Identification of immune-related prognostic biomarkers in triple-negative breast cancer

Introduction

Breast cancer is the most common lethal cancer in women (1,2). Triple-negative breast cancer (TNBC) is a distinct subset of breast cancer, accounting for approximately 15% of total occurrences. TNBC has a bleak outlook because of its high histological grade and aggressive behavior, which are not easy for the attainment of effective treatment (3). TNBC does not respond to conventional hormonal treatments and is resistant to human epidermal growth factor receptor-2 (HER2)-targeted therapy. Additionally, chemotherapy and radiotherapy may not always provide effective treatment for TNBC (4-7). Thus, there is an urgent need to develop novel effective targeted therapies.

Studies based on clinical data and experiments have indicated that immunotherapy has the potential to significantly extend the lifespan of individuals. In recent years, several clinical studies have investigated immunotherapy treatments for TNBC (8-10). Compared to other subtypes, TNBC is considered to be sensitive to immunotherapy due to certain traits, such as genomic instability, a high tumor mutation burden, and elevated levels of immune infiltration (11,12). In addition, compared with other subtypes, TNBC patients exhibit significant expression of programmed cell death ligand 1 (PD-L1) (13). This discovery opens up new possibilities and guidelines for the advancement of effective immunotherapy treatments for TNBC patients (14). While TNBC exhibits a greater reaction to immune checkpoint inhibitors (ICIs) than other subtypes, there are TNBC patients who demonstrate limited effectiveness of immunotherapy (15). Hence, it is imperative to create innovative and efficient immune-associated indicators for forecasting the outcome and effectiveness of immunotherapy.

This study aimed to discover new prognostic biomarkers associated with the immune system. Initially, we discovered 2,008 genes that were expressed differently between the immunomodulatory (IM) group (n=87) and the remaining subtypes (n=273). Survival was significantly predicted by 33 immune-related genes (IRGs) according to the findings of univariate Cox regression analysis. Using the least absolute shrinkage and selection operator (LASSO) regression analysis, a model was created to predict breast cancer survival outcomes, including 12 out of 33 IRGs. A prognostic model was built and then validated in The Cancer Genome Atlas (TCGA) TNBC cohort. The identification of immune-related prognostic indicators for TNBC in our study aids in the precise immunotherapy of TNBC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1554/rc).

Methods

Study cohort

Our previous study (16) described a total of 465 patients diagnosed with TNBC by pathology from Fudan University Shanghai Cancer Center (FUSCC) in the entire study cohort. The cohort contained 360 samples with RNA-sequencing data and 279 samples with whole exome sequencing (WES) data. All samples were previously untreated primary breast cancers. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the independent ethics committee at Fudan University Shanghai Cancer Center Ethical Committee (No. 2019171). Informed consent were obtained. Data on TNBC (n=145), including expression, mutation, and clinical information, were obtained by utilizing the TCGA database. We acquired the single-cell RNA sequencing (scRNA-seq) information of five TNBC individuals from the Gene Expression Omnibus (GEO) repository using the study identifier GSE148673.

Currently, there is an ongoing multicenter trial called I-SPY2, which is adaptively randomized and open-label in nature. The study examined the use of neoadjuvant chemotherapy (NACT) in treating early-stage breast cancer patients with a high risk of recurrence (NCT01042379) (17,18). The platform trial simultaneously evaluates multiple investigational arms, each comprising NACT as a common control arm, along with an investigational agent or combination. For women diagnosed with breast cancer, the main objective is achieving pathologic complete response (pCR), which means there are no invasive tumors present in the breast or nearby lymph nodes following surgical treatment. The current investigation assessed the expression of TDO2, HLA-C, and OPLAH as distinct indicators for predicting pCR to the combination of paclitaxel and pembrolizumab.

Identification of differentially expressed IRGs and survival-associated IRGs

We used the limma package in R to analyze differential expression. Differential expression analysis was carried out comparing the IM group (n=87) with the remaining subtypes (n=273). Genes that satisfied the filtering conditions of adjust P<0.05 and |Log2 fold change (FC)| >0.58 were categorized as genes exhibiting differential expression. Survival-associated IRGs were identified using univariate Cox regression analysis in R with the ‘survival’ package.

Perform GO and KEGG analyses

To elucidate the role of the dysregulated redox-associated genes, we utilized the R package ‘clusterProfiler’ to conduct enrichment analyses for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The q value and the P value were the selection criteria, which must be less than 0.05.

