Classification of triple-negative breast cancers based on Immunogenomic profiling

Abstract

BackgroundAbundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.MethodsUsing four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification.ResultsWe identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K–Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype.ConclusionsThe identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-1002-1) contains supplementary material, which is available to authorized users.

Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy

“…Mounting evidence has identified that the immune cell dysfunction within the HNSC-TME promotes immunosuppression and so the associated tumor survival and progression, which later demands therapeutic intervention to counteract this process [35,36]. In this study, we analyzed the immune cell infiltration in a meta-cohort of 1029 HNSC samples and categorized the HNSC into three distinct immune subtypes.Our analysis indicated that density of CD4 + T cells, CD8 + T cells, plasma cells, and M1 Macrophages cells along with the higher immune score were correlated to the patient's prognosis, which is in line with the previous studies [37,38]. This emphasizes the fact that the pre-existing immune response has an anti-tumor effect and positively affects the response to immunotherapy.…”

GNG5 is a Novel Oncogene Associated with Poor Prognosis in Glioma Patients

“…Previous studies have reported a correlation between programmed death-1/ligand-1 (PD-1/PD-L1) and immune activity in tumors in triple-negative breast cancer [20]; Moreover, a large number of studies have shown that ssGSEA and ESTIMATE are highly reliable in the evaluation of tumor immune strati cation and immune invasion analysis [22,[49][50][51][52]. Furthermore, we analyzed the correlation between the expression of GNG5 and in ltration level of tumor-in ltrating immune cells using TIMER.…”

“…The resulting enriched pathways were analyzed based on nominal (NOM) P-values and normalized enrichment scores (NES). We examined the relationship between the expression of GNG5 in glioma tissues and the immune microenvironment using the single-sample gene set enrichment analysis (ssGSEA) to calculate the enrichment of 29 immune cell geneset signatures in each glioma sample based on data downloaded from the CGGA database [19][20][21].…”

Immune cell confrontation in the papillary thyroid carcinoma microenvironment

“…To understand the overall situation of immune in ltration and facilitate visualization, we corrected the data as follows. For each ssGSEA score, Xi was replaced with Xi ′ with the equation Xi ′ = (Xi − Xmin)/(Xmax − Xmin), where Xmin and Xmax represent the minimum and maximum values of the ssGSEA score in the THCA sample, respectively [17]. The same method was also applied to the other nine GEO datasets.…”