Tumor mutational burden is an emerging biomarker to predict response to immune checkpoint inhibitors, but its utility as a prognostic indicator in ovarian cancer is unclear.
Understanding the tumor mutation burden (TMB) genetic signature in patients with ovarian cancer may predict their response to immunotherapy, according to a recent study in International Immunopharmacology.
TMB is an emerging biomarker to predict response to immune checkpoint inhibitors (ICIs), but its utility as a prognostic indicator in ovarian cancer is unclear.
Response to immune checkpoint inhibitors—programmed cell death-ligand 1 (PD-L1), programmed cell death 1 (PD1), and cytotoxic T lymphocyte antigen 4 (CTLA4)—have not been as successful in patients with ovarian cancer compared with lung cancer and melanoma.
Given that TMB can predict ICI response, and high TMB may be linked with better response to CTLA4, PD1 or PD-L1, as well as combination therapy, the authors of this study sought to examine the prognostic value of TMB and any correlations with response to immune therapy and immune cell infiltration in ovarian cancer.
The researchers used patient samples from 3 datasets to create a genetic signature: 587 patients from The Cancer Genome Atlas (TCGA), 260 patients from the GSE32062, and 94 patients from the International Cancer Genome Consortium (ICGC).
Patients from TCGA dataset were used as a training cohort, and the datasets from GSE32062 and ICGC were used for validation.
The TMB for each patient was estimated as the number of variants per exon length, and was merged with the corresponding clinical data (outcomes, immune infiltrates, and immunotherapy responsiveness) from each patient. Patients from the TCGA dataset (the training group) were divided into high- and low-TMB groups according to their median TMB.
Higher TMB was associated with better survival in the TCGA and ICGC OC cohorts. The high-TMB group had higher CD8+ T-cell infiltration than the low-TMB group.
While no significant correlation was found between TMB and immunotherapy response was found, further analysis suggested that patients in the low-risk group might have a better response to anti-PD1 therapy.
Using weighted gene co-expression network analysis, the researchers identified 8 prognostic and TMB-related genes to construct a TMB-related signature that could distinguish between the high- and low-risk patients. Its predictive power was validated in the GSE32062 and ICGC datasets.
Among the 8 genes, RAB34, CDH22, and LRRC4B were associated with higher risk; the remaining 5 (CAMK1G, ISG20, HLA-DOB, USP51, and PRSS16) were identified as protective factors.
Further work needs to use a larger sample size of patients in a prospective study; in addition, the researchers said experiments such as flow cytometry and immunohistochemistry are necessary to verify the relationship between the TMB and TMB-related genes and immune cell infiltration.
Fan S, Gao Xm Qin Q, Li H, Yuan Z, Zhao S. Association between tumor mutation burden and immune infiltration in ovarian cancer. Published online November 11, 2020. Int Immunopharmacol. doi: 10.1016/j.intimp.2020.107126