Development and verification of the IRG signature

To construct the most precise and feasible prediction model, one can utilize the LASSO technique, which assigns weights to model parameters and identifies the crucial variables. We performed LASSO Cox regression using the R package ‘glmnet’ (19). Based on the findings of this analysis, a model was created using a 12-gene signature to forecast clinical outcomes in patients with TNBC. For each sample, risk scores were calculated by using coefficients assigned to each prognostic IRG. Risk scores were used to classify patients into high- and low-risk groups during training and validation, which was determined using the median value.

Performance analysis

The survival package in R was utilized to analyze data concerning patients in the model. A log-rank test and Kaplan-Meier curves were employed to examine disparities between the two groups. The sensitivity and specificity of a model in predicting outcome events are often determined using a receiver operating characteristic curve (20). We utilized the R package ‘survival’ for the purpose of the receiver operator characteristic (ROC) analysis. Using the median risk score as a threshold, we plotted clinical outcome data against the risk score for patients with breast cancer. Our research produced ROC curves and computed the area under the curve (AUC) for survival at 3 and 5 years. Bootstrap resampling (1,000 resamplings) was utilized to assess the calibration capability of the nomograms. The line at a 45-degree angle symbolized ideal calibration, and the proximity of the line indicated the quality of calibration.

Estimation of immune infiltration

The CIBERSORT algorithm (21) was utilized to examine the infiltration of immune cells. The Wilcoxon rank-sum test was used to analyze the disparities in immune infiltrating cell scores between the low- and high-risk groups.

Mutation spectrum characteristics

The analysis involved utilizing genetic somatic mutation data from the FUSCC TNBC cohort to compare the disparities between low- and high-risk groups. The analysis of genetic mutation differences between low-risk and high-risk groups was conducted using Maftools (22).

Single-cell RNA-seq analysis

The t-distributed Stochastic Neighbor Embedding (tSNE) method was employed with the ‘seurat’ data processing package to decrease nonlinear dimensions (1). An annotated cell cluster was then created using the “singleR” package in combination with canonical markers (2). Additionally, the expression of genes in each cluster was plotted using the ‘seurat’ package.

Statistical analysis

The data were assessed for statistical significance using GraphPad Prism software version 8 (GraphPad Software, San Diego, CA, USA) and R software version 3.5.2. Recurrence-free survival (RFS) was defined as the period from surgery to recurrence or last follow-up. Plots were created to assess the patients’ prognosis for 1-, 3-, and 5-year RFS using ROC curves. Survival analysis was performed using Kaplan-Meier with the Log-rank test. Student’s t-test was used to compare the variances between two groups. Analysis items with P<0.05 were considered statistically significant.

Results

IRG model construction

We identified a total of 2008 distinct genes with differential expression (P<0.05, |log FC| >0.58) between the IM subtype and the remaining subtypes (Figures 1,2A). Univariate Cox analysis was conducted to detect survival-associated IRGs, resulting in the identification of 33 genes as survival-related genes (Figure 2B). Afterwards, the implementation of LASSO Cox regression analysis led to the discovery of twelve genes associated with the immune system: TDO2, CHIT1, CARMIL2, HLA-C, ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL (Figure 2C). We used these genes to establish a prognostic risk model. The risk score for each individual was computed using the following equation: risk score = [The expression of TDO2 × (−0.13)] + [The expression of CHIT1 × (−0.13)] + [The expression of CARMIL2 × (−0.0004)] + [The expression of HLA-C × (−0.05)] + (The expression of ADIRF × 0.07) + (The expression of C19orf33 × 0.07) + (The expression of CA8 × 0.03) + (The expression of AHNAK2 × 0.05) + (The expression of RHOV × 0.03) + (The expression of OPLAH × 0.006) + (The expression of THEM6 × 0.16) + (The expression of NEBL × 0.16). After calculating their risk scores, the patients were divided into two groups based on whether their scores were above or below the median value.

Figure 1 Schematic of the research strategy. FUSCC, Fudan University Shanghai Cancer Center; TNBC, triple-negative breast cancer; IM, immunomodulatory; DEGs, different expression genes; FC, fold change; LASSO, least absolute shrinkage and selection operator; TCGA, The Cancer Genome Atlas; ROC, receiver operator characteristic.

Figure 2 Identification of differentially expressed IRGs and construction of prognostic model. (A) Volcano plot of IRGs in the IM subtype and other subtypes. Through differential expression analysis, downregulated genes are labeled in blue and upregulated genes are labeled in red, respectively. (B) An analysis of univariate regression revealed 33 genes that were linked to prognosis (P<0.05). (C) The LASSO Cox regression model was used to plot partial likelihood deviations against log(λ). HR, hazard ratio; CI, confidence interval; IRGs, immune-related genes; IM, immunomodulatory; LASSO, least absolute shrinkage and selection operator.

