Cancer Diagnostic Methods Take Innovative Strides
A research team from the University of Pittsburgh Cancer Institute has developed a novel computational strategy in unraveling the intricacy of breast cancer tumors. The method comprises of identifying different types of cells in the tumor and assessing the fate of the tumor based on the kind of interactions between normal cells and cancer cells.
"We have developed a computational method that allows us to better understand what goes inside a tumor," Vitor Onuchic, a graduate student in Structural and Computational Biology and Molecular Physics at the Bioinformatics .esearch Lab said,
According to Sci Feeds, following this approach may help in inferring the cell-type composition of breast tumors. It was then added that they were not able to detect "changes in the epigenomic and transcriptomic profiles of individual cell-types."
In layman terms, a tumor behaves like a community. It houses a variety of cell-types both cancerous and non-cancerous that are continuously interacting with each other, the result of which can either be a growth of the tumor or its riddance. This approach of cell interaction is capable of opening doors for developing new and improved therapeutic ways.
"Without understanding the composition of tumors we couldn't understand how cells engage in that disease-causing exchange of information," Dr. Aleksandar, the director at Baylor's Computational Biology Department, stated during an interview.
According to Medical Xpress, it's only normal to ask the advantages of incorporating this new approach of diagnosing cancer tumors. Previous approaches to diagnosis only used to physically separate the non-cancerous cells from the cancerous ones. This strategy can be costly and requires a lot of time. Another drawback of this approach is that by creating a separation between the types of cells, the interaction between the cells within the tissue is hampered.
The new computational approach's primary advantage is that it delves deeper within a cancerous tumor and identifies the problem at a cellular level and determines the interactions between cells as the deciding factor whether the cancer is there to stay or not.