Lung cancer, illustration

Research published today in Nature Communications, from investigators at Perlmutter Cancer Center at NYU Langone Health, says that genetic information from healthy tissues near lung adenocarcinoma tumors may better predict cancer recurrence after initial treatments. Adenomcarcinoma accounts for roughly one-third of all lung cancer in the United States according to the Centers for Disease Control and Prevention. While most patients can be cured in the early stages of the disease via surgery, about 30% of patients will have their residual cancer regrow.

The study, conducted in 147 men and women treated for early-stage lung cancer, explored the value of analyzing the transcriptome to search for clues of disease recurrence. Analysis of the RNA from apparently healthy tissue near the tumor showed that testing these cells could predict a return of cancer in 83% of the cases, while the RNA from the original tumors themselves only predicted it 63% of the time.

“Our findings suggest that the pattern of gene expression in apparently healthy tissue might serve as an effective and until now elusive biomarker to help predict lung cancer recurrence in the earliest stages of the disease,” said study co-lead author Igor Dolgalev, PhD, director of the Cellular Analytic Laboratory and assistant professor, department of medicine at NYU Grossman School of Medicine.

According to Dolgalev, this research is the largest study of its kind to date that compares analysis of healthy adjacent tissue with tumor tissue for their abilities to predict cancer recurrence. For this study, the investigators gathered nearly 300 healthy and tumor tissue samples from lung cancer patients and then used RNA sequencing. Information from the RNAseq of the tissue samples was then compared with patient records using an AI algorithm to model estimated disease risk.

The researchers showed that the reason the healthy tissue was more accurate a predictor of disease recurrence was because it identified the expression of genes that are known to be associated with inflammation or heightened immune system activity. This building of a defensive reaction in the apparently healthy tissue would not be present in tissue that was actually healthy and indicated an early warning of disease.

“Our results suggest that seemingly normal tissue that sits close to a tumor may not be healthy after all,” said Hua Zhou, PhD, a bioinformatician at NYU Grossman and co-lead author of the study. “Instead, escaped tumor cells might be triggering this unexpected immune response in their neighbors.”

This finding also suggests a treatment approach for these patients that would include immunotherapy to help ward off disease that may be present in the adjacent tissue, but is not yet visible in current methods of detection, according to co-senior author Aristotelis Tsirigos, PhD.

More work needs to be done to better determine the utility of the algorithm as the investigation was retrospective and trained the model using cases the investigators already knew had recurrent cancer. Now, the team will apply the model prospectively to see how it performs in predicting cancer recurrence risk of newly treated patients.

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