Background
Breast cancer is the most common cancer type worldwide. In 2020, there were approximately 2.3 million newly diagnosed breast cancer cases and 680,000 related deaths worldwide [
1]. Currently, surgical resection with adjuvant chemoradiotherapy or hormone therapy are the gold standard for treating breast cancer patients [
2]. Immune checkpoint therapy (ICT), as a new therapeutic approach, has been used to treat breast cancer patients [
3‐
5], but most patients do not respond to ICT [
6], and there are no available biomarkers to predict the response. Recent studies have shown that breast cancer is an immunogenic cancer type and contains large quantities of tumour-infiltrating lymphocytes (TILs) [
7,
8], suggesting that TILs may be associated with the immunotherapy outcomes of breast cancer.
TILs are composed of T cells and B cells and have been demonstrated to be associated with the development of breast cancer [
9]. CD103 is expressed on subsets of CD8
+ T cells and is essential for antitumour cytotoxic T-cell activity because it triggers lytic granule polarization and release at contact sites [
10]. In addition, CD103 binds to its ligand, E-cadherin, on epithelial tumour cells, leading to the retention of antigen-specific lymphocytes within epithelial tumours [
11]. Thus, CD103 is considered a crucial marker of tissue-resident memory T (TRM) cells. Patients with advanced-stage breast cancers with high levels of TRM cells have better response rates to anti-PD-1 antibodies than those with low levels of TRM cells [
7]. Lymphocyte activation gene 3 (LAG3), an immune checkpoint molecule, is expressed on multiple cell types, including CD4
+ and CD8
+ T cells [
12]. Persistent antigen stimulation in cancer leads to upregulation of LAG3 expression, promoting T-cell exhaustion [
12,
13]. Thus, an increasing number of studies have used LAG3 to mark exhausted T cells [
14‐
19]. Single-cell RNA sequencing (scRNA-seq) is a powerful technique for dissecting the heterogeneity of solid tumours [
20], which will pave the way for individualized treatment. ScRNA-seq analysis of the tumour microenvironment contributes to identifying immune cell subsets associated with prognosis and understanding their molecular characteristics, which provides an effective way to predict the immunotherapy response and prognosis of cancer patients. Therefore, identification of potential prognostic markers associated with TIL subpopulations based on integrated analysis of scRNA and bulk RNA sequencing and machine learning algorithms might provide effective ICT outcome prediction and therapeutic indicators for breast cancer patients.
In our present study, two CD103+LAG3+ TIL subsets that were associated with antitumour immunity were identified by scRNA-seq analysis. Based on the expression profiles of marker genes in these two subsets, we constructed a CD103+LAG3+ TIL-related risk score prognostic model (CLTRP) by performing least absolute shrinkage and selection operator (LASSO) regression and Cox analysis. We used the model to predict overall survival and explored the molecular characteristics, immune infiltration, and chemotherapeutic sensitivity of different CLTRP subgroups. Furthermore, the ability of this risk score prognostic model to predict patient response to chemotherapy and immunotherapy was assessed.
Discussion
As one of the most powerful techniques for analysing the complexity of solid tumours [
20], scRNA-seq analysis of primary tumours has enabled the discovery of novel, clinically relevant cell subsets defined by a unique signature of gene expression [
39‐
41]. In primary human tumours, transcriptome analysis based on scRNA-seq not only reveals the heterogeneity of T cells and B cells but also has begun to clarify dynamic relationships between T-cell subpopulations [
42‐
44]. These strategies can be used to assess the conditional relationships between T-cell subsets or B-cell subsets and the clinical features of cancer. For example, Peter and colleagues used scRNA-seq analysis to uncover that breast cancer tissues contain large quantities of TILs [
30]. Their work led to the discovery of an intratumoural CD8
+ tissue-resident memory T (TRM) cell subset associated with improved prognosis [
30]. In the present work, based on integrated analysis of scRNA and bulk RNA sequencing data and machine learning algorithms, we identified CD8
+ T-cell and B-cell subsets with high expression of CD103 and LAG3 and developed a CLTRP scoring system based on signature genes from the above subsets as a predictive model for immunotherapy outcomes of breast cancer patients.
CD103, a marker expressed on CD8
+ T cells, triggers lytic granule polarization and release at contact areas, leading to the killing of tumour cells [
10,
45]. Thus, CD103 is essential for antitumour cytotoxic T-cell activity. Moreover, CD103 binds to its ligand E-cadherin on epithelial tumour cells, leading to the retention of antigen-specific lymphocytes within epithelial tumours [
46]. Therefore, CD103 functions as a marker of TRM cells. CD103-positive TILs have been reported to be associated with improved prognosis in patients with triple-negative breast cancer [
47]. In this study, we found that CD103 alone could not predict breast cancer survival. This may be attributed to tumour heterogeneity. LAG3, an immune checkpoint molecule, is used to mark exhausted T cells in an increasing number of studies [
14‐
19].
In the present work, we identified two tumour-infiltrating lymphocyte subsets with high expression of CD103 and LAG3, including a CD8
+ T-cell and a B-cell subset. These two subsets were named CD103
+LAG3
+ lymphocytes for convenience of description. The CD103
+LAG3
+CD8
+ T-cell subset is similar to TRM cells, and this subset highly expressed cytotoxic genes, such as PRF1 and GZMB. This molecular phenotype suggests a relationship between the CD103
+LAG3
+CD8
+ T-cell subset and improved prognosis of breast cancer patients. In addition, effector memory B cells have recently emerged as crucial targets for immunotherapy that could be clinically beneficial for patients with solid tumours [
48‐
50]. Furthermore, our present work showed that the CD103
+LAG3
+ B-cell subset highly expressed marker genes of effector memory B cells, suggesting that this subset is associated with improved prognosis. Based on TCGA datasets, we screened two genes (CXCL13 and BIRC3) from the above two subset signatures by a machine learning algorithm called LASSO Cox regression and developed a CLTRP scoring system. In melanoma, lung cancer, and colorectal cancers, CXCL13, along with CCR5, has been identified as a T-cell-intrinsic marker of ICT sensitivity [
51]. In high-grade serous ovarian cancer, CXCL13 increases infiltration of TILs and is helpful to enhance efficacy of ICT [
52]. In present study, integrated analysis of scRNA-sequencing data derived from primary breast cancer and bulk RNA-sequencing data from patients receiving ICT identified CXCL13 and BIRC3 as TIL-related markers of ICT sensitivity in breast cancer.
In this study, there are some limitations. First, all prognostic analyses were performed solely on data from public databases. Therefore, larger preclinical studies and retrospective clinical trial analyses are required to confirm our findings. Second, given that breast cancer cohorts in our study were from different public datasets, intratumor or interpatient heterogeneity was unavoidable. It has been reported that tumour heterogeneity is closely associated with the efficacy of immunotherapy or chemotherapy. Despite these limitations, the present study suggests that CLTRP is a promising biomarker for determining prognosis, chemotherapeutic drug sensitivity and immune benefit from ICT in breast cancer patients and may be helpful for clinical decision-making in breast cancer patients.
Conclusions
In the present study, we identified two TIL subsets with high expression of CD103 and LAG3 via scRNA-seq analysis. Based on The Cancer Genome Atlas (TCGA) dataset, we constructed a CD103+LAG3+ TIL-related risk score prognostic model (CLTRP) for patients with breast cancer. This CLTRP signature could accurately predict the prognosis, drug sensitivity, molecular and immune characteristics, chemotherapy benefit and immunotherapy outcomes of breast cancer patients. CLTRP could therefore serve as a predictor of both prognosis and treatment response for breast cancer.
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