  Background Precise prediction of cancer types is vital for cancer diagnosis
and therapy. Important cancer marker genes can be inferred through predictive
model. Several studies have attempted to build machine learning models for this
task however none has taken into consideration the effects of tissue of origin
that can potentially bias the identification of cancer markers. Results In this
paper, we introduced several Convolutional Neural Network (CNN) models that
take unstructured gene expression inputs to classify tumor and non-tumor
samples into their designated cancer types or as normal. Based on different
designs of gene embeddings and convolution schemes, we implemented three CNN
models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and
tested on combined 10,340 samples of 33 cancer types and 731 matched normal
tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent
prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal).
Furthermore, we interpreted one of the models, known as 1D-CNN model, with a
guided saliency technique and identified a total of 2,090 cancer markers (108
per class). The concordance of differential expression of these markers between
the cancer type they represent and others is confirmed. In breast cancer, for
instance, our model identified well-known markers, such as GATA3 and ESR1.
Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes
and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be
found at https://github.com/chenlabgccri/CancerTypePrediction.
