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.

  • Opinion
  • Published:

Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

Abstract

Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.

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

Access options

Buy this article

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

Figure 1: An example of the backward elimination principle.

Similar content being viewed by others

References

  1. World Health Organization. Cancer control: early detection: WHO guide for effective programmes. WHO http://www.who.int/cancer/publications/cancer_control_detection/en/ (2007).

  2. Shimizu, Y., Yasui, K., Matsueda, K., Yanagisawa, A. & Yamao, K. Small carcinoma of the pancreas is curable: new computed tomography finding, pathological study and postoperative results from a single institute. J. Gastroenterol. Hepatol. 20, 1591–1594 (2005).

    Article  Google Scholar 

  3. Brennan, D. J. et al. Antibody-based proteomics: fast-tracking molecular diagnostics in oncology. Nat. Rev. Cancer 10, 605–617 (2010).

    Article  CAS  Google Scholar 

  4. Neagu, M., Constantin, C., Tanase, C. & Boda, D. Patented biomarker panels in early detection of cancer. Recent Pat. Biomark. 1, 10–24 (2011).

    CAS  Google Scholar 

  5. Vlahou, A. Network views for personalized medicine. Proteomics Clin. Appl. 7, 384–387 (2013).

    Article  CAS  Google Scholar 

  6. Franzi, M., Bhat, A. & Latosinska, A. Clinical proteomic biomarkers: relevant issues on study design and technical considerations in biomarker development. Clin. Transl Med. 3, 7–22 (2014).

    Article  Google Scholar 

  7. Polanski, M. & Anderson, N. L. A list of candidate cancer biomarkers for targeted proteomics. Biomark. Insights 2, 1–48 (2007).

    Google Scholar 

  8. Füzey, A. K., Levin, J., Chan, M. M. & Chan, D. W. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin. Proteomics 10, 13–27 (2013).

    Article  Google Scholar 

  9. Menon, U. et al. Risk algorithm using serial biomarker measurements doubles the number of screen-detected cancers compared with a single-threshold rule in the United Kingdom Collaborative Trial of Ovarian cancer screening. J. Clin. Oncol. 33, 2062–2075 (2015).

    Article  Google Scholar 

  10. Pavlou, M. P., Diamandis, E. P. & Blasutig, I. M. The long journey of cancer biomarkers from the bench to the clinic. Clin. Chem. 59, 147–157 (2013).

    Article  CAS  Google Scholar 

  11. Smart, A. A multi-dimensional model of clinical utility. Int. J. Qual. Health Care 18, 377–382 (2006).

    Article  Google Scholar 

  12. Zhang, Z. & Chan, D. W. The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidemiol. Biomarkers Prev. 19, 2995–2299 (2010).

    Article  CAS  Google Scholar 

  13. Kiyonami, R. et al. Increased selectivity, analytical precision, and throughput in targeted proteomics. Mol. Cell. Proteomics 10, M110.002931 (2011).

    Article  Google Scholar 

  14. Tighe, P. J., Ryder, R. R., Todd, I. & Fairclough, L. C. ELISA in the multiplexed era: potentials and pitfalls. Proteomics Clin. Appl. 9, 406–422 (2015).

    Article  CAS  Google Scholar 

  15. Haab, B. B. Applications of antibody array platforms. Curr. Opin. Biotechnol. 4, 415–421 (2006).

    Article  Google Scholar 

  16. Borrebaeck, C. A. K. & Wingren, C. Design of high-density antibody microarrays for disease proteomics: key technological issues. J. Proteomics 72, 928–935 (2009).

    Article  CAS  Google Scholar 

  17. Bradbury, A. & Pluckthun, A. Reproducability: standardized antibodies used in research. Nature 518, 27–29 (2015).

    Article  CAS  Google Scholar 

  18. Alhamdani, M. S. S., Schröder, C. & Hoheisel, J. D. Oncoproteomic profiling with antibody microarrays. Genome Med. 1, 1–7 (2009).

    Article  Google Scholar 

  19. Liotta, L. A. et al. Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 3, 317–325 (2003).

    Article  CAS  Google Scholar 

  20. Borrebaeck, C. A. K. & Wingren, C. Recombinant antibodies for the generation of antibody arrays. Methods Mol. Biol. 785, 247–262 (2011).

