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Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes

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Abstract

Personalized or precision medicine as a diagnostic and therapeutic paradigm was introduced some 10–15 years ago, with the advent of biomarker discovery as a mechanism for identifying prognostic and predictive attributes associated with treatment indication and outcome. While the concept is not new, the successful development and implementation of novel ‘companion diagnostics’, especially in oncology, continues to represent a significant challenge and is currently at the forefront of smart trial design and therapeutic choice. The ability to determine patient selection for a specific therapy has broad implications including better chances for a positive outcome, limited exposure to potentially toxic drugs and improved health economics. Importantly, a significant step in this paradigm is the role of predictive pathology or the accurate assessment of morphology at the microscopic level. In breast cancer, this has been most useful where histologic attributes such as the classification of tubular and cribriform carcinoma dictates surgery while neoadjuvant studies suggest that patients with lobular carcinoma are not likely to benefit from chemotherapy. The next level of ‘personalized pathology’ at the tissue-cellular level is the use of ‘protein biomarker panels’ to classify the disease process and ultimately drive tumor characterization and treatment. The following review article will focus on the evolution of predictive pathology from a subjective, ‘opinion-based’ approach to a quantitative science. In addition, we will discuss the individual components of the precise pathology platform including advanced image analysis, biomarker quantitation with mathematical modeling and the integration with fluid-based (i.e. blood, urine) analytics as drivers of next generation precise patient phenotyping.

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References

  1. Hertz DL, Rae JM. Pharmacogenetic predictors of response. Adv Exp Med Biol. 2016;882:191–215.

    Article  PubMed  Google Scholar 

  2. Gleason DF. Classification of prostate carcinomas. Cancer Chemother Rep. 1966;50:125–8.

    CAS  PubMed  Google Scholar 

  3. Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. The value of histologic grade in breast cancer: experience from a large study with long term follow-up. Histopathology. 1991;19:403.

    Article  CAS  PubMed  Google Scholar 

  4. Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am J Surg Pathol. 2016;40:244–52.

    PubMed  Google Scholar 

  5. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small cell lung cancer to gefitinib. N Engl J Med. 2004;350:2129–39.

    Article  CAS  PubMed  Google Scholar 

  6. Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisingewr KR, Yatabe Y, et al. International association for the study of lung cancer/american thoracic society/European respoiroty society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol. 2011;6:244–85.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Prabhakar C. Epidermal growth factor receptor in non-small cell lung cancer. Transl Lung Cancer Res. 2015;4:110–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Servant N, Romejon J, Gestraud P, La Rosa P, Lucotte G, Lair S, et al. Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial. Front Genet. 2014;5:152.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lopez-Chavez A, Thomas A, Rajan A, Raffeld M, Morrow B, Kelly R. Molecular profiling and targeted therapy for advanced thoracic malignancies: a biomarker-derived, multiarm, multihistology phase II basket trial. J Clin Oncol. 2015;33:1000–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ping Z, Xia Y, Shen T, Parekh Siegal G, Eltoum I-E, et al. A microscopic landscape of the invasive breast cancer genome. Sci Rep. 2016;6:27545.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Natrajan R, Sailem H, Mardakheh F, Garcia MA, Tape CJ, Dowsett M, et al. Microenvironmental heterogeneity parallels breast cancer progression: a histology–genomic integration analysis. PLoS Med. 2016;13:e1001961.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Nanda R, Chow LQ, Dees EC, Berger R, Gupta S, Geva R, et al. Pembrolizumab in patients with advanced triple-negative breast cancer: phase 1b KEYNOTE—12 study. JCO. 2016;34:2460–7.

    Article  CAS  Google Scholar 

  14. Vuong D, Simpson PT, Green B, Cummings MC, Lakhani SR. Molecular classification of breast cancer. Virchows Arch. 2014;465:1–14.

    Article  CAS  PubMed  Google Scholar 

  15. Cardoso F, van’t Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med. 2016;8:717–29.

    Article  Google Scholar 

  16. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30.

    Article  PubMed  Google Scholar 

  17. Barbieri CE, Tomlins SA. The prostate cancer genome: perspectives and potential. Urol Oncol. 2014;32:53.e15–22.

    Article  CAS  Google Scholar 

  18. Fraser M, Berlin A, Bristow RG, van der Kwast T. Genomic, pathological, and clinical heterogeneity as drivers of personalized medicine in prostate cancer. Urol Oncol. 2015;33:85–94.

