Only about one in four people diagnosed with acute myelogenous leukemia (AML) survive five years after the initial diagnosis. To improve that survival rate, Texas researchers have created an online atlas to identify and classify the protein signatures present at AML diagnosis. The new protein classifications will help researchers and clinicians recommend better treatment and personalized medicine for patients suffering from this aggressive cancer. The breakthrough research was published this week in Nature Biomedical Engineering.
AML occurs in the blood and bone marrow, and it “presents as a cancer so heterogeneous that it is often described as not one, but a collection of diseases,” according to co–senior author Amina Qutub of University of Texas at San Antonio.
The researchers examined the genetic, epigenetic, and environmental diversity that occurs in cancerous cells due to AML. Analyzing proteomic screens of 205 patient biopsies, they developed a new computational method called MetaGalaxy to identify molecular hallmarks of leukemia and categorize the protein signatures into 154 different patterns based on their cellular functions and pathways.
“These hallmarks are analogous to the way constellations guide navigation of the stars: they provide a map to protein changes for leukemia,” Qutub says.
By approaching this challenge through the unique lens of developing a quantitative map for each leukemia patient from protein expression in their blood and bone marrow, rather than the standard lens of qualitative metrics and genetic risks alone, the team will be able to more precisely categorize patients into risk groups and better predict their treatment outcomes.
To better understand the AML hallmarks at the proteomic level and to share the results of their work with other researchers, the team built a web portal known as the Leukemia Proteome Atlas. This online portal gives oncologists and cancer scientists the tools they need to investigate AML protein expression patterns from one patient to the next. It also provides investigators around the world with leads for new leukemia research and new computational tools.
Since many genetic mutations cannot be targeted, the proteomic profiling and target identification process used in this research study will accelerate the identification of therapeutic targets. It also propels researchers toward the development of personalized combination therapies for patients based on their unique protein signatures.
Image: UTSA’s Amina Qutub with fellow researchers in UT MD Anderson Cancer Center develop a quantitative map for each leukemia patient from protein expression in their blood and bone marrow. Qutub developed a new computational method called MetaGalaxy to categorize the protein signatures into 154 different patterns based on their cellular functions and pathways. Image courtesy of UTSA.