Classification of prostate cancer based on clinical and omics data using neural networks techniques to improve prognostic power.

Abstract

e16569 Background: In 2017, prostate cancer (PCa) was the second most common cancer in men after lung cancer. While there are different courses of action to treat the disease, its mortality in Peru is higher than 50%. Conventionally, PCa is diagnosed by evaluating tissue biopsies, and classified according to the Gleason grading system. Novel molecular classifications of PCa have been proposed for diagnostic and prognostic purposes. The main goal of this work is to implement a tool predicting the disease free time of patient according to the genomic expression and highlight the genes playing an influential role on the prediction. Methods: Modern techniques to classify data keep getting broader and more accurate, in particular with the introduction of Neural Networks(NN). We implement an Artificial Neural Network automatic genomic classification strategy based on a Local Interpretable Model-Agnostic Explanations (LIME) algorithm because it allows the network to choose the features of major discriminative significance. As a proof-of-concept, we selected a sub-set of 3530 genes related to recurrence from 499 PCa genomes to build the neural networks. Results: The resulting neural network, trained and tested on cancer cell 2010 database and validate on the MSKCC data the can predict the time of recurrence within a range of three months based on the genomic expression with an accuracy of 96,9% and a loss of less than 9%. Using the implemented LIME algorithm, our results indicate that this subset of genes is informative of recurrence and plays a substantial role in the prediction. Conclusions: Instead of using a classic fully connected layer, we implemented different types of Deep Learning networks where the final network provides the predicted survival rate or time to recurrence. This information will allow the doctors to propose the best course of treatment. Our method is able to generate an augmented score, enabling a more accurate evaluation of risk and personalized treatment strategy