A Prognostic Model Based on Residual Cancer Burden and Tumor-Infiltrating Lymphocytes on Residual Disease after Neoadjuvant Therapy in HER2+ Breast Cancer

Interaction between redox regulation, immune activation, and response to treatment in HER2+ breast cancer

In HER2+ breast cancer (BC), neoadjuvant therapy represents an ideal scenario for translational research, considering pathological complete response (pCR) as an endpoint. In these patients, achieving pCR after neoadjuvant therapy is associated with a better prognosis. However, biomarkers are needed to tailor optimal treatment for each patient. Evaluating tumour-infiltrating lymphocytes (TILs) has gained attention in predicting pCR. In the context of metastatic disease, TILs also appear to play a role in predicting outcomes. The interaction between the presence of TILs and reactive oxygen species (ROS) remains an area to be explored. ROS are critical for tumour cell homeostasis, and different levels can trigger differential biological responses in cancer cells and their microenvironment. Nevertheless, the influence of ROS on treatment efficacy and prognosis in patients with HER2+ BC remains to be elucidated. In this article, we reviewed the interplay between treatment response, immune system activation, and ROS production in HER2+ BC and suggested novel areas of intervention and research. We also present a bioinformatic analysis demonstrating that the altered expression of several redox-related genes could be associated with the prevalence of immune cell populations in the tumour microenvironment and with patient survival. New biomarkers are thus suggested and should be further explored to tailor the best treatment to each patient.

Tumor-infiltrating lymphocytes in HER2-positive breast cancer: potential impact and challenges

INTRODUCTION

In this review, we evaluate the role of stromal tumor-infiltrating lymphocytes (sTILs) as a biomarker in human epidermal growth factor receptor 2 (HER2)-positive breast cancer, exploring the prognostic and predictive potential in various treatment settings.

METHODS

Data from multiple clinical trials in the early and metastatic settings, focusing on TILs' correlation with pathologic complete response (pCR), progression-free survival (PFS), and overall survival across early and metastatic HER2-positive breast cancer were summarized. This review also discusses TILs' assessment methods, interobserver variability, and emerging technologies to assess TILs.

RESULTS

TILs have been identified as a highly reproducible biomarker that predicts pCR in patients receiving neoadjuvant therapy and serves as a prognostic indicator for long-term outcomes in several breast cancer subtypes, including HER2-positive. Studies indicate that higher TIL levels correlate with better recurrence-free survival rates. Despite these findings, there is no consensus on the optimal TIL threshold for clinical decision making, and further research is required on how to incorporate TILs into routine clinical practice.

CONCLUSIONS

TILs represent a promising biomarker in HER2-positive breast cancer, particularly in early disease settings. This assessment could guide treatment de-escalation or intensification, tailoring therapies to individual patient profiles. Due to their prognostic importance, TILs can be added to pathology reports. However, further validation in clinical trials is essential for the widespread adoption of TILs in clinical practice.

TNIP1 Impacts Prognosis by Modulating the Immune Microenvironment in BRCA

Breast invasive carcinoma (BRCA) affects women worldwide, and despite advancements in diagnosis, prevention, and treatment, outcomes remain suboptimal. TNIP1, a novel target involved in multiple immune signaling pathways, influences tumor development and survival. However, the connection between BRCA and TNIP1 remains unclear. Analysis of data from the TCGA, GEO, Sangerbox, and Ualcan databases revealed that TNIP1 is underexpressed in BRCA tissues. This finding was corroborated by RT-PCR and immunohistochemistry. Furthermore, data from the TCGA and GEPIA2 databases, along with Sangerbox, identified TNIP1 as a marker of poor prognosis in BRCA patients. TNIP1 expression shows significant positive correlations with the BRCA Tumor Microenvironment (TME) StromalScore (R = 0.22), ImmuneScore (R = 0.25), and ESTIMATEScore (R = 0.27). Various algorithms have demonstrated a strong association between TNIP1 expression and BRCA tumor-infiltrating immune cells (TIICs). Further analysis using EPIC, TIMER, MCPCounter, QUANTISEQ, xCell, and other computational tools revealed that elevated TNIP1 expression is significantly associated with increased immune cell scores. TNIP1 expression in BRCA tumor tissues also shows a strong correlation with immune checkpoint markers. Data from the HAP database indicate that TNIP1 expression is predominantly involved in the normal skin microenvironment. Subsequent analysis using the TISCH platform with the BRCA single-cell dataset demonstrated that TNIP1 exhibits higher expression levels in immune cells compared to non-immune cells in BRCA patients. This expression is significantly positively correlated with inflammation (R = 0.25) and differentiation (R = 0.28) within the TME, while showing negative correlations with BRCA stemness (R = - 0.34) and invasion (R = - 0.22). Consequently, TNIP1 is proposed as a potential prognostic marker and therapeutic target for BRCA.

