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Researchers Develop qPCR Assay to Gauge Prognosis in NSCLC Patients

NEW YORK (GenomeWeb) – Stanford University researchers have developed a nine-gene panel to gauge prognosis in non-small cell lung cancer patients, as they reported in the Journal of the National Cancer Institute yesterday.

Stanford's Maximilian Diehn and his colleagues drew upon more than 1,100 nonsquamous NSCLC samples to generate and validate a nine-gene molecular prognostic index (MPI). They then developed the MPI into a qPCR assay that they said outperformed other prognostic signatures. By adding together clinical and pathological data with the MPI, the researchers further developed a composite risk model that they said provided the most accurate risk stratification.

"Both the MPI and CRM identify early-stage NSCLC patients at high risk for death, with patients in the high-risk group having an approximately 50 percent chance of death by five years," Diehn and his colleagues wrote in their paper.

The researchers pulled together four cohorts of NSCLC patients, totaling 1,106 patients, that they divided into training and validation sets. In the training set, they uncovered 1,012 genes whose expression was associated with survival.

By clustering and analyzing these genes, the researchers found that they largely fell into four groups linked to certain biological processes and pathways like proliferation, airway basal stem cells, and lymph node-expressed genes.

To build their MPI, the researchers chose the most prognostic genes from each of those four clusters, which they then developed into that nine-gene set. This set, they noted, included genes linked with an adverse outcome — MAD2L1, GINS1, SLC2A1, and KRT6A — and ones associated with a favorable outcome — TNIK, BCAM, KDM6A, FCGRT, and FAIM3.

In the validation set, this nine-gene panel was strongly associated with overall survival, the researchers reported. It remained linked with survival even when only early stage NSCLC cases from the cohort were considered. It was also prognostic in two additional, external cohorts.

Diehn and his colleagues then implemented their MPI along with two control housekeeping genes as a qPCR assay, which they validated in an additional cohort of 98 patients.

They compared their newly created prognostic gene panel with three other published signatures by applying those other qPCR assays to their training and testing meta-cohorts. By computing the net reclassification improvement and integrated discrimination improvement statistics for MPI versus the other signatures, the researchers said their signature could outperform the others.

Adding in standard clinical and pathological covariates like age, sex, and smoking status made the risk index even more robust, Diehn and his colleagues reported. By combining a clinical prognostic index with their MPI into a composite risk model (CRM), the researchers were able to identify patients at higher risk of death across all NSCLC stages. The CRM, they noted, outperformed the MPI, as gauged by the net reclassification improvement and integrated discrimination improvement statistics, indicating that the integration of molecular and clinical risk-associated variables yields a more robust assessment of prognosis.

"As far as we are aware, our study is the first to provide a composite model that incorporates both gene expression and clinical data from a large, population-based database to leverage the independent prognostic content of these two types of data," the researchers said.

These indexes, they added, could then be useful for gauging recurrence risk in patients with NSCLC.