    Article  PubMed  Google Scholar 

  19. Chatterjee P, Choudhary GS, Sharma A, Singh K, Heston WD, Ciezki J, et al. PARP inhibition sensitizes to low dose-rate radiation TMPRSS2-ERG fusion gene-expressing and PTEN-deficient prostate cancer cells. PLoS One. 2013;8:e60408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chen X, Rycaj K, Liu X, Tang DG. New insights into prostate cancer stem cells. Cell Cycle. 2013;12:579–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Domingo-Domenech J, Vidal SJ, Rodriguez-Bravo V, Castillo-Martin M, Quinn SA, Rodriguez-Barrueco R, et al. Suppression of acquired docetaxe resistance in prostate cancer through depletion of notch-and hedgehog-dependent tumor-initiating cells. Cancer Cell. 2012;22:373–88.

    Article  CAS  PubMed  Google Scholar 

  22. Kannan N, Nguyen LV, Eaves CJ. Integrin B3 links therapy resistance and cancer stem cell properties. Nat Cell Biol. 2014;16:397–9.

    Article  CAS  PubMed  Google Scholar 

  23. Vidal SJ, Rodriguez-Bravo V, Quinn SA, Rodriguez-Bravo R, Lujambio A, Williams E, et al. A targetable GATA-IGF2 axis confers aggressiveness in lethal prostate cancer. Cancer Cell. 2015;27:223–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tsiatas M, Grivas P. Immunobiology and immunotherapy in genitourinary malignancies. Ann Transl Med. 2016;4:270.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mountzios G, Linardou H, Kosmidis P. Immunotherapy in non-small cell lung cancer: the clinical impact of immune response and targeting. Ann Transl Med. 2016;4:268.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Cordon-Cardo C, Kotsianti A, Verbel DA, Teverovskiy M, Capodieci P, Hamann S, et al. Improved prediction of prostate cancer recurrence through systems pathology. J Clin Investig. 2007;117:1876–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Donovan MJ, Hamann S, Clayton M, Khan FM, Sapir M, Bayer-Zubek V, et al. Systems pathology approach for the prediction of prostate cancer progression after radical prostatectomy. J Clin Oncol. 2008;26:3923–9.

    Article  PubMed  Google Scholar 

  28. Donovan MJ, Kotsianti A, Bayer-Zubek V, Verbel D, Teverovskiy M, Cordon-Cardo C, et al. A systems pathology model for predicting overall survival in patients with refractory, advanced non-small cell lung cancer treated with gefitinib. Eur J Cancer. 2009;45:18–26.

    Article  Google Scholar 

  29. Donovan MJ, Khan FM, Fernandez G, Mesa-Tejada R, Sapir M, Zubek VB, et al. Personalized prediction of tumor response and cancer progression on prostate needle biopsy. J Urol. 2009;182:125–32.

    Article  PubMed  Google Scholar 

  30. Donovan MJ, Khan F, Powell D, Fernandez G, Feliz A, Hansen T, et al. Previously developed systems-based model (Prostate Px+) identifies favorable-risk prostate cancer for men enrolled in an active surveillance program. J Urol. 2011;185:e517–8.

    Article  Google Scholar 

  31. Kyi C, Sabado RL, Saenger YM, Marshall PR, Donovan M, Loging W, et al. In situ, therapeutic vaccination against refractory solid cancers with intratumoral Poly-ICLC: a phase I study. J Clin Oncol. 2016;34:suppl abstr 3086.

    Google Scholar 

  32. Ullal A, Peterson V, Agasti SS, Tuang S, Juric D, Castro CM, et al. Cancer cell profiling by barcoding allows multiplexed protein analysis in fine-needle aspirates. Sci Transl Med. 2014;6:219ra9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacob SA, Hattendorf B, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11:417–22.

    Article  CAS  PubMed  Google Scholar 

  34. Wolf C, Jarutat T, Vega Harring S, Haupt K, Babitzki G, Bader K, et al. Determination of phosphorylated proteins in tissue specimens requires high-quality samples collected under stringent conditions. Histopathology. 2014;64:431–44.

    Article  PubMed  Google Scholar 

  35. Abel EJ, Bauman TM, Weiker M, Shi F, Downs TM, Jarrard DF, et al. Analysis and validation of tissue biomarkers for renal cell carcinoma using automated high-throughput evaluation of protein expression. Hum Pathol. 2014;45:1092–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Steurer S, Singer JM, Rink M, Chun F, Dahlem R, Simon R, et al. MALDI imaging-based identification of prognostically relevant signals in bladder cancer using large-scale tissue microarrays. Urol Oncol. 2014;32:1225–33.

