How healthy tissue can help catch lung cancer’s return
The new study focuses on lung adenocarcinoma, which forms in alveolar epithelial cells
NEW YORK: Genetic information collected from seemingly healthy tissue near lung tumours may be a better predictor of whether cancer will come back after treatment than analysis of the tumours themselves, according to new research.
The new study focuses on lung adenocarcinoma, which forms in alveolar epithelial cells.
Most patients are cured if tumours are surgically removed early in the disease’s progression, but residual cancer cells regrow in about 30 per cent of cases and can lead to death. Consequently, experts have long searched for biomarkers that are predictors of recurrence and whose presence might prompt more-aggressive initial treatment.
The study included 147 men and women treated for early-stage lung cancer. It explored the utility value of the transcriptome, the complete set of RNA molecules that tell cells what proteins to make.
Analysis of RNA collected from apparently healthy tissue adjacent to tumour cells accurately predicted that cancer would recur 83 per cent of the time, while RNA from tumours themselves was only informative 63 per cent 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 Igor Dolgalev, Assistant Professor, Department of Medicine at NYU Langone Health in the US.
Published online in the journal Nature Communications, the investigation is the largest to date to compare genetic material from tumours and adjacent tissue for their ability to predict recurrence, Dr. Dolgalev said.
For the study, the research team collected almost 300 tumour and healthy tissue samples from patients with lung cancer. The study investigators then sequenced the RNA from each sample and fed these data, along with whether or not recurrence occurred within five years of surgery, into an artificial intelligence algorithm. Using machine learning, the program built mathematical models that estimated recurrence risk.
The findings revealed that the expression of genes associated with inflammation, or heightened immune-system activity, in adjacent, apparently normal lung tissue was especially useful for making predictions. This defensive reaction, the study authors say, ought not to be present in tissue that is truly healthy and may be an early warning sign of disease.
“Our results suggest that seemingly normal tissue that sits close to a tumour may not be healthy after all,” said Hua Zhou, a bioinformatician at NYU Grossman School of Medicine. “Instead, escaped tumour cells might be triggering this unexpected immune response in their neighbours.”