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Digital and Computational Pathology for Biomarker Discovery

  • Peter Hamilton
  • Paul O’Reilly
  • Peter Bankhead
  • Esther Abels
  • Manuel Salto-Tellez
Chapter

Abstract

Digital pathology is now centre stage in tissue analytics by providing a range of new advanced tools for biomarker research, analysis, discovery and translation. The advantages of digital image analytics in tissue research have been recognized for many years. However, recent advances in high-resolution whole slide scanning, web-enabled multisite collaboration, image analytics, machine learning and imaging informatics are now, for the first time, enabling researchers to accelerate quantitative biomarker discovery and transform the discovery and clinical translation of companion diagnostics for precision therapeutics. This chapter will summarize some of the most important advances in digital pathology and the transformative impact this is having on cancer biomarker discovery and development.

Keywords

Digital pathology Computational pathology Image analysis Biomarker Precision medicine Tissue microarrays Artificial intelligence Deep learning Algorithms FDA Companion diagnostics 

Notes

Disclosure

The opinions expressed in this presentation are solely those of the author or presenters and do not necessarily reflect those of Philips. The information presented herein is not specific to any product of Philips or their intended uses. The information contained herein does not constitute, and should not be construed as, any promotion of Philips products or company policies.

References

  1. 1.
    Hamilton PW, van Diest PJ, Williams R, Gallagher AG. Do we see what we think we see? The complexities of morphological assessment. J Pathol [Internet]. 2009 [cited 2017 Jun 4];218(3):285–91. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19291709.CrossRefPubMedGoogle Scholar
  2. 2.
    Hamilton PW, Bankhead P, Wang Y, Hutchinson R, Kieran D, McArt DG, et al. Digital pathology and image analysis in tissue biomarker research. Methods [Internet]. 2014 [cited 2017 Jun 5];70(1):59–73. Available from: http://www.sciencedirect.com/science/article/pii/S1046202314002370.CrossRefGoogle Scholar
  3. 3.
    Prescott JW. Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making. J Digit Imaging [Internet]. 2013 [cited 2017 Jun 5];26(1):97–108. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22415112.CrossRefGoogle Scholar
  4. 4.
    Masmoudi H, Hewitt SM, Petrick N, Myers KJ, Gavrielides MA. Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans Med Imaging [Internet]. 2009 [cited 2017 Jun 11];28(6):916–25. Available from: http://ieeexplore.ieee.org/document/4752752/.CrossRefPubMedGoogle Scholar
  5. 5.
    Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, James JA, Manuel Salto-Tellez PWH. QuPath: open source software for digital pathology image analysis. bioRxiv. 2017;  https://doi.org/10.1101/099796.
  6. 6.
    Vandenberghe ME, Scott MLJ, Scorer PW, Soderberg M, Balcerzak D, Barker C. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci Rep [Internet]. 2017 [cited 2017 Jun 12];7:45938. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28378829.
  7. 7.
    Klümper N, Syring I, Vogel W, Schmidt D, Müller SC, Ellinger J, et al. Mediator complex subunit MED1 protein expression is decreased during bladder cancer progression. Front Med [Internet]. 2017 [cited 2017 Jun 11];4:30. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28367434.
  8. 8.
    Parra ER, Behrens C, Rodriguez-Canales J, Lin H, Mino B, Blando J, et al. Image analysis–based assessment of PD-L1 and tumor-associated immune cells density supports distinct intratumoral microenvironment groups in non–small cell lung carcinoma patients. Clin Cancer Res [Internet]. 2016 [cited 2017 May 16];22(24). Available from: http://clincancerres.aacrjournals.org/content/22/24/6278.CrossRefPubMedGoogle Scholar
  9. 9.
    Neumeister VM, Anagnostou V, Siddiqui S, England AM, Zarrella ER, Vassilakopoulou M, et al. Quantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues. JNCI J Natl Cancer Inst [Internet]. 2012 [cited 2017 Jun 4];104(23):1815–24. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23090068.CrossRefPubMedGoogle Scholar
  10. 10.
    Neumeister VM, Parisi F, England AM, Siddiqui S, Anagnostou V, Zarrella E, et al. A tissue quality index: an intrinsic control for measurement of effects of preanalytical variables on FFPE tissue. Lab Invest [Internet]. 2014 [cited 2017 Jun 4];94(4):467–74. Available from: http://www.nature.com/doifinder/10.1038/labinvest.2014.7.CrossRefGoogle Scholar
  11. 11.
    Lykkegaard Andersen N, Brugmann A, Lelkaitis G, Nielsen S, Friis Lippert M, Vyberg M. Virtual double staining. Appl Immunohistochem Mol Morphol [Internet]. 2017 [cited 2017 Jun 12];1. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28248729.
  12. 12.
    Wang Y, McCleary D, Wang C-W, Kelly P, James J, Fennell DA, et al. Ultra-fast processing of gigapixel tissue MicroArray images using high performance computing. Cell Oncol [Internet]. 2011 [cited 2017 Jun 6];34(5):495–507. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21559926.CrossRefGoogle Scholar
  13. 13.