    Article  CAS  Google Scholar 

  21. Yu, X., Schneiderhan-Marra, N. & Joos, T. O. Protein microarray for personalized medicine. Clin. Chem. 56, 376–387 (2010).

    Article  CAS  Google Scholar 

  22. Ellmark, P. et al. Identification of protein expression signatures associated with Helicobacter pylori infection and gastric adenocarcinoma using recombinant antibody microarrays. Mol. Cell. Proteomics 5, 1638–1646 (2006).

    Article  CAS  Google Scholar 

  23. Carlsson, A. et al. Serum protein profiling of metastatic breast cancer using recombinant antibody microarrays. Eur. J. Cancer 44, 472–480 (2008).

    Article  CAS  Google Scholar 

  24. Shao, C. et al. Antibody microarray analysis of serum glycans in esophageal squamous cell carcinoma cases and controls. Proteomics Clin. Appl. 3, 923–931 (2009).

    Article  CAS  Google Scholar 

  25. Chapman, C. J. et al. Immunobiomarkers in small cell lung cancer: potential early cancer signals. Clin. Cancer Res. 17, 1474–1480 (2011).

    Article  CAS  Google Scholar 

  26. Sonntag, J. et al. Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer. Transl Proteomics 2, 52–59 (2014).

    Article  CAS  Google Scholar 

  27. Brand, R. E. et al. Serum biomarker panels for the detection of pancreatic cancer. Clin. Cancer Res. 17, 805–816 (2011).

    Article  CAS  Google Scholar 

  28. Sauer, G. et al. Molecular indicators of non-sentinel node status in breast cancer determined in preoperative biopsies by multiplexed sandwich immunoassays. J. Cancer Res. Clin. Oncol. 137, 1175–1184 (2011).

    Article  CAS  Google Scholar 

  29. Nolen, B. M. et al. Prediagnostic serum biomarkers as early detection tools for pancreatic cancer in a large prospective cohort study. PLoS ONE 9, e94928 (2014).

    Article  Google Scholar 

  30. Rosskopf, S. et al. The pre-analytical processing of blood samples for detecting niomarkers on protein microarrays. J. Immunol. Methods 418, 39–51 (2015).

    Article  CAS  Google Scholar 

  31. Rai, A. J. et al. HUPO plasma proteome project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5, 3262–3277 (2005).

    Article  CAS  Google Scholar 

  32. Alonzo, T. A., Pepe, M. S. & Moskowitz, C. S. Sample size calculations for comparative studies of medical tests for detecting presence of disease. Stat. Med. 21, 835–852 (2002).

    Article  Google Scholar 

  33. Melo, S. A. et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523, 177–182 (2015).

    Article  CAS  Google Scholar 

  34. Borrebaeck, C. A. K. Viewpoints in clinical proteomics. Proteomics Clin. Appl. 6, 343 (2012).

    Article  CAS  Google Scholar 

  35. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289–300 (1995).

    Google Scholar 

  36. Carlsson, A. et al. Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases. Proc. Natl Acad. Sci. USA 108, 14252–14257 (2011).

    Article  CAS  Google Scholar 

  37. Kullback, S. & Leibler, R. A. On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951).

    Article  Google Scholar 

  38. Hansh, S. M., Pitteri, S. J. & Faca, V. M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    Article  Google Scholar 

  39. Zethelius, B. et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N. Engl. J. Med. 358, 2107–2116 (2008).

    Article  CAS  Google Scholar 

  40. Van 't Veer, L. J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002).

    Article  CAS  Google Scholar 

  41. Van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002).

    Article  CAS  Google Scholar 

  42. Cusumano, P. G. et al. European inter-institutional impact study of MammaPrint. Breast 23, 423–428 (2014).

    Article  CAS  Google Scholar 

  43. Sparano, J. A. et al. Prospective validation of a 21-gene expression assay in breast cancer. N. Engl. J. Med. 373, 2005–2014 (2015).

    Article  CAS  Google Scholar 

  44. Olsson, E. et al. Serial monitoring of circulation tumor DNA in patients with primary breast cancer for detection of occult metastatic disease. EMBO Mol. Med. 7, 1034–1047 (2015).