We displayed the distribution of risk scores between the low-risk and high-risk groups in the training and validation sets (Figure 3A,3B). Next, we displayed the survival status and survival time of patients in various risk categories in both the training and validation sets (Figure 3C,3D). In addition, the expression levels of 12 immune-related prognostic genes were examined in both the training and validation sets for each patient, as shown in Figure 3E,3F. According to the findings, patients who experienced recurrence exhibited elevated risk scores (Figure 3G). The analysis of survival indicated that participants classified as high-risk experienced a lower rate of RFS than those classified as low-risk (FUSCC TNBC cohort, P=0.001; TCGA TNBC cohort, P=0.005) (Figure 3H).

Figure 3 Validation of prognostic risk scoring model. (A) An illustration of the distribution of risk scores in the FUSCC TNBC cohort is shown below. The color progression from green to red indicates the progression from low- to high-risk scores. (B) TCGA TNBC cohort risk score distribution. Low risk is indicated by the color green, while high risk is indicated by the color red. (C) The FUSCC TNBC cohort includes information about the survival status and duration of survival for each patient with TNBC. (D) Survival status and survival time were examined for patients in the TCGA TNBC group. (E) The FUSCC TNBC cohort displays a heatmap illustrating the expression of 12 immune-related genes that are prognostic for TNBC patients. (F) The TCGA TNBC cohort displays a heatmap illustrating the expression of 12 immune-related genes that are prognostic for TNBC patients. (G) Risk score in the no recurrence and recurrence groups in the FUSCC TNBC (left) and TCGA TNBC cohorts (right). Comparison using an unpaired t-test. (H) The survival rates of patients with TNBC in the high-risk and low-risk groups were compared in the FUSCC TNBC cohort (on the left) and the TCGA TNBC cohort (on the right). Statistical tests were performed using the log rank method. FUSCC, Fudan University Shanghai Cancer Center; TNBC, triple-negative breast cancer; TCGA, The Cancer Genome Atlas.

Validation of the prognostic risk model

By integrating the risk score with the pathologic T and N stage, we constructed a nomogram (Figure 4A). When solely utilizing pathologic T stage for prognosis prediction, the anticipated AUC values for the 1-, 3-, and 5-year operating curves in the training and validation sets were 0.50, 0.55, and 0.54 and 0.78, 0.74, and 0.68, respectively (Figure S1A,S1B). Using only the pathologic N stage as a predictor of prognosis, the predicted AUC values for the 1-, 3-, and 5-year operating curves in the training and validation sets were 0.74, 0.74, and 0.70 and 0.82, 0.77, and 0.74, respectively (Figure S1C,S1D). Moreover, when we integrated the risk score with pathologic T and N stage to forecast prognosis, the projected AUC values for the 1-, 3-, and 5-year operating curves in the training and validation sets were 0.78, 0.83, and 0.75 and 0.91, 0.85, and 0.81, respectively (Figure 4B). The calibration graph additionally demonstrated that the forecast likelihood of the nomogram aligned with the real probability of RFS at 1, 3, and 5 years (Figure 4C), demonstrating the model’s prediction ability with satisfactory discrimination and accuracy.

Figure 4 Combination of risk score and clinicpathological features to improve risk stratification and survival prediction. (A) A nomogram that merges a risk score with clinical attributes. (B) The time-ROC curves of the risk score combined with T and N stage in the FUSCC TNBC (left) and TCGA TNBC (right) cohorts. (C) Calibration plots of the risk score combined with clinicopathological features in the FUSCC TNBC (left) and TCGA TNBC (right) cohorts. AUC, area under the curve; RFS, recurrence-free survival; ROC, receiver operator characteristic; FUSCC, Fudan University Shanghai Cancer Center, TNBC, triple-negative breast cancer; TCGA, The Cancer Genome Atlas; T, tumor; N, lymph node.