    Kerr KM, Nicolson MC. Non–small cell lung cancer, PD-L1, and the pathologist. Arch Pathol Lab Med [Internet]. 2016 [cited 2017 Jun 6];140 5858. Available from: http://www.archivesofpathology.org/doi/pdf/10.5858/arpa.2015-0303-SA?code=coap-site.
  14. 14.
    McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelekanou V, Rehman J, et al. Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer. JAMA Oncol [Internet]. 2016 [cited 2017 May 16];2(1):46–54. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26562159.CrossRefPubMedGoogle Scholar
  15. 15.
    Xing X, Li Z, Wang J, Ji J. Analysis of PDL1 expression and T cells infiltration in 1014 gastric cancer patients. J Clin Oncol [Internet]. 2017 [cited 2017 May 16];35(4_suppl):50–50. Available from: http://ascopubs.org/doi/10.1200/JCO.2017.35.4_suppl.50.CrossRefGoogle Scholar
  16. 16.
    Turkki R, Linder N, Kovanen P, Pellinen T, Lundin J. Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J Pathol Inform [Internet]. 2016 [cited 2017 Jun 12];7(1):38. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27688929.CrossRefPubMedGoogle Scholar
  17. 17.
  18. 18.
    Tuominen VJ, Ruotoistenmaki S, Viitanen A, Jumppanen M, Isola J. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res [Internet]. 2010 [cited 2017 Jun 11];12(4):R56. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20663194.
  19. 19.
    Tuominen VJ, Tolonen TT, Isola J. ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology [Internet]. 2012 [cited 2017 Jun 11];60(5):758–67. Available from: http://doi.wiley.com/10.1111/j.1365-2559.2011.04142.x.
  20. 20.
    Yu K-H, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun [Internet]. 2016 [cited 2017 Jun 11];7:12474. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27527408.
  21. 21.
    Dong F, Irshad H, Oh E-Y, Lerwill MF, Brachtel EF, Jones NC, et al. Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. In: Sapino A, editor. PLoS One [Internet]. 2014 [cited 2017 Jun 11];9(12):e114885. Available from: http://dx.plos.org/10.1371/journal.pone.0114885.
  22. 22.
    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature [Internet]. 2015 [cited 2017 Sep 1];521(7553):436–44. Available from: http://www.nature.com/doifinder/10.1038/nature14539.CrossRefGoogle Scholar
  23. 23.
    Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Int Conf Med Image Comput Comput Interv 2015 (p. 234–41) Springer Int Publ. Available from https://arxiv.org/abs/150504597.
  24. 24.
    Erickson BJ, Korfiatis P, Akkus Z, Kline T, Philbrick K. Toolkits and libraries for deep learning. J Digit Imaging [Internet]. 2017 [cited 2017 Sep 1];30(4):400–5. Available from: http://link.springer.com/10.1007/s10278-017-9965-6.CrossRefPubMedGoogle Scholar
  25. 25.
    Paeng K, Hwang S, Park S, Kim M, Kim S. A unified framework for tumor proliferation score prediction in breast histopathology. arXiv Prepr arXiv161207180 2016. Available from https://arxiv.org/abs/161207180.
  26. 26.
    Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. 2016 [cited 2017 Sep 1]; Available from: http://arxiv.org/abs/1606.05718.
  27. 27.
    Deng J, Dong W, Socher R, Li L-J, Li K, Li F-F. ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition [Internet]. IEEE; 2009 [cited 2017 Sep 1]. p. 248–55. Available from: http://ieeexplore.ieee.org/document/5206848/.
  28. 28.
    Xing F, Yang L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev Biomed Eng [Internet]. 2016 [cited 2017 Jun 11];9:234–63. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26742143.CrossRefPubMedGoogle Scholar
  29. 29.
    Hamilton PW, Wang Y, Boyd C, James JA, Loughrey MB, Hougton JP, et al. Automated tumor analysis for molecular profiling in lung cancer. Oncotarget [Internet]. 2015 [cited 2017 Jun 12];6(29):27938–52. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26317646.
  30. 30.
    Viray H, Li K, Long TA, Vasalos P, Bridge JA, Jennings LJ, et al. A prospective, multi-institutional diagnostic trial to determine pathologist accuracy in estimation of percentage of malignant cells. Arch Pathol Lab Med. 2013;137(11):1545–9.CrossRefGoogle Scholar
  31. 31.
    Williams BJ, DaCosta P, Goacher E, Treanor D. A systematic analysis of discordant diagnoses in digital pathology compared with light microscopy. Arch Pathol Lab Med [Internet]. 2017; [cited 2017 Sep 1];arpa.2016-0494-OA. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28467215.
  32. 32.
    Abels E, Pantanowitz L. Current state of the regulatory trajectory for whole slide imaging devices in the USA. J Pathol Inform [Internet]. 2017 [cited 2017 Jun 11];8(1):23. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28584684.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Peter Hamilton
    • 1
  • Paul O’Reilly
    • 2
  • Peter Bankhead
    • 2
  • Esther Abels
    • 3
  • Manuel Salto-Tellez
    • 4
  1. 1.Department of Digital PathologyPhilips UKBelfastUK
  2. 2.Belfast Development HubPhilips Digital Pathology SolutionsBelfastUK
  3. 3.Digital Pathology Solutions, Pharma SolutionsPhilips Digital Pathology SolutionsBestThe Netherlands
  4. 4.Northern Ireland Molecular Pathology Laboratory, Centre for Cancer Research, Department of Cell BiologyQueens University BelfastBelfastUK

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