Biomarkers May Improve Prediction of a Lung Cancer Diagnosis

A group of international researchers discovered protein biomarkers that may help refine eligibility for lung cancer screening with LDCT.

The Challenge

Lung cancer is the world’s leading cause of cancer death. That’s largely because by the time most people are diagnosed with lung cancer it has advanced and spread to areas outside the lungs, making it more difficult to treat and when curative treatment is rarely possible.

For about the past 10 years, there has been a guideline for health care providers to recommend screening with low-dose CT scanning for people with a smoking history and a high risk of developing lung cancer. Annual scans can help find lung cancer before it has spread when there are typically more treatment options with a curative intent.

Lung cancer screening and early detection are the most promising ways to reduce the number of people who die from lung cancer each year. Yet, several questions remain about how to identify people with a smoking history who are likely to benefit from screening.

Since 2018, the United States National Cancer Institute (NCI) has funded the Integrative Analysis of Cancer Risk and Etiology (INTEGRAL) program, which includes objectives to develop early-detection biomarkers and risk-prediction tools to refine the guideline for lung cancer screening.

The INTEGRAL program has 3 projects: the Genetics project, the Risk Biomarker project to study pre-diagnostic blood biomarkers, and the Nodule Malignancy project to evaluate nodules found during screening with LDCT scans.

The Research

American Cancer Society (ACS) Senior Principal Scientist of Epidemiology, Ying Wang, PhD, is part of an international team of researchers working on the INTEGRAL program who have recently published papers about the Risk Biomarker. They’ve been systematically studying circulating protein biomarkers before a diagnosis of lung cancer with the goal of finding a way to better refine and predict a person’s risk for lung cancer.

The current guideline for lung cancer screening uses the risks of age and smoking history to determine eligibility for getting a yearly LDCT scan. Biomarkers may provide additional or complementary information on a person’s risk for developing lung cancer, the scientists explain in Annals of Epidemiology as they describe their motivation for their study design and initial study of protein biomarkers.

The researchers are also working on new ways to tell the difference between cancerous and noncancerous (benign) nodules after an LDCT scan. They plan to develop one test including multiple biomarkers that can help achieve both goals.

The problem is if a person has a noncancerous nodule that’s identified as cancerous (called a false positive nodule), they can end up receiving treatments and interventions that may cause long-term problems. On the other hand, if a person has a nodule that’s identified as noncancerous when it is cancer, they may have missed an opportunity to receive a curative treatment.

The researchers described their search for protein biomarkers associated with the risk of developing lung cancer, in Nature Communications. Using data from the Lung Cancer Cohort Consortium (which includes data from the ACS CPS-II Nutrition Cohort), they measured 1,162 proteins in blood samples drawn no more than 3 years before a diagnosis of lung cancer in 731 people diagnosed with lung cancer and 731 controls with a smoking history.

They identified 36 protein biomarkers that appear linked to the risk of “imminent smoking-related lung cancer,” which they define as a diagnosis of lung cancer within 3 years after the blood sample was taken. Most of the proteins had not previously been identified as pre-diagnostic biomarkers of lung cancer. These proteins include tumor biomarkers, inflammation biomarkers, and angiogenesis-related biomarkers.

In a related study, published in the Journal of the National Cancer Institute, the researchers focused on developing a lung-cancer-prediction model using protein biomarkers in the blood. They compared their model to two current lung cancer prediction models and found that it outperformed both.

For the final phase of the INTEGRAL biomarker project, researchers will study people who ever smoked with more than 1,500 participants with lung cancer and 3,000 cancer-free participants to provide risk models that can be used in clinical practice to assess eligibility for lung cancer screening.

Why Does it Matter? 

More research around protein biomarkers and the risk of lung cancer needs to be done to determine how well protein biomarkers can predict future diagnoses of lung cancer. For instance, researchers need to study repeat blood samples from the same people over time, cohorts need to include people with non-European ancestry, and protein biomarkers need to be studied in a population of never smokers.