This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2012
Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
- Equal contributors
1 Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy
2 Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa 16149, Italy
3 Department of Human Genetics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, Amsterdam 1100, The Netherlands
4 Department of Pediatric Oncology and Hematology, University Children's Hospital Essen, Hufelandstr. 55, Essen 45122, Germany
5 Department of Hematology-Oncology, Gaslini Institute, Largo Gaslini 5, Genoa 16147, Italy
6 Departments of Pediatric Pathology, Gaslini Institute, Largo Gaslini 5, Genoa 16147, Italy
BMC Bioinformatics 2013, 14(Suppl 7):S12 doi:10.1186/1471-2105-14-S7-S12Published: 22 April 2013
Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting.
Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging.
The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.