Implications of complement activation in the efficacy of the monoclonal antibody cetuximab against lung cancer cells

  1. Hsu, Yi-Fan
Supervised by:
  1. Rubén Pío Osés Director

Defence university: Universidad de Navarra

Fecha de defensa: 04 October 2010

Committee:
  1. José Luis González Larriba Chair
  2. Jesús García-Foncillas López Secretary
  3. María Mercedes Garayoa Berrueta Committee member
  4. Sergio Portal Núñez Committee member
  5. Alfonso Calvo González Committee member
Department:
  1. (FC) Bioquímica y Genética

Type: Thesis

Teseo: 111409 DIALNET

Abstract

Background: Cetuximab, an antibody targeting the epidermal growth factor receptor (EGFR), increases survival in patients with advanced EGFR-positive non-small cell lung cancer when administrated in combination with chemotherapy. In this study, we investigated the role of complement activation in the antitumor mechanism of this therapeutic drug. Results: EGFR-expressing lung cancer cell lines were able to bind cetuximab and initiate complement activation by the classical pathway, irrespective of the mutational status of EGFR. This activation led to deposition of complement components and increase in complement-mediated cell death. The influence of complement activation on the activity of cetuximab in vivo was evaluated in xenografts of A549 lung cancer cells on nude mice. A549 cells express wild-type EGFR and have a KRAS mutation. Cetuximab activity against A549 xenografts was highly dependent on complement activation, since complement depletion completely abrogated the antitumor efficacy of cetuximab. Moreover, cetuximab activity was significantly higher on A549 cells in which a complement inhibitor, factor H, was genetically downregulated. Conclusions: We demonstrate for the first time that the in vivo antitumor activity of cetuximab can be associated with a complement-mediated immune response. These results may have important implications for the development of new cetuximab-based therapeutic strategies and for the identification of markers that predict clinical response.