Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer

Introduction

Breast cancer, one of the most common causes of cancer in women, has drawn great attention around the globe. In recent years, breast cancer incidence and mortality rates are still on the rise, which seriously threatens women’s health (1). At the molecular level, breast cancer can be generally divided into four subtypes: Luminal A, Luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and basal-like (2,3). Luminal A breast cancer is usually sensitive to endocrine therapy and has a relatively good prognosis. Luminal B subtype includes all types of hormone receptor-positive breast cancer, except for Luminal A, accounting for about one-fourth of the breast cancer cases (4). Compared with Luminal A breast cancer, the survival rate of patients with Luminal B breast cancer is much lower, and their prognosis and treatment sensitivity are inconsistent and varied (5,6). Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy as adjuvant treatment, and their prognosis cannot be accurately assessed and remains a matter of controversy. Only a few studies focus on the precise reclassification of Luminal B breast cancer and various appropriate treatment strategies. Therefore, it has become indispensable to find valuable biomarkers to reclassify Luminal B breast cancer more accurately.

Immunotherapy is a newly emerging treatment modality in cancer therapy that has promising potential. However, most patients with breast cancer treated by immunotherapy show negative results, except for several trials in selected patients with triple-negative breast cancer. Some studies attempt to examine the effectiveness of immunotherapy by subtyping triple-negative breast cancer to classify their characteristics and prognosis (7-13), whereas no such positive evidence has been observed in patients with Luminal B breast cancer.

In the previous studies, several reclassification methods for breast cancer have been proposed, and some of them have shown significant clinical benefits (3,14,15). However, a more precise and effective treatment could be found by using machine learning algorithm to construct a prognostic prediction model based on immune-related genes to reclassify patients with Luminal B breast cancer. Therefore, we constructed an immune-related gene signature (IRGS) to predict clinical outcomes of patients with Luminal B breast cancer, aiming to bring a more accurate and individualized treatment plan with fewer-to-no side effects, which may also provide a foothold for the clinical practice of precision medicine. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-24-377/rc).

Methods

Patients

We collected the gene expression profile of patients with Luminal B breast cancer from two datasets to identify an IRGS. We included 738 patients with Luminal B breast cancer in this study: 488 from the Metabric dataset (training cohort) and 250 from The Cancer Genome Atlas (TCGA) dataset (validation cohort). The level 3 RNA expression profile data of TCGA cohort were downloaded from the GDC Data Portal (https://portal.gdc.cancer.gov/), and log₂-transformed transcripts per million (TPM) were utilised. ‘Combat’ algorithm in R package ‘sva’ was used to remove batch effects. The exclusion criterion was patients without sound survival information. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Construction and validation of a prognostic signature

From the ImmPort database (www.immport.org), we downloaded the gene list of 2,498 immune-related genes. By overlapping them with the Metabric and TCGA dataset, 1,090 commonly expressed immune-related genes were retained. Then, we screened out top 50% highly variant and highly expressed genes with a median absolute deviation of >0.5. After resampling 1,000 times and performing a univariate Cox regression analysis each time, those genes robustly associated with prognosis were retained. Least absolute shrinkage and selection operator (LASSO)-Cox regression were used to establish the final prognostic prediction model.

The cut-off of the risk score (RS) was determined by the time-dependent receiver operating characteristic (ROC) curve at 5 years; subsequently, patients were divided into immune high- and low-risk groups. Kaplan-Meier survival analysis and log-rank tests were performed to compare disease-free survival (DFS) of the two risk groups. Univariate analysis and multivariate analysis integrating with clinical and pathologic variables were used to test the independent prognostic value of IRGS in patients with Luminal B breast cancer.

Functional annotation and analysis

Enrichment analysis and pathway annotations were used for signature genes and differentially expressed genes (DEGs) in the two risk groups by R package ‘gProfleR’. Gene set enrichment analysis (GSEA) was conducted to further explore the underlying pathways through the Bioconductor package ‘HTSanalyzeR’. We investigated the distribution of immune and stromal composition by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. In addition, we compared the distribution of immune cells in low- and high-risk groups in the Metabric and TCGA datasets by EPIC algorithm (16).

