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Cancer. 2012 Mar 15;118(6):1543-53. doi: 10.1002/cncr.26449. Epub 2011 Aug 25.

Adhesion molecule protein signature in ovarian cancer effusions is prognostic of patient outcome.

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Molecular Signaling Section, Medical Oncology Branch, National Cancer Institute, Bethesda, Maryland, USA.



Ovarian cancer cells in malignant effusions lack attachment to solid-phase matrix substrata and receive survival stimuli through cell-cell and cell-soluble matrix molecule interactions. We hypothesized that adhesion-related survival and proliferation pathway signals can inform clinical outcomes and guide targeted therapeutics.


Lysed cell pellets from a blinded set of benign (n = 20) and malignant (n = 51) peritoneal and pleural ovarian cancer patient effusions were applied to reverse-phase protein arrays and examined using validated antibodies to adhesion-associated protein endpoints. Results were subjected to hierarchical clustering for signature development. Association between specimen type, protein expression, and clinicopathologic associations were analyzed using the Mann-Whitney U test. Survival outcomes were estimated using the Kaplan-Meier method with log-rank comparison.


A cell adhesion protein signature obtained from unsupervised clustering distinguished malignant from benign effusions (P = 6.18E-06). Protein subset analyses from malignant cases defined 3 cell adhesion protein clusters driven by E-cadherin, epithelial cell adhesion molecule, and N-cadherin, respectively. The components of the E- and N-cadherin clusters correlated with clinical outcome by Kaplan-Meier statistics. Univariate analysis indicated that FAK and phosphorylated AKT were associated with higher overall and progression-free survival (PFS) (P = .03), and Akt, phosphorylated paxillin, and E- and N-cadherin were associated with improved PFS (P ≤ .05). If 4 or 5 of the index adhesion proteins were high, PFS was improved by multivariate analysis (P ≤ .01).


This hypothesis-testing examination of tumor cell adhesion molecules and pathways yielded potential predictive biomarkers with which to triage patients to selected molecular therapeutics and may serve as a platform for biomarker-based stratification for clinical application.

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