Advancing personalized cancer medicine

Cancer is complex. It can be triggered by many factors, be driven by a myriad of genetic mutations, and change over time. Clinicians need to be able to provide precise diagnoses, identify the best treatment option available, monitor disease progression, and determine prognosis.

To this end, next-generation sequencing is often used to analyse millions of DNA fragments in tumour biopsies to precisely determine a cancer’s genetic profile. “But obtaining tissue biopsies is not always possible or even necessarily helpful,” explains medical oncologist Jae-yong Cho, general director of Yonsei University’s Gangnam Severance Cancer Hospital in Seoul, South Korea.

For example, some cancer patients develop metastases whose profiles differ from the original mass depending on the organ in which they occur. Also, tumours are not uniform: a biopsy from one area of a tumour will not reflect what is happening in the whole tumour or how it is changing.

Cho and his colleagues at Gangnam Severance Cancer Hospital, together with collaborators in industry, are now developing time- and cost-effective approaches to obtain more regular and complete genetic profiling snapshots from cancer patients. Their approaches involve extracting genetic content from liquid samples (such as blood or the fluid around the lungs), amplifying it, analysing it and comparing it with the latest data in the literature using artificial intelligence.

Extracting data from CNA

Specifically, Cho’s team is interested in circulating nucleic acids (CNAs) — DNA circulating in the bloodstream. When cells die they release nucleic-acid fragments called cell-free DNA into the bloodstream. Normally, immune cells called macrophages clean them up, but macrophages can’t keep up with their overproduction by tumours, so more cell-free DNA remains. Researchers, including those at Gangnam Severance Cancer Hospital, have been evaluating the efficacy of various technologies for analysing liquid samples for a fraction of cell-free DNA that originates from tumours, known as circulating tumour DNA.

Sometimes only tiny amounts of circulating tumour DNA are present, making it difficult to detect. Cho’s team has been exploring various approaches for amplifying cell-free DNA to improve the detection of circulating tumour DNA. They are also assessing the merits of extracting cancer gene data from another type of CNA called exosomes — vesicles that transport genetic information and proteins between cells, making them especially enriched in DNA. Growing evidence suggests that exosomes are associated with tumour development and metastasis, making them attractive candidates for cancer mutation testing.

Cho and his colleagues used various methods to amplify cell-free and exosomal genetic material extracted from the liquid biopsies of people with non-small-cell lung cancer. This type of lung cancer is sometimes caused by mutations in the epidermal growth factor receptor (EGFR) gene, warranting chemotherapy with drugs called tyrosine kinase inhibitors. But the EGFR gene often develops other mutations over time that confer resistance to these drugs. Being able to spot the development of this mutation is important for adjusting ongoing treatment regimens.

Gangnam Severance Cancer Hospital researchers found amplification approaches were more sensitive for detecting the chemotherapy-resistant mutation in plasma samples when they analysed both cell-free DNA and the total nucleic acid in the exosomes. On the other hand, exosomal total nucleic acid alone could be used for analyses when extracted from the fluid around the patients’ lungs, because it was enriched in the mutations.

The researchers were also able to use cell-free DNA analyses to identify a rare EGFR-related mutation in an advanced gastric cancer patient who was not responding to treatment. Cho and his colleagues suggest EGFR-targeting therapies could be used to treat such cases.

Bring on artificial intelligence

Extracting mutated genetic material from cancer patients is not the only challenge faced by clinicians. “I used to take quite a bit of time to sift through databases to find meaningful information on the mutations identified in a patient’s next-generation-sequencing result,” says Cho.

To address this, Cho and his colleagues worked with Korean medtech company Waycen to develop Waymed Gene. This software uses artificial intelligence to sift through the literature, curate a database, and then search through this and another manually curated database to provide clinicians with the latest information on the detected mutations. “Waymed Gene provides a dashboard that can be used to check gene mutation information from the relevant clinical and thesis data at a glance,” says Cho. “It helps me find the right drug or clinical trial for patients.”

The Gangnam Severance Cancer Hospital team is also using the technology in Waymed Gene to discover new targets for anti-cancer drugs and to find molecules that can be used as drugs. They are currently using the software to look for molecules that can target mutations in the KRAS gene found in some types of cancer, including pancreatic, colorectal and non-small-cell lung cancer.

The research at Gangnam Severance Cancer Hospital on detecting circulating cancer mutations in liquid biopsies, and the use of state-of-the-art artificial-intelligence technology for providing up-to-date mutation information promises further advances for personalized cancer treatments.