Identification of a Potential PGK1 Inhibitor with the Suppression of Breast Cancer Cells Using Virtual Screening and Molecular Docking

BACKGROUND/OBJECTIVES

Breast cancer is the second most common malignancy worldwide and poses a significant threat to women's health. However, the prognostic biomarkers and therapeutic targets of breast cancer are unclear. A prognostic model can help in identifying biomarkers and targets for breast cancer. In this study, a novel prognostic model was developed to optimize treatment, improve clinical prognosis, and screen potential phosphoglycerate kinase 1 (PGK1) inhibitors for breast cancer treatment.

METHODS

Using data from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified in normal individuals and breast cancer patients. The biological functions of the DEGs were examined using bioinformatics analysis. A novel prognostic model was then constructed using the DEGs through LASSO and multivariate Cox regression analyses. The relationship between the prognostic model, survival, and immunity was also evaluated. In addition, virtual screening was conducted based on the risk genes to identify novel small molecule inhibitors of PGK1 from Chemdiv and Targetmol libraries. The effects of the potential inhibitors were confirmed through cell experiments.

RESULTS

A total of 230 up- and 325 down-regulated DEGs were identified in HER2, LumA, LumB, and TN breast cancer subtypes. A new prognostic model was constructed using ten risk genes. The analysis from The Cancer Genome Atlas (TCGA) indicated that the prognosis was poorer in the high-risk group compared to the low-risk group. The accuracy of the model was confirmed using the ROC curve. Furthermore, functional enrichment analyses indicated that the DEGs between low- and high-risk groups were linked to the immune response. The risk score was also correlated with tumor immune infiltrates. Moreover, four compounds with the highest score and the lowest affinity energy were identified. Notably, D231-0058 showed better inhibitory activity against breast cancer cells.

CONCLUSIONS

Ten genes (ACSS2, C2CD2, CXCL9, KRT15, MRPL13, NR3C2, PGK1, PIGR, RBP4, and SORBS1) were identified as prognostic signatures for breast cancer. Additionally, results showed that D231-0058 (2-((((4-(2-methyl-1H-indol-3-yl)-1,3-thiazol-2-yl)carbamoyl)methyl)sulfanyl)acetic acid) may be a novel candidate for treating breast cancer.

Prognostic stratification ability of the CPS+EG scoring system in HER2-low and HER2-zero early breast cancer treated with neoadjuvant chemotherapy

BACKGROUND

The CPS+EG scoring system was initially described in unselected early breast cancer (eBC) patients treated with neoadjuvant chemotherapy (NAC), leading to refined prognostic stratification, and thus helping to select patients for additional post-NAC treatments. It remains unknown whether the performance is the same in new biological breast cancer entities such as the HER2-low subtype.

PATIENTS AND METHODS

Outcomes (disease-free (DFS) and overall survival OS)) of 608 patients with HER2-non amplified eBC and treated with NAC were retrospectively analyzed according to CPS-EG score. We compared the prognostic stratification abilities of the CPS+EG in HER2-low and HER2-0 eBC, analyzing ER+ and ER- tumors separately.

