Highlights d We build the genomic and transcriptomic landscape of 465 primary TNBCs d Chinese TNBC cases demonstrate more PIK3CA mutations and LAR subtype d Transcriptomic data classify TNBCs into four subtypes d Multi-omics profiling identifies potential targets within specific TNBC subtypes

Purpose: The tumor microenvironment has a profound impact on prognosis and immunotherapy. However, the landscape of the triple-negative breast cancer (TNBC) microenvironment has not been fully understood. Experimental Design: Using the largest original multiomics dataset of TNBC (n ¼ 386), we conducted an extensive immunogenomic analysis to explore the heterogeneity and prognostic significance of the TNBC microenvironment. We further analyzed the potential immune escape mechanisms of TNBC. Results: The TNBC microenvironment phenotypes were classified into three heterogeneous clusters: cluster 1, the "immune-desert" cluster, with low microenvironment cell infiltration; cluster 2, the "innate immune-inactivated" cluster, with resting innate immune cells and nonimmune stromal cells infiltration; and cluster 3, the "immuneinflamed" cluster, with abundant adaptive and innate immune cells infiltration. The clustering result was validated internally with pathologic sections and externally with The Cancer Genome Atlas and METABRIC cohorts. The microenvironment clusters had significant prognostic efficacy. In terms of potential immune escape mechanisms, cluster 1 was characterized by an incapability to attract immune cells, and MYC amplification was correlated with low immune infiltration. In cluster 2, chemotaxis but inactivation of innate immunity and low tumor antigen burden might contribute to immune escape, and mutations in the PI3K-AKT pathway might be correlated with this effect. Cluster 3 featured high expression of immune checkpoint molecules. Conclusions: Our study represents a step toward personalized immunotherapy for patients with TNBC. Immune checkpoint inhibitors might be effective for "immuneinflamed" cluster, and the transformation of "cold tumors" into "hot tumors" should be considered for "immune-desert" and "innate immune-inactivated" clusters.

Epithelial-to-mesenchymal transition (EMT) is a complex multistep process in which phenotype switches are mediated by a network of transcription factors (TFs). Systematic characterization of all dynamic TFs controlling EMT state transitions, especially for the intermediate partial-EMT state, represents a highly relevant yet largely unexplored task. Here, we performed a computational analysis that integrated time-course EMT transcriptomic data with public cistromic data and identified three synergistic master TFs (ETS2, HNF4A and JUNB) that regulate the transition through the partial-EMT state. Overexpression of these regulators predicted a poor clinical outcome, and their elimination readily abolished TGF-β-induced EMT. Importantly, these factors utilized a clique motif, physically interact and their cumulative binding generally characterized EMT-associated genes. Furthermore, analyses of H3K27ac ChIP-seq data revealed that ETS2, HNF4A and JUNB are associated with super-enhancers and the administration of BRD4 inhibitor readily abolished TGF-β-induced EMT. These findings have implications for systematic discovery of master EMT regulators and super-enhancers as novel targets for controlling metastasis.