A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Centre philtres out the biological noise in liquid biopsy samples, helping clinicians better match therapies to their patients’ tumours.

The research was published May 1 in Clinical Cancer Research and was funded in part by the National Institutes of Health.

Liquid biopsies, which analyse cell-free DNA (cfDNA) fragments from tumours in blood samples, are commonly used to identify mutations in a patient’s solid tumour, enabling clinicians to select mutation-targeted therapies.

However, liquid biopsies may also pick up mutations that accumulate in white blood cells through an ageing-related process called clonal hematopoiesis.

These white blood cell mutations are common in older individuals and in patients who have previously undergone chemotherapy or radiation.

“When you do a liquid biopsy, and you get the report back, and you see mutations, you do not know if the mutations are coming from the tumour or the white blood cells,” explains Jenna Canzoniero, M.D., M.S., a co-first author of the paper and an assistant professor of oncology at the Johns Hopkins University School of Medicine.

“If you want to select a mutation-targeted drug to treat the cancer, you want to make sure you are targeting mutations in the cancer and not mutations in the white blood cells.”

To solve this problem, Canzoniero and her colleagues in the molecular oncology laboratory developed a machine learning model called plasmaCHORD that uses characteristics of the DNA fragments to estimate whether a mutation found in a liquid biopsy originates from the tumour or white blood cells.

The DNA fragments from tumours and the DNA fragments from white blood cells are “chopped up” in different ways, Canzoniero says, creating distinct “cfDNA fragmentation profiles.” The model also uses factors like the patient’s age and the type of gene and mutation.

The team trained the model on liquid biopsy samples from 225 patients with breast, colorectal, esophageal, ovarian or non-small cell lung cancer.

They verified the model’s accuracy by using matched genetic sequencing of patients’ tumour cells and white blood cells to identify the true source of the mutations.

Next, they tested plasmaCHORD in a separate set of 114 patients with breast, prostate or non-small cell lung cancer from another institution that uses a different type of liquid biopsy sequencing platform, and found that the model had a similar ability to identify the true source of the mutations.

In particular, within that cohort, plasmaCHORD improved the ability to correctly distinguish tumour from white blood cell mutations from approximately 50% to 83% for a set of clinically relevant mutations.

Finally, they provided proof of concept that the information was clinically useful by showing that plasmaCHORD’s prediction of mutation origin helped clinicians avoid selecting likely ineffective therapies for patients evaluated at the Johns Hopkins Molecular Tumour Board.

“About one-third of mutations detected in tumour-naive liquid biopsies can originate from white blood cells, and our ability to match targeted therapies to each patient’s genomic profile depends on our ability to distinguish tumour mutation from biological noise,” says senior study author Valsamo Anagnostou, M.D., Ph.D., the Alex Grass Professor of Oncology and leader of the Johns Hopkins Molecular Tumour Board at the Johns Hopkins University School of Medicine.

“An artificial intelligence model applied to standard liquid biopsy tests could be both clinically valuable and quickly scalable.”

“PlasmaCHORD can be used going forward for both research and potentially for clinical purposes to identify the origin of mutations in a liquid biopsy if you’re not sure,” Canzoniero says.

“We are thinking about working on a future version that would hopefully have even better performance.”

Source: Johns Hopkins Medicine