In this study, we developed SMFS based on the gene expression ratio that was calculated by dividing the expression of genes enhancing the tumor-promoting effect of M2 macrophages by the expression of genes improving the antitumor immune effect of CTLs. This ratio can take into account the possible interaction between CTLs and M2 macrophages to some extent [31]. Therefore, we speculate that the higher the SMFS value is, the higher the degree of tumor immunosuppression, and the lower the SMFS value is, the stronger the antitumor immune effect. SMFS was validated stratified for TNBC status and RT, creating four groups (TNBC with RT, non-TNBC with RT, TNBC with no RT, and non-TNBC with no RT). In the RT group (GSE2034), SMFS was an independent prognostic factor for MFS in both patients with TNBC and non-TNBC. However, SMFS was not associated with prognosis in terms of MFS in the no RT group (GSE12276), regardless of TNBC or non-TNBC. GSE2034 and GSE12276 were two independent datasets, and the administration of RT was not random, so it was impossible to verify the interaction between SMFS and RT in one cohort in a rigorous manner. However, these results could indirectly indicate the RT specificity of SMFS. In addition, SMFS showed a trend of interaction with the TNBC subtype in GSE2034 (Pinteraction=0.081), demonstrating that SMFS may be more strongly linked to prognosis in TNBC patients than in non-TNBC patients. Compared with non-TNBC patients, TNBC patients with rapid distant recurrence (MFS < 1 year) had a higher median SMFS (P = 0.001). These results suggest that SMFS could be a prognostic indicator for MFS specific to TNBC patients treated with adjuvant RT.
We also compared the performance of SMFS with previously published immune or RT signatures in breast cancer. First, we found that proliferation and SMFS were independent prognostic factors for MFS in the non-TNBC with RT group (Table 4). This finding indicates that proliferation is a very important factor affecting the effects of RT on patients with non-TNBC. Previous studies have also demonstrated that the radiosensitivity signature (RSS) and single-sample predictors (SSPs) could predict the prognosis for IBTR in patients with ER + tumors owing to their biological effect on proliferation [8, 9]. Second, previous studies found that RSI was associated with the prognosis for IBTR in the ER-RT + group [8, 27]. However, our results showed that RSI was associated with prognosis in terms of MFS in the training set (TNBC with RT) by univariate Cox regression, but the association was not significant by multivariate analysis (Table 2). Furthermore, the performance of RSI was poor in all groups of the validation sets, probably because RSI and ARTIC were developed with survival fraction at 2 Gy (SF2) [32] and locoregional recurrence (LRR) [7] as endpoints, which mainly reflected the local control effect of RT, that is, the direct effect of RT. However, RT might have an indirect effect on long-term survival through the immune system, which could not be reflected by ARTIC or RSI. In fact, there is evidence that the RT sensitivity of solid tumors is associated with immune activation [33]. Third, in our analysis, IMS showed a prognostic effect on MFS only in the non-TNBC with RT group (GSE2034) (univariate P = 0.003, multivariate P = 0.069). IMS was developed in E-TABM-158 dataset [34], which only contains 15% TNBC patients, which might explain why IMS did not show prognostic efficacy in the TNBC with RT group. Finally, we found that T3/T4 patients had a worse prognosis than T1 patients in the no RT group (GSE12276), but no such phenomenon was observed in the RT group (GSE2034) (Table 3, Table 4). According to the clinical guidelines of breast cancer, T3 (tumor size > 5 cm) tumors should be treated with RT after surgery [6], which likely indicates that the T3/T4 patients in GSE12276 could obtain a better prognosis by receiving RT.
