Discovering cross-cancer patients with a semi-supervised deep clustering approach

Official URL: https://risc01.sabanciuniv.edu/record=b2486365 _(Table of contents)

In traditional medicine, the treatment decisions for a cancer patient are typically based on the patient’s cancer type. The availability of molecular profiles for a large cohort of multiple cancer patients opens up possibilities to characterize patients at the molecular level. There have been reports of cases where patients with different cancers bear similarities. Motivated from these observations, in this thesis, we specifically focus on developing a method to discover cross-cancer patients. We define cross-cancer patients as those who have molecular profiles that bear a high level of similarity to other patient(s) diagnosed with a different cancer type and are not representative of their cancer type. To find cross-cancer similar patients, we develop a framework where we identify patients that co-cluster frequently when clustered based on their transcriptomic profiles. To solve the clustering problem, we propose a semi-supervised deep learning clustering in which the clustering task is guided by the cancer types of the patients and the survival times. The deep representation obtained in the network is used in the clustering module of DeepCrossCancer. Applying the method to nine different cancers from The Cancer Genome Atlas project using patient tumor gene expression data, we discover twenty patients similar to a patient or multiple patients in another cancer type. We analyze these patients in light of other genomic alterations. Our results find significant similarities both in mutation and copy number variations of the crosscancer patients. The detection of cross-cancer patients opens up possibilities for transferring clinical decisions from one patient to another and expediting the investigation of novel cancer drivers shared among them. The method is available at https://github.com/Tastanlab/DeepCrossCancer.