RESULTS

In ER+ eBC, the CPS+EG scoring system seems to retain a prognostic value, both in HER2-low and HER2-0 tumors, by distinguishing populations with significantly different outcomes (good: score 0-1, poor: score 2-3, and very poor: score 4-5). Using C-indices for DFS and OS, CPS+EG provided the highest prognostic information in ER+ eBC, especially in HER2-0 tumors. In contrast, in ER- eBC, the CPS+EG does not appear to be able to distinguish different outcome groups, either in HER2-low or HER2-0 tumors. In ER- eBC, C-indices for DFS and OS were highest for pathological stage, reflecting the predominant prognostic importance of residual disease in this subtype.

CONCLUSIONS

HER2-low status does not influence the prognostic performance of the CPS+EG score. Our results confirm the usefulness of the CPS+EG score in stratifying the prognosis of ER+ eBC after NAC, for both HER2-0 and HER2-low tumors. For ER- eBC, HER2-low status does not influence the performance of the CPS+EG score, which was lower than that of the pathological stage alone.

The residual cancer burden index as a valid prognostic indicator in breast cancer after neoadjuvant chemotherapy

PURPOSE

The residual cancer burden index (RCB) was proposed as a response evaluation criterion in breast cancer patients treated with Neoadjuvant Chemotherapy (NAC). This study evaluated the relevance of RCB with replase-free survival (RFS).

METHODS

The clinical data of 254 breast cancer patients who received NAC between 2016 and 2020 were retrospectively collected. The relationship between clinicopathologic factors and RFS was evaluated using Cox proportional hazards regression models. RFS estimates were determined by Kaplan-Meier(K-M) analysis and compared using the log-rank test. Multivariate logistic regression analysis was used to evaluate the risk factors associated with RCB. Receiver operating characteristic (ROC) curves showed the potential of the RCB and MP grading systems as biomarkers for RFS.

RESULTS

At a median follow-up of 52 months, 59 patients(23.23%) developed relapse. Multivariate Cox regression showed that older age (P = 0.022), high Pathologic T stage after NAC (P = 0.023) and a high RCB score(P = 0.003) were risk factors for relapse. The outcomes of the multivariate logistic analysis indicated that RCB 0 (pathologic complete response [pCR]) was associated with HER2-positive patients (P = 0.002) and triple-negative breast cancer (TNBC) patients (P = 0.013). In addition, the RCB and MP scoring systems served as prognostic markers for patients who received NAC, and their area under curves (AUCs) were 0.691 and 0.342, respectively.

CONCLUSION

These data suggest that RCB can be equally applied to predict RFS in Chinese patients with NAC. The application of RCB may help guide the selection of treatment strategies.

Hepatitis C virus infection is associated with high risk of breast cancer: a pooled analysis of 68,014 participants

Introduction

Breast cancer is the most common malignancy among women. Previous studies had shown that hepatitis C virus (HCV) infection might serve as a risk factor for breast cancer, while some studies failed to find such an association.

Methods

In this study, we presented a first attempt to capture and clarify this clinical debate via a cumulative analysis (registration ID: CRD42023445888).

Results

After systematically searching and excluding the irrelevant publications, five case-control or cohort studies were finally included. The synthetic effect from the eligible studies showed that patients with HCV infection had a significantly higher prevalence of breast cancer than non-HCV infected general population (combined HR= 1.382, 95%CI: 1.129 to 1.692, P=0.002). There was no evidence of statistical heterogeneity during this pooled analysis (I2 = 13.2%, P=0.33). The sensitivity analyses confirmed the above findings. No significant publication bias was observed among the included studies. The underlying pathophysiological mechanisms for this relationship might be associated with persistent infection/inflammation, host immune response, and the modulation of HCV-associated gene expression.

Discussion

Though the causal association between HCV infection and breast cancer did not seem quite as strong, screening for HCV might enable the early detection of breast cancer and help to prevent the progression of the disease. Since the topic of this study remains a matter of clinical debate, further studies are still warranted to validate this potential association.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023445888.