Prognostic immune-related gene models for breast cancer: a pooled analysis

Immune-related Gene-based Prognostic Signature for the Risk Stratification Analysis of Breast Cancer

Background:

Breast cancer has plagued women for many years and caused many deaths around the world.

Method:

In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study.

Results:

Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion:

Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

Characterization of 5-inflammatory-gene signature to affect the immune status and predict prognosis in breast cancer

Introduction

Breast cancer (BC) is associated with an inflammatory microenvironment. In BC, epidemiological evidence suggests that inflammation is associated with a poor prognosis. However, approaches to determine the extent of inflammation in the tumor microenvironment remain unclear.

Material and methods

We downloaded the expression profiles and corresponding clinicopathological information of 1050 BC tissues and 59 cases of normal breast tissue from The Cancer Genome Atlas (TCGA) dataset. Similarly, data of 1050 BC tissues were downloaded from Gene Expression Omnibus (GEO) and 200 inflammation-related genes were downloaded from the MSigDB database. We developed an inflammatory risk model to reflect the immune microenvironment in BC.

Results

Multivariate Cox analysis showed that the risk score was an independent predictor of overall survival (OS). Inflammatory signature was significantly associated with clinical and molecular features and could serve as an independent prognostic factor for BC patients. Furthermore, most immune cells were significantly less infiltrated in the high-risk group than in the low-risk group. There was a significant difference in survival time between the group with a high and low tumor mutational burden (TMB) score, and the survival time of the patients with a low TMB was significantly higher than that of the high-risk group. The risk scores were significantly lower in patients who responded to immunotherapy (complete response/partial response - CR/PR) than in patients who did not respond to immunotherapy (stable disease/progressive disease - SD/PD).

Conclusions

We developed and validated an inflammatory risk model, which served as an independent prognostic indicator and reflected immune response intensity in the BC microenvironment.

Construction and Validation of an Immune Infiltration-Related Gene Signature for the Prediction of Prognosis and Therapeutic Response in Breast Cancer

Breast cancer patients show significant heterogeneity in overall survival. Current assessment models are insufficient to accurately predict patient prognosis, and models for predicting treatment response are lacking. We evaluated the relationship between various immune cells and breast cancer and confirmed the association between immune infiltration and breast cancer progression. Different bioinformatics and statistical approaches were combined to construct a robust immune infiltration-related gene signature for predicting patient prognosis and responses to immunotherapy and chemotherapy. Our research found that a higher immune infiltration-related risk score (IRS) indicates that the patient has a worse prognosis and is not very sensitive to immunotherapy. In addition, a new nomogram was constructed based on the gene signature and clinicopathological features to improve the risk stratification and quantify the risk assessment of individual patients. Our study might contribute to the optimization of the risk stratification for survival and the personalized management of breast cancer.

Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

Prognostic Value of Natural Killer Cells Besides Tumor-Infiltrating Lymphocytes in Breast Cancer Tissues

BACKGROUND

Each subgroup of immune cells has a different prognostic role in breast cancer; however, the prognostic impact of tumor-infiltrating natural killer cells (TINKs) is still not well established. Our aim was to assess the prognostic impact of natural killer (NK) cells in breast carcinomas.

MATERIALS AND METHODS

NK cells infiltration were assessed by immunohistochemistry (IHC). Statistical analyses were performed to evaluate the correlation of NK cells with clinical-pathological features and outcome.

RESULTS

CD56 IHC was realized in 126 patients. NK cells infiltration showed significant and positive association with tumor high Scarff-Bloom-Richardson (SBR) grade. NK cells were significantly associated with HER2-positive breast cancer and triple-negative breast cancer subtypes. Analyses showed significant and inverse correlation with progesterone and estrogen receptors expression status. High NK cells were significantly related to high Ki-67 labeling index. Our data showed that high NK cells infiltrate was significantly associated with tumor-infiltrating lymphocytes in breast cancer tissues. At a median follow-up of 5.5 years, high CD56 expression (≥ 5 cells/10 high power field) was associated significantly with a good overall survival and with good disease-free survival.

CONCLUSION

In this study, we assessed the important prognostic role of TINKs in breast carcinomas, which seems to be evident despite its association with aggressive pathological features. Thus evaluation of NK cells can be standardized and integrated in daily routine.

Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.

Bioinformatic profiling of prognosis-related genes in the breast cancer immune microenvironment

In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.

Not Everyone Fits the Mold: Intratumor and Intertumor Heterogeneity and Innovative Cancer Drug Design and Development

Disruptive innovations in medicine are game-changing in nature and bring about radical shifts in the way we understand human diseases, their treatment, and/or prevention. Yet, disruptive innovations in cancer drug design and development are still limited. Therapies that cure all cancer patients are in short supply or do not exist at all. Chief among the causes of this predicament is drug resistance, a mechanism that is much more dynamic than previously understood. Drug resistance has limited the initial success experienced with biomarker-guided targeted therapies as well. A major contributor to drug resistance is intratumor heterogeneity. For example, within solid tumors, there are distinct subclones of cancer cells, presenting profound complexity to cancer treatment. Well-known contributors to intratumor heterogeneity are genomic instability, the microenvironment, cellular genotype, cell plasticity, and stochastic processes. This expert review explains that for oncology drug design and development to be more innovative, we need to take into account intratumor heterogeneity. Initially thought to be the preserve of cancer cells, recent evidence points to the highly heterogeneous nature and diverse locations of stromal cells, such as cancer-associated fibroblasts (CAFs) and cancer-associated macrophages (CAMs). Distinct subpopulations of CAFs and CAMs are now known to be located immediately adjacent and distant from cancer cells, with different subpopulations exerting different effects on cancer cells. Disruptive innovation and precision medicine in clinical oncology do not have to be a distant reality, but can potentially be achieved by targeting these spatially separated and exclusive cancer cell subclones and CAF subtypes. Finally, we emphasize that disruptive innovations in drug discovery and development will likely come from drugs whose effect is not necessarily tumor shrinkage.

Clinical significance of the immune microenvironment in ovarian cancer patients

Tumor immune infiltrates of ovarian cancer were quite cohort and subtype dependent.