A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib - PubMed (original) (raw)

. 2012 Jan;109(1):213-24.

doi: 10.1002/bit.23297. Epub 2011 Aug 23.

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A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Rexxi D Prasasya et al. Biotechnol Bioeng. 2012 Jan.

Abstract

Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.

Copyright © 2011 Wiley Periodicals, Inc.

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Figures

Figure 1

Figure 1

(A) Overview of the ErbB network. Arrows indicate the potential autocrine interactions between ligands and receptors. ErbB2 has no known ligand while ErbB3 has minimal kinase activity. (B) Overview of PLSR modeling, where the X matrix composed of receptor and ligand levels is regressed against the Y matrix of CI-1033 sensitivity.

Figure 2

Figure 2

Levels of ErbB1-4 in the serous EOC cell lines panel, relative to concurrently run master lysate. N/D indicates non-detectable level.

Figure 3

Figure 3

ErbB autocrine loop characterization. (A) Screen of ErbB1 and ErbB3 phosphorylation under serum-starved conditions. pErbB1 positive control is OVCA433 treated with 100 ng/mL of EGF for 10 minutes prior to lysing. pErbB3 positive control is OVCA432 treated with 100 ng/mL of NRG1-β for 10 minutes prior to lysing. (B) Levels of autocrine ErbB ligands in EOC cell lysates.

Figure 4

Figure 4

Representative CI-1033 cytotoxicity dose-response curves. (A) OVCAR5, (B) OVCA433.

Figure 5

Figure 5

A multi-protein model is needed to predict cell line cytotoxicity to CI-1033. (A) Full PLSR model of CI-1033 cytotoxicity, and (B) reduced CI-1033 cytotoxicity models built using subsets of proteins. N/A indicates inability to build a model using that protein subset.

Figure 6

Figure 6

Expression of ErbB1-4 in EOC cell lines in the prediction set. The grey box represents the data range included in the original full PLSR model of CI-1033 cytotoxicity.

Figure 7

Figure 7

ErbB autocrine ligands in the EOC cell lines in the prediction set. The grey box represents the data range included in the original full PLSR model of CI-1033 cytotoxicity.

Figure 8

Figure 8

Model predictions of additional EOC cell lines. (A) Original full PLSR model, (B) full PLSR model with expanded ErbB2 range, (C) reduced PLSR model excluding ErbB2. Note that due to the inclusion of SKOV-3 in the training set of (B), that model has only 5 members in the prediction set.

Figure 9

Figure 9

Top nine reduced models. (A) Subsets of proteins used in the top nine models, shaded box indicates the protein was included in the X matrix. (B) Representative predictions of a reduced CI-1033 cytotoxicity model (X matrix composed of ErbB1, NRG1-β, and TGFα).

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