Dataset Information

Tumor mutational burden and immune infiltrates in renal cell carcinoma and matched brain metastases.

ABSTRACT:

Background

Tumor mutational burden (TMB) and density of tumor-infiltrating lymphocytes (TIL) have been postulated as predictive biomarkers for immunotherapy. Therefore, we investigated the concordance of TMB and TIL of primary/extracranial renal cell carcinoma (RCC) specimens and matched brain metastases (BM).

Patients and methods

Twenty specimens from 10 patients were retrieved from the Vienna Brain Metastasis Registry (6/10 primary tumor, 4/10 lung metastasis, 10/10 matched BM). TMB was assessed using the TruSight Oncology 500 gene panel with libraries sequenced on a NextSeq instrument. TIL subsets (CD3+, CD8+, CD45RO+, FOXP3+, PD-L1+) were investigated using immunohistochemistry (Ventana Benchmark Ultra system) and automated tissue analysis (Definiens software).

Results

No significant difference in TMB, CD3+, CD8+, CD45RO+, FOXP3+ or PD-L1+ expression was observed between extracranial and matched intracranial specimens (P > 0.05). Higher CD8+ TIL (P = 0.053) and CD45RO+ TIL (P = 0.030) densities in the primary tumor compared with the intracranial samples were observed in specimens collected after exposure to systemic treatment. Neither extracranial sample origin (lung metastasis versus primary RCC) nor extracranial disease status at BM diagnosis (progressive versus stable disease) were significantly associated with TMB or TIL densities in extracranial and intracranial samples (P > 0.05). No significant correlation was found between the median differences of TMB or TIL densities from extracranial to intracranial samples and BM-free survival.

Conclusion

The comparable immunological microenvironment of extra- and intracranial tumor samples in our study underscores the immunological activation also in BM from RCC, and therefore, supports the development of immune modulatory treatments also in patients with brain metastatic RCC.

SUBMITTER: Steindl A

PROVIDER: S-EPMC7890370 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Tumor mutational burden and immune infiltrates in renal cell carcinoma and matched brain metastases.

Steindl A A Alpar D D Heller G G Mair M J MJ Gatterbauer B B Dieckmann K K Widhalm G G Hainfellner J A JA Schmidinger M M Bock C C Müllauer L L Preusser M M Berghoff A S AS

ESMO open 20210212 2

<h4>Background</h4>Tumor mutational burden (TMB) and density of tumor-infiltrating lymphocytes (TIL) have been postulated as predictive biomarkers for immunotherapy. Therefore, we investigated the concordance of TMB and TIL of primary/extracranial renal cell carcinoma (RCC) specimens and matched brain metastases (BM).<h4>Patients and methods</h4>Twenty specimens from 10 patients were retrieved from the Vienna Brain Metastasis Registry (6/10 primary tumor, 4/10 lung metastasis, 10/10 matched BM). ...[more]

PMID: 33588158

Similar Datasets

Project description:BackgroundWhether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to investigate the prognosis of TMB and the potential association with immune infiltrates in clear cell renal cell carcinoma (ccRCC).MethodsWe downloaded the somatic mutation data of 336 ccRCC patients from the Cancer Genome Atlas (TCGA) database, and analyzed the mutation profiles with "maftools" package. TMB was calculated and we classified the samples into high-TMB and low-TMB group. Differential analysis was conducted to compare the expression profiles between two groups using "limma" package, and we identified the 9 hub TMB-related signature from batch survival analysis. Gene ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were performed to screen significantly enriched pathways between two groups. Based on the TIMER database, we further assessed the relationships of the mutants of 9 TMB-related signature with immune infiltration levels in ccRCC. Besides, we utilized the "CIBERSORT" package to estimate the abundance of 22 immune fractions between low- and high-TMB groups, and the significant difference were determined by Wilcoxon rank-sum test. Furthermore, Cox regression model combined with survival analysis were used to evaluate the prognostic value of immune cells. Last, we constructed a Tumor Mutation Burden Prognostic Index (TMBPI) from multivariate Cox results and Receiver Operating Characteristic (ROC) curve was drawn to assess the predictive accuracy.ResultsSingle nucleotide polymorphism (SNP) occurred more frequently than insertion or deletion, and C>T was the most common of SNV in ccRCC. Higher TMB levels conferred poor survival outcomes, associated with higher tumor grades and advanced pathological stages. A total of 1,265 differentially expressed genes were obtained and top 19 immune-related genes were identified in Venn diagram. GSEA revealed that patients in higher TMB groups correlated with MAPK signaling pathway, Wnt signaling pathway and pathway in cancers. Moreover, we identified 9 hub TMB-related immune genes related with survival and mutants of 9 signature were associated with lower immune infiltrates. In addition, infiltration levels of CD8+ T cell, CD4+ memory resting T cell, M1 and M2 macrophages, as well as dendritic resting cells in high-TMB group were lower than that in low-TMB group, especially the level of CD8+ T cell and macrophage correlated negatively with prognosis of ccRCC. Last, the TMBPI was constructed and the AUC of ROC curve was 0.666.ConclusionsHigher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in ccRCC. The mutants of 9 hub TMB-related immune signature conferred lower immune cells infiltration which deserved further validation.

Dataset Information

Tumor mutational burden and immune infiltrates in renal cell carcinoma and matched brain metastases.

Background

Patients and methods

Results

Conclusion

Publications

Tumor mutational burden and immune infiltrates in renal cell carcinoma and matched brain metastases.

Similar Datasets

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