Now Arriving: A Lung Cancer Biomarker Blood Test that Can Effectively Distinguish Between Malignant
Now arriving: the next step in early diagnosis of lung cancer.
The prior step? Major use of low dose CT scans to find at an early stage developing lung nodules that may be an early part of a cancer.
The new step: a very good (not perfect) and reliable biomarker that can distinguish between benign and malignant nodules. It’s a blood test that uses 13 proteins expressed during early tumor formation.
The consequences? Potentially significant, for humans and for the litigation industry.
The back story? Dr. Harvey Pass, biomarkers and government funded research. Future posts will cover the back story.
The company developing the test? Integrated Diagnostics – see its press release here.
The medical article? Online here in Nature Translational Medicine; the abstract is pasted below:
"Each year, millions of pulmonary nodules are discovered by computed tomography and subsequently biopsied. Because most of these nodules are benign, many patients undergo unnecessary and costly invasive procedures. We present a 13-protein blood-based classifier that differentiates malignant and benign nodules with high confidence, thereby providing a diagnostic tool to avoid invasive biopsy on benign nodules. Using a systems biology strategy, we identified 371 protein candidates and developed a multiple reaction monitoring (MRM) assay for each. The MRM assays were applied in a three-site discovery study (n = 143) on plasma samples from patients with benign and stage IA lung cancer matched for nodule size, age, gender, and clinical site, producing a 13-protein classifier. The classifier was validated on an independent set of plasma samples (n = 104), exhibiting a negative predictive value (NPV) of 90%. Validation performance on samples from a nondiscovery clinical site showed an NPV of 94%, indicating the general effectiveness of the classifier. A pathway analysis demonstrated that the classifier proteins are likely modulated by a few transcription regulators (NF2L2, AHR, MYC, and FOS) that are associated with lung cancer, lung inflammation, and oxidative stress networks. The classifier score was independent of patient nodule size, smoking history, and age, which are risk factors used for clinical management of pulmonary nodules. Thus, this molecular test provides a potential complementary tool to help physicians in lung cancer diagnosis."