Fig. 5
In silico dissection of the immune clusters. a We used the CIBERSORT algorithm to assess the composition of the immune microenvironment of breast cancer samples. For each cluster, we calculated the median of the absolute score of the 22 cell types given by the CIBERSORT in each cohort. Immune cluster-specific cell-type median scores were used in an unsupervised clustering using maximum linkage and the ward.D2 method. The heatmap obtained allows to visualize which cell types are enriched across the immune clusters. Immune clusters are annotated on the top and bottom of the heatmap. b Density plots represents the distribution of the absolute CIBERSORT scores for selected cell types across the clusters for the METABRIC cohort; the vertical lines crossing the distribution identify the median value for the score. Kruskal–Wallis test p value are denoted. c Estimates of multivariable logistic regression analysis and the 95% confidence interval (CI) are illustrated by forest plot to assess which immune cells inferred by CIBERSORT explain the most the poor prognosis cluster (Cluster B) vs Clusters A and C. Box size is inversely proportional to the width of the confidence interval. Asterisks denote FDR-corrected p value < 0.05. Immune cell types from the lymphoid or myeloid lineage are identified.
