Estimating and Comparing immune cell infiltration in cancer Open Access

Jinjing He (Spring 2020)

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Background: During last several years, cancer immunology and immunotherapy have become a promising field in cancer research. It is known that the tumor-infiltrating immune cells are related to tumor progression. In particular, the proportions of immune cells in tumor samples are often indicators for cancer stages and predictive for survival rate, thus these quantities are of great interest. To estimate the immune cell proportions, a practical approach is to perform signal deconvolution from high-throughput omics data. In this study, we aim to compare the immune cell proportions estimation from different omics data, using different method. To be specific, we apply two deconvolution methods on gene expression and DNA methylation data from individuals with and without breast invasive carcinoma (BRCA).

Methods: We used CIBERSORT and TOAST methods to estimate the proportions of B cells, CD4 T cell, CD8 T cell, Natural killer (NK) cell, Monocytes and Granulocytes in gene expression and DNA methylation. CIBERSORT is based on regression framework for reference-based deconvolution, and TOAST is based on matrix factorization for reference-free deconvolution. The Pearson correlation was applied to assess the relationship of estimates from gene expression and DNA methylation with the same method, or between the estimates with two methods.

Conclusion: Results from CIBERSORT showed that there is no significant difference in estimated proportions from tumor and normal samples. The estimations from gene expression and DNA methylation data are very low. With TOAST, the estimated proportions in tumor samples are significantly different from those in normal samples. Moreover, the estimated immune cells proportions from gene expression and DNA methylation show some correlation. Comparing CIBERSORT and TOAST, the correlations between proportion estimates from both gene expression and DNA methylation data are not very high.

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