Identifying Cross-Cancer Similar Patients via a Semi-Supervised Deep Clustering Approach
Ay, D.; Tastan, O.
Show abstract
The treatment decisions for a cancer patient are typically based on the patients diagnosed cancer type. With the characterization of cancer tumors at the molecular level, there have been reports of patients being similar despite being diagnosed with different cancer types. Motivated from these observations, we aim at discovering cross-cancer patients, which we define as patients whose tumors are more similar to patient tumors diagnosed with another cancer type. We develop DeepCrossCancer to identify cross-cancer patients that always co-cluster with the other patient from another cancer type. The input to DeepCrossCancer is the transcriptomic profiles of the patient tumors, the age, and sex of the patient. To solve the clustering problem, we use a semi-supervised deep learning-based clustering method in which the clustering task is supervised by cancer type labels and the survival times of the patients. Applying the method to patient data from nine different cancers, we discover 20 cross-cancer patients that consistently co-cluster. By analyzing the predictive genes of the cross-cancer patients and other genomic information available for the patient such as somatic mutations and copy number variations, we identify striking genomic similarities across these patients providing support. The detection of cross-cancer patients opens up possibilities for transferring clinical decisions across patients at a single patient level. The source code is available at github.com/tastanlab/DeepCrossCancer
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge 96%
- Exploring tumor-normal cross-talk with TranNet: role of the environment in tumor progression 96%
- Neural Network Models for Sequence-Based TCR and HLA Association Prediction 96%
Similar papers in this journal
Similar papers in this journal
- Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks 96%
- Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping 96%
- Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis 96%
Similar papers in this journal
Similar papers in this journal
- Learning universal knowledge graph embedding for predicting biomedical pairwise interactions 97%
- Trans-Driver: a deep learning approach for cancer driver gene discovery with multi-omics data 96%
- Multivariate optimization of k for k-nearest-neighbor feature selection with dichotomous outcomes: complex associations, class imbalance, and application to RNA-Seq in Major Depressive Disorder 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.