Research Portfolio

Funding Opportunities

Join our Mailing List
Join our mailing list to be notified of new funding opportunities.

Your Email

To receive information about funding opportunities, events, and program updates.

Predicting cell-cell interactions in the tumor microenvironment for non-small cell lung carcinoma

Institution: Stanford University
Investigator(s): Alice Yu,
Award Cycle: 2019 (Cycle 29) Grant #: T29DT0760 Award: $85,522
Subject Area: Cancer
Award Type: Dissertation Awards

Initial Award Abstract
Heavy tobacco intake from cigarette smoking and second hand smoke greatly increases people's chance of being diagnosed with lung cancer. Once people are diagnosed with lung cancer, they have approximately 50% chance of survival within the next five years. Although there are drugs available to treat lung cancer, such as chemotherapy and target treatment, there is still a chance the treatment will not work. To develop more precise and accurate treatment options, we need to better understand genetically what is causing drug resistance. Specifically, how do cancer cells interact with other types of cells in the lung to continue growth and evade drugs? Current studies show that cancer cells interact with surrounding immune cells to turn off their functions, so specific immune cells will not attack the cancer cells. Within the suite of immune cells, some are activated when they are assisting cancer cells and dormant otherwise. There are also many more cell types surrounding the cancer cells, such as blood and connective tissue cells, that also exist in different activated states. We hypothesize that all the surrounding cells in their various states communicate with each other to assist cancer cells with growth. To test this, we obtained data containing measurements of genetic activity in each cell type surrounding the lung cancer cells on a single cell level from a publicly available database. This allows us to peek into the genetic behavior of active and dormant cells from various cell types while they were present near cancer cells. With this data, we will first develop a machine learning algorithm to predict what communication interactions are occurring between the different states of cell types in lung cancer. This will provide insight into what cell behaviors are triggered in the different cell types. Next, we will match existing US Food and Drug Administration (FDA)-approved drugs to the interactions to suggest potential drug combinations that can be used to treat tobacco-related lung disease.