Data Tool That Predicts Lung Cancer Treatment

Researchers have developed a personalized risk assessment tool that can potentially predict the survival rate and treatment outcomes of early-stage lung cancer patients.

 The tool uses a panel of 29 unique extracellular matrix (ECM) genes that the researchers identified based on their abnormal expression in lung cancers compared to healthy lung tissues.

 ECM is the space that surrounds cells and provides structural and biochemical support to surrounding cells. Recent studies have shown an association between tissue stiffness and the risk of cancer, particularly breast cancer. This is because some of the cells in the tumor produce fibrous-like collagen proteins that form into a scaffolding structure (ECM) for these cancer cells to attach to.

   “…the potential of personalized medicine is fast becoming a reality.”

 “The traditional way of targeting cancer has been a ‘one size fits all’ approach for patients. Yet, although two persons may have the same type of cancer, how the disease manifests and progresses is unique to each individual,” says Lim Su Bin, a PhD candidate at the National University of Singapore who is also the acting director of BIGHEART (Biomedical Institute for Global Health Research and Technology) at the university.

 “In our research, we look at non-small cell lung cancer, which is the most common type of lung cancer. Our novel tool successfully identified early-stage patients who derived survival benefit from adjuvant chemotherapy. This is a very exciting development as we have taken a big step forward in enabling treatments to be customized for cancer patients to improve survival rates.

 “As we begin to know more about such tumor variability, or heterogeneity, the potential of personalized medicine is fast becoming a reality. The goal of precision medicine is to provide the right treatment to the right person at the right dose and at the right time,” Lim adds.

 “When precision medicine meets big data, its potential is even greater. With the increase of global joint efforts in sharing large-scale data, we were able to explore the genomic data across multiple cancer types through various databases,” she says.

 From their examination of the open databases, the team found a wide heterogeneity in terms of ECM gene expression within early-stage lung cancer patients. They identified 29 specific ECM components that could potentially serve as biomarkers for the disease’s diagnosis and prognosis, considering their abnormal dynamics during cancer progression. The research team then developed these biomarkers into a novel gene panel for clinical application.

 Tests with more than 2,000 early-stage lung cancer patients validated the gene panel’s robust performance in predicting survival outcomes and chemotherapy success rate. 

 The researchers also determined a common cut-off score for patient stratification.

 The team is currently looking into the relevance of the 29-ECM gene panel biomarkers in predicting patient survival rate and treatment outcomes in 11 other cancer types.


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