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Assessment of Dendritic Cell Therapy Effectiveness Based on the Feature Extraction from Scientific Publications

Dendritic cells (DCs) vaccination is a promising way to contend cancer metastases especially in the case of 
 
immunogenic tumors. Unfortunately, it is only rarely possible to achieve a satisfactory clinical outcome in 
 
the majority of patients treated with a particular DC vaccine. Apparently, DC vaccination can be successful 
 
with certain combinations of features of the tumor and patients immune system that are not yet fully revealed. 
 
Difficulty in predicting the results of the therapy and high price of preparation of individual vaccines prevent 
 
wider use of DC vaccines in medical practice. Here we propose an approach aimed to uncover correlation 
 
between the effectiveness of specific DC vaccine types and personal characteristics of patients to increase 
 
efficiency of cancer treatment and reduce prices. To accomplish this, we suggest two-step analysis of published 
 
clinical trials results for DCs vaccines: first, the information extraction subsystem is trained, and, second, the 
 
extracted data is analyzed using JSM and AQ methodology.