Blood test plus risk model may help improve selection for lung cancer screening
A blood test based on a four-marker protein panel combined with an independent risk prediction model that accounts for smoking history significantly improved patient selection for lung cancer screening, according to study results.
The findings, published in Journal of Clinical Oncology, showed the personalized approach outperformed current U.S. Preventive Services Task Force lung cancer risk assessment criteria.
“This study is the culmination of years of painstaking research to identify blood-based biomarkers that inform about lung cancer risk among smokers to better determine the need for CT screening than the current criteria,” Samir M. Hanash, MD, PhD, professor of clinical cancer prevention and leader of the McCombs Institute for the Early Detection and Treatment of Cancer at The University of Texas MD Anderson Cancer Center, told Healio.
“The research spanned studies of circulating DNA, RNA, proteins, metabolites, immune response markers and [others]. A four-protein marker panel was found to be optimal for implementation, which prompted the blinded validation study conducted using samples from the NCI Prostate, Lung, Colorectal and Ovarian [PLCO] Cancer Screening Trial cohort,” he said.
In the blinded validation study, Hanash and colleagues assessed the performance of the four-marker protein panel — including the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen and cytokeratin-19 fragment — in combination with a lung cancer risk prediction model. They compared the combination with current USPSTF lung cancer screening criteria among prediagnostic case and non-case sera from the PLCO Cancer Screening Trial.
Results showed that the blood-based test alone resulted in an area under the receiver operating characteristic curve of 0.74 (95% CI, 0.72-0.76) among all specimens and an AUC of 0.79 (95% CI, 0.77-0.82) among case sera gathered within 1 year before diagnosis.
The combined blood-based test and lung cancer risk prediction model resulted in an AUC of 0.85 (95% CI, 0.82-0.88) for case sera gathered within 1 year of diagnosis.
Compared with current USPSTF criteria, the model had significantly higher sensitivity (88.4% vs. 78.5%) and specificity (56.2% vs. 49.3%) among PLCO participants with a smoking history of 10 or more pack-years, and would have identified 9.2% more lung cancers cases and reduced referral among noncases by 13.7% among these individuals.
Based on these findings, the researchers recommended personalized risk assessment to reduce the lung cancer burden.
“A blood test can be used through shared decision-making to inform of lung cancer risk and the need for CT screening,” Hanash said.
For more information:
Samir M. Hanash, MD, PhD, can be reached at The University of Texas MD Anderson Cancer Center, 6767 Bertner Ave., Houston, TX 77030; email: firstname.lastname@example.org.