according to the results of a database analysis.
A total of 33 genes formed the signature that could be transformed into a risk score, according to a study by Santosh Khanal, a senior bioinformatics scientist at Children’s Mercy Kansas City (Mo.), and colleagues published in Cancer Genetics.
Their study used gene expression and clinical parameters from the Lymphoma/Leukemia Molecular Profiling Project from 233 patients who received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) therapy to identify genes whose expression was associated with overall survival (OS). They refined the information to develop prognostic gene signature that could be used to calculate risk scores for each individual and predict OS.
Significant separation
The researchers initially found 61 genes individually associated with OS that had a nonadjusted P ≤ .001 using the univariate Cox regression model. The 61 genes were then assessed using multivariate Cox analysis to identify a minimal set of genes that could predict OS, resulting in a minimal set of 33 genes that were used to develop a survival risk score for each individual.
The OS of the high-risk group was significantly reduced, compared with the low-risk group (hazard ratio, 0.046; P < .0001). Upon stratification of individuals by risk score into quartiles, patients in the lowest quartile risk score had a 100% probability of survival, while individuals in the highest quartile had a 9.2% OS by year 5.
In order to validate their results, the researchers calculated risk scores using their prognostic gene set in three additional published DLBCL studies. For all three studies, individuals with low risk score had significantly better OS, “indicating the robustness of the gene signature for multiple external datasets,” according to the researchers.
The top biological pathways and processes that were significantly overrepresented in the 33-gene set were the thioester biosynthetic process (P = .00005), cellular response to hormone stimulus (P = .002), G protein–coupled receptor ligand binding (P = .003), and myeloid cell activation involved in immune responses (P = 0.006).
“As new therapies for lymphoma become available, including new immunotherapies and personalized medicine approaches such as [chimeric antigen receptor] T cells, it will be important to identify candidate individuals that are at high risk and may benefit from experimental therapeutic approaches compared with individuals who will have lower risk of death with current therapies,” the researchers concluded.
The authors reported that they had no competing interests.