Advertisement

Development of Gene Expression-Based Biomarkers on the nCounter® Platform for Immuno-Oncology Applications

  • Sarah WarrenEmail author
  • Patrick Danaher
  • Afshin Mashadi-Hossein
  • Lynell Skewis
  • Brett Wallden
  • Sean Ferree
  • Alessandra Cesano
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)

Abstract

Biomarkers based on transcriptional profiling can be useful in the measurement of complex and/or dynamic physiological states where other profiling strategies such as genomic or proteomic characterization are not able to adequately measure the biology. One particular advantage of transcriptional biomarkers is the ease with which they can be measured in the clinical setting using robust platforms such as the NanoString nCounter system. The nCounter platform enables digital quantitation of multiplexed RNA from small amounts of blood, formalin-fixed, paraffin-embedded tumors, or other such biological samples that are readily available from patients, and the chapter uses it as the primary example for diagnostic assay development. However, development of diagnostic assays based on RNA biomarkers on any platform requires careful consideration of all aspects of the final clinical assay a priori, as well as design and execution of the development program in a way that will maximize likelihood of future success. This chapter introduces transcriptional biomarkers and provides an overview of the design and development process that will lead to a locked diagnostic assay that is ready for validation of clinical utility.

Key words

Biomarker development Clinical diagnostics RNA transcription Gene expression NanoString nCounter 

