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Constructing a Lung Cancer Map of Drug Resistance States with Single-Cell Analysis

Institution: Stanford University
Investigator(s): Loukia Karacosta,
Award Cycle: 2019 (Cycle 29) Grant #: T29FT0569 Award: $138,450
Subject Area: Cancer
Award Type: Postdoctoral Fellowship Awards

Initial Award Abstract
One of the main reasons lung cancer remains deadly, is the ability of cancer cells to acquire resistance to drug therapies and ultimately become non-responsive. This leads to eventual cancer recurrence in lung cancer patients and poor survival outcome. Understanding what helps cancer cells adjust and change during drug treatments is vital towards our efforts in preventing and counteracting the development of drug resistance. This project proposes to study mechanisms that are thought to help lung cancer cells become resistant, yet are not fully understood or used in current therapeutic strategies. Furthermore, published studies show that smoking history is correlated with the ability of lung cancer cells to avoid drug effects and become more aggressive, and this could be used to the advantage of lung cancer patients with smoking history. By using technology that will allow me to study in depth each lung cancer cell individually, will reveal the many different ways each cancer cell can come up with to become resistant to drugs that are used in lung cancer standard therapies. My goal is to treat in the lab lung cancer cells with a number of drugs used in the clinic, and record the changes and responses that take place in surviving cells. By utilizing computational methods, these responses will then be used to build a reference map of possible responses a cancer cell may have to certain treatments. This way one could analyze a patient sample and test what type of a response that patient's cancer cells are having towards a certain drug at different time-points of therapy (before, during and after). Through collaborating with Stanford clinical oncologists, I will test this idea by analyzing patient samples obtained at different times during their therapy and see if by comparing with the constructed reference map, we can draw conclusions on how each patient is responding to therapy and see how this is correlated with smoking history. This could set the foundation towards developing prognostic and therapeutic tools that may help anticipate and counteract drug resistance at a personalized level, enabling precision medicine for current and former smokers suffering from lung cancer.