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Background: Previous studies have shown that high level of TILs in invasive lobular carcinoma (ILC) is associated with poor prognosis, contrary to that in TNBC and HER2-positive breast cancer., Methods: The densities of six immune cell markers and three immune checkpoints in the ILC microenvironment were detected by computational pathology analysis. Then, the LASSO cox regression model was used to construct an immune score (IS) and further evaluate its prognostic value., Results: In our ILC cohort, the low density of CD4, CD8, CD20, CD56, CD68, FOXP3, PD-1, and PD-L1 had significantly longer disease-free survival (DFS) and overall survival (OS); however, the low density of CTLA-4 was associated with shorter DFS and OS. Based on this, an IS was constructed, and patients with low-IS had significantly prolonged DFS (p < 0.0001) and OS (p < 0.0001). Multivariate analysis revealed that IS was an independent prognostic indicator for DFS and OS. Further analysis showed that IS may increase the prognostic value of TNM stage. We further explored the prognostic role of CD68 and FOXP3 in the transcriptional level and the corresponding ISm in the METABRIC dataset, and found that low proportion of CD68 and FOXP3 and their ISm were associated with longer OS, and ISm was also an independent prognostic factor for OS., Conclusion: IS was a promising biomarker to distinguish the prognosis in ILC patients., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and the current prognostic system cannot meet the clinical need. Interactions between immune responsiveness and tumor cells plays a key role in the progression of TNBC and macrophages are vital component of immune cells. A prognostic model based on macrophages may have great accuracy and clinical utility., Methods: For model development, we screened early stage (without metastasis) TNBC patients from The Cancer Genome Atlas (TCGA) database. We extracted messenger RNA (mRNA) expression data and clinical data including age, race, tumor size, lymph node status and tumor stage. The follow up time and vital status were also retrieved for overall survival calculation. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to calculate the immune cell composition of each sample. Weighted gene co-expression network analysis (WGCNA) was used to identify M1-like macrophage-related genes. Combining least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, the M1-like macrophage polarization-related prognostic index (MRPI) was established. We obtained TNBC patients in Gene Expression Omnibus (GEO) database through PAM50 method and retrieved the mRNA expression data and survival data. The Harrell's concordance index (CI), the area under the receiver operating characteristic (ROC) curves (AUCs) and the calibration curve were used to evaluate the developed model., Results: We obtained 166 early TNBC cases and 113 normal tissue cases for model building, along with 76 samples from GSE58812 cohort for model validation. CIBERSORT analysis suggested obvious infiltration of macrophages, especially M1-like macrophages in early TNBC. Four genes were eventually identified for the construction of MPRI in the training set. The AUCs at 2 years, 3 years, and 5 years in the training cohort were 0.855, 0.881 and 0.893, respectively; and the AUCs at 2 years, 3 years, and 5 years in the validation cohort were 0.887, 0.792 and 0.722, respectively. Calibration curves indicated good predictive ability and high consistency of our model., Conclusions: MRPI is a promising biomarker for predicting the prognosis of early-stage TNBC, which may indicate personalized treatment and follow-up strategies and thus may improve the prognosis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-23-6/coif). The authors have no conflicts of interest to declare., (2023 Gland Surgery. All rights reserved.)

Aim: The aim of this study was to assess the prognostic value of lymph node ratio (LNR) in premenopausal patients with luminal breast carcinoma., Materials and Methods: A total of 885 female patients who presented with axillary lymph node-positive luminal breast cancer between 2000 and 2009 were investigated. Using X-tile, we classified patients into low-, intermediate- and high-risk groups based on LNR. The Kaplan-Meier method was used to determine cumulative survival curves. Cox proportional hazards analyses were used to identify the factors that contributed to disease-free (DFS) and overall (OS) survival., Results: The median age of patients was 42 years (range=21-58 years). A training set of 295 patients and a validation set of 590 patients were used to determine the optimal LNR cut-off points (0.20 and 0.63). DFS was 87.7%, 77.4% and 53.9% (p<0.001) and OS was 91.5%, 76.7% and 50.9% (p<0.0001) for the low- (≤0.20), intermediate- (0.21-0.63) and high-risk (>0.63) groups, respectively. The 10-year DFS and OS rates were significantly longer in the low-risk group than in the high-risk group. Nomogram analysis demonstrated that LNR contributed more compared to nodal stage in predicting both DFS and OS., Conclusion: We conclude that LNR strongly predicts prognosis in premenopausal patients with lymph node-positive luminal breast cancer., (Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)