Sequential Liquid Biopsy Sampling May Be a Predictive Tool for Early Disease Progression in Patients With Colorectal Cancer
According to the American Cancer Society, excluding skin cancers, colorectal cancer is the third most common cancer among men and women in the United States, with over 97,000 new cases expected this year, and is the third leading cause of cancer-related death, with over 50,000 deaths predicted in 2018. Studies show that although genetic alterations in the RAS pathway are responsible for primary and acquired resistance to anti-EGFR monoclonal antibodies, between 65% and 70% of patients with the disease progress within 3 to 12 months after starting therapy despite tailored patient selection based on genetic screening for somatic RAS mutations.
A prospective phase II study investigating the value of profiling subclonal mutations in the RAS pathway in the plasma cell–free DNA (cfDNA) of patients with metastatic colorectal cancer to predict their response to anti-EGFR therapies has found that serial liquid biopsies, coupled with a mathematical framework of tumor evolution, could predict which patients are at risk for disease progression. The study’s findings provide novel opportunities for adaptive personalized therapies for patients with metastatic colorectal cancer. The study by Khan et al is published in Cancer Discovery.
Study Methodology
The researchers analyzed data from the PROSCPECT-C trial, which evaluated biomarkers of both response and resistance to anti-EGFR therapies in 47 patients with RAS wild-type metastatic colorectal cancer being treated with cetuximab (Erbitux). Study participants underwent tissue biopsies at predefined time points, including pretreatment (baseline) and post-treatment (disease progression), and at partial response in some. In addition, plasma samples were collected every 4 weeks until disease progression.
The researchers combined profiling of serial cfDNA and matched sequential tissue biopsies with imaging and mathematical modeling of cancer evolution. The mathematical models the researchers generated utilized cfDNA and carcinoembryonic antigen (CEA) levels from patients’ plasma to predict time to progression. The results were validated using RECIST measurements from radiological imaging data.
The mathematical model utilizing CEA measurements was applied to six patients to predict time to clinical progression. Of these predictions, three were within 10% of progression time as measured by RECIST.
Study Findings
The researchers found that a significant proportion of the patients defined as having RAS wild-type colorectal cancer based on diagnostic tissue analysis harbor aberrations in the RAS pathway in pretreatment cfDNA and do not benefit from EGFR inhibition. They also found that predictions generated with high-sensitivity cfDNA profiling allowed for the prediction of progression time several weeks in advance, compared with models utilizing CEA measurements, allowing individualized quantitative forecasting of disease progression and providing new opportunities for adaptive personalized therapies.
“Integration of novel monitoring technologies like cfDNA, in combination with mathematical modeling of tumor forecasting, may offer the opportunity to act early, stop therapy, or change treatment to stay one step ahead of the disease,” said Nicola Valeri, MD, PhD, team leader in Gastrointestinal Cancer Biology and Genomics at The Institute of Cancer Research, London, in a statement. “Our method allows for a more accurate prediction as well as improved monitoring of response to therapy.”
Funding for this study was provided by Cancer Research IK, the National Institute for Health Research Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, and the European Union.
Conflict of interest disclosures may be found at the end of the study abstract.
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
