Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging

Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient’s likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

[1]  E. Lundberg,et al.  The emerging landscape of spatial profiling technologies , 2022, Nature reviews genetics.

[2]  I. Chetty,et al.  The 2021 landscape of FDA-approved artificial intelligence/machine learning-enabled medical devices: An analysis of the characteristics and intended use , 2022, Int. J. Medical Informatics.

[3]  A. Vladzymyrskyy,et al.  Changes in software as a medical device based on artificial intelligence technologies , 2022, International Journal of Computer Assisted Radiology and Surgery.

[4]  P. Boor,et al.  Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology , 2022, Modern Pathology.

[5]  M. J. van de Vijver,et al.  The impact of a pathologist's personality on the interobserver variability and diagnostic accuracy of predictive PD-L1 immunohistochemistry in lung cancer. , 2022, Lung cancer.

[6]  C. Marquette,et al.  Analytical validation of automated multiplex chromogenic immunohistochemistry for diagnostic and predictive purpose in non-small cell lung cancer. , 2022, Lung cancer.

[7]  F. Nahm Receiver operating characteristic curve: overview and practical use for clinicians , 2022, Korean journal of anesthesiology.

[8]  P. Lorgelly,et al.  Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device , 2022, JMIR medical informatics.

[9]  A. Parwani,et al.  Bridging the Gap: The Critical Role of Regulatory Affairs and Clinical Affairs in the Total Product Life Cycle of Pathology Imaging Devices and Software , 2021, Frontiers in Medicine.

[10]  T. Kiehl,et al.  Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP) , 2021, Diagnostics.

[11]  J. Lee,et al.  An analysis of research biopsy core variability from over 5000 prospectively collected core samples , 2021, npj Precision Oncology.

[12]  D. Larsimont,et al.  Fluorescent Multiplex Immunohistochemistry Coupled With Other State-Of-The-Art Techniques to Systematically Characterize the Tumor Immune Microenvironment , 2021, Frontiers in Molecular Biosciences.

[13]  E. Parra,et al.  Best Practices for Technical Reproducibility Assessment of Multiplex Immunofluorescence , 2021, Frontiers in Molecular Biosciences.

[14]  E. Torlakovic,et al.  Quantitative comparison of PD-L1 IHC assays against NIST standard reference material 1934 , 2021, Modern Pathology.

[15]  E. Parra,et al.  Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies , 2021, Frontiers in Molecular Biosciences.

[16]  J. Taube,et al.  Multi-institutional TSA-amplified Multiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study , 2021, Journal for ImmunoTherapy of Cancer.

[17]  E. Parra Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment , 2021, Frontiers in Molecular Biosciences.

[18]  Ludmila V. Danilova,et al.  Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade , 2021, Science.

[19]  C. Hoyt Multiplex Immunofluorescence and Multispectral Imaging: Forming the Basis of a Clinical Test Platform for Immuno-Oncology , 2021, Frontiers in Molecular Biosciences.

[20]  Y. Tsutsumi Pitfalls and Caveats in Applying Chromogenic Immunostaining to Histopathological Diagnosis , 2021, Cells.

[21]  Gregory Campbell,et al.  The role of statistics in the design and analysis of companion diagnostic (CDx) studies , 2021 .

[22]  G. Litjens,et al.  Deep learning in histopathology: the path to the clinic , 2021, Nature Medicine.

[23]  I. Wistuba,et al.  Multiplex Immunofluorescence Tyramide Signal Amplification for Immune Cell Profiling of Paraffin-Embedded Tumor Tissues , 2021, Frontiers in Molecular Biosciences.

[24]  Giovanni M. Lujan,et al.  Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association , 2021, Journal of pathology informatics.

[25]  L. Pantanowitz,et al.  Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association , 2021, Applied immunohistochemistry & molecular morphology : AIMM.

[26]  James Ziai,et al.  Clinical and research applications of multiplexed immunohistochemistry and in situ hybridization , 2021, The Journal of pathology.

[27]  Anika K Schaedle,et al.  Development and Validation of Measurement Traceability for In Situ Immunoassays. , 2021, Clinical chemistry.

[28]  P. Tan,et al.  Multi-protein spatial signatures in ductal carcinoma in situ (DCIS) of breast , 2021, British Journal of Cancer.

[29]  Salil S. Bhate,et al.  Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma , 2020, Nature Communications.

[30]  M. Willrich,et al.  A High-Level Overview of the Regulations Surrounding a Clinical Laboratory and Upcoming Regulatory Challenges for Laboratory Developed Tests. , 2020, Laboratory medicine.

