Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Targeting PTPRK-RSPO3 colon tumours promotes differentiation and loss of stem-cell function

Nature volume 529, pages 97–100 (2016)Cite this article

Subjects

Abstract

Colorectal cancer remains a major unmet medical need, prompting large-scale genomics efforts in the field to identify molecular drivers for which targeted therapies might be developed1,2,3. We previously reported the identification of recurrent translocations in R-spondin genes present in a subset of colorectal tumours4. Here we show that targeting RSPO3 in PTPRK-RSPO3-fusion-positive human tumour xenografts inhibits tumour growth and promotes differentiation. Notably, genes expressed in the stem-cell compartment of the intestine were among those most sensitive to anti-RSPO3 treatment. This observation, combined with functional assays, suggests that a stem-cell compartment drives PTPRK-RSPO3 colorectal tumour growth and indicates that the therapeutic targeting of stem-cell properties within tumours may be a clinically relevant approach for the treatment of colorectal tumours.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Anti-RSPO3 inhibits tumour growth of RSPO3-fusion-positive PDX models.
Figure 2: Anti-RSPO3 promotes differentiation.
Figure 3: RSPO2 and RSPO3 are required for normal stem-cell function in the intestine.
Figure 4: Anti-RSPO3 treatment reduces tumour propagation.

Similar content being viewed by others

Accession codes

Data deposits

Sequence data have been deposited at the European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/home) under accession number EGAS00001001462.

References

  1. The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012)

  2. De Sousa E Melo, F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nature Med. 19, 614–618 (2013)

    Article  Google Scholar 

  3. Sadanandam, A. et al. Reconciliation of classification systems defining molecular subtypes of colorectal cancer: interrelationships and clinical implications. Cell Cycle 13, 353–357 (2014)

    Article  CAS  Google Scholar 

  4. Seshagiri, S. et al. Recurrent R-spondin fusions in colon cancer. Nature 488, 660–664 (2012)

    Article  CAS  ADS  Google Scholar 

  5. Siegel, R., Desantis, C. & Jemal, A. Colorectal cancer statistics, 2014. CA Cancer J. Clin. 64, 104–117 (2014)

    Article  Google Scholar 

  6. Kim, K. A. et al. R-spondin proteins: a novel link to beta-catenin activation. Cell Cycle 5, 23–26 (2006)

    Article  CAS  Google Scholar 

  7. de Lau, W. et al. Lgr5 homologues associate with Wnt receptors and mediate R-spondin signalling. Nature 476, 293–297 (2011)

    Article  CAS  ADS  Google Scholar 

  8. Hao, H. X. et al. ZNRF3 promotes Wnt receptor turnover in an R-spondin-sensitive manner. Nature 485, 195–200 (2012)

    Article  CAS  ADS  Google Scholar 

  9. Clevers, H. The intestinal crypt, a prototype stem cell compartment. Cell 154, 274–284 (2013)

    Article  CAS  Google Scholar 

  10. Vermeulen, L. et al. Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nature Cell Biol. 12, 468–476 (2010)

    Article  CAS  Google Scholar 

  11. Schuijers, J. et al. Ascl2 acts as an R-spondin/Wnt-responsive switch to control stemness in intestinal crypts. Cell Stem Cell 16, 158–170 (2015)

    Article  CAS  Google Scholar 

  12. Jung, P. et al. Isolation and in vitro expansion of human colonic stem cells. Nature Med. 17, 1225–1227 (2011)

    Article  CAS  Google Scholar 

  13. Kosinski, C. et al. Gene expression patterns of human colon tops and basal crypts and BMP antagonists as intestinal stem cell niche factors. Proc. Natl Acad. Sci. USA 104, 15418–15423 (2007)

    Article  CAS  ADS  Google Scholar 

  14. Dow, L. E. et al. Apc restoration promotes cellular differentiation and reestablishes crypt homeostasis in colorectal cancer. Cell 161, 1539–1552 (2015)

    Article  CAS  Google Scholar 

  15. Madan, B. et al. Wnt addiction of genetically defined cancers reversed by PORCN inhibition. Oncogene (2015)

  16. Scholer-Dahirel, A. et al. Maintenance of adenomatous polyposis coli (APC)-mutant colorectal cancer is dependent on Wnt/beta-catenin signaling. Proc. Natl Acad. Sci. USA 108, 17135–17140 (2011)

