The GENSIGNIA™ MSC (miRNA Signature Classifier) Lung Cancer Test is based on research performed by the academic groups of Drs. Sozzi and Pastorino (Boeri et al. 2011). The initial study was designed to address the unmet clinical need for a molecular marker-based non-invasive test for early diagnosis of lung cancer in subjects at risk.|
MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer
Boeri et al. 2011, Proc. Nat. Acad. Sci.
A large validation study was completed in 2013 and published in the Journal of Clinical Oncology in early 2014 (Sozzi et al. 2014).
Discovery of miRNA ratio signatures for prediction and diagnosis of lung cancer
Initial training and testing of MSC in two independent cohorts
MSC – three risk groups (low, intermediate and high)
Clinical Utility of a Plasma-based miRNA Signature Classifier within Computed Tomography Lung Cancer Screening: a Correlative MILD Trial Study
Sozzi et al. 2014, J. Clin. Oncol.
Discovery of MSC for Lung Cancer Detection
Determine performance of MSC in 939 subjects from randomized prospective screening trial using LDCT
Diagnostic performance: 87% Sensitivity, 81% Specificity with 99% NPV
Prognostic performance: MSC risk categories significantly correlate with overall survival
Clinical utility: 5-fold reduction in false positive rates of LDCT
The study conducted by Dr. Sozzi and colleagues included an extensive miRNA profiling of primary lung tumors, paired normal lung tissues, and multiple plasma samples collected before and at the time of disease, from two independent spiral CT-screening trials. The objective was to identify biomarkers able to predict tumor development and prognosis, therefore improving lung cancer diagnosis and treatment options.
In the study, miRNA expression patterns significantly distinguished: (i) tumors from normal lung tissues, (ii) tumor histology and growth rate, (iii) clinical outcome, and (iv) year of lung cancer CT detection. In addition, miRNA profiles in normal lung tissues also displayed remarkable associations with clinical features, suggesting the influence of a permissive microenvironment for tumor development.
miRNA expression analyses in plasma samples collected 1–2 years before the onset of disease, at the time of CT detection and in disease-free smokers enrolled in the screening trial, resulted in the generation of miRNA signatures with strong predictive, diagnostic, and prognostic potential (area under the ROC curves ≥ 0.85). These signatures were validated in an independent cohort from a second randomized spiral-CT trial (in press).
The results suggest a potential role for miRNAs in lung tissues and plasma as molecular predictors of lung cancer development and aggressiveness and may have clinical implication for lung cancer management.
Validation of the MSC - Lung Cancer Test
The purpose of the validation study conducted by Dr. Sozzi and collegues was to determine the diagnostic performance of the MSC algorithm. 939 subjects that had been enrolled in the MILD trials were tested using pre-specified cutpoints of the MSC for detection of cancer.
The sample cohort selected for the validation study was divided as follows:
The results of the study show that the overall diagnostic performance of the MSC was 87% sensitivity and 81% specificity. Moreover, the MSC was independent of the lung cancer stage and of the time between testing and lung cancer diagnosis (up to 24 months prior to cancer diagnosis).
The study included 594 subjects in the LDCT arm without lung cancer. Of those 346 (58%) had a nodule detected by LDCT and by adding the information obtained using the MSC Lung Cancer Test, this rate was reduced to 20%. In the same group of subjects, 115 (19.4%) had a nodule with a diameter greater or equal to 5 mm; in clinical practice those nodules are actionable and by combining the LDCT result to the classification provided by the MSC test, the rate was reduced to 3.7%.