    Article  CAS  Google Scholar 

  45. Moyer, V. A. Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann. Int. Med. 157, 120–134 (2012).

    Article  Google Scholar 

  46. Sartori, D. A. & Chan, D. W. Biomarkers in prostate cancer: what's new? Curr. Opin. Oncol. 26, 259–264 (2014).

    Article  CAS  Google Scholar 

  47. Heijnsdijk, E. A., Denham, D. & de Koning, H. J. The cost-effectivness of prostate cancer detection with the use of prostate health index. Value Health 19, 153–157 (2016).

    Article  Google Scholar 

  48. Grönberg, H. et al. Prostate cancer screening in men aged 50–69 years (STHLM3): a prospective population-based diagnostic study. Lancet Oncol. 16, 1667–1676 (2015).

    Article  Google Scholar 

  49. Schully, S. D. et al. Leveraging biospecimen resources for discovery or validation of markers for early cancer detection. J. Natl Cancer Inst. 107, djv012 (2015).

    Article  Google Scholar 

  50. Ransohoff, D. F. Proteomics research to discover markers: what can we learn from Netflix? Clin. Chem. 56, 172–176 (2010).

    Article  CAS  Google Scholar 

  51. Zhang, Z. et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res. 64, 5882–5890 (2004).

    Article  CAS  Google Scholar 

  52. Bast, R. C. et al. Reactivity of a monoclonal antibody with human ovarian carcinoma. J. Clin. Invest. 68, 1331–1337 (1981).

    Article  Google Scholar 

  53. Bristow, R. E. et al. Ovarian malignancy risk classification of adnexal mass using a multivariate index assay. Gynecol. Oncol. 128, 252–259 (2013).

    Article  Google Scholar 

  54. Coleman, R. L. et al. Validation of a second-generation multivariate index assay for malignancy risk of adnexal masses. Am. J. Obstet. Gynecol. 82, 1–11 (2016).

    Google Scholar 

  55. Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 11, 845–867 (2002).

    Article  Google Scholar 

  56. Makarov, A., Denisov, E., Lange, O. & Horning, S. Dynamic range of mass accuracy in LTQ Orbitrap hybrid mass spectrometer. J. Am. Mass Spectrom. 17, 977–982 (2006).

    Article  CAS  Google Scholar 

  57. Parker, C. E. & Borchers, C. H. Mass spectrometry based biomarker discovery, verification, and validation — quality assurance and control of protein biomarker assays. Mol. Oncol. 8, 840–858 (2014).

    Article  CAS  Google Scholar 

  58. Abbatiello, S. E. et al. Large-scale interlaboratory study to develop, analytically validate and apply highly multiplexed, quantitative peptide assays to measure cancer-relevant proteins in plasma. Mol. Cell. Proteomics 14, 2357–2374 (2015).

    Article  CAS  Google Scholar 

  59. Razavi, M., Anderson, N. L., Yip, R., Pope, M. E. & Pearson, T. W. Multiplexed longitudinal measurements of protein biomarkers in DBS using an automated SISCAPA workflow. Bioanalysis 8, 1597–1160 (2016).

    Article  CAS  Google Scholar 

  60. Waldemarson, S. et al. Proteomic analysis of breast tumors confirms the mRNA intrinstic molecular subtypes using different classifiers: a large-scale analysis using fresh frozen tissue samples. Breast Cancer Res. 18, 69 (2016).

    Article  Google Scholar 

  61. Nordström, M. et al. Identification of plasma protein profiles associated with risk groups of prostate cancer patients. Proteomics Clin. Appl. 8, 951–962 (2014).

    Article  Google Scholar 

  62. Lee, M.-S. et al. Prognostic significance of CREB-binding protein and CD81 expression in primary high grade non-muscular invasive bladder cancer: identification of novel biomarkers for bladder cancer using antibody microarray. PLoS ONE 10, e0125405 (2015).

    Article  Google Scholar 

  63. Hartwell, L., Mankoff, D., Paulovich, A., Ramsey, S. & Swisher, E. Cancer biomarkers: a systemic approach. Nat. Biotechnol. 24, 905–908 (2006).

