Mapping the Tumor Microenvironment in TNBC and Deep Exploration for M1 Macrophages-Associated Prognostic Genes

IFI35 regulates non-canonical NF-κB signaling to maintain glioblastoma stem cells and recruit tumor-associated macrophages

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor characterized by a highly heterogeneous and immunosuppressive tumor microenvironment (TME). The symbiotic interactions between glioblastoma stem cells (GSCs) and tumor-associated macrophages (TAM) in the TME are critical for tumor progression. Here, we identified that IFI35, a transcriptional regulatory factor, plays both cell-intrinsic and cell-extrinsic roles in maintaining GSCs and the immunosuppressive TME. IFI35 induced non-canonical NF-kB signaling through proteasomal processing of p105 to the DNA-binding transcription factor p50, which heterodimerizes with RELB (RELB/p50), and activated cell chemotaxis in a cell-autonomous manner. Further, IFI35 induced recruitment and maintenance of M2-like TAMs in TME in a paracrine manner. Targeting IFI35 effectively suppressed in vivo tumor growth and prolonged survival of orthotopic xenograft-bearing mice. Collectively, these findings reveal the tumor-promoting functions of IFI35 and suggest that targeting IFI35 or its downstream effectors may provide effective approaches to improve GBM treatment.

The role of tumor-associated macrophages in tumor immune evasion

BACKGROUND

Tumor growth is closely linked to the activities of various cells in the tumor microenvironment (TME), particularly immune cells. During tumor progression, circulating monocytes and macrophages are recruited, altering the TME and accelerating growth. These macrophages adjust their functions in response to signals from tumor and stromal cells. Tumor-associated macrophages (TAMs), similar to M2 macrophages, are key regulators in the TME.

METHODS

We review the origins, characteristics, and functions of TAMs within the TME. This analysis includes the mechanisms through which TAMs facilitate immune evasion and promote tumor metastasis. Additionally, we explore potential therapeutic strategies that target TAMs.

RESULTS

TAMs are instrumental in mediating tumor immune evasion and malignant behaviors. They release cytokines that inhibit effector immune cells and attract additional immunosuppressive cells to the TME. TAMs primarily target effector T cells, inducing exhaustion directly, influencing activity indirectly through cellular interactions, or suppressing through immune checkpoints. Additionally, TAMs are directly involved in tumor proliferation, angiogenesis, invasion, and metastasis. Developing innovative tumor-targeted therapies and immunotherapeutic strategies is currently a promising focus in oncology. Given the pivotal role of TAMs in immune evasion, several therapeutic approaches have been devised to target them. These include leveraging epigenetics, metabolic reprogramming, and cellular engineering to repolarize TAMs, inhibiting their recruitment and activity, and using TAMs as drug delivery vehicles. Although some of these strategies remain distant from clinical application, we believe that future therapies targeting TAMs will offer significant benefits to cancer patients.

IFI35 limits antitumor immunity in triple-negative breast cancer via CCL2 secretion

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with poor prognosis due to the lack of therapeutic targets. Although immunotherapy brings survival benefits to patients diagnosed with TNBC, it remains limited and treatment resistance is widespread. Here we demonstrate that IFI35 is highly expressed in tumor tissues and can be induced by Interferon-γ in a time-dependent and concentration-dependent manner in breast cancer cells. In xenograft models, we reveal that IFI35 dramatically increases myeloid-derived suppressor cells infiltration in tumors, along with depletion and anergy of CD8+T cells. IFI35 ablation leads to prolonged survival of the mice. Mechanistically, RNA-sequencing reveals that IFI35 promotes CCL2 secretion, resulting in the remodeling of TNBC immune microenvironment. Ablation of IFI35 promotes the infiltration of effector CD8+T cells, and thereby sensitizes TNBC to anti-PD-1 immunotherapy. Our data suggest that IFI35 limits antitumor immunity and may be expected to become a new immunotherapy target in TNBC.

Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

PURPOSE

Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy.

MATERIALS AND METHODS

We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response.

RESULTS

Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group.

CONCLUSION

Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.

Construction and validation of a macrophage polarization-related prognostic index to predict the overall survival in patients with early-stage triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and the current prognostic system cannot meet the clinical need. Interactions between immune responsiveness and tumor cells plays a key role in the progression of TNBC and macrophages are vital component of immune cells. A prognostic model based on macrophages may have great accuracy and clinical utility.

Methods

For model development, we screened early stage (without metastasis) TNBC patients from The Cancer Genome Atlas (TCGA) database. We extracted messenger RNA (mRNA) expression data and clinical data including age, race, tumor size, lymph node status and tumor stage. The follow up time and vital status were also retrieved for overall survival calculation. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to calculate the immune cell composition of each sample. Weighted gene co-expression network analysis (WGCNA) was used to identify M1-like macrophage-related genes. Combining least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, the M1-like macrophage polarization-related prognostic index (MRPI) was established. We obtained TNBC patients in Gene Expression Omnibus (GEO) database through PAM50 method and retrieved the mRNA expression data and survival data. The Harrell's concordance index (CI), the area under the receiver operating characteristic (ROC) curves (AUCs) and the calibration curve were used to evaluate the developed model.

Results

We obtained 166 early TNBC cases and 113 normal tissue cases for model building, along with 76 samples from GSE58812 cohort for model validation. CIBERSORT analysis suggested obvious infiltration of macrophages, especially M1-like macrophages in early TNBC. Four genes were eventually identified for the construction of MPRI in the training set. The AUCs at 2 years, 3 years, and 5 years in the training cohort were 0.855, 0.881 and 0.893, respectively; and the AUCs at 2 years, 3 years, and 5 years in the validation cohort were 0.887, 0.792 and 0.722, respectively. Calibration curves indicated good predictive ability and high consistency of our model.

Conclusions

MRPI is a promising biomarker for predicting the prognosis of early-stage TNBC, which may indicate personalized treatment and follow-up strategies and thus may improve the prognosis.