Statistical analysis

For comparison, the independent t-test was used for continuous variables, and the Chi-squared test or Fisher’s exact test was performed for categorical variables. LASSO regression was performed via the ‘glmnet’ R package (version 2.0-16). We performed univariate and multivariate analyses to test the association between IRGS and DFS. Variables with P<0.05 in the univariate analysis were included in the multivariate analysis through the Cox proportional hazards regression model. Statistical significance was set at P<0.05. Statistical analysis was performed in R software (version 3.5.1; http://www.Rproject.org).

Results

Construction and definition of the IRGS

In order to establish an IRGS to predict the prognosis of patients with Luminal B breast cancer, we collected data on the gene expression profiles of those patients from the Metabric and TCGA datasets for preprocessing. Next, we filtered out 488 patients from the Metabric and 250 patients from TCGA datasets. There was no significant difference in the characteristics of these two cohorts (Table 1). We obtained 2,498 immune-related genes from the ImmPort dataset. Then we screened out 396 highly expressed and highly variant immune-related genes as the candidate genes for constructing the prognostic model.

Cox regression analysis was performed to select the genes related to recurrence and metastasis in Luminal B breast cancer from the Metabric dataset. After resampling 1,000 times, 14 genes with statistically significant times >900 times (P<0.05) were retained. Through LASSO-Cox regression, 12 immune-related genes were then chosen to construct the IRGS, and the corresponding parameters were obtained. Therefore, a preliminary prognostic model comprising 12 genes for patients with Luminal B breast cancer was established (Figure 1A,1B). The RS in patients with Luminal B breast cancer was calculated using the following formula: RS = (0.090 × THBS1) + (0.213 × S100A1) − (0.047 × LANCL1) + (0.254 × PDGFRB) − (0.289 × ACO1) − (0.287 × SEMA3G) + (0.343 × ACVR1B) − (0.157 × IGF1R) + (0.093 × NR2F1) − (0.156 × PGRMC) − (0.555 × PPARA) − (0.070 × TNFRSF18).

Figure 1 The establish of IRGS. (A) Schematic flow chart of the study procedure; (B) 12 immune-related genes selected in LASSO Cox regression; (C) time-dependent ROC analysis. IRGS, immune-related gene signature; ROC, receiver operating characteristic; LASSO, least absolute shrinkage and selection operator; TCGA, The Cancer Genome Atlas; KM, Kaplan-Meier.

We defined a cut-off score of −1.169 by the time-dependent ROC at 5 years (Figure 1C, https://cdn.amegroups.cn/static/public/10.21037gs-24-377-1.xlsx).

IRGS powerfully predicted DFS of patients with Luminal B breast cancer

We classified the patients into high- (RS ≥−1.169) and low-risk groups (RS <−1.169) by constructing the IRGS. The distribution of the IRGS RS for individual patients in the training and validation cohorts is shown in Figure 2A,2B. Furthermore, Kaplan-Meier analysis showed that the 5-year DFS in the high-risk group was significantly lower as compared to the low-risk group, both in the training cohort [hazard ratio (HR) =4.95, 95% confidence interval (CI): 3.22–7.62, P<0.001, Figure 2C] and the validation cohort (HR =2.47, 95% CI: 1.29–4.75, P=0.005, Figure 2D), using this novel IGRS model.

Figure 2 The outcomes of low and high immune risk in Luminal B breast cancer patients. (A,B) Distribution of the IRGS risk score for individual patients in the Metabric and the TCGA cohorts; (C,D) Kaplan-Meier curves comparing patients with low or high immune risk in Metabric and TCGA. P values comparing patients with low or high immune risk were calculated using the log-rank test. IRGS, immune-related gene signature; TCGA, The Cancer Genome Atlas; HR, hazard ratio; CI, confidence interval; DFS, disease-free survival.

IRGS is an independent risk factor for patients with Luminal B breast cancer

To analyse the factors impacting the prognosis of Luminal B patients, we performed a univariate analysis of multiple prognostic-related indicators of breast tumours, such as RS; age; tumour location; tumour, node, metastasis (TNM) stage; hormone therapy; and chemotherapy. Among them, the RS and TNM stage were independent prognostic factors for Luminal B breast cancer, both in the training and validation cohort (P<0.05). Subsequently, multivariate analysis was used to confirm the effect of these two independent prognostic factors (training cohort RS: HR =4.96, 95% CI: 3.00–8.18, P<0.001; TNM staging: HR =1.69, 95% CI: 1.20–2.39, P=0.003; validation cohort RS: HR =2.56, 95% CI: 1.28–5.09, P=0.007; TNM staging: HR =2.25, 95% CI: 1.43–3.53, P<0.001) (Table 2).