    Article  CAS  ADS  Google Scholar 

  17. Yan, K. S. et al. The intestinal stem cell markers Bmi1 and Lgr5 identify two functionally distinct populations. Proc. Natl Acad. Sci. USA 109, 466–471 (2012)

    Article  CAS  ADS  Google Scholar 

  18. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009)

    Article  CAS  ADS  Google Scholar 

  19. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5 . Nature 449, 1003–1007 (2007)

    Article  CAS  ADS  Google Scholar 

  20. Metcalfe, C., Kljavin, N. M., Ybarra, R. & de Sauvage, F. J. Lgr5+ stem cells are indispensable for radiation-induced intestinal regeneration. Cell Stem Cell 14, 149–159 (2014)

    Article  CAS  Google Scholar 

  21. Tian, H. et al. A reserve stem cell population in small intestine renders Lgr5-positive cells dispensable. Nature 478, 255–259 (2011)

    Article  CAS  ADS  Google Scholar 

  22. O’Brien, C. A., Pollett, A., Gallinger, S. & Dick, J. E. A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature 445, 106–110 (2007)

    Article  ADS  Google Scholar 

  23. Ricci-Vitiani, L. et al. Identification and expansion of human colon-cancer-initiating cells. Nature 445, 111–115 (2007)

    Article  CAS  ADS  Google Scholar 

  24. Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J. & Clarke, M. F. Prospective identification of tumorigenic breast cancer cells. Proc. Natl Acad. Sci. USA 100, 3983–3988 (2003)

    Article  CAS  ADS  Google Scholar 

  25. Bonnet, D. & Dick, J. E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nature Med. 3, 730–737 (1997)

    Article  CAS  Google Scholar 

  26. Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014)

    Article  CAS  Google Scholar 

  27. Robinson, D. et al. Integrative clinical genomics of advanced prostate cancer. Cell 161, 1215–1228 (2015)

    Article  CAS  Google Scholar 

  28. Wu, C. et al. RSPO2–LGR5 signaling has tumour-suppressive activity in colorectal cancer. Nature Commun. 5, 3149 (2014)

    Article  ADS  Google Scholar 

  29. Fevr, T., Robine, S., Louvard, D. & Huelsken, J. Wnt/b-catenin is essential for intestinal homeostasis and maintenance of intestinal stem cells. Mol. Cell. Biol. 27, 7551–7559 (2007)

    Article  CAS  Google Scholar 

  30. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    Article  CAS  Google Scholar 

  31. Tso, K. Y., Lee, S. D., Lo, K. W. & Yip, K. Y. Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts? BMC Genomics 15, 1172 (2014)

    PubMed  Google Scholar 

  32. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genet. 43, 491–498 (2011)

    Article  CAS  Google Scholar 

  33. Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012)

    Article  CAS  Google Scholar 

  34. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001)

    Article  CAS  Google Scholar 

  35. Fu, W. et al. Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220 (2013)

    Article  CAS  ADS  Google Scholar 

  36. Forbes, S. A. et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res. 38, D652–D657 (2010)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank D. Jakubiak, Z. Modrusan, J. Stinson, P. Haverty and the Sequencing and Microarray laboratories for assistance with the animal studies and RNA-seq data generation.

Author information

Authors and Affiliations

  1. Molecular Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Elaine E. Storm, Felipe de Sousa e Melo, Noelyn Kljavin & Frederic J. de Sauvage

  2. Molecular Biology, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Steffen Durinck & Eric W. Stawiski

  3. Translational Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Jarrod Tremayne, Elizabeth Blackwood & Ciara Metcalfe

  4. Antibody Engineering, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Christine Tan, Cecilia Chiu, Jo-Anne Hongo & Yan Wu

