Introduction

A common goal in cancer research and pharmaceutical drug development is to reveal correlations between signaling pathway activity and clinical outcome. Correlations support target validation, trial design, patient selection, response assessment, and, if trials are successful, the diagnostic component of theranostics. However, the predictive power of measurements of protein expression depends on the precision and accuracy of tissue analysis tools. For example, many techniques

deployed today, such as those based on immunohistochemistry (IHC) and micro-array detection, or analysis of sample lysates, provide data that are averages from volumes of tissue, including many cells not of interest. These methods blur out key proteomic information that resides at the cellular level, relating to the signaling states of individual cells.2

Get Specific

Until now, classification of disease and the assessment of therapeutic response have been based largely on molecular information from individual tissue sections, one protein at a time. The sections are usually stained with hematoxylin and an IHC stain, and manually assessed using the visual acuity of a pathologist. These techniques provide only average values for whole sections, for one signaling protein at a time. What’s needed is information of multiple proteins at a time, within individual cells.

Quantitative Pathology Imaging & Analysis

A P P L I C A T I O N N O T E

Analyzing Signaling Pathway Activity in FFPE Tissue Sections for Cancer Research and Drug Development

2

Technology now offers the opportunity to access this level of information, by utilizing an effective, practical and reliable platform for cytometric analysis of intact tissue sections, or ‘tissue cytometry’. The platform supports preclinical and clinical studies through the integration of multiplexed immunofluorescence (IF) labeling strategies, robotic slide handling, and automated multispectral image acquisition and analysis. PerkinElmer’s Nuance multiplex biomarker and Vectra quantitative pathology imaging systems and inForm advanced image analysis software together provide the ideal platform for this application.

During the course of tumor progression, cancer cells acquire characteristics that reflect alterations in the signal transduction pathways that in normal cells control cell proliferation, motility, and survival. Many of the proteins currently under investigation as possible targets for cancer therapy are involved in these signaling pathways. The mechanisms and roles of these pathways in tumorigenesis are the basis of much of the progress made recently in the scientific understanding of cancer and in the development of cancer drugs by pharmaceutical companies. In this example, the goal is to provide a general purpose signaling activity detection kit, to assess the AKT, ERK, and S6 pathways.

Automated Multiplexed Tissue Cytometry

The ideal imaging platform integrates: a) easy-to-implement multiplexed immunofluorescence staining protocols, b) an automated slide analysis system (PerkinElmer’s Vectra) that utilizes multispectral imaging to isolate marker signals from one another and from autofluorescence, and c) a pattern-recognition-based image-analysis software (inForm®) for automatically segmenting images and extracting data from cells-of-interest.

Figure 1. Illustration of signaling pathways in a cell: in this case for apoptotic signaling in a normal cell.1 Reproduced from the University of Delaware. Figure 2. Residual clinical tissue samples in a tissue micro array (TMA) are

used in order to confirm that staining is specific, and labels can be reliably unmixed from autofluorescence. Row A: raw color images of two TMA cores, Row B: same with autofluorescence unmixed away to reveal label signals, Row C: with inForm tissue and cell segmentations, and Item D: a ‘tissue cytometry’ scatter plot showing relatively expression of two signaling pathway proteins, on a per-cell basis. Samples courtesy Novartis Institutes of Biomedical Research, Cambridge, MA, U.S.3

Validation of Specificity of Staining and Spectral Unmixing of Autofluorescence

pS6 vs pERK

180

160

140

120

100

80

60

40

20

00 50 100 150 200 250 300 350 400 450

pERK (Alexa 555)

pS6

(Ale

xa 6

47)

A

B

C

D

3

Multispectral Imaging Separates Multiple Markers in the Same Tissue

Quantitative, independent, and specific multi-label protocols have been developed that, in conjunction with easy-to-use multispectral imaging systems and advanced learn-by-example software, can greatly accelerate clinical and pre-clinical studies.4

Multispectral imaging captures information from many narrow wavelength bands instead of simply one broad band for each fluorescence emission filter. This added information enables the isolation of label emissions from often obscuring autofluorescence, which is a common problem with formalin-fixed paraffin-embedded tissue sections, and from other label emissions, even if they are spatially and spectrally overlapping.

Conventional fixed-bandpass filter approaches cannot do this. Multispectral imaging capability comes in two levels of product – the award winning Nuance, which is a manual system that can be mounted on research-grade microscopes, and the Vectra, which is a fully integrated system consisting of robotic slide handling, automated image acquisition, and tools for automated image analysis. PerkinElmer’s Vectra system can acquire images and analyze 200 slides at a time once it has been trained with a small number of representative images. Like the Nuance system, it can be used for both brightfield and fluorescence imaging.