Function annotation of IRGS

Furthermore, we carried out gene ontology analysis of the 12 immune-related genes in the predictive model to examine their functional pathways. The results revealed that the 12 immune-related genes were mainly enriched in pathways related to chemotherapy response (Figure 3A). The expression level of each gene showed a distinctly opposite trend between the high- and low-risk groups in both the training and the validation cohorts (Figure 3B). The result of gene ontology analysis of DEGs showed an enrichment trend in stromal related microenvironment pathways in the Metabric cohort (Figure 3C). GSEA showed that epithelial-mesenchymal transit, angiogenesis, interferon-alpha response, and interferon-gamma response were the leading pathways (Figure 3D).

Figure 3 GO analysis and GSEA analysis of IRGS. (A) GO analysis of 12 genes in the prognostic prediction model; (B) expression distribution of 12 genes in the high- and low-risk groups in the training cohort and validation cohort; (C) GO analysis of DEGs in the Metabric cohort; (D) GSEA analysis showed epithelial mesenchymal transit, angiogenesis, IFN-α response, IFN-γ response were leading pathways. GO, Gene Ontology; GSEA, gene set enrichment analysis; IRGS, immune-related gene signature; DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; ES, enrichment score; IFN, interferon.

Through the ESTIMATE, immune infiltration of the high-risk group was significantly higher than the low-risk group with a significant difference in the immune score (P=0.006), the stromal score (P<0.001), and the ESTIMATE score (P<0.001) in the Metabric cohort (Figure 4A). Interestingly, we found that the immune high-risk group had a higher percentage of cancer-associated fibroblast (CAF) (P<0.001) in both the Metabric and TCGA cohorts (Figure 4B, Figures S1,S2).

Figure 4 ESTIMATE and CAF analysis of IRGS. (A) Through ESTIMATE, Stromal score, immune score and ESTIMATE score were calculated in the Metabric cohort. (B) CAF had a higher percentage in high immune risk group. IRGS, immune-related gene signature; ESTIMATE, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data; CAF, cancer-associated fibroblasts; TCGA, The Cancer Genome Atlas.

Discussion

Precision medicine has received widespread attention since its discovery, and treatment based on the molecular biology of breast cancer has become a consensus in clinical practice. Previous studies have shown that breast cancer is a heterogeneous malignant tumour (17,18). The tumour of patients with the same clinicopathological characteristics can still exhibit different biological behaviours, and their responses to treatment and their prognosis are not the same (19). This is especially seen in patients with Luminal B breast cancer, where the sensitivity of personalised treatment shows strong heterogeneity (20,21). Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy. However, there is still no effective and feasible biomarker that predicts the prognosis and the therapeutic effects of drugs on patients with Luminal B breast cancer.

At present, there are several precision treatment options at the genetic level. In patients with hormone receptor-positive breast cancer, Oncotype DX (21-gene) and Mamaprint (70-gene) can be used to detect patients with a low risk of tumour recurrence, which may be avoided with additional chemotherapy (22-24). Patients with germline BRCA gene mutations in triple-negative breast cancer can be specifically treated with poly ADP-ribose polymerase (PARP) inhibitors which have shown excellent clinical effect. There are also a small group of patients specifically treated with androgen receptor (AR) (25,26). Therefore, we have constructed this 12-gene prognostic prediction model through deep-learning analysis of the database, and this project has been identified to have a good prognostic prediction performance for patients with Luminal B breast cancer. For patients in different risk groups, the recommended treatment plans may differ. The result of gene ontology analysis revealed that the 12 genes are mainly enriched in pathways related to chemotherapy response, which may indicate its clinical potential in predicting the therapeutic response to chemotherapy.

