M1 Polarization Markers Are Upregulated in Basal-Like Breast Cancer Molecular Subtype and Associated With Favorable Patient Outcome

Abstract

Background: Breast cancer heterogeneity is an essential element that plays a role in the therapy response variability and the patient's outcome. This highlights the need for more precise subtyping methods that focus not only on tumor cells but also investigate the profile of stromal cells as well as immune cells.

Objectives: To mine publicly available transcriptomic breast cancer datasets and reanalyze their transcriptomic profiling using unsupervised clustering in order to identify novel subsets in molecular subtypes of breast cancer, then explore the stromal and immune cells profile in each subset using bioinformatics and systems immunology approaches.

Materials and methods: Transcriptomic data from 1,084 breast cancer patients obtained from The Cancer Genome Atlas (TCGA) database were extracted and subjected to unsupervised clustering using a recently described, multi-step algorithm called Iterative Clustering and Guide-gene Selection (ICGS). For each cluster, the stromal and immune profile was investigated using ESTIMATE and CIBERSORT analytical tool. Clinical outcomes and differentially expressed genes of the characterized clusters were identified and validated in silico and in vitro in a cohort of 80 breast cancer samples by immunohistochemistry.

Results: Seven unique sub-clusters showed distinct molecular and clinical profiles between the well-known breast cancer subtypes. Those unsupervised clusters identified more homogenous subgroups in each of the classical subtypes with a different prognostic profile. Immune profiling of the identified clusters showed that while the classically activated macrophages (M1) are correlated with the more aggressive basal-like breast cancer subtype, the alternatively activated macrophages (M2) showed a higher level of infiltration in luminal A and luminal B subtypes. Indeed, patients with higher levels of M1 expression showed less advanced disease and better patient outcomes presented as prolonged overall survival. Moreover, the M1 high basal-like breast cancer group showed a higher expression of interferon-gamma induced chemokines and guanylate-binding proteins (GBPs) involved in immunity against microbes.

Conclusion: Adding immune profiling using transcriptomic data can add precision for diagnosis and prognosis and can cluster patients according to the available modalities of therapy in a more personalized approach.

Keywords: basal like; breast cancer; macrophages; transcriptomic; tumor infiltrated immune cells.

Figure 1

Flow chart of the bioinformatics methodology used.

Figure 2

Unsupervised clustering of breast cancer subtypes revealed the presence of seven distinct sub-clusters. (A) Unsupervised clustering of the 1,084 breast cancer samples obtained from TCGA using ICGS2 option in the AltAnalyze tool. It showed the hierarchical cosine Euclidean option. (B) The distribution of the severe clusters in luminal A, luminal B, and basal-like subtypes and showing the representative groups that match more than 50% of the total patients in that molecular subtype.

Figure 3

The association between the dominant sub-cluster within each molecular subtype and patient outcome.

Figure 4

Box plot of NR2E1, INGX, C1QL2, POU5F1, A2ML1, ROPN1, VGLL1, FZD9 expression in different breast cancer subtypes using Breast Cancer Gene-Expression Miner v4.0 database.

Figure 5

FZD9 and NR2E1 immunoreactivity in our patient cohort that consists of 80 breast cancer patients (A) Representative images of strong, moderate, weak as well as negative FZD9 immunoreactivity. (B) Representative images of positive and negative NR2E1 immunoreactivity.

Figure 6

The cellular and stromal profile of clusters representative of luminal A, B, and basal-like breast cancer subtypes. (A) Estimation of Stromal and Immune cells profile in clusters representative of luminal A, B, and basal-like breast cancer subtypes using Expression data (ESTIMATE) tool. (B) The percentage of immune cell infiltration in basal versus luminal A and B breast cancer cells as predicted by CIBERSORT analytical tool. (C) Non-parametric Pearson correlation matrix for immune cells showing different correlation of M1 and M2 to other infiltrating immune cells.

Figure 7

The genetic and molecular profile of M1H basal-like breast cancer subtype compared to the M1L group (A) The association between M1H and M1L basal-like breast cancer subtypes and patient outcome presented as overall survival (OV) and disease-free survival (DFS). (B) The expression levels of key M1 phenotype markers including specific cytokines and chemokines (CXCL9, IDO1, CXCL13, CXCR2P1, GBP1P1, GBP4and) in M1H and M1L basal-like breast cancer subtypes. (C) Top pathways enriched in M1H basal-like breast cancer subtype. ****p < 0.01.

Figure 8

Possible mechanisms through which M1 might mediate the favorable outcome in basal-like triple-negative breast cancer (TNBC) patients.