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Distant metastasis remains the major cause of morbidity for breast cancer. Individuals with liver or brain metastasis have an extremely poor prognosis and low response rates to anti-PD-1/L1 immune checkpoint therapy compared to those with metastasis at other sites. Therefore, it is urgent to investigate the underlying mechanism of anti-PD-1/L1 resistance and develop more effective immunotherapy strategies for these patients. Using single-cell RNA sequencing, a high-resolution map of the entire tumor ecosystem based on 44 473 cells from breast cancer liver and brain metastases is depicted. Identified by canonical markers and confirmed by multiplex immunofluorescent staining, the metastatic ecosystem features remarkable reprogramming of immunosuppressive cells such as FOXP3+ regulatory T cells, LAMP3+ tolerogenic dendritic cells, CCL18+ M2-like macrophages, RGS5+ cancer-associated fibroblasts, and LGALS1+ microglial cells. In addition, PD-1 and PD-L1/2 are barely expressed in CD8+ T cells and cancer/immune/stromal cells, respectively. Interactions of the immune checkpoint molecules LAG3-LGALS3 and TIGIT-NECTIN2 between CD8+ T cells and cancer/immune/stromal cells are found to play dominant roles in the immune escape. In summary, this study dissects the intratumoral heterogeneity and immunosuppressive microenvironment in liver and brain metastases of breast cancer for the first time, providing insights into the most appropriate immunotherapy strategies for these patients.

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Fatty acid metabolism has been deciphered to augment tumorigenesis and disease progression in addition to therapy resistance via strengthened lipid synthesis, storage, and catabolism. Breast cancer is strongly associated with the biological function of fatty acid metabolism owing to the abundant presence of adipocytes in breast tissue. It has been unraveled that tumor cells exhibit considerable plasticity based on fatty acid metabolism, responding to extra-tumoral and a range of metabolic signals, in which tumor microenvironment plays a pivotal role. However, the prognostic significance of fatty acid metabolism in breast cancer remains to be further investigated. Alongside these insights, we retrieved 269 reliable fatty acid metabolism-related genes (FMGs) and identified the landscape of copy number variations and expression level among those genes. Additionally, 11 overall survival-related FMGs were clarified by univariate Cox hazards regression analysis in The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Subsequently, a prognostic signature based on 6 overall survival (OS)-related FMGs was generated using Lasso Cox hazards regression analysis in TCGA dataset and was validated in two external cohorts. The correlation between the signature and several essential clinical parameters, including T, N, and PAM50 subtypes, was unveiled by comparing the accumulating signature value in various degrees. Furthermore, an optimal nomogram incorporating the signature, age, and American Joint Committee on Cancer (AJCC) stage was constructed, and the discrimination was verified by C-index, the calibration curve, and the decision curve analysis. The underlying implications for immune checkpoints inhibitors, the landscape of tumor immune microenvironment, and the predictive significance in therapy resistance to diverse strategies were depicted ultimately. In conclusion, our findings indicate the potential prognostic connotation of fatty acid metabolism in breast cancer, supporting novel insights into breast cancer patients’ prognosis and administrating effective immunotherapy.

Objective: To evaluate whether radiomic features extracted from intra and peri-nodular lesions can enhance the ability to differentiate between invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), and adenocarcinoma in situ (AIS) manifesting as ground-glass nodule (GGN). Materials and Methods: This retrospective study enrolled 120 patients with a total of 121 pathologically confirmed lung adenocarcinomas (85 IA and 36 AIS/MIA) from January 2015 to May 2019. The recruited patients were randomly divided into training (84 nodules) and validation sets (37 nodules), with a ratio of 7:3. The minority group in the training set was balanced by the synthetic minority over-sampling (SMOTE) method. The intra-, peri-nodular, and gross region of interests (ROI) were delineated with manual annotation. Image features were quantitatively extracted from each ROI on CT images. The minimum redundancy maximum relevance (mRMR) feature ranking method and the least absolute shrinkage and selection operator (LASSO) classifier were used to eliminate unnecessary features. The intra-and peri-nodular radiomic features were combined to produce the gross radiomic signature. A combined clinical-radiomic model was constructed by multivariable logistic regression analysis. The predicted performances of different models were evaluated using receiver operating curve (ROC) and calibration curve. Results: The gross radiomic signature (AUC: training set = 0.896; validation set = 0.876) showed a good ability to discriminate the invasiveness of adenocarcinoma, comparing to intra-nodular (AUC: training set = 0.862; validation set = 0.852) or perinodular radiomic signature (AUC: training set = 0.825; validation set = 0.820). The AUC of the combined clinical-radiomic model was 0.917 for the training and 0.876 for the validation cohort, respectively. Wu et al. Peri-Nodular Radiomics of Invasive GGNs Conclusions: The gross radiomic signature of intra-and peri-nodular regions improved the prediction ability and aided predicting the invasiveness of lung adenocarcinoma appearing as GGN.