The genes constituting the IRGS (Table S1) have been reported to correlate with tumour progression and immunotherapy. IGF1R is tightly linked with cancer incidence, which might be related to BRCA1 (27). A combined inhibiting effect of IGF1R and EGFR could promote immune activation in pancreatic cancer treatment (28). In breast cancer, lncRNA NR2F1 has been reported to promote angiogenesis by activating the IGF1R pathway (29). THBS1, an angiogenesis inhibitor as well as a potential therapeutic target, plays a vital role in tumour progression and acts as a promotor in certain pathways but as an inhibitor in others (30). THBS1 accelerates liver metastasis via epithelial-mesenchymal transition in patients with colorectal cancer (31), while activating the THBS1 signal axis in breast cancer facilitates focal adhesion and cancer aggressiveness (32). Furthermore, activating the interferon pathway with suppression of the tumour microenvironment and resistance to the immune checkpoint is an effective treatment option for breast cancer (33,34). Those echoed with GSEA analysis. The tumour stroma is closely related to the malignancy of epithelial cells, and studies have reported that the genetic inactivation of PTEN in mouse mammary stromal fibroblasts changed the tumour microenvironment through the large-scale remodelling of the extracellular matrix (ECM), innate immune cell infiltration, and increased angiogenesis, thereby affecting the development and deterioration of breast cancer (35). Moreover, the amount of stromal collagen affects the infiltration of immune-competent cells in gastric cancer, thereby changing the immune characteristics (36). Another report focused on the stromal and immune infiltration genes related to the prognosis of breast cancer, highlighting the underlying genes behind the regulation of the tumour microenvironment and cell infiltration (37). The above findings are consistent with the results of DEGs and ESTIMATE in our model. In the four consensus molecular subtypes (CMSs) of colorectal cancer, CMS1, with stronger immune activity, showed better prognosis while CMS4, with higher stromal invasion, usually had a poorer prognosis (38); comparing this to the results of the DEGs and ESTIMATE in our model, the high-risk group also exhibited the characteristics of higher stromal infiltration. Through single-cell analysis, the coordinated relationship between stromal heterogeneity and cancer immune response has been previously validated (39). In solid cancers, mesenchymal stromal cells have become vital mediators of immune function and immunotherapy response and even serve as novel predictors of drug response and new drug targets. Distinct subclasses of CAF play a critical role in immune regulation (40). A growing number of studies have reported that CAF, which are highly heterogeneous components in the tumour microenvironment, can regulate immune activity and anti-tumour immune response (41). Evidence shows that CAF, which plays a key role in breast cancer progression, is influenced by PDGFRB (42), which blocks invasive growth and migration in triple-negative breast cancer (43). The immune-checkpoint blockade treatment with PDGFRB aptamer in triple-negative breast cancer (44) gives enlightenment in treating Luminal B breast cancer. This indicates that IGRS may have potential immunotherapy significance; however, more studies are needed for further exploration and validation.

Interestingly, in our research, we found that the 12 immune-related genes mainly existed in chemotherapy-related pathways, and the gene expression is inconsistent in high- and low-risk patients. This might suggest that patients in different risk groups have different sensitivity to chemotherapy and immunotherapy. At present, immunotherapy has been selectively used for patients with triple-negative breast cancer in neoadjuvant and palliative treatment (45,46). Based on these results, we suppose that similar therapeutic plans containing immunotherapy seem feasible for high-risk patients with Luminal B breast cancer. A prospective phase II clinical trial of neoadjuvant chemotherapy plus immunotherapy in Luminal B breast cancer provided evidence for our results (47). On the other hand, the better prognosis of patients with low risk showed a possibility of reducing treatment in patients with low immune risk in the future.

Conclusions

We have developed a novel IRGS for predicting clinical outcomes in patients with Luminal B breast cancer. Furthermore, those 12 genes mostly related to response to chemical, and the expression levels were completely opposite in patients of immune low- and high-risk groups. More studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for Luminal B breast cancer patients, thereby assisting clinicians to make individualised treatment plans.

Acknowledgments

The authors acknowledge all the participants and the helpful discussions with other members of The First Affiliated Hospital and The Sixth Affiliated Hospital of Sun Yat-sen University.

Funding: This study was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596), the Fundamental Research Funds for the Central Universities of Sun Yat-sen University (No. 19ykpy57), and Science and Technology Planning Project of Guangzhou City (No. 202102020641). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-24-377/rc

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-24-377/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-24-377/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).

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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