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Methodological requirements for valid tissue-based biomarker studies that can be used in clinical practice

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Abstract

Paralleling the growth of ever more cost efficient methods to sequence the whole genome in minute fragments of tissue has been the identification of increasingly numerous molecular abnormalities in cancers—mutations, amplifications, insertions and deletions of genes, and patterns of differential gene expression, i.e., overexpression of growth factors and underexpression of tumor suppressor genes. These abnormalities can be translated into assays to be used in clinical decision making. In general terms, the result of such an assay is subject to a large number of variables regarding the characteristics of the available sample, particularities of the used assay, and the interpretation of the results. This review discusses the effects of these variables on assays of tissue-based biomarkers, classified by macromolecule—DNA, RNA (including micro RNA, messenger RNA, long noncoding RNA, protein, and phosphoprotein). Since the majority of clinically applicable biomarkers are immunohistochemically detectable proteins this review focuses on protein biomarkers. However, the principles outlined are mostly applicable to any other analyte. A variety of preanalytical variables impacts on the results obtained, including analyte stability (which is different for different analytes, i.e., DNA, RNA, or protein), period of warm and of cold ischemia, fixation time, tissue processing, sample storage time, and storage conditions. In addition, assay variables play an important role, including reagent specificity (notably but not uniquely an issue concerning antibodies used in immunohistochemistry), technical components of the assay, quantitation, and assay interpretation. Finally, appropriateness of an assay for clinical application is an important issue. Reference is made to publicly available guidelines to improve on biomarker development in general and requirements for clinical use in particular. Strategic goals are formulated in order to improve on the quality of biomarker reporting, including issues of analyte quality, experimental detail, assay efficiency and precision, and assay appropriateness.

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References

  1. Meshinchi S, Hunger SP, Aplenc R, Adamson PC, Jessup JM (2012) Lessons learned from the investigational device exemption review of Children’s Oncology Group trial AAML1031. Clin Cancer Res 18:1547–1554

    Article  PubMed Central  PubMed  Google Scholar 

  2. Hewitt SM, Badve SS, True LD (2012) Impact of preanalytic factors on the design and application of integral biomarkers for directing patient therapy. Clin Cancer Res 18:1524–1530

    Article  PubMed Central  PubMed  Google Scholar 

  3. O’Huallachain M, Karczewski KJ, Weissman SM, Urban AE, Snyder MP (2012) Extensive genetic variation in somatic human tissues. Proc Natl Acad Sci U S A 109:18018–18023

    Article  PubMed Central  PubMed  Google Scholar 

  4. Engel KB, Moore HM (2011) Effects of preanalytical variables on the detection of proteins by immunohistochemistry in formalin-fixed, paraffin-embedded tissue. Arch Pathol Lab Med 135:537–543

    PubMed  Google Scholar 

  5. Neumeister VM, Anagnostou V, Siddiqui S, England AM, Zarrella ER, Vassilakopoulou M, Parisi F, Kluger Y, Hicks DG, Rimm DL (2012) Uantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues. J Natl Cancer Inst 104:1815–1824

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Bordeaux J, Welsh A, Agarwal S, Killiam E, Baquero M, Hanna J, Anagnostou V, Rimm D (2010) Antibody validation. Biotechniques 48:197–209

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Gross DS, Rothfeld JM (1985) Quantitative immunocytochemistry of hypothalamic and pituitary hormones: validation of an automated, computerized image analysis system. J Histochem Cytochem 33:11–20

    Article  CAS  PubMed  Google Scholar 

  8. Zrazhevskiy P, Gao X (2009) Multifunctional quantum dots for personalized medicine. Nano Today 4:414–428

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Fandel TM, Pfnür M, Schäfer SC, Bacchetti P, Mast FW, Corinth C, Ansorge M, Melchior SW, Thüroff JW, Kirkpatrick CJ, Lehr HA (2008) Do we truly see what we think we see? The role of cognitive bias in pathological interpretation. J Pathol 216:193–200

    Article  CAS  PubMed  Google Scholar 

  10. Wells WA, Rainer RO, Memoli VA (1993) Equipment, standardization, and applications of image processing. J Clin Pathol 99:48–56

