relative expression +/- SD compared to ACTB gene expression from three independent experiments. Statistical analysis was performed using One way non-parametric Kruskal-Wallis test with *p=0.0146, #p=0.0141, &p=0.0146.

Source data

Extended Data Fig. 3 uPA, uPAR, PAI1 and CDCP1 expression in cancer cells.

a, Gene expression analysis of uPA, uPAR, PAI1 and CDCP1 by RT-qPCR on cDNA samples from TKCC10, TKCC05, CAOV3, PC3 and OVMZ6 cells. Results are expressed as mean of relative expression +/- SD compared to ACTB gene expression from three independent experiments. b, Western blot analysis under reducing conditions of patient-derived PDAC TKCC10 and TKCC05 cells, ovarian cancer CAOV3 cells and prostate cancer PC3 cells using anti-CDCP1 antibody 4115 (reacts with CDCP1-FL and CTF) and an anti-GAPDH antibody.

Source data

Extended Data Fig. 4 uPA, uPAR and CDCP1 expression in bioengineered cancer cells.

TKCC05 and CAOV3 cells were bioengineered to stably express control ShRNA control (NT), uPA-targeting ShRNA (ShuPA#1 and ShuPA#2) or uPAR-targeting ShRNA (ShuPAR#1 and ShuPAR#2). Gene expression analysis of uPA, uPAR and CDCP1 was performed by RT-qPCR. Results are expressed as mean +/- SD of relative expression compared to ACTB gene expression from three independent experiments. Statistical analysis was performed using One way non-parametric Kruskal-Wallis test with *p=0.0185, #p=0.0325, $p=0.0479, &p=0.0325.

Source data

Extended Data Fig. 5 uPA, uPAR and PAI1 expression in bioengineered OVMZ6 cells.

Gene expression analysis of uPA, uPAR and PAI-1 by RT-qPCR on cDNA samples from OVMZ6 cells stably expressing WT- CDCP1 and transiently transfected with plasmid encoding uPA, uPAR or PAI-1. Results are expressed as mean +/- SD of relative expression compared to ACTB gene expression from three independent experiment.

Source data

Extended Data Fig. 6 Correlation analysis between uPAR expression and PDAC patient survival.

a, Kaplan-Meier analysis showing correlation between uPAR mRNA expression levels and PDAC survival in the ICGC patient dataset (n=267). For this analysis patients with expression below the first quartile in the entire population were segregated into ‘Bottom 25%’ and those with expression above the third quartile of expression level in the entire population were segregated into the ‘Top 25%’ expression group. Statistical differences between Kaplan-Meier curves were determined by Mantel-Cox test. b, Kaplan-Meier analysis showing correlation between gene pair (CDCP1 and uPAR; uPA and uPAR) expression levels and PDAC survival in the ICGC patient dataset (n=267). For this analysis patients with expression below the first quartile in the entire population were segregated into ‘Low’ expression group and those with expression above the third quartile in the entire population were segregated into the ‘High’ expression group. Statistical differences between Kaplan-Meier curves were determined by Mantel-Cox test.

Source data

Extended Data Fig. 7 Correlation analysis between CDCP1, uPA and uPAR expression and PDAC patient survival in TCGA dataset.

a, Kaplan-Meier analysis showing correlation between CDCP1, uPA and uPAR mRNA expression levels and PDAC survival in the TCGA patient dataset (n=170). For this analysis patients with expression below the first quartile in the entire population were segregated into ‘Bottom 25%’ and those with expression above the third quartile of expression level in and PDAC survival in the TCGA patient dataset (n=170). For this analysis patients with expression below the first quartile in the entire population were segregated into ‘Low’ expression group and those with expression above the third quartile in the entire population were segregated into the ‘High’ expression group. Statistical differences between Kaplan-Meier curves were determined by Mantel-Cox test.

Source data

Extended Data Fig. 8 Integration of sbABP technology with other technologies available to study proteolytic networks.

Conceptual workflow indicating how sbABP technology (a cleavage-centric approach) could be integrated with substrate-centric and protease-centric approaches to identify proteolytic mediators and develop inhibitors/ABPs to control a particular proteolytic event. TAILS, Terminal amine isotopic labeling of substrates; COFRADIC, Combined fractional diagonal chromatography; sbABP, substrate-biased activity-based probe.

Supplementary information

Supplementary Information

Supplementary Note (Synthetic procedures), Supplementary Tables 1and 2 and Supplementary Figs. 1–3

Reporting Summary

Supplementary Dataset 1

List of proteins identified in MS analysis (Human proteome database)

Supplementary Dataset 2

List of peptides corresponding to uPA identified in MS analysis (Human proteome database)

Supplementary Dataset 3

List of proteins identified in MS analysis (Bovin proteome database)

Supplementary Dataset 4

List of peptides corresponding to Plasminogen identified in MS analysis (Bovin proteome database)

Source data

Source Data Fig. 2

Unprocessed Western Blots

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Source Data Fig. 5

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Kryza, T., Khan, T., Lovell, S. et al. Substrate-biased activity-based probes identify proteases that cleave receptor CDCP1. Nat Chem Biol 17, 776–783 (2021). https://doi.org/10.1038/s41589-021-00783-w

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