Ajay Goel, Ph.D., AGAF, chair of the Department of Molecular Diagnostics and Experimental Therapeutics at City of Hope speaks about the DDW 2021 abstract – Transcriptomic Profiling Identifies a Risk Stratification Signature for Predicting Peritoneal Recurrence and Micrometastasis in Gastric Cancer
Link to Article:
https://pubmed.ncbi.nlm.nih.gov/33558424/
Summary –
The goal is to:
Peritoneal carcinomatosis is a kind of gastric cancer that is deadly. The need for biomarkers to help identify patients at high risk of peritoneal recurrence or metastasis is highlighted by the need to create biomarkers that can help identify patients at high risk of peritoneal recurrence or metastasis.
Design of the experiment:
By analyzing expression profiling datasets from 249 patients with gastric cancer, we performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers, followed by clinical validation of 426 patients from three cohorts.
The following are the outcomes:
A 12-gene panel for strong prediction of peritoneal recurrence in patients with gastric cancer was found using genome-wide expression profiling (AUC = 0.95), and it was effectively verified in a second dataset (AUC = 0.86). We developed a six gene-based risk-prediction model based on 216 specimens from a training cohort [AUC = 0.72; 95 percent confidence interval (CI): 0.66-0.78], which was then validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95 percent confidence interval (CI): 0.67-0.83). Combining tumor shape and degree of invasion enhanced the prediction model’s predictive performance in both cohorts (AUC = 0.84). The effectiveness of the similar six-gene panel for detecting peritoneal metastasis was next assessed by evaluating 210 gastric cancer specimens (previous 111 patients + extra 99 cases), which distinguished patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was very good at detecting peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was greatly improved when the depth of invasion was included in (AUC = 0.85).
Final Thoughts:
Our unique transcriptome signature for risk stratification and identification of high-risk peritoneal carcinomatosis patients should be useful in clinical decision-making in gastric cancer patients.