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Big Data Analytics Used for Personalized Assessment Tool for Cancer Diagnosis

Team of researchers from National University of Singapore (NUS) developed a tool that is able to detect the incidence of cancer, predict patient survivability and determine patient suitability for immunotherapy cancer treatment.

In a research led by Professor Lim Chwee Teck from the Department of Biomedical Engineering at NUS and recent Ph.D. graduate Dr Lim Su Bin, the Tumour Matrisome Index (TMI) was developed to act as a “scorecard” for assessing cancer diagnosis, effectiveness of treatment, and cancer recurrence rate and survivability.

The TMI includes a panel of 29 selected genes which are found in the extracellular matrix (ECM) of the human body. These genes were selected through a series of studies done by the NUS team which shoed that the 29 genes were consistent throughout patient diagnosis with non-small-cell lung cancer (NSCLC) which accounts for approximately 85 percent of all lung cancers.

Development and validation of TMI was done using big data and predictive analysis of over 30,000 patient-derive biopsies.

“Parallel analyses in over 30,000 patient-derived biopsies revealed that the TMI scores are closely associated with mutational load, tumour histopathology and predictive of patient outcomes.” Shared Dr Lim Su Bin.

Prof Lim Chwee Teck explained, “TMI can be used together with liquid biopsy, which is less invasive and less painful for the patient compared to conventional tumour biopsies. As it only requires a blood test instead of day surgery, it can be done more frequently over the course of treatment, providing doctors with real-time information on how the patient is responding to treatment. Tissue biopsy is often done at the start and end of treatment, while liquid biopsies can be done frequently, allowing doctors to track more efficiently how well treatment is progressing. This is a big step forward in personalising cancer treatment and ensuring better patient outcomes.”

Analysis using public datasets of health individuals and cancer patients, the researchers were able to confirm that cancer patients produced a higher set of TMI scores. Upon examination of TMI on 11 major cancer types, the team found that TMI scores were distinguishable between cancer and normal tissue, and that each cancer type has a specific TMI signature. At the moment only lung cancer has been validated and further testes are required for the other 10 cancer types.

The team also showed that TMI scores could be used to predict how successful a patient might react to cancer treatments, such as immunotherapy.

A patient’s TMI scores could give a better gauge on his or her survivability, the team found. In their work with NSCLC the team found that high TMI scores were consistently associated with early recurrence of cancer and metastatic spread, leading to an increased risk of death.

The team’s findings were published in two scientific journals – on 15 August 2019 in the Proceedings of the National Academy of Sciences and on 22 May 2019 in the Nature Partner Journal’s Precision Oncology.

Prof Lim and his team plan to collaborate with clinical partners to conduct further clinical tests to validate the use of TMI on other cancer types. This will determine the accuracy and specificity of TMI in diagnosis and prognosis of patients via liquid biopsy. [APBN]