New research published in Nature Medicine finds two subtypes of gene expression in peritoneal carcinomatosis (PC) that correlate with patient survival. PC is a kind of metastatic gastric cancer in the peritoneal (abdominal) cavity. The research comes from scientists at The University of Texas MD Anderson Cancer Center.
PC occurs when cancer cells spread to the peritoneal cavity and attach to the stomach and other organs, resulting in fluid accumulation; unfortunately, patients have an overall survival of fewer than six months.
"PC represents a major unmet clinical need, as we don't have effective treatment options available for these patients," said co-corresponding author Jaffer Ajani, M.D., professor of Gastrointestinal Medical Oncology. "Based on our findings, we need to move toward profiling these cells in each patient in order to offer more tailored treatment options."
In carrying out the study, the researchers analyzed over 45,000 individual cells from patients with PC. This in-depth analysis is the most extensive that has been conducted.
"In order to better treat patients with PC, we first have to understand the populations of metastatic cells in the peritoneal cavity," said co-corresponding author Linghua Wang, M.D., Ph.D., assistant professor of Genomic Medicine. "This is the most detailed analysis of these cells performed to date. That is the power of single-cell analysis -- we are able to look at every single cell and get a picture of the landscape."
In their analysis of the cells, the team noticed a clear distinction between two different types of cells and their correlations with survival. Wang explains: "The intriguing aspect is that, by classifying tumor cells based on lineage compositions, we noted two groups of patients. The more gastric-like PC cells had an aggressive phenotype and were associated with shorter survival. However, the more intestine-like PC cells were less aggressive, and patients had longer survival."
Based on this information, the researchers were able to develop a gene expression signature that allows for a more accurate prediction of patient survival than current tools. The team says they hope that their findings can be used to guide treatment decision-making processes.