The powerful learn-by-example image analysis algorithms in PerkinElmer’s inForm software provides trainable tissue segmentation, a key component of automated image analysis and data extraction. The software can be trained to differentiate relevant tissue regions (e.g., malignant and normal epithelia, stroma, necrosis, etc.) and segment cellular compartments (nuclei, cytoplasm, and membrane) to allow for detailed, spatially resolved multiparameter quantitation.

References

1. Illustration obtained from the University of Delaware website, www.udel.edu/~apickard/Cancer%20Website_files/basics.htm

2. Multispectral imaging and pathology: seeing and doing more. Expert Opin. Med. Diagn. (2008) 2(9):1067-1081, Richard M Levenson, Alessandro Fornari & Massimo Loda

3. Tissue cytometry platform for quantitating multiple signaling pathways proteins in intact tissue sections. Poster at 2009 AACR-NCI-EORTC conference, Clifford C. Hoyt*, Randall K. Wetzel**, David Yang‡, J. Carl Barrett‡, Humphrey Gardner‡.

*PerkinElmer, Inc., Hopkinton, MA, U.S.

**Cell Signaling Technology, Danvers, MA, U.S.

‡Novartis Institutes for Biomedical Research, Cambridge, MA, U.S.

For this particular study, a staining panel was developed, targeting phosphoeptitopes of AKT, ERK, and S6, using antibodies of three different isotypes, with secondaries conjugated to Alexa fluorophores (A488, A555, and A647). DAPI was used as a counterstain.3

Staining protocols were optimized using tissue microarrays (TMA) generated from lung tissue samples prepared using methods designed to preserve phosphoepitopes. Spectral unmixing libraries were generated with single-stained control samples. Image analysis algorithms were trained to segment tissue regions (e.g., malignant and normal epithelia, stroma, necrosis, etc.) and then cells and cell compartments within tumor regions, to extract per-cell data for cytological analysis. Validation of the platform’s ability to quantitate changes of phosphoepitope expression was performed with cell blocks of cell lines treated with the relevant inhibitors.

Pilot studies of the TMA and cell lines reveal robust and specific signal levels, localized to tissue and cellular structures appropriate for the target molecules, despite being applied in four-plex. Pattern-recognition-based, automated image analysis algorithms reliably detected tumor cells and segmented associated cellular compartments, after having been trained on less than 10% of pilot study images. Tissue segmentation accuracy was estimated at greater than 90%, based on visual review by pathologists.

Figure 3. Spectral unmixing explained using examples of four-component fluorescence unmixing.3

Nuance Multispectral

ImagingSystems

Color (RGB) Representation

of Spectral Cube

Spectra of Labels

Image Acquisition

Unmixing of Overlapping Fluorophores using Pure Component Spectra

Color (RGB) Representation

of Spectral Cube

Label A Label C

Label DLabel B

Once unmixed, labels can be measured accurately.

Wavelength

Spectral Unmixing

Figure 4. Images of antibody validation tests with a muscle myoblast cell line. The cell line (C2C12) was treated with compounds to express combinations of pAKT, pERK, and pS6. Figure courtesy Cell Signaling Technology, Danvers, MA, U.S.3

For a complete listing of our global offices, visit www.perkinelmer.com/ContactUs

Copyright ©2013-2014, PerkinElmer, Inc. All rights reserved. PerkinElmer® is a registered trademark of PerkinElmer, Inc. All other trademarks are the property of their respective owners. 010905_01

PerkinElmer, Inc. 940 Winter Street Waltham, MA 02451 USA P: (800) 762-4000 or (+1) 203-925-4602www.perkinelmer.com

For more information, please visit our website at www.perkinelmer.com/vectra

PerkinElmer’s Tissue Imaging Portfolio

Nuance® multispectral imaging systems for quantitative, multiple-marker analysis in immunohistochemistry and immuno- fluorescence microscopy.

Vectra® slide analysis system for automated high-throughput slide imaging and analysis, the world’s only multispectral fluorescence- and brightfield-capable system.

Untreated bFGF Insulin TPA LY/U0126/RapapS

6 (S

235/

236)

pER

K

(T20

2/Y2

04)

pAK

T

(T30

8)M

erge

Acquisition Manual Automated*

Imaging Mode Multimodal Brightfield Immunohistochemistry (IHC) and Immunofluorescence (IF)

Optical Imaging Mounts to any existing microscope Integrated

Wavelength Range 420-720 nm or 450-950 nm 420-720 nm

inForm Software Optional Included

*maximum capacity of 200 slides