[31]  Heeva Baharlou,et al.  AFid: A tool for automated identification and exclusion of autofluorescent objects from microscopy images. , 2020, Bioinformatics.

[32]  A. LaCasce,et al.  Spatial Signatures Identify Immune Escape via PD-1 as a Defining Feature of T-cell/Histiocyte-rich Large B-cell Lymphoma. , 2020, Blood.

[33]  Salil S. Bhate,et al.  Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front , 2020, Cell.

[34]  A. Marchevsky,et al.  Pathologists should probably forget about kappa. Percent agreement, diagnostic specificity and related metrics provide more clinically applicable measures of interobserver variability. , 2020, Annals of diagnostic pathology.

[35]  J. Taube,et al.  The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation , 2020, Journal for immunotherapy of cancer.

[36]  J. T. Jørgensen Companion and complementary diagnostics: an important treatment decision tool in precision medicine , 2020, Expert review of molecular diagnostics.

[37]  L. Morrison,et al.  Brightfield multiplex immunohistochemistry with multispectral imaging , 2020, Laboratory Investigation.

[38]  T. Lim,et al.  Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy , 2020, Cancer communications.

[39]  V. Prasad,et al.  Estimation of the Percentage of US Patients With Cancer Who Are Eligible for Immune Checkpoint Inhibitor Drugs , 2020, JAMA network open.

[40]  Mei Jiang,et al.  Procedural Requirements and Recommendations for Multiplex Immunofluorescence Tyramide Signal Amplification Assays to Support Translational Oncology Studies , 2020, Cancers.

[41]  T. Bauer,et al.  Precise Identification of Cell and Tissue Features Important for Histopathologic Diagnosis by a Whole Slide Imaging System , 2020, Journal of pathology informatics.

[42]  Salil S. Bhate,et al.  Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front , 2019, Cell.

[43]  R. Levenson,et al.  New Technologies to Image Tumors. , 2020, Cancer treatment and research.

[44]  Edwin Roger Parra,et al.  Multiplex Immunofluorescence Assays. , 2020, Methods in molecular biology.

[45]  Heeva Baharlou,et al.  AFid: A tool for automated identification and exclusion of autofluorescent objects from microscopy images , 2019, bioRxiv.

[46]  David L Rimm,et al.  Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. , 2019, JAMA oncology.

[47]  Douglas Bowman,et al.  Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association , 2019, Journal of pathology informatics.

[48]  Paul Hofman,et al.  Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer—Just About Ready for Prime-Time? , 2019, Cancers.

[49]  Jaime Rodriguez-Canales,et al.  Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens. , 2019, Journal of visualized experiments : JoVE.

[50]  Timo Kohlberger,et al.  Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection , 2019, Journal of pathology informatics.

[51]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[52]  Philipp Berens,et al.  The art of using t-SNE for single-cell transcriptomics , 2018, Nature Communications.

[53]  B. Besse,et al.  Clinical utility of tumor mutational burden in patients with non-small cell lung cancer treated with immunotherapy. , 2018, Translational lung cancer research.

[54]  A. Ganser,et al.  Signatures of T and B Cell Development, Functional Responses and PD-1 Upregulation After HCMV Latent Infections and Reactivations in Nod.Rag.Gamma Mice Humanized With Cord Blood CD34+ Cells , 2018, Front. Immunol..

[55]  E. Pisano,et al.  What Can Be Done to Improve Research Biopsy Quality in Oncology Clinical Trials? , 2018, Journal of oncology practice.

[56]  M. Fernö,et al.  Stability of oestrogen and progesterone receptor antigenicity in formalin‐fixed paraffin‐embedded breast cancer tissue over time , 2018, APMIS : acta pathologica, microbiologica, et immunologica Scandinavica.

[57]  Z. Werb,et al.  Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity , 2018, Nature Communications.

[58]  Toby C. Cornish,et al.  US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised. , 2018, Archives of pathology & laboratory medicine.

[59]  Ron Kikinis,et al.  Implementing the DICOM Standard for Digital Pathology , 2018, Journal of pathology informatics.

[60]  E. Eisemann,et al.  Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types , 2017, Nature Communications.

[61]  R. Eisen Controls, Fit-for-purpose Assays, Verification Versus Validation, and Tissue Tools for IHC: Announcing a Workshop From the International Society for Immunohistochemistry and Molecular Morphology, Held at the 12th Annual Retreat for Applied Immunohistochemistry and Molecular Morphology, February 4, 2 , 2017, Applied immunohistochemistry & molecular morphology (Print).

