Big data tool predicts best lung cancer treatment

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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.”

Although lung cancer is a leading cause of cancer death among both men and women, there is still a lack of definitive genetic “signature” to effectively predict how early-stage lung cancer patients would respond to adjuvant therapy like chemotherapy, before patients begin treatment.

“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 professor Lim Chwee Teck from the department of biomedical engineering at the National University of Singapore.

“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,” he says.

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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.

“Our study demonstrates how we can harness and transform unprecedented amount of genomic data into a useful decision-making tool that can be implemented in routine clinical practice. We are excited about the potential of applying our novel bioinformatics approach into the emerging area of liquid biopsy, which serves as an alternative to invasive and painful tissue biopsy,” says Lim.

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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They’re also developing an integrative platform using the principles of bioinformatics, microfluidics, and cancer genomics for actual testing of local patient’s samples, and to translate these scientific findings for true precision medicine in the future.

A research paper describing the team’s work appears in Nature Communications.

Source: National University of Singapore