Skip to main content
Premium Trial:

Request an Annual Quote

Researchers ID Autoantibodies for Distinguishing Between Benign and Malignant Lung Nodules


NEW YORK (GenomeWeb) – A team led by Arizona State University researcher Joshua LaBaer has identified a panel of autoantibodies that could be useful in diagnosing lung cancer.

The markers, detailed in a paper published this month in the Journal of Thoracic Oncology, could be particularly useful in helping clinicians determine whether lung lesions detected on CT scans are benign or malignant, LaBaer told GenomeWeb.

This intended use is the focus of much proteomic diagnostic work in lung cancer, including Integrated Diagnostics' Xpresys Lung test, which uses a panel of 11 proteins to rule out CT-detected lesions as likely benign.

While the Indi test uses multiple reaction-monitoring mass spec, the ASU panel is based on the nucleic acid programmable protein array (NAPPA) technology developed by LaBaer, which uses printed cDNA vectors to synthesize proteins in situ.

Using these arrays, LaBaer and his colleagues screened serum from 40 early-stage lung cancer patients and 40 smoker controls against panels of 10,000 full-length human proteins, identifying 17 protein antigens that showed higher reactivity in lung cancer cases than in controls. They then used ELISAs to further evaluate those proteins, measuring them in a set of 137 lung cancer patients, 127 smoker controls, and 170 subjects with benign pulmonary nodules.

From this, the researchers put together a five-protein panel that could distinguish between lung cancer patients and smoker controls with sensitivity of 30 percent and specificity of 89 percent. They also put together a panel that could distinguish between benign and malignant lesions with sensitivity of 30 percent and specificity of 88 percent.

LaBaer said that this second use would likely be the focus of his team's work going forward, noting that while achieving the sensitivity necessary for an effective screening test would be challenging, the markers could prove useful in evaluating CT scan results.

"There's not enough sensitivity for a screening test, but if you ran this along with a CT scan it would at least give you some indication that if someone tested positive for this test, you would be much more inclined to follow them more closely or consider a follow-up biopsy," he said. "That is how we envision using this, which is why we set [the study] up this way [to] include people with benign findings and smoker controls as well."

In addition to LaBaer and Indi, a number of researchers and companies, including Somalogic and Quest Diagnostics, have explored proteomic tests for determining lung nodule status. It is an attractive space given that the majority of nodules identified via CT scan are benign and workup of these nodules can involve a variety of expensive and invasive procedures.

In a 2013 interview with GenomeWeb, Indi CEO Albert Luderer put the number of lung nodules requiring evaluation in the US at around 3 million annually, and recent study results will likely drive increased adoption of CT scans in patients at risk of lung cancer. Most notable, in 2011, the National Lung Screening Trial – a National Cancer Institute study involving more than 53,000 current and former heavy smokers – released findings that indicated that CT scans reduced lung cancer mortality by 20 percent compared to standard chest X-rays.

Indi's Xprsys has been on the market since 2013, and while the privately held company has not released sales numbers, it has maintained that they have met expectations. LaBaer said, though, that he sees space in the market for additional tests and markers addressing the problem.

"None of these tests are perfect, but I think by adding more we are going to get that much better at doing what we need to do," he said. "I think combining this with existing assays could definitely improve things quite a bit."

With autoantibody markers such as those the ASU researchers are pursuing, a pattern is becoming evident, particularly in heterogeneous diseases like cancer, where the markers generally offer better specificity than sensitivity, LaBaer said.

"This is the way we are finding a lot of these autoantibody-type markers work," he said. "It seems that each individual may produce different antibodies — some will produce antibody A, some will produce antibody B. And when the antibody is present, it is a pretty good sign that something is wrong, which is what the high specificity tells you: there aren't a lot of false positives."

"But you can't always rely on each person making a particular antibody," he added, noting that this limits the sensitivity of individual markers. "So, you have to combine markers to get better sensitivity but still with reasonable specificity."

LaBaer said his team is now in the process or organizing a larger study to further test the markers. The goal, he said, would be to analyze each of the 17 potential markers both individually and as part of panels, looking at them prospectively in groups of patients with CT-detected lung nodules. The Journal of Thoracic Oncology study was conducted with help from the National Cancer Institute's Early Detection Research Network, which supports work on development and evaluation of biomarkers for the early detection of cancer. LaBaer said he and his colleagues might go back to the organization for support for additional studies.

He said he would in the future be interested in licensing out markers that emerge from the work to a diagnostics company, similar to the arrangement his lab has with proteomic firm Provista Diagnostics for breast cancer markers they discovered and developed using the NAPPA platform.

Provista signed a licensing agreement with LaBaer's lab in 2014 for certain biomarkers and autoantibody technologies. LaBaer is also on the company's scientific advisory board.

LaBaer and his colleagues are also continuing work on expanding the NAPPA platform and are currently approaching 15,000 different gene products. The primary challenge to adding content is collecting the genes to the full-length proteins, he said. "Adding the proteins [themselves] is easy. We just need the genes. So, we are really working on collecting more full-length genes."