Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.

ABSTRACT: The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor simi

SUBMITTER: Flashner-Abramson E 

PROVIDER: S-EPMC6691586 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature