Using machine learning to identify undiagnosable cancers

The first step in choosing the appropriate treatment for a cancer patient is to identify their specific type of cancer, including determining the primary site -- the organ or part of the body where the cancer begins. In rare cases, the origin of a cancer cannot be determined, even with extensive testing. Although these cancers of unknown primary tend to be aggressive, oncologists must treat them with non-targeted therapies, which frequently have harsh toxicities and result in low rates of survival. A new deep-learning approach developed by researchers at the Koch Institute for Integrative Cancer Research at MIT and Massachusetts General Hospital (MGH) may help classify cancers of unknown primary by taking a closer look the gene expression programs related to early cell development and differentiation. "Sometimes you can apply all the tools that pathologists have to offer, and you are still left without an answer," says Salil Garg, a Charles W. (1955) and Jennifer C. Johnson Clinical Investigator at the Koch Institute and a pathologist at MGH. "Machine learning tools like this one could empower oncologists to choose more effective treatments and give more guidance to their patients."