Molecular features of the high- and low-risk groups

Prognostic genes were identified by conducting differential expression analysis between the high-risk and low-risk groups. A total of 885 distinct genes with differential expression (P<0.05, |log FC| >0.58) between the high-risk and low-risk groups were identified. The results of the enrichment analysis for biological process (BP) indicated that the differentially expressed genes were primarily enriched in the promotion of leukocyte activation and cell activation (Figure 5A). The results of the enrichment analysis for cell component (CC) indicated that the immunoglobulin complex and the external side of the plasma membrane were the main areas where differentially expressed genes were enriched (Figure 5B). The results of the analysis on molecular functions (MFs) indicated that various genes with differential expression were primarily enriched in antigen binding and binding to immunoglobulin receptors (Figure 5C). The KEGG pathway enrichment analysis showed that the genes with differential expression were predominantly enriched in cell adhesion molecules and infection caused by Staphylococcus aureus (Figure 5D). Next, we conducted additional analysis on the impacts of genetic mutations in both the high-risk and low-risk groups (Figure S2A,S2B). The top 15 genes with the highest mutation frequencies were identified. In the low-risk group, the TP53, PIK3CA, TTN, KMT2C, and ABCA13 genes exhibited the most frequent mutations. In the high-risk group, the TP53, TTN, PIK3CA, MUC16, and OBSCN genes exhibited the most mutations.

Figure 5 Molecular features of the high- and low-risk groups. (A-D) Enrichment analysis for differentially expressed genes in the high- and low-risk groups was performed using (A-D). BP (A), CC (B), MF (C), and KEGG (D) methods. (E) The proportions of infiltrating immune cells across different risk groups are illustrated by the Violin plot. Comparison using an unpaired t-test. Significant results were observed at ***, P<0.001; **, P<0.01, and *, P<0.05, respectively. BP, biological process; CC, cell component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Differences between high-risk and low-risk groups in terms of immunity

Our calculation of immune infiltration percentages based on the LM22 signature matrix was performed using CIBERSORT, and the integration of CIBERSORT and differential expression profiling revealed an abundance of immune-activated cells and immunostimulators within the low-risk group (Figure 5E and Figure S3A) (21,23). Meanwhile, we found that the low-risk group had a significant increase in the expression of immunoinhibitors, which further supports the use of immune checkpoint blockade as a therapeutic strategy (Figure S3B) (23).

To examine the association between IRGs and the effectiveness of immunotherapy, a total of 29 patients who received neoadjuvant immunotherapy in conjunction with chemotherapy were included in the study. The findings indicated that TDO2 and HLA-C exhibited reduced expression in non-pCR individuals (n=10). Conversely, OPLAH was overexpressed in non-pCR patients (Figure 6). The utilization of these genes is anticipated for the prediction of the clinical effectiveness of TNBC immunotherapy.

Figure 6 Correlation between IRGs expression and the efficacy of immunotherapy. Comparison using an unpaired t-test. Significant results were observed at ***, P<0.001; and *, P<0.05, respectively. pCR, pathologic complete response; IRGs, immune-related genes.

Single-cell RNA-seq analysis of 12 genes of the signature

Using the tSNE technique and Single R annotation, we categorized TNBC tumor cells and microenvironmental cells from five patients in the GEO (GSE148673) repository. Our analysis revealed eight distinct cell clusters comprising malignant cells, B cells, T cells, macrophages, monocytes, fibroblasts, endothelial cells, and tissue stem cells (Figure 7A). The expression of the 12 genes in the signature revealed that HLA-C, ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL were frequently expressed in tumor cells, as depicted in Figure 7B-7M. However, TDO2, CHIT1, and CARMIL2 had higher expression in tumor stem cells, macrophages and T cells, respectively. In addition, consistent with previous studies (24,25), we observed that HLA-C was expressed at a high level in all other immune cells. In addition, the Dim plot illustrates the relative expression of the 12 genes in the signatures (Figure 7N).

Figure 7 Single-cell RNA-seq analysis of 12 IRGs. (A) Distribution of cell clusters shown by the tSNE method. (B-M) The mRNA expression of (B) TDO2, (C) CHIT1, (D) CARMIL2, (E) HLA-C, (F) ADIRF, (G) C19orf33, (H) CA8, (I) AHNAK2, (J) RHOV, (K) OPLAH, (L) THEM6, and (M) NEBL in different cell clusters. (N) Dim plot of gene distribution. IRGs, immune-related genes; tSNE, t-distributed Stochastic Neighbor Embedding.

Discussion

In our prior investigation, we categorized TNBCs into four transcriptome-derived subcategories: (I) luminal androgen receptor (LAR), (II) IM, (III) immune-suppressed basal-like, and (IV) mesenchymal-like. Among each subtype, we discovered potential therapeutic targets or biomarkers (16). For this research, we incorporated 360 individuals with TNBC from the FUSCC TNBC group and discovered 2008 different expression genes (DEGs) between IM and the remaining subtypes, which were selected for subsequent investigation. Potential prognostic factors were identified through regression analysis, revealing 12 immune-related DEGs (TDO2, CHIT1, CARMIL2, HLA-C, ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL). A risk score model was constructed using these genetic factors, enabling accurate prediction of RFS in patients with TNBC. Furthermore, the validation of the model was conducted using a dataset acquired from the TCGA TNBC cohort, suggesting its potential broad applicability for patients with TNBC.