[62]  C. Gridelli,et al.  The reproducibility of PD-L1 scoring in lung cancer: can the pathologists do better? , 2017, Translational lung cancer research.

[63]  Tuan Bui,et al.  Multiparametric immune profiling in HPV- oral squamous cell cancer. , 2017, JCI insight.

[64]  E. Hsueh,et al.  Utility of PD-L1 immunohistochemistry assays for predicting PD-1/PD-L1 inhibitor response , 2017, Biomarker Research.

[65]  L. Essioux,et al.  Current Status of Companion and Complementary Diagnostics: Strategic Considerations for Development and Launch , 2017, Clinical and translational science.

[66]  S. Prost,et al.  Choice of Illumination System & Fluorophore for Multiplex Immunofluorescence on FFPE Tissue Sections , 2016, PloS one.

[67]  M. Fassan,et al.  HER2 heterogeneity in gastric/gastroesophageal cancers: From benchside to practice. , 2016, World journal of gastroenterology.

[68]  A. Caliendo,et al.  Point-Counterpoint: The FDA Has a Role in Regulation of Laboratory-Developed Tests , 2016, Journal of Clinical Microbiology.

[69]  S. Nielsen External quality assessment for immunohistochemistry: experiences from NordiQC , 2015, Biotechnic & histochemistry : official publication of the Biological Stain Commission.

[70]  M. Baker Reproducibility crisis: Blame it on the antibodies , 2015, Nature.

[71]  Christopher-Paul Milne,et al.  Complementary versus companion diagnostics: apples and oranges? , 2015, Biomarkers in medicine.

[72]  Clive R. Taylor,et al.  Standardization of Positive Controls in Diagnostic Immunohistochemistry: Recommendations From the International Ad Hoc Expert Committee , 2015, Applied immunohistochemistry & molecular morphology : AIMM.

[73]  Chichung Wang,et al.  Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. , 2014, Methods.

[74]  J. T. Jørgensen,et al.  Companion Diagnostics for Targeted Cancer Drugs – Clinical and Regulatory Aspects , 2014, Front. Oncol..

[75]  Clive R. Taylor,et al.  Standardization of Negative Controls in Diagnostic Immunohistochemistry: Recommendations From the International Ad Hoc Expert Panel , 2014, Applied immunohistochemistry & molecular morphology : AIMM.

[76]  Sabina Sanghera,et al.  ECONOMIC EVALUATIONS AND DIAGNOSTIC TESTING: AN ILLUSTRATIVE CASE STUDY APPROACH , 2013, International Journal of Technology Assessment in Health Care.

[77]  C. Isacke,et al.  Multiple immunofluorescence labeling of formalin-fixed paraffin-embedded tissue. , 2011, Methods in molecular biology.

[78]  D. Atha,et al.  Standards for Immunohistochemical Imaging: A Protein Reference Device for Biomarker Quantitation , 2010, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[79]  R. Herbert,et al.  Useful Immunohistochemical Markers of Tumor Differentiation , 2010, Toxicologic pathology.

[80]  A. Šimundić Measures of Diagnostic Accuracy: Basic Definitions , 2009, EJIFCC.

[81]  Clifford Hoyt,et al.  Visualization of Microscopy‐Based Spectral Imaging Data from Multi‐Label Tissue Sections , 2008, Current protocols in molecular biology.

[82]  C. Loos Multiple Immunoenzyme Staining: Methods and Visualizations for the Observation With Spectral Imaging , 2008 .

[83]  D. Rimm What brown cannot do for you , 2006, Nature Biotechnology.

[84]  Richard M Levenson,et al.  Spectral imaging perspective on cytomics , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[85]  S. Taube,et al.  Standard Reference Material for Her2 Testing: Report of a National Institute of Standards and Technology-sponsored Consensus Workshop , 2003, Applied immunohistochemistry & molecular morphology : AIMM.

[86]  M. Bobrow,et al.  Tyramide Signal Amplification (TSA) Systems for the Enhancement of ISH Signals in Cytogenetics , 2000, Current protocols in cytometry.

[87]  G. Timberlake,et al.  Feature-Based Registration of Retinal Images , 1987, IEEE Transactions on Medical Imaging.

[88]  G. B. Pierce,et al.  ENZYME-LABELED ANTIBODIES: PREPARATION AND APPLICATION FOR THE LOCALIZATION OF ANTIGENS , 1966, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.