Researchers at the University of Texas MD Anderson Cancer Center have profiled over 45,000 individual cells from patients with peritoneal carcinomatosis (PC), a particular form of metastatic gastric cancer, to create extensive cell heterogeneity. We defined and identified two different subtypes that correlate with patient survival.
Based on their findings, today Nature medicine, Researchers have developed and validated gene expression signatures that can more accurately predict patient survival than other clinical features. When validated in a prospective study, this tool may help stratify patients with gastric cancer and lead to more effective treatment strategies.
“To better treat PC patients, we first need to understand the population of metastatic cells in the abdominal cavity,” said co-author Linghua Wang, MD, Ph.D., an assistant professor of genomic medicine. Says. “This is the most detailed analysis of these cells ever performed. This is the power of single cell analysis. You can examine every single cell and take a picture of the landscape.”
Peritoneal carcinomatosis is a condition in which cancer cells infiltrate the peritoneum or abdominal cavity and attach to the stomach and other organs. It can also occur in other gastrointestinal cancers, but it is most commonly seen in patients with advanced gastric cancer, and about 45% of patients are diagnosed with PC at some point. This condition results in a significant accumulation of water in the abdominal cavity, and the patient’s overall survival is less than 6 months.
“PC represents a major unmet clinical need due to the lack of effective treatment options available to these patients,” said co-author Dr. Jaffer Ajani, a professor of gastrointestinal oncology. I will. “Based on our findings, we need to move towards profiling these cells in each patient to provide more tailored treatment options.”
In this study, researchers isolated PC cells from ascites collected from 20 patients with advanced gastric cancer. Ten of the patients were long-term survivors who survived more than one year after the PC diagnosis, and 10 patients were short-term survivors who survived less than six months after the PC diagnosis.
After performing a single-cell RNA sequence to analyze gene expression, researchers were able to create the first “map” of PC cells. It describes the different cell types that exist and their functional state. The variability of cancer cells present in the tumor is known as intratumoral heterogeneity, and different subtypes of cancer cells respond differently to a particular treatment, which can lead to treatment failure or recurrence. there is.
Gene expression information also allowed researchers to identify the origin of PC cells, known as tumor cell lines. They found that these were all gastric cancer cells, but some appeared to be derived from gastric cells, while others were more similar to intestinal cells.
“The interesting thing is that we focused on two groups of patients by classifying tumor cells based on their lineage composition,” Wang said. “Stomach-like PC cells showed an aggressive phenotype and shorter survival, but intestinal-like PC cells were less aggressive and patients survived longer.”
Based on these findings, researchers have developed a genetic signature that more reliably predicts patient survival than various clinical features. They validated signatures in a second cohort of patients with advanced gastric cancer and PC and four large localized gastric cancer cohorts of more than 1,300 patients in total.
In the future, researchers will validate their signatures in prospective studies and perform further analysis of PC cells in more patients to provide regulatory mechanisms for tumor cell lineage plasticity and better treatment options for patients. We hope to identify new therapeutic targets available.
“This is an important first step towards a better understanding of the single-cell biology of these cancer cells, but there is still much to do,” Ajani said. “Understanding this heterogeneity predicts that one day it could be used to guide the most beneficial clinical decisions for each patient.”