  5. Discovery Oncology, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Xiaofen Ye & Robert L. Yauch

  6. Research Pathology, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Thinh Pham & Ron Firestein

  7. Protein Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, 94080, California, USA

    Travis Bainbridge

Authors
  1. Elaine E. Storm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steffen Durinck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felipe de Sousa e Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jarrod Tremayne
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noelyn Kljavin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christine Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaofen Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cecilia Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Thinh Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jo-Anne Hongo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Travis Bainbridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ron Firestein
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Elizabeth Blackwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ciara Metcalfe
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Eric W. Stawiski
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Robert L. Yauch
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Frederic J. de Sauvage
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.E.S. collected and analysed data from antibody characterization, tumour response characterization, normal intestinal response experiments, and wrote the manuscript. S.D. analysed the RNA-seq data and performed the GSEA. F.d.S.e.M. assisted in the analysis of the RNA-seq and flow cytometry data. J.T. and E.B. assisted in the design and interpretation of efficacy studies. N.K. assisted in the irradiation experiments. C.T., C.C., J.-A.H. and Y.W. generated and assisted in the characterization of the antibodies. T.B. purified proteins and antibodies. R.F. assisted in the histopathology. C.M. assisted with normal homeostasis experiments. E.W.S. analysed exome sequencing data. C.M., X.Y. and B.Y. screened samples to identify tumour models. T.P. generated RNA in situ hybridization data. F.d.S. was involved in study design, interpretation and manuscript preparation.

Corresponding author

Correspondence to Frederic J. de Sauvage.

Ethics declarations

Competing interests

The authors are employees of Genentech, Inc. and E.E.S, S.D., J.T., N.K., C.T., X.Y., C.C., T.P., J.-A.H., T.B., R.F., E.B., C.M., E.W.S., R.L.Y., Y.W. and F.d.S. own shares in Roche.

Extended data figures and tables

Extended Data Figure 1 Characteristics of anti-RSPO2 and anti-RSPO3 antibodies.

a, Surface plasmon resonance sensograms of anti-RSPO2 (0–125 nM) binding to human and mouse R-spondins (as indicated; h, human; m, mouse). b, Surface plasmon resonance sensograms of anti-RSPO3 (0–125 nM) binding to human and mouse R-spondins (as indicated). c, Affinities of anti-RSPO2 and anti-RSPO3 binding to human and mouse R-spondins as indicated. Kd, equilibrium dissociation constant calculated as the ratio of koff/kon. Data are means ± s.d. d, Functional blocking of Wnt reporter assay stimulated by 11.6 pM human RSPO2 or 10.5 pM human RSPO3 and 10 ng ml−1 rmWNT3a (as indicated). Data represent the mean ± s.e.m. of three independent experiments.

Extended Data Figure 2 Tumour growth inhibition persists following anti-RSPO3 treatment cessation.

a, RNA-seq expression of RSPO1-4 and identification of RSPO3-fusion by fusion-specific quantitative reverse transcription PCR (qRT–PCR) in colon tumour, normal and PDX models. Expression data from normal colon and colon tumours were analysed from the publicly available data EGA accession EGAS00001000288 (ref. 4). Data are mean ± s.d. Normal n = 72; non-fusion tumour n = 67; PTPRK(e1)-RSPO3(e2) n = 4; CRCA n = 3 different tumours from the model; CRCB n = 3 different tumours from the model. RPKM, reads per kilobase per million. Expression levels greater than 0 but less than 0.5 RPKM are graphed as 0.5. b, Individual tumour measurements from CRCA treated with anti-RSPO3 (30 mg kg−1) twice a week for 4 weeks. Tumours were monitored for 30–40 days following the last dose of antibody (n = 10). c, Individual tumour measurements of CRCB data presented in Fig. 1. Anti-ragweed, n = 9; anti-RSPO3, n = 10. Tumour growth was followed for an additional 30–40 days following the last dose of antibody. Closed circles, anti-ragweed. Open circles, anti-RSPO3. Black bar indicates the time during which mice were treated with antibodies.

Source data

Extended Data Figure 3 Histopathological analysis of tumours from CRCA and CRCB.

Sections from CRCA and CRCB (representative tumour from n = 3 analysed) collected 4 days following the initiation of dosing and stained as indicated. CC3, cleaved caspase 3. Black arrows highlight extracellular MUC2 positivity, red arrows highlight intracellular MUC2 positivity. Scale bar, 100 μm.

Extended Data Figure 4 Effects of anti-RSPO3 treatment on Wnt pathway activity.

a, Heat map of a CTNNB1 gene signature29 in CRCA samples, n = 3 tumours per group. b, Heat map of a CTNNB1 gene signature29 in CRCB, n = 3 tumours per group. c, Gene set enrichment analysis of CRCA using a CTNNB1 gene signature29. RW, anti-Ragweed-treated; R3, anti-RSPO3-treated; NES, normalized enrichment score; FWER, family-wise error rate. d, Gene set enrichment analysis of CRCB using a CTNNB1 gene signature29. e, f, RNA-seq expression of a subset of Wnt target genes in CRCA (e) and CRCB (f). Data are normalized to control group. Horizontal line indicates the mean. Closed circles, anti-ragweed treated. Open circles, anti-RSPO3 treated. n = 3 tumours.

