BIOMARKER-BASED CANCER THERAPY PERSONALIZATION

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Abstract

The efficacy of cancer treatments varies significant from case to case, even if their tumors are categorized into the same histological subtypes and stages. Thus it would be necessary to identify a biomarker that could provide evidence about the probability of benefit or toxicity from a specific therapeutic intervention. EGFR gene mutation, for example, is a predictive biomarker for epidermal growth factor receptor tyrosine kinase inhibitors, but its identification is a rare successful case and only a limited number of predictive biomarkers are currently available for cancer therapeutics. Technologies such as microarray, high-speed sequencing, and mass spectrometry have advanced rapidly in recent years, allowing comprehensive analyses of the cancer genome and proteome. It is anticipated that the application of these technologies will contribute to the discovery of new cancer biomarkers. Antibody-based proteomics is a concept that emerged in alliance with the progression of genome-wide antibody production projects. The completion of proteome-scale antibody libraries is expected to enable not only rapid verification of biomarkers, but also direct screening of antibodies by high-throughput assays. Here we report new methods that can screen antibodies on the basis of their reactivity with a large number of patient samples with reasonable comprehensiveness and throughput: automated quantification of immunofluorescence tissue microarray and immunofluorescence reverse-phase protein microarray (RPPM). Using these antibody-based proteomics technologies, we were able to identify biomarkers that can predict the efficacy of combinational chemotherapy, radiotherapy, and molecular targeting therapy. We also discuss the potential application of the antibody-based proteomics technologies to early-phase drug development.

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