Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry.

PURPOSE: Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation. EXPERIMENTAL DESIGN: Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling. RESULTS: Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression. CONCLUSIONS: Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

Standardization of HER2 immunohistochemistry in breast cancer by automated quantitative analysis.

CONTEXT: There is critical need for standardization of HER2 immunohistochemistry testing in the clinical laboratory setting. Recently, the American Society of Clinical Oncology and the College of American Pathologists have submitted guidelines recommending that laboratories achieve 95% concordance between assays and observers for HER2 testing. OBJECTIVE: As a potential aid to pathologists for achieving these new guidelines, we have conducted an examination using automated quantitative analysis (AQUA analysis) to provide a standardized HER2 immunohistochemistry expression score across instruments (sites), operators, and staining runs. DESIGN: We analyzed HER2 expression by immunohistochemistry in a cohort (n = 669) of invasive breast cancers in tissue microarray format across different instruments (n = 3), operators (n = 3), and staining runs (n = 3). Using light source, instrument calibration techniques, and a new generation of image analysis software, we produced normalized AQUA scores for each parameter and examined their reproducibility. RESULTS: The average percent coefficients of variation across instruments, operators, and staining runs were 1.8%, 2.0%, and 5.1%, respectively. For positive/negative classification between parameters, concordance rates ranged from 94.5% to 99.3% for all cases. Differentially classified cases only occurred around the determined cut point, not over the entire distribution. CONCLUSIONS: These data demonstrate that AQUA analysis can provide a standardized HER2 immunohistochemistry test that can meet current guidelines by the American Society of Clinical Oncology/College of American Pathologists. The use of AQUA analysis could allow for standardized and objective HER2 testing in clinical laboratories.

Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated QUantitative Analysis.

Inherent to most tissue image analysis routines are user-defined steps whereby specific pixel intensity thresholds must be set manually to differentiate background from signal-specific pixels within multiple images. To reduce operator time, remove operator-to-operator variability, and to obtain objective and optimal pixel separation for each image, we have developed an unsupervised pixel-based clustering algorithm allowing for the objective and unsupervised differentiation of signal from background, and differentiation of compartment-specific pixels on an image-by-image basis. We used the Automated QUantitative Analysis (AQUA) platform, a well-established automated fluorescence-based immunohistochemistry image analysis platform used for quantification of protein expression in specific cellular compartments to demonstrate utility of this methodology. As a metric for cellular compartmentalization, we examined correlation of percentage nuclear volume with histologic grade in 3 serial sections of a large cohort (n=669) of invasive breast cancer samples. We observed a significant (P=0.002, 0.006, and 0.08) difference in mean percentage nuclear volume between low and high-grade tumors. Reproducibility of percentage nuclear volume was also significant (P<0.001) across 3 serial sections. We then quantified compartment-specific expression of 5 biomarkers in 3 cancer types for association with outcome: estrogen receptor (nuclear), progesterone receptor (nuclear), HER2 (membrane/cytoplasm), ERCC1 (nuclear), and PTEN (cytoplasm). All 5 markers showed an expected and significant (P<0.05) association with survival. This new clustering algorithm thus produces accurate and precise compartmentalization for assessment of target gene expression, and will enhance the efficiency and objectivity of the current Automated QUantitative Analysis and other image analysis platform.