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Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.

Development of a PD-L1 multiplex immunofluorescence assay with advanced visual analysis for understanding the tumor microenvironment.

Sub-category:
Tissue-Based Biomarkers

Category:
Developmental Immunotherapy and Tumor Immunobiology

Meeting:
2019 ASCO Annual Meeting

Abstract No:
e14283

Citation:
J Clin Oncol 37, 2019 (suppl; abstr e14283)

Author(s): Sean Downing, Aditi Sharma, Courtney Hebert, Jamie Buell, Douglas Wood; PerkinElmer, Inc., Hopkinton, MA; Ultivue, Inc, Cambridge, MA; Ultivue, Cambridge, MA; Ultivue, Inc., Cambridge, MA

Abstract Disclosures

Abstract:

Background: Current PD-L1 Immunohistochemistry (IHC) assays utilizing conventional brightfield, chromogenic 3,3′-Diaminobenzidine (DAB) staining modalities are the norm in both translational and clinical research. With the rise of immuno-oncology, multiplexed immunofluorescence (mIF) has the potential to revolutionize pathology research as it enables the identification of complex cell phenotypes and their potential interactions in the tumor microenvironment (TME). Ultivue has developed a mIF assay for profiling PD-L1, CD8, CD68, pan-Cytokeratin, and Sox10 enabling whole-slide imaging and data analysis in a single workday. Methods: With an expanding number of biomarkers being interrogated, the complexity of the data analysis and visualization tasks increase exponentially. For N markers, a total of 2N phenotypes are possible. To address this problem and reveal the biologically relevant information embedded in the data, we have developed software tools to reduce the complexity, visualize, and quantify spatial distributions of cells across the full spectrum of possible phenotypes. In this study we demonstrate the utility, robustness, and ability to derive meaningful biological insights by validating the accuracy, precision, sensitivity, and specificity of the PD-L1 multiplex immunofluorescence assay. Results: Here we present results that identify all distinct binary phenotypes within cohorts of lung cancer and melanoma samples using qualitative pathology review and an image processing technique called Phenotypic Surface Density Mapping (PSDM). Important features of this quantitative technique are robustness, use of real physical units (e.g. cells/µm2 or intensity/µm2), and data generation in an unbiased fashion to reveal information about every possible phenotype. Conclusions: Applying the pathology review and PSDM to the cohorts of samples confirmed a high level of concordance demonstrating sensitivity and specificity with high degrees of confidence above 85%. Analysis of intra-run, inter-run, and inter-technician runs revealed that cell counts were within a CV less than 15% across all markers. Data presented here indicates the potential for use in a high throughput testing laboratory to aid in collection and interpretation of meaningful biological insights.

 
Other Abstracts in this Sub-Category:

 

1. Phase I trial of the combination of the heat shock protein-90 inhibitor onalespib (AT13387) and the cyclin-dependent kinase inhibitor AT7519M in patients with advanced solid tumors.

Meeting: 2019 ASCO Annual Meeting Abstract No: 2619 First Author: Khanh Tu Do
Category: Developmental Immunotherapy and Tumor Immunobiology - Tissue-Based Biomarkers

 

2. Non-small cell lung cancer (NSCLC) next generation sequencing (NGS) using the Oncomine Comprehensive Assay (OCA) v3: Integrating expanded genomic sequencing into the Canadian publicly funded health care model.

Meeting: 2019 ASCO Annual Meeting Abstract No: 2620 First Author: Kirstin Perdrizet
Category: Developmental Immunotherapy and Tumor Immunobiology - Tissue-Based Biomarkers

 

3. Overestimation of tumor mutational burden (TMB) using algorithms compared to germline subtraction.

Meeting: 2019 ASCO Annual Meeting Abstract No: 2621 First Author: Kaushal Parikh
Category: Developmental Immunotherapy and Tumor Immunobiology - Tissue-Based Biomarkers

 

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