Biomarkers for immunostimulatory monoclonal antibodies in combination strategies for melanoma and other tumor types Academic Article uri icon

Overview

MeSH Major

  • Antibodies, Monoclonal
  • Biomarkers
  • Cancer Vaccines
  • Immunization
  • Immunologic Factors
  • Melanoma
  • Neoplasms

abstract

  • Modulation of the immune system by targeting coinhibitory and costimulatory receptors has become a promising new approach of immunotherapy for cancer. The recent approval of the CTLA-4-blocking antibody ipilimumab for the treatment of melanoma was a watershed event, opening up a new era in the field of immunotherapy. Ipilimumab was the first treatment to ever show enhanced overall survival (OS) for patients with stage IV melanoma. However, measuring response rates using standard Response Evaluation Criteria in Solid Tumors (RECIST) or modified World Health Organization criteria or progression-free survival does not accurately capture the potential for clinical benefit for ipilimumab-treated patients. As immunotherapy approaches are translated into more tumor types, it is important to study biomarkers, which may be more predictive of OS to identify the patients most likely to have clinical benefit. Ipilimumab is the first-in-class of a series of immunomodulating antibodies that are in clinical development. Anti-PD1 (nivolumab and MK-3475), anti-PD-L1 (BMS-936 559, RG7446, and MEDI4736), anti-CD137 (urelumab), anti-OX40, anti-GITR, and anti-CD40 monoclonal antibodies are just some of the agents that are being actively investigated in clinical trials, each having the potential for combination with the ipilimumab to enhance its effectiveness. Development of rational combinations of immunomodulatory antibodies with small-molecule pathway inhibitor therapies such as vemurafenib makes the discovery of predictive biomarkers even more important. Identifying reliable biomarkers is a necessary step in personalizing the treatment of each patient's cancer through a baseline assessment of tumor gene expression and/or immune profile to optimize therapy for the best chance of therapeutic success.

publication date

  • March 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

Digital Object Identifier (DOI)

  • 10.1158/1078-0432.CCR-12-2982

PubMed ID

  • 23460532

Additional Document Info

start page

  • 1009

end page

  • 20

volume

  • 19

number

  • 5