    CAS  Google Scholar 

  11. True LD (1988) Quantitative immunohistochemistry: a new tool for surgical pathology? Am J Clin Pathol 90:324–325

    CAS  PubMed  Google Scholar 

  12. Rubin MA, Zerkowski MP, Camp RL, Kuefer R, Hofer MD, Chinnaiyan AM, Rimm DL (2004) Quantitative determination of expression of the prostate cancer protein alpha-methylacyl-CoA racemase using automated quantitative analysis (AQUA): a novel paradigm for automated and continuous biomarker measurements. Am J Pathol 164:831–840

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Etzioni R, Hawley S, Billheimer D, True LD, Knudsen B (2005) Analyzing patterns of staining in immunohistochemical studies: application to a study of prostate cancer recurrence. Cancer Epidemiol Biomarkers Prev 14:1040–1046

    Article  CAS  PubMed  Google Scholar 

  14. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892

    Article  CAS  PubMed  Google Scholar 

  15. Deutsch EW, Ball CA, Berman JJ, Bova GS, Brazma A, Bumgarner RE, Campbell D, Causton HC, Christiansen JH, Daian F, Dauga D, Davidson DR, Gimenez G, Goo YA, Grimmond S, Henrich T, Herrmann BG, Johnson MH, Korb M, Mills JC, Oudes AJ, Parkinson HE, Pascal LE, Pollet N, Quackenbush J, Ramialison M, Ringwald M, Salgado D, Sansone SA, Sherlock G, Stoeckert CJ Jr, Swedlow J, Taylor RC, Walashek L, Warford A, Wilkinson DG, Zhou Y, Zon LI, Liu AY, True LD (2008) Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). Nat Biotechnol 26:305–312

    Article  CAS  PubMed  Google Scholar 

  16. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM, Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics (2005) Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 97:1180–1184

    Article  CAS  PubMed  Google Scholar 

  17. McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM, Mesirov JP, Polley MY, Kim KY, Tricoli JV, Taylor JM, Shuman DJ, Simon RM, Doroshow JH, Conley BA (2013) Criteria for the use of omics-based predictors in clinical trials. Nature 502:317–320

    Article  CAS  PubMed  Google Scholar 

  18. Poste G, Carbone DP, Parkinson DR, Verweij J, Hewitt SM, Jessup JM (1996) Eveling the playing field: bringing development of biomarkers and molecular diagnostics up to the standards for drug development. J Natl Cancer Inst 88:1456–1466

    Article  Google Scholar 

  19. Hayes DF, Bast RC, Desch CE, Fritsche H Jr, Kemeny NE, Jessup JM, Locker GY, Macdonald JS, Mennel RG, Norton L, Ravdin P, Taube S, Winn RJ (2012) Tumor marker utility grading system: framework to evaluate clinical utility of tumor markers. Clin Cancer Res 18:1515–1523

    Article  Google Scholar 

  20. Febbo PG, Ladanyi M, Aldape KD, De Marzo AM, Hammond ME, Hayes DF, Iafrate AJ, Kelley RK, Marcucci G, Ogino S, Pao W, Sgroi DC, Birkeland ML (2011) NCCN Task Force report: evaluating the clinical utility of tumor markers in oncology. J Natl Compr Canc Netw 9(Suppl 5):S1–S32

    CAS  Google Scholar 

  21. Williams PM, Lively TG, Jessup JM, Conley BA (2012) Bridging the gap: moving predictive and prognostic assays from research to clinical use. Clin Cancer Res 18:1531–1539

    Article  PubMed Central  PubMed  Google Scholar 

  22. Begley CG, Ellis LM (2012) Drug development: raise standards for preclinical cancer research. Nature 483:531–533

    Article  CAS  PubMed  Google Scholar 

  23. Leong AS, Leong TY (2011) Standardization in immunohistology. Methods Mol Biol 724:37–68

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Cancer Institute Pacific Northwest Prostate Cancer Specialized Program of Research Excellence (SPORE; P50 CA 097186-06).

Conflict of interest

I declare that I have no conflict of interest.

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Authors and Affiliations

  1. Department of Pathology, University of Washington Medical Center, 1959 NE Pacific St., Box 356100, Seattle, WA, USA

    Lawrence D. True

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  1. Lawrence D. True
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Correspondence to Lawrence D. True.

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True, L.D. Methodological requirements for valid tissue-based biomarker studies that can be used in clinical practice. Virchows Arch 464, 257–263 (2014). https://doi.org/10.1007/s00428-013-1531-0

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  • DOI: https://doi.org/10.1007/s00428-013-1531-0

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