Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer

Abstract

The majority of the targeted therapeutic agents in clinical use target proteins and protein function. Although DNA and RNA analyses have been used extensively to identify novel targets and patients likely to benefit from targeted therapies, these are indirect measures of the levels and functions of most therapeutic targets. More importantly, DNA and RNA analysis is ill-suited for determining the pharmacodynamic effects of target inhibition. Assessing changes in protein levels and function is the most efficient way to evaluate the mechanisms underlying sensitivity and resistance to targeted agents. Understanding these mechanisms is necessary to identify patients likely to benefit from treatment and to develop rational drug combinations to prevent or bypass therapeutic resistance. There is an urgent need for a robust approach to assess protein levels and protein function in model systems and across patient samples. While "shot gun" mass spectrometry can provide in-depth analysis of proteins across a limited number of samples, and emerging approaches such as multiple reaction monitoring have the potential to analyze candidate markers, mass spectrometry has not entered into general use because of the high cost, requirement of extensive analysis and support, and relatively large amount of material needed for analysis. Rather, antibody-based technologies, including immunohistochemistry, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), and more recently protein arrays, remain the most common approaches for multiplexed protein analysis. Reverse-phase protein array (RPPA) technology has emerged as a robust, sensitive, cost-effective approach to the analysis of large numbers of samples for quantitative assessment of key members of functional pathways that are affected by tumor-targeting therapeutics. The RPPA platform is a powerful approach for identifying and validating targets, classifying tumor subsets, assessing pharmacodynamics, and identifying prognostic and predictive markers, adaptive responses and rational drug combinations in model systems and patient samples. Its greatest utility has been realized through integration with other analytic platforms such as DNA sequencing, transcriptional profiling, epigenomics, mass spectrometry, and metabolomics. The power of the technology is becoming apparent through its use in pathology laboratories and integration into trial design and implementation.

Keywords: Biomarker; Drug development; Pharmacodynamic; Proteomics; RPPA.

Figure 1

(A) An RPPA slide for measuring a single protein in a set of samples. (B) Magnified view of the slide showing its layout based on the MD Anderson RPPA core specifications. Each slide can accommodate up to 1056 samples and 96 controls. (C) The samples and controls are printed in a 5-step dilution series, with 2-fold dilution at each step, in grids of 11 × 11 spots. Each of the 48 grids can accommodate up to 22 samples and 2 controls. (Reproduced from Tabchy et. al. Copyright © 2011–2014 Prous Science, S.A.U. or its licensors. All rights reserved.)

Figure 2

(A) A single unified intensity vs. relative concentration curve output from SuperCurve that uses spots from all the samples. (B) A map of the slide showing spot residual values that deviate from the curve in part A. Dark green represents near zero residual; white represents large residuals.

Figure 3

Response to the drug lapatinib (IC₅₀ values capped at 8.0) vs. phospho-EGFR expression in lung cancer cell lines. IC₅₀ is inversely correlated with phospho-EGFR expression (Pearson correlation = −0.465, P-value = 0.00077), indicating that cell lines with higher expression of phospho-EGFR are more sensitive to the drug. The line of best fit is shown in red. Correlation with these previously-known findings further validate the data produced by the RPPA platform and the methods described.

Figure 4

An example of the PI3K signaling network elucidated by RPPA from a TCGA cohort of 3,467 tumor samples across 11 lineages. Interplay between proteins was quantified using scores from a probabilistic graphical model analysis that identified links between proteins. Only the strongest links are shown. The color of a link indicates tumor lineage, which is specified by the standard TCGA disease acronym. Green nodes are individual proteins; white nodes are related proteins that were highly correlated and therefore merged prior to network analysis. Positive (negative) correlations are indicated with continuous (dotted) lines. The graph shows that correlations between proteins and their associated pathways are highly lineage dependent. Only a handful of proteins are shown to be correlated across virtually all tumor lineages (Adapted from Akbani et al.).

Figure 5

(A) Clustered heat map of TCGA breast cancer RPPA data with samples in columns and proteins in rows. Five different clusters can be seen. The clusters are associated with PAM50 calls, HER2 amplification status, TP53 mutation, and PIK3CA mutation status (p < 0.001, χ² test). Some known biomarkers can be seen to be differentially expressed between the clusters, such as HER2, phosphoHER2, PR, AR, ER-alpha, and phosphoER-alpha. A newly discovered “Reactive” subtype (cluster 5) based on RPPA data can also be seen with biomarkers MYH11, Caveolin1, and Collagen6. (B) Kaplan-Meier survival curves for the 5 clusters. The Reactive subtype (in dark blue) has good outcome (overall p = 0.005). (Heat map dynamically explorable at: http://bioinformatics.mdanderson.org/TCGA/NGCHMPortal/)