References

  1. 1.
    Food and Drug Administration (2018) Draft developing and labeling in vitro companion diagnostic devices for a specific group or class of oncology therapeutic products guidance for industry. https://www.fda.gov/ucm/groups/fdagov-public/@fdagov-afda-gen/documents/document/ucm627805.pdf. Accessed 30 Jan 2019
  2. 2.
    Sheerens H et al (2017) Current status of companion and complementary diagnostic. Clin Transl Sci 10(2):84–92CrossRefGoogle Scholar
  3. 3.
    Committee on Policy Issues in the Clinical Development and Use of Biomarkers for Molecularly Targeted Therapies; Board on Health Care Services; Institute of Medicine; National Academies of Sciences, Engineering, and Medicine (2016) In: Graig LA, Phillips JK, Moses HL (eds) Biomarker tests for molecularly targeted therapies: key to unlocking precision medicine. National Academies Press (US), Washington, DC.  https://doi.org/10.17226/21860. Available from: https://www.ncbi.nlm.nih.gov/books/NBK349100/CrossRefGoogle Scholar
  4. 4.
    Cesano A, Warren S (2018) Bringing the next generation of Immuno-oncology biomarkers to the clinic. Biomedicine 6(1).  https://doi.org/10.3390/biomedicines6010014
  5. 5.
    Butterfield L (2017) The society for immunotherapy of cancer biomarkers task force recommendations review. Semin Cancer Biol 52(2):12–15PubMedPubMedCentralGoogle Scholar
  6. 6.
    Gnjatic S et al (2017) Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J Immunother Cancer 5:44CrossRefGoogle Scholar
  7. 7.
    US Food and Drug Administration (2007) Guidance for industry and FDA staff—Class II special controls guidance document: gene expression profiling test system for breast cancer prognosis. http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm079163.htm
  8. 8.
    Nielsen T et al (2014) Analytical validation of the PAM50-based prosigna breast cancer prognostic gene signature assay and nCounter analysis system using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer 14:177CrossRefGoogle Scholar
  9. 9.
    Wallden B et al (2015) Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med Genet 8:54Google Scholar
  10. 10.
    Geiss GG et al (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26(3):317–325CrossRefGoogle Scholar
  11. 11.
    Jiang L et al (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21(9):1543–1551CrossRefGoogle Scholar
  12. 12.
    Clinical and Laboratory Standards Institute (2018) Validation and verification of multiplex nucleic acid assays, 2nd edn. Wayne, PA, USAGoogle Scholar
  13. 13.
    US Food and Drug Administration (2014) Guidance for industry and FDA staff: qualification process for drug development tools. https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM230597.pdf
  14. 14.
    Marton MJ, Weiner R (2013) Practical guidance for implementing predictive biomarkers into early phase clinical studies. Biomed Res Int 2013:891391CrossRefGoogle Scholar
  15. 15.
    Masucci GV et al (2016) Validation of biomarkers to predict response to immunotherapy in cancer: volume I — pre-analytical and analytical validation. J Immunother Cancer 4:7CrossRefGoogle Scholar
  16. 16.
    Dobbin KK et al (2016) Validation of biomarkers to predict response to immunotherapy in cancer: volume II - clinical validation and regulatory considerations. J Immunother Cancer 4:77CrossRefGoogle Scholar
  17. 17.
    Plebani M et al (2014) Harmonization of pre-analytical quality indicators. Biochem Med 24(1):105CrossRefGoogle Scholar
  18. 18.
    Office of Biorepositories and Biospecimen Research (2011) National Cancer Institute, National Institutes of Health, US Department of Health and Human Services. National Cancer Institute Best Practices for Biospecimen Resources. https://biospecimens.cancer.gov/bestpractices/2016-NCIBestPractices.pdf. Accessed 30 Jan 2019
  19. 19.
    Chau CH et al (2008) Validation of analytic methods for biomarkers used in drug development. Clin Cancer Res 14(19):5967CrossRefGoogle Scholar
  20. 20.
    Lee JW et al (2005) Method validation and measurement of biomarkers in nonclinical and clinical samples in drug development: a conference report. Pharm Res 22(4):499CrossRefGoogle Scholar
  21. 21.
    Danaher P et al (2017) Gene expression markers of tumor infiltrating leukocytes. J Immunother Cancer 5:18CrossRefGoogle Scholar
  22. 22.
    Wang A, Sarwal MM (2015) Computational models for transplant biomarker discovery. Front Immunol 6.  https://doi.org/10.3389/fimmu.2015.00458
  23. 23.
    Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. New York, NY Springer; 2001Google Scholar
  24. 24.
    Bair E (2004) Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2(4):E108CrossRefGoogle Scholar
  25. 25.
    Dabney AR (2006) Classification of microarrays to nearest centroids. Bioinformatics 21(22):4148–4154CrossRefGoogle Scholar
  26. 26.
    Dudoit S et al (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 97:77–87CrossRefGoogle Scholar
  27. 27.
    Tibshirani R (1994) Regression selection and shrinkage via the lasso. J R Stat Soc Series B 58:267–288Google Scholar
  28. 28.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67:301–320CrossRefGoogle Scholar
  29. 29.
    Tibshiani R et al (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99(10):6567–6572CrossRefGoogle Scholar
  30. 30.
    Ayers M et al (2017) IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127(8):2930–2940CrossRefGoogle Scholar
  31. 31.
    Scott DW (2014) Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue. Blood 123(8):1214–1217CrossRefGoogle Scholar
  32. 32.
    Prat A et al (2010) Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res 12(5):R68CrossRefGoogle Scholar
  33. 33.
    Burstein et al (2015) Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res 21(7):1688–1698CrossRefGoogle Scholar
  34. 34.
    Guinney J et al (2015) The consensus molecular subtypes of colorectal cancer. Nat Med 21:1350–1356CrossRefGoogle Scholar
  35. 35.
    Sjödahl G et al (2017) Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol 242(1):113–125CrossRefGoogle Scholar
  36. 36.
    Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine; Micheel CM, Nass SJ, Omenn GS, eds (2012) Washington, DC: National Academies Press (US). Available from: https://www.ncbi.nlm.nih.gov/books/NBK202168/
  37. 37.
    Richard AC et al (2014) Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation. BMC Genomics 15:649CrossRefGoogle Scholar
  38. 38.
    Vandesompele J et al (2002 Jun) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7):research0034–research0031CrossRefGoogle Scholar
  39. 39.
    Warren S et al (2017) Pretreatment gene expression signature correlation with clinical response to pembrolizumab or nivolumab in metastatic melanoma. Poster presented at Society for Immunotherapy of Cancer Annual Meeting, Washington, DC, 3 Nov. 2017Google Scholar
  40. 40.
    Damotte D et al (2018) The Tumor Inflammation Signature is predictive of anti-PD1 treatment benefit in the CERTIM pan-cancer cohort. Poster presented at the American Association for Cancer Research Annual Meeting, Chicago, 14 Apr 2018Google Scholar
  41. 41.
    Rozeman EA et al (2017) Biomarker Analysis for the OpACIN Trial (Neo-/adjuvant ipilimumab + nivoluman (IPI+NIVO) in palpable stage 3 melanoma. Poster presented at the Society for Immunotherapy of Cancer Annual Meeting, Washington, DC, 3 Nov. 2017Google Scholar
  42. 42.
    Danaher P et al (2018) Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from the Cancer genome atlas (TCGA). J Immunother Cancer 6(1):63CrossRefGoogle Scholar
  43. 43.
    Ott PA et al (2018, 2018) T-cell-inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J Clin Oncol.  https://doi.org/10.1200/JCO.2018.78.2276CrossRefGoogle Scholar
  44. 44.
    Cristescu R et al (2018) Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362(6411):eaar3593CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Sarah Warren
    • 1
    Email author
  • Patrick Danaher
    • 1
  • Afshin Mashadi-Hossein
    • 1
  • Lynell Skewis
    • 1
  • Brett Wallden
    • 1
  • Sean Ferree
    • 1
  • Alessandra Cesano
    • 1
    • 2
  1. 1.NanoString Technologies, Inc.SeattleUSA
  2. 2.ESSA PharmaSouth San FranciscoUSA

Personalised recommendations