Widgets Magazine

Algorithm maps cancer trajectory

Researchers at the Stanford University School of Medicine have developed a mathematical algorithm to help predict the severity of bladder cancer.

 

The results were published online Monday, Jan. 16, in the Proceedings of the National Academy of Sciences, and may influence treatment plans for patients with bladder cancer.

 

“There were two main findings,” said Chad Tang, a medical student who contributed to the study. The researchers “divided up the bladder cancer into different subtypes, which have different prognostic implications. Second, one of the markers they used was a robust predictor of clinical outcomes.”

 

Before applying the algorithm, the team also researched bladder cancer stem cells.

 

“The algorithm was originally published in the context of predicting genes in development and differentiation,” said co-author Debashis Sahoo, instructor at the Pathology Stem Cell Center. “What we have now is an application to cancer.”

 

The work with the algorithm started approximately two years ago after a 2009 paper identified three different types of bladder cancer: basal, intermediate and differentiated. These subgroups are identified by markers of the keratin (KRT) protein family. The basal subtype is marked by KRT14, intermediate is identified by KRT5 and differentiated is identified by KRT20.

 

The most severe cases of bladder cancer are consistently related to the basal subgroup. This knowledge will allow doctors to plan the treatment of patients diagnosed with this type of cancer.

 

“The clinician has to remove the bladder in the case of certain patients because of the progress of the cancer, and the way they do it now is based on state and the grade of the cancer, which is very subjective,” Sahoo said. “This would be the first time that a molecular marker, which is very objective, can guide the clinician.”

 

Removing the bladder is a complicated operation. Markers identified by this algorithm will provide better indication of whether surgery is necessary when compared to traditional methods.

 

“I was motivated to do research where I could use my expertise in computer science to contribute to healthcare, in particular cancer,” Sahoo said. “Some of the relatives in my family were victims of cancer.”

 

Tang cited intellectual interest as motivating his involvement.

 

“I got involved because I was interested in cancer and biology,” Tang said. “I knew the post docs pretty well, and I was really interested in stem cell studies.”

 

The study will motivate further research on the subject.

 

“So far, breast cancer is the only kind with a similar way of subtyping,” said Keith Chan, assistant professor at the Baylor College of Medicine and a co-author of the 2009 paper. “Hopefully we can extend it to other types of cancer and provide better prognostic information for the patients.”

 

“The next step is to try to move it to a prospective clinical trial to validate its results,” Chan said. “The second thing is to try to understand the biology of these cells, so we can target them. That’s what we’re working on right now.”