According to our discovery, the risk-scoring model consisting of 12 genes has the potential to be used as a prognostic predictor for TNBC. Notably, TDO2, CHIT1, CARMIL2, and HLA-C were identified as protective factors that contribute to a favorable prognosis. ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6 and NEBL were risk factors unfavorable to prognosis. TDO2 participates in the metabolism of tryptophan (26), catalyzing the production of kynurenine, which undermines the immune surveillance of the host and facilitates the progression of cancer (27). CHIT1, also known as chitotriosidase, is a member of the GH18 glycosyl hydrolase family 18 in humans (28,29). A prior investigation indicated that CHIT1 levels are increased in individuals diagnosed with primary breast cancer (30). Invadopodia formation necessitates the presence of CARMIL2, a regulator of capping protein and linker of myosin 1 (31). Impaired T-cell activation has been reported as a characteristic of CARMIL2 deficiency (32). The HLA-C gene is part of the major histocompatibility complex and has become a prominent target of biomedical research due to its involvement in a number of diseases, such as cancer and autoimmune disorders (33-36). Studies have demonstrated variations in C10orf116 among various pathological grades of ovarian carcinoma and nonmuscle-invasive bladder cancer (37-39). C10orf116 deficiency in prostate cancer (PCa) is linked to unfavorable disease-free-survival (DFS) outcomes (37). Pancreatic cancer, along with other types of cancer, has been found to exhibit abnormal expression of C19orf33 in previous research (40). According to reports, the excessive expression of CA8 has been found to enhance the proliferative and migratory capabilities of renal cell carcinoma (RCC) (41). In thyroid carcinoma tissues, AHNAK2 was discovered to be increased, and it stimulates the advancement of thyroid carcinoma by activating the NF-κB pathway (42). According to a recent investigation, it was suggested that RHOV enhances the growth and spread of lung adenocarcinoma cells via the JNK/c-Jun pathway (43). Earlier research indicated that OPLAH functions as a standalone prognostic factor for gastric cancer and squamous cell carcinoma, as evidenced by previous studies (44,45). In a prior investigation, it was discovered that THEM6, a member of the thioesterase superfamily, serves as an indicator of resistance to ADT in PCa (46). According to prior research, NEBL has been identified as a crucial element in the advancement of ovarian cancer (47). These genes, as indicated by the aforementioned studies, might have an impact on the advancement of TNBC. Furthermore, immune-associated DEGs have the capability to accurately forecast the outcomes of patients with TNBC.

In addition to accurately predicting tumor progression and prognosis, the risk score more effectively distinguishes immune cells infiltrating TNBC from nontumor cells. The low-risk group exhibited notably elevated levels of infiltration by CD8⁺ T cells, CD4 memory-activated T cells, and M1 macrophages. CD8⁺ T cells play a significant role in the immune response by identifying and acknowledging tumor cells in tumor immunotherapy (48). In the traditional sense, CD4⁺ T cells are regarded as assisting cells that stimulate CD8⁺ T cells (49,50). Upon different stimuli, uncommitted macrophages (M0) transform into proinflammatory macrophages (M1 and M2), and the production of proinflammatory cytokines by M1 macrophages hinders tumor growth (51,52). To some degree, within the low-risk category, there was a notable increase in the expression of substances that boost the immune system and substances that suppress the immune system, suggesting positive immune responses and advantages of immunotherapy.

Nevertheless, our study still has certain limitations. The predictive model still needs additional validation through extensive clinical trials. Furthermore, it is necessary to conduct biological experiments to investigate the underlying mechanisms of immune-related prognostic genes in TNBC.

Conclusions

In summary, a signature consisting of twelve genes was created by utilizing IRGs that exhibited differential expression between the IM subtype and other subtypes. This signature has been shown to be sufficiently accurate to predict prognosis in TNBC patients. In addition, the signature is linked to the expression of immune checkpoints and the infiltration of immune cells, providing a means to identify potential populations that could potentially benefit from immunotherapy.

Acknowledgments

We are grateful to the AJE platform for checking and modifying the language. We also thank Dr. Yang Ouyang and Xi-Yi Sui for editing the manuscript.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1554/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1554/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1554/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-1554/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the independent ethics committee at Fudan University Shanghai Cancer Center Ethical Committee (No. 2019171). Informed consent were obtained.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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