Intratumoral Tumor Infiltrating Lymphocytes (TILs) are Associated With Cell Proliferation and Better Survival But Not Always With Chemotherapy Response in Breast Cancer

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Impacts of tumor microenvironment during neoadjuvant chemotherapy in patients with esophageal squamous cell carcinoma

With the advent of immune checkpoint inhibitors (ICIs), a better understanding of tumor microenvironment (TME) is becoming crucial in managing esophageal squamous cell carcinoma (ESCC) patients. We investigated the survival impact of TME status and changes in patients with ESCC who underwent neoadjuvant chemotherapy (NAC) followed by surgery (n = 264). We examined immunohistochemical status (CD4+, CD8+, CD20+, Foxp3+, HLA class-1+, CD204+, and programmed death ligand-1 [PD-L1+]) on 264 pre-NAC and 204 paired post-NAC specimens. Patients were classified by their pre- and post-NAC immune cell status and their changes following NAC. Our findings showed that pre-NAC TME status was not significantly associated with survival outcomes. In contrast, post-NAC TME status, such as low level of T cells, CD4+ T cells, and high PD-L1 combined positive score (CPS), were significantly associated with poor overall survival (OS). Notably, TME changes through NAC exerted significant survival impacts; patients with consistently low levels of T cells, low levels of CD4+ T cells, or high levels of PD-L1 (CPS) had very poor OS (3-year OS: 35.5%, 40.2%, and 33.3%, respectively). Tumor microenvironment changes of consistently low T cells, low CD4+ T cells, and high PD-L1 were independent predictors of poor OS in multivariate Cox hazards analyses, while factors indicating post-NAC status (T cells, CD4+, and PD-L1 [CPS]) alone were not. Therefore, we suggest that the consistently low T/high PD-L1 group could benefit from additional therapies, such as ICIs, and the importance of stratification by the TME, which has recently been recognized.

Identification of cuproptosis-related miRNAs in triple-negative breast cancer and analysis of the miRNA-mRNA regulatory network

Introduction

The close association between cuproptosis and tumor immunity in triple-negative breast cancer (TNBC) allows its monitoring for predicting the prognosis of patients with TNBC. Nevertheless, the biological function and prognostic value of cuproptosis-related miRNAs and their target genes have not been reported.

Purpose

To construct the miRNA and mRNA-based risk models associated with cuproptosis for patients with TNBC.

Methods

Comparison of expression levels for genes associated with cuproptosis was executed between patients in the normal individuals and the TCGA-TNBC cohort. Conducting differential analysis resulted in the identification of differentially expressed miRNA (DE-miRNAs) and differentially expressed genes (DEGs) between the TNBC and Control samples. Screening for prognostic miRNAs and biomarkers involved employing univariate Cox analysis and least absolute shrinkage and selection operator regression analyses. These methods were utilized to construct risk models aimed at predicting the survival of patients with TNBC. Based on the median value of risk scores, patients were then stratified into low- and high-risk groups. Functional enrichment analysis was employed to explore the potential function and pathways of prognostic genes. Additionally, independent prognostic analysis was performed through univariate and multivariate Cox regression. Immune infiltration analysis was performed to examine disparities in the infiltration of immune cells between the two risk groups. Finally, the prognostic gene expression was mined in key cell types of TNBC.

Results

We obtained 5213 DEGs and 204 DE-miRNAs related to cuproptosis between TNBC and Control samples. Five prognostic miRNAs (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were closely associated with TNBC. Significant differences in the functions of prognostic genes between the two risk groups were observed, encompassing adipogenesis, inflammatory response, and hormone metabolic process. The prognostic gene regulatory network revealed that miR200C-3p regulated ZFPM2 and CFL2, and miR-1277-3p regulated BMP2 and RORA. A nomogram was created based on riskScore, cancer status, and pathologic stage to predict 1/3/5-year survival of patients with TNBC. Immune infiltration analysis indicated that the immune microenvironment may be associated with the progression of TNBC. Interestingly, prognostic genes exhibited higher expression levels in T cells, fibroblasts, endothelial cells, and monocytes compared to other cells.

Conclusions

Five prognostic miRNA (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were significantly associated with TNBC, it provides new therapeutic targets for the treatment and prognosis of TNBC.

A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer

Purpose

This study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors.

Methods

This retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People's Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People's Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis.

Results

In the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes.

Conclusion

The developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.

High EIF4EBP1 expression reflects mTOR pathway activity and cancer cell proliferation and is a biomarker for poor breast cancer prognosis

Eukaryotic translation initiation factor 4E binding protein 1 (EIF4EBP1) is regulated by the mTOR (mammalian target of rapamycin) signaling pathway. Phosphorylated EIF4EBP1 protein leads to pathway activation and correlates with aggressive breast cancer features. However, the clinical relevance of EIF4EBP1 gene expression as a prognostic biomarker in bulk breast tumors is not understood. In this study, EIF4EBP1 expression was analyzed in over 5000 breast cancers from three large independent cohorts, TCGA, METABRIC, and SCAN-B (GSE96058), and expression was dichotomized into low and high groups by the median. We also performed gene set enrichment analysis (GSEA) and cell cybersorting via the xCell algorithm to investigate EIF4EBP1 biology and expression patterns within the tumor microenvironment (TME). We additionally confirmed EIF4EBP1 expression location in the TME via single cell RNA sequencing. EIF4EBP1 expression was highest in both triple negative and high-grade tumors (both P<0.001), and tumor mutational burden scores were highest in the high EIF4EBP1-expression groups (all P<0.001). High EIF4EBP1 expression significantly correlated to worse overall survival in all three cohorts (hazard ratios (HR) 1.4-1.9), and worse distant relapse-free survival in patients treated with neoadjuvant taxane-anthracycline chemotherapy (HR 2.4). GSEA demonstrated enriched mTOR and cell proliferation-related gene sets, including, MYC, G2M checkpoint, and E2F targets across all three bulk tumor and single cell RNA sequencing cohorts. Phenotypically, these pathways were reflected by increased Ki67 gene expression and signaling via pharmacologically-activated mTOR gene sets in EIF4EBP1 high-expressing tumors (all P<0.001). EIF4EBP1 expression was increased in whole breast tumors compared to normal breast tissue (P<0.001), and was expressed predominantly in cancer epithelial cells, particularly in basal epithelial cell subclasses. EIF4EBP1 expression did not correlate to a consistent immune system phenotype across all three cohorts. Overall, these findings support that high EIF4EBP1 gene expression in bulk breast tumors could represent a poor prognostic marker via mTOR signaling pathways activation and upregulation of cell cycling, ultimately leading to increased tumorigenesis.