Ly Vu, PhD

Acute myeloid leukemia (AML) is one of the most common and aggressive types of blood cancers. Even though we have made exciting progress and have stronger treatments available, around 30% of AML patients who receive treatment will experience a relapse and have a very low chance of survival. Therefore, we need to figure out how these diseases develop and become resistant to treatment. It has been proposed in AML, there are certain cells that have stem cell-like qualities, which allow them to evade therapy and cause the cancer to come back even after treatment. In this project, we will use advanced techniques to investigate how these cells acquire such characteristics by having specific chemical changes on messenger RNAs. Our ultimate goal is to develop new treatments that can improve the lives of people suffering from these deadly diseases.

Andrew Roth, PhD

Vintner Grant funded by the V Foundation Wine Celebration in honor of Rich and Leslie Frank and in memory of Edythe Frank

When a patient is diagnosed with Follicular Lymphoma (FL) the effect the disease will have is unpredictable. Many patients will do well and live many years. But, some patients will have what are called transformation events. 

Transformation is when a new, more aggressive type of lymphoma develops. When this happens patients do not do well. With no way to know which patients will transform, doctors cannot determine the best strategy for treatment. But even if they could predict transformation, it is not clear what the best course of action is since we do not understand the biology of transformation. 

Recent research has shown that the non-cancerous cells in a tumor can have a major impact on how the tumor behaves. 

These cells can create an environment that either encourages or limits tumor growth. The way cancerous and non- cancerous cells are organized can be thought of as the architecture of the tumor. By comparing the architecture of patients that do and do not transform, we believe that we can find better ways of predicting and preventing transformation. To do this we will employ cutting edge technologies that allow us to precisely measure features of thousands of single cells and look at how they are organized. We will use artificial intelligence to build a new approach to predict transformation using this information. This will also let us learn about the causes of transformation and how to prevent it. 

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