Using Big Data To Personalize Cancer Treatment

Researchers have identified a panel of 29 extracellular matrix genes that can determine how different lung cancer patients respond to treatment.

AsianScientist (Apr. 24, 2018) – A team of researchers from Singapore has developed a personalized risk assessment tool that can predict the survival rate and treatment outcomes of early-stage lung cancer patients. Their findings have been published in Nature Communications.

Although lung cancer is the leading cause of cancer death among both men and women, there is still a lack of definitive genetic ‘signature’ to effectively predict how 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,” said Professor Lim Chwee Teck from the National University of Singapore.

To develop a more personalized approach, Lim and his team studied open genetic databases and found that non-small cell lung cancer patients varied in their expression of extracellular matrix (ECM) components. They were also able to identify 29 specific ECM components that could potentially serve as biomarkers for the disease’s diagnosis and prognosis.

The gene panel’s robust performance in predicting survival outcomes and chemotherapy success rate was validated in more than 2,000 early-stage lung cancer patients. 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,” said Lim, who is also the acting director of BIGHEART (Biomedical Institute for Global Health Research and Technology).

“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.”

The team is currently looking into the relevance of this gene panel biomarkers in predicting patient survival rate and treatment outcomes in 11 other cancer types. They are also developing an integrative platform using the principles of bioinformatics, microfluidics and cancer genomics to test patient samples and translate these scientific findings for precision medicine.

The article can be found at: Lim et al. (2017) An Extracellular Matrix-related Prognostic and Predictive Indicator for Early-stage Non-small Cell Lung Cancer.


Source: National University of Singapore; Photo: Shutterstock.
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