Extended Data Figure 5 Validation of gene expression changes by qRT–PCR.

a, b, Relative gene expression in CRCA and CRCB tumour samples as assessed by qRT–PCR. Closed circles, anti-ragweed-treated tumours. Open circles, anti-RSPO3-treated tumours. Horizontal line indicates the mean. n = 3 for CRCA data and n = 4 for CRCB. qRT–PCR data were validated from at least two independent experiments.

Extended Data Figure 6 Anti-RSPO3 treatment promotes differentiation.

a, Gene set enrichment using the Kosinsky et al. gene set13 in CRCA. b, Gene set enrichment using the Kosinsky et al. gene set13 in CRCB. n = 3 per group. RW, anti-ragweed-treated; R3, anti-RSPO3-treated; NES, normalized enrichment score.

Extended Data Figure 7 APC mutant PDX models fail to respond to anti-RSPO3 treatment.

Tumour growth inhibition (left) and qRT–PCR of target genes (right) in APC mutant PDX models. a, APC(1554fs, 8fs, E941*);BRAF(V600E);PIK3CA(E545K), TP53(R248W) mutant PDX model. n = 10, anti-ragweed treated, n = 9, anti-RSPO3-treated. b, APC(757fs, E1408*);KRAS(G12D) model. Mice were treated with antibodies (30 mg kg−1) for 3–4 weeks (n = 10 animals per group). Tumour growth data were normalized to initial tumour volume upon treatment (100–150 mm3). qRT–PCR data (n = 5) were collected at 8 (a) or 4 (b) days after the initiation of dosing. Data are plotted as mean ± s.e.m. Closed circles, anti-ragweed. Open circles, anti-RSPO3. UND, undetermined Ct value.

Source data

Extended Data Figure 8 The downregulation of stem-cell marker gene expression is an early and robust response to anti-RSPO3 treatment.

a, Relative gene expression in CRCA tumour samples collected 1, 4 and 10 days following a single injection of anti-RSPO3 (30 mg kg−1). Closed circles, anti-ragweed-treated. Open circles, anti-RSPO3-treated. Horizontal line indicates the mean. n = 5 tumours per group per time point. UND, undetermined Ct value.

Extended Data Figure 9 Combined treatment of anti-RSPO2 and anti-RSPO3 reduces LGR5.

a, Quantification of Lgr5+ cells in the jejenum of mice treated with antibody (indicated on x axis) for 4 days. Lgr5–GFP-positive crypts/total crypts (n = 10 for control, n = 20 for antibody treated) were counted in three different mice. Horizontal line indicates the mean. b, Relative expression assessed by qRT–PCR from RNA isolated from the ileum of animals treated with antibody for 9 days. Data are normalized to the control antibody treated group. n = 5, horizontal line indicates the mean. c, Haematoxylin and eosin stained sections from the intestine of mice treated with indicated antibodies for 9 days. Scale bar, 100 μm.

Extended Data Figure 10 Anti-RSPO3 treatment reduces tumour-initiating cell content.

a, Percentage of CD133 (left) and CD44 (right)-positive cells as a percentage of live cells following 14 days of anti-RSPO3 treatment of CRCA. b, Percentage of CD133 (left) and CD44 (right)-positive cells as a percentage of live cells following 8 days of anti-RSPO3 treatment of CRCB. Closed circles, anti-ragweed treated. Open circles, anti-RSPO3 treated. Horizontal line indicates the mean. n = 5.

Supplementary information

Supplementary Table 1

This file contains RNAseq data of differentially expressed genes in response to anti-RSPO3 in models CRCA and CRCB. (XLSX 128 kb)

Supplementary Table 2

This file contains RNAseq data of differentially expressed genes between models CRCA and CRCB. (XLSX 656 kb)

PowerPoint slides

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Storm, E., Durinck, S., de Sousa e Melo, F. et al. Targeting PTPRK-RSPO3 colon tumours promotes differentiation and loss of stem-cell function. Nature 529, 97–100 (2016). https://doi.org/10.1038/nature16466

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature16466

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer