Analyzing the cells around lung cancer tumors may provide a better prediction of lung cancer prediction than analyzation of the tumor itself.
Analyzing genetic information and data from healthy tissue near lung tumors could be a more precise tool to predict cancer recurrence post-treatment than analyzing the actual tumors in patients with lung cancer, according to research conducted at by NYU Langone Health and it’s Perlmutter Cancer Center.
The study focuses on lung adenocarcinoma, which is a type of lung cancer that starts in alveolar epithelial cells. Lung adenocarcinoma represents about one-third of all lung cancer cases within the United States, according to the U.S Centers for Disease Control and Prevention. Although early surgery helps for patients to become cured, typically, 30% experience recurrence, which can become fatal. Due to this, researchers have been investigating biomarkers to come to a conclusion as to when recurrence could happen and thereby to justify more aggressive treatment.
Within the study, 300 sample of tumor and tissue were collected from patients with lung cancer. Researchers within the study then sequenced the RNA, took the data and gained recurrence information within a five-year post-surgery period. This all went into an artificial intelligence algorithm. Researchers used “machine learning” to take mathematical methods, which predicted the recurrence rate and risk, according to the press release.
The study included 147 patients, both men and women, who were treated for early-stage lung cancer. Transcriptome (a set of RNA molecules that alert cells to what proteins need to be made) was explored within the study. RNA analysis of the healthy tissue, which was close to the tumor cells had evaluated that cancer recurrence would be evident 83% of the time, in comparison to RNA from tumors themselves, which only predicted this 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,” study co-lead author Igor Dolgalev, an assistant professor in the Department of Medicine at NYU Grossman School of Medicine and a member of Perlmutter Cancer Center, said in a press release.
This study, which was published in Nature Communications, is the largest to date when it comes to genetic information from tumors and surrounding tissue, alongside the ability to detect recurrence within lung tumors, according to Dolgalev.
Results from the study showed that gene expression that coincided with inflammation or heightened immune system activity in healthy lung tissue was useful in detecting recurrence. The reaction shouldn’t be found in healthy tissue and if it is, that could be a sign of early disease signals, according to the study authors.
“Our results suggest that seemingly normal tissue that sits close to a tumor may not be healthy after all,” said study co-lead author Hua Zhou, a bioinformatician at NYU Grossman and a member of Perlmutter Cancer Center. “Instead, escaped tumor cells might be triggering this unexpected immune response in their neighbors.”
“Immunotherapy, which bolsters the body’s immune defenses, might therefore help combat tumor growth before it becomes visible to traditional methods of detection,” added study co-senior author and cancer biologist Aristotelis Tsirigos, a professor in the Department of Pathology at NYU Grossman and a member of Perlmutter Cancer Center
Tsirigos became worried that the experiment was training the computer program backwards, knowing that the disease had recurred in those specific patients.
Due to this, the next plan for researchers is to use the program in order to access recurrence risk within these patients, according to Tsirigos.
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