Many previous studies have shown that the immune system plays an important role in mediating the antitumor effects of RT [35, 36]. For instance, RT can activate the antitumor immune effect by inducing the maturation of dendritic cells and enhancing the activation of T cells [37, 38]. That is, RT can eliminate the immunosuppressive state of cancer and turn immunologically “cold” tumors “hot” [39]. In our study, patients in the low-risk group (low SMFS) may derive a greater benefit from indirect RT effects through the immune system, leading to a better prognosis. SMFS developed by our approach may be able to select populations of breast cancer, especially TNBC, suitable for RT from the perspective of immunity. RT is generally considered to be an important local control method in breast cancer treatment [40], but the endpoint we used in the training set and validation sets was MFS, rather than the usual endpoints of IBTR or LRR. The reasons are as follows. First, the clinical outcome indicators available in public datasets are limited, especially satisfying the conditions of both RT and a sufficient number of patients with TNBC. Second, at present, the local control effect of RT for breast cancer has been very good, and there are many indicators to predict the local control efficacy of RT [7, 8, 27]. Our focus is on the interaction between immunity and RT in TNBC, and the antitumor immune effect is usually considered to be related to the long-term outcome of cancer [41, 42]. At present, TNBC patients are prone to developing distant metastasis after first-line treatment, and the survival time after distant metastasis is very short [4, 5]. Antitumor immune effects play an important role in the distant metastasis of tumors [43, 44]. Therefore, investigating how to combine the antitumor effects of RT and the immune system and maximize their role in the treatment of TNBC is the fundamental goal of this study. Recently, a study on the application of RT combined with immunotherapy in metastatic TNBC showed encouraging results [45]. However, there are currently no predictive biomarkers for RT combined with immunotherapy. We developed and validated a CTL/M2 macrophage-related four-gene signature (SMFS) that had prognostic value for MFS in TNBC patients undergoing RT, which could provide some information to achieve this goal, but it needs to be further verified in large randomized clinical trials or even trials of checkpoint blockade plus RT.
It was observed that each of our four subtypes of TNBC seemed to be comprised of two or several Burstein subtypes illustrating moderate heterogeneity between these two classification systems. We found that the c1 and c4 subtypes had the highest proportion of BLIA tumors but had a lower SMFS value and longer MFS. This further confirms that the lower the SMFS is, the stronger the antitumor immune activation. We also found that SMFS was significantly positively correlated with the abundance of endothelial cells inferred by MCP-counter. Single-cell sequencing research of lung cancer showed that endothelial cells in tumors can downregulate immune cell homing and genes correlated with T-cell activity [46]. This suggests that the angiogenesis of tumors may be different from that of normal tissues and may impair the antitumor immune effect. SMFS included 4 genes, among which ELP3 (Gene ID: 55140) and MSRA (4482) were genes that could improve antitumor immune effect of CTLs, while FAM160B2 (64760) and PCDH12 (51294) were genes that could enhance the tumor-promoting effect of M2 macrophages. However, these genes did not overlap with the markers of endothelial cells or CTLs inferred by MCP-counter or the markers of M2 macrophages inferred by CIBERSORTx (data not shown). These four genes were incorporated into the model of SMFS in the form of PCDH12/ELP3, PCDH12/MSRA and FAM160B2/MSRA gene pairs. Therefore, these genes may be expressed on any cells of the tumor bulk and reflect the immunosuppression status of the tumor and immune ecosystem in the form of SMFS.
There are some limitations in our study. First, the number of TNBC cases in the validation sets was too small, and the administration of RT was not randomized which means that it was impossible to verify the predictive effect of SMFS and the interaction effect on RT. Second, the current clinical treatment of invasive breast cancer almost always includes chemotherapy and RT, and even neoadjuvant therapy has been widely used in the treatment of TNBC. However, most patients in GSE58812 and GSE2034 only received adjuvant RT, and patients in GSE12276 received chemotherapy but not RT. Third, in terms of the long-term survival influenced by the antitumor immune effects of RT, overall survival may be more appropriate as the endpoint to further develop classifiers [42]. Fourth, the biological characteristics of SMFS need to be further analyzed with the single-cell sequencing data of TNBC to evaluate the precise intercellular communications by which the functions of TILs or macrophages are impacted.
In conclusion, we developed a CTL/M2 macrophage-related four-gene signature (SMFS) that had prognostic value for MFS in TNBC patients undergoing RT and then validated our SMFS in two independent datasets of patients with or without RT. Our research provides an idea on how to use transcriptional data to screen genes interacting with tumor-infiltrating immune cells to develop prognostic or predictive indicators for RT. This study may provide new ideas for the development of biomarkers guiding the combined use of RT with immunotherapy in the future.