Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients

A cut-off of 5 circulating tumor cells (CTCs) per 7.5 ml of blood in metastatic breast cancer (MBC) patients is highly predictive of outcome. We analyzed the relationship between CTCs as a continuous variable and overall survival in immunohistochemically defined primary tumor molecular subtypes using an artificial neural network (ANN) prognostic tool to determine the shape of the relationship between risk of death and CTC count and to predict individual survival. We analyzed a training dataset of 311 of 517 (60%) consecutive MBC patients who had been treated at MD Anderson Cancer Center from September 2004 to 2009 and who had undergone pre-therapy CTC counts (CellSearch(A (R))). Age; estrogen, progesterone receptor, and HER2 status; visceral metastasis; metastatic disease sites; therapy type and line; and CTCs as a continuous value were evaluated using ANN. A model with parameter estimates obtained from the training data was tested in a validation set of the remaining 206 (40%) patients. The model estimates were accurate, with good discrimination and calibration. Risk of death, as estimated by ANN, linearly increased with increasing CTC count in all molecular tumor subtypes but was higher in ER+ and triple-negative MBC than in HER2+. The probabilities of survival for the four subtypes with 0 CTC were as follows: ER+/HER2- 0.947, ER+/HER2+ 0.959, ER-/HER2+ 0.902, and ER-/HER2- 0.875. For patients with 200 CTCs, they were ER+/HER2- 0.439, ER+/HER2+ 0.621, ER-/HER2+ 0.307, ER-/HER2- 0.130. In this large study, ANN revealed a linear increase of risk of death in MBC patients with increasing CTC counts in all tumor subtypes. CTCs' prognostic effect was less evident in HER2+ MBC patients treated with targeted therapy. This study may support the concept that the number of CTCs, along with the biologic characteristics, needs to be carefully taken into account in future analysis.