    Article  PubMed  Google Scholar 

  37. Pereira LH, Reis IM, Reategui EP, Gordon C, Saint-Victor S, Duncan R, et al. Risk stratification system for oral cancer screening. Cancer Prev Res. 2016;9:445–55.

    Article  CAS  Google Scholar 

  38. Strotman LN, Millner LM, Valides R Jr, Linder MW. Liquid biopsies in oncology and the current regulatory landscape. Mol Diagn Ther. 2016;20:429–36.

  39. Jia S, Zocco D, Samuels MI, Chou MF, Chammas R, Skog J, et al. Emerging technologies in extracellular vesicle-based diagnostics. Expert Rev Mol Diagn. 2014;14:307–21.

    Article  CAS  PubMed  Google Scholar 

  40. Lianidou ES, Strati A, Markou A. Circulating tumor cells as promising novel biomarkers in solid cancers. Crit Rev Clin Lab Sci. 2014;51:160–7.

    Article  CAS  PubMed  Google Scholar 

  41. Kolbl AC, Jeschke U, Andergasses U. The significance of epithelial-to-mesenchymal transition for circulating tumor cells. Int J Mol Sci. 2016;17:E1308.

    Article  PubMed  Google Scholar 

  42. Antonarakis E, Lu C, Wang H, Luber B, Nakazawa M, Roeser J, et al. AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N Engl J Med. 2014;371:1028–38.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Nicolazzo C, Raimondi C, Mancini ML, Caponnetto S, Gradilone A, Gandini O, et al. Monitoring PD-L1 positive circulating tumor cells in non-small cell lung cancer patients treated with the PD-1 inhibitor Nivolumab. Sci Rep. 2016;6:31726.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Esposito A, Bardelli A, Criscitiello C, Colombo N, Gelao L, Fumagalli L, et al. Monitoring tumor-derived cell-free DNA in patients with solid tumors: clinical perspectives and research opportunities. Cancer Treat Rev. 2014;40:648–55.

    Article  CAS  PubMed  Google Scholar 

  45. Lewis JM, Heineck DP, Heller MJ. Detecting cancer biomarkers in blood: challenges for new molecular diagnostic and point-of-care tests using cell-free nucleic acids. Expert Rev Mol Diagn. 2015;15:1187–200.

    Article  CAS  PubMed  Google Scholar 

  46. Mok T, Wu YL, Lee JS, Yu CJ, Sriuranpong V, Sandoval-Tan J, et al. Detection and dynamic changes of EGFR mutations from circulating tumor DNA as a predictor of survival outcomes in NSCLC patients treated with first-line intercalated erlotinib and chemotherapy. Clin Cancer Res. 2015;21:3196–203.

    Article  CAS  PubMed  Google Scholar 

  47. Legendre C, Gooden GC, Johnson K, Martinez RA, Liang WS, Salhia B. Whole-genome bisulfite sequencing of cell-free DNA identifies signature associated with metastatic breast cancer. Clin Epigenet. 2015;7:100.

    Article  Google Scholar 

  48. Skog J, Wurdinger T, van Rijn S, Meijer DH, Gainche L, Sena-Esteves M, et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008;10:1470–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mizutani K, Terazawa R, Kameyama K, Kato T, Horie K, Tsuchiya T, et al. Isolation of prostate cancer-related exosomes. Anticancer Res. 2014;34:3419–23.

    CAS  PubMed  Google Scholar 

  50. Donovan MJ, Noerholm M, Bentink S, Belzer S, Skog J, O’Neill V, et al. A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostatic Dis. 2015;18:370–5.

    Article  CAS  PubMed  Google Scholar 

  51. McKiernan J, Donovan MJ, O’Neill V, Bentink S, Noerholm M, Belzer S, et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol. 2016;2:882–9.

    Article  PubMed  Google Scholar 

  52. Montamedinia P, Scott AN, Bate KL, Sadeghi N, Salazar G, Shapiro E, et al. Urine exosomes for non-invasive assessment of gene expression and mutations of prostate cancer. PLoS One. 2016;11:e154507.

    Google Scholar 

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Correspondence to Michael J. Donovan.

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Donovan, M.J., Cordon-Cardo, C. Implementation of a Precision Pathology Program Focused on Oncology-Based Prognostic and Predictive Outcomes. Mol Diagn Ther 21, 115–123 (2017). https://doi.org/10.1007/s40291-016-0249-5

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