Classifying Pancreatic Cancer Tumors May Improve Treatment Options

The findings of a new study may help predict resistance to treatments for pancreatic cancer. The study was published in the journal Clinical Cancer Research.

“Our study evaluated the best way to classify tumors according to available treatment response data from prior clinical trials,” said Jen Jen Yeh, MD, who is a professor of surgery and pharmacology and vice chair for research in the UNC School of Medicine Department of Surgery in a press release. “Our hope is that we can use this information to tailor treatments, and potentially avoid giving therapies that may not work well for certain patients.”

In 2015, the research team discovered two major subtypes of pancreatic cancer based on the molecular and genetic features of the disease. However, other research groups reported different classification systems with three and four subtypes. To address this, the team analyzed data from two recent clinical trials for pancreatic cancer to gain a better understanding of which tumor classifications aligned with treatment responses. They observed two-subtype classification best aligned with treatment outcome data from two clinical trials.

After analyzing five independent pancreatic cancer studies, they also found that the two-subtype system best explained differences in overall patient survival, with patients classified as having basal-like tumors showing worse survival outcomes. “We found that this simpler, two-subtype system best explained treatment responses and survival outcomes,” said Naim Rashid, PhD, Rashid, the study’s co-corresponding author and an assistant professor in the UNC Gillings School of Global Public Health Department of Biostatistics.

Their new subtype classification method, generated using machine-learning approaches, relied on comparisons of how nine pairs of genes are expressed. They observed that this method was accurate, even when it was used to classify tumor samples that were processed and stored differently and used different methods of gene expression measurement. “This study basically provides the evidence that this is something we can feasibly do in the clinic,” Dr. Yeh said.

Dr. Rashid added that: “We want to use the prediction model we developed in actual trials to ensure patients are placed on optimal therapies up-front in order to optimize survival and other outcomes.”