    Article  CAS  Google Scholar 

  64. Kamisawa, T., Wood, L. D., Itoi, T. & Takaori, K. Pancreatic cancer. Lancet 388, 73–85 (2016).

    Article  CAS  Google Scholar 

  65. Ghatnekar, O. et al. Modelling the benefits of early diagnosis of pancreatic cancer using a biomarker signature. Int. J. Cancer 133, 2392–2397 (2013).

    Article  CAS  Google Scholar 

  66. Mirus, J. E. et al. Spatiotemporal proteomic analyses during pancreas cancer progression identifies serine/threonine stress kinase 4 (STK4) as a novel candidate biomarker for early stage disease. Mol. Cell. Proteomics 13, 3484–3496 (2014).

    Article  CAS  Google Scholar 

  67. Mirus, J. E. Cross-species antibody microarray interrogation identifies a 3-protein panel of plasma biomarkers for early diagnosis of pancreas cancer. Clin. Cancer Res. 21, 1764–1771 (2015).

    Article  CAS  Google Scholar 

  68. Wingren, C. et al. Identification of serum biomarker signatures associated with pancreatic cancer. Cancer Res. 72, 2481–2490 (2012).

    Article  CAS  Google Scholar 

  69. Sandström, A. et al. Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures. Proteomics Clin. Appl. 6, 486–496 (2012).

    Article  Google Scholar 

  70. Gerdtsson, A. S. et al. A multicenter trial defining a serum protein signature associated with pancreatic ductal adenocarcinoma. Int. J. Proteomics 2015, 587250 (2015).

    Article  Google Scholar 

  71. Wu, T.-C., Shao, Y.-F., Shan, Y., Wu, J.-X. & Zhao, P. Surgical effect of malignant tumor of body and tail of the pancreas: compare with pancreatic head cancer. Chin. J. Surg. 45, 30–33 (2007).

    PubMed  Google Scholar 

  72. Gerdsson, A. S. et al. Plasma protein profiling in a stage defined pancreatic cancer cohort - implications for early diagnosis. Mol. Onc. 10, 1305–1316 (2016).

    Article  Google Scholar 

  73. Mansfield, E. A. FDA perspective on companion diagnostics: an evolving paradigm. Clin. Cancer Res. 20, 1453–1457 (2014).

    Article  CAS  Google Scholar 

  74. Dinakarpandian, D. in Big Data Analysis for Bioinformatics and Biomedical Discoveries (ed. Ye, S. Q. ) 249–264 (CRC Press, 2016).

    Google Scholar 

Download references

Acknowledgements

The critical reading and comments suggested by J. Borrebaeck (University of California, Berkeley, USA) and C. Rose, C. Wingren, S. Ek, and expert bioinformatic guidance by C. Peterson and M. Ohlsson (CREATE Health Translational Cancer Center, Lund University, Sweden) are gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carl A. K. Borrebaeck.

Ethics declarations

Competing interests

The author is one of the founders of a start-up diagnostic company in complex diseases.

Related links

PowerPoint slides

Glossary

Bead-based arrays

Similar to antibody microarrays but the antibodies are deposited on micro-beads instead of on a planar surface.

Biomarker velocity

The change in signal of a biomarker over time.

Enzyme-linked immunosorbent assay

(ELISA). A solid-phase immunoassay that measures the interaction between proteins and specific antibodies.

510(k)

A premarketing submission made to the US Food and Drug Administration (FDA) to demonstrate that the test is safe and effective. If cleared by the FDA, the test can be marketed in the United States.

Gleason score

A score given to a prostate cancer based on its microscopic appearance, whereby a higher Gleason score indicates a more aggressive tumour.

Antibody microarrays

Miniaturized enzyme-linked immunosorbent assay format.

Laboratory developed tests

(LDTs). In vitro diagnostic tests that are designed, manufactured and used in a single laboratory and not approved by the US Food and Drug Administration.

Reverse phase protein arrays

Arrays in which protein samples are deposited in micro-scale on a planar surface and probed with specific antibodies.

Selected reaction monitoring or multiple reaction monitoring

(SRM/MRM). Two names for a method used in tandem mass spectrometry to quantitatively target individual proteins or peptides.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borrebaeck, C. Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer. Nat Rev Cancer 17, 199–204 (2017). https://doi.org/10.1038/nrc.2016.153

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc.2016.153

This article is cited by

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