Single-cell atlas reveals a distinct immune profile fostered by T cell-B cell crosstalk in triple negative breast cancer

Background:Characterizing the unique immune microenvironment of each tumor is of great importance for better predicting prognosis and guiding cancer immunotherapy.However,the unique features of the immune microenvironment of triple negative breast cancer(TNBC)compared with other subtypes of breast cancer remain elusive.Therefore,we aimed to depict and compare the immune landscape among TNBC,human epidermal growth factor receptor 2-positive(HER2^(+))breast cancer,and luminal-like breast cancer.Methods:Single-cell RNA sequencing(scRNA-seq)was performed on CD45^(+)immune cells isolated from human normal breast tissues and primary breast tumors of various subtypes.By analyzing the scRNA-seq data,immune cell clusters were identified and their proportions as well as transcriptome features were compared among TNBC,human HER2^(+)breast cancer,and luminal-like breast cancer.Pseudotime and cell-cell communication analyses were also conducted to characterize the immune microenvironment.Results:ScRNA-seq data of 117,958 immune cells were obtained and 31 immune clusters were identified.A unique immunosuppressive microenvironment in TNBC was decoded as compared to that in HER2^(+)or luminal-like breast cancer,which was characterized by higher proportions of regulatory T cells(Tregs)and exhausted CD8+T cells and accompanied by more abundant plasma cells.Tregs and exhausted CD8+T cells in TNBC exhibited increased immunosuppression signature and dysfunctional scores.Pseudotime analyses showed that B cells tended to differentiate to plasma cells in TNBC.Cell-cell communication analyses indicated that these unique features are fostered by the diversified T cell-B cell crosstalk in TNBC.Based on the T cell-B cell crosstalk,a prognostic signaturewas established that could effectively predict the prognosis status for patients with TNBC.Additionally,it was found that TNBC had a higher proportion of cytotoxic natural killer(NK)cells,whereas HER2^(+)or luminal-like breast cancer lost this feature,suggesting thatHER2^(+)or luminal-like breast cancer,but not TNBC,may benefit from NK-based immunotherapy.Conclusions:This study identified a distinct immune feature fostered by T cell-B cell crosstalk in TNBC,which provides better prognostic information and effective therapeutic targets for breast cancer.

Estimating Multiple Socioeconomic Attributes via Home Location-A Case Study in China

Inferring people’s Socioeconomic Attributes(SEAs),including income,occupation,and education level,is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation.Previous works mainly focus on estimating SEAs from peoples’cyberspace behaviors and relationships,such as the content of tweets or the social networks between online users.Besides cyberspace data,alternative data sources about users’physical behavior,like their home location,may offer new insights.More specifically,in this paper,we study how to predict a person’s income level,family income level,occupation type,and education level from his/her home location.As a case study,we collect people’s home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China.We further enrich home location with the knowledge from real estate websites,government statistics websites,online map services,etc.To learn a shared representation from input features as well as attribute-specific representations for different SEAs,we propose H2SEA,a factorization machine-based multi-task learning method with attention mechanism.Extensive experiment results show that:(1)Home location can clearly improve the estimation accuracy for all SEA prediction tasks(e.g.,80.2%improvement in terms of F1-score in estimating personal income level);(2)The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics,such as Area Under Curve(AUC),F-measure,and specificity;(3)The performance of specific SEA prediction tasks(e.g.,personal income)can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China;(4)Compared with online crawled housing price data,the area-level average income and Points Of Interest(POI)are more important features for SEA inferences in China.