Luminal Breast Cancer: Risk of Recurrence and Tumor-Associated Immune Suppression

Prognostici of ER-staining patterns and heterogeneity of ER positive HER2 negative breast cancer

BACKGROUND

Estrogen receptor (ER) expression is critical in breast cancer treatment. While low ER (1-9%) resembles triple-negative cancer with chemotherapy efficacy, the significance of "intermediate expression" (≥ 10%) and the therapeutic efficacy remain unclear. This study explores the differences in staining patterns and molecular characteristics of ER-low to intermediate expression to guide treatment.

METHODS

A total of 104 breast cancer patients treated between January 2008 and July 2024 with an Allred Proportion Score (PS) of 2-4 were included. PS2 (n = 21) was classified as ER-low, while PS3 (n = 26) and PS4 (n = 57) as ER-intermediate (ER-int). ER-int was further divided by ER staining pattern: "Island" (heterogeneous) and "Scatter," (uniform) subgroups. The prognosis, clinical factors, and gene expression profiles (n = 11) were analyzed.

RESULTS

The Island subgroup was associated with poorest prognosis (p = 0.0116), particularly among the patients treated with endocrine-only treatment patients (p < 0.0001). Elevated tumor-infiltrating lymphocyte (TIL) levels correlated with worse prognosis in endocrine-only treatment patients (p < 0.0043), with TIL levels highest in ER-low, followed by Island and Scatter subgroups. Island tumors were enriched in CD36, GZMB, and type I interferon genes; additionally, 23 "ISLAND" genes showed significant prognostic differences in the TCGA BRCA ER-int (10-69%) cohort.

CONCLUSION

This study emphasizes the importance of recognizing heterogeneity within the ER-int subtype. Identifying distinct ER staining patterns and prognostic significance of TILs and transcriptome in ER-int tumors suggests the need for individualized treatment strategies for Island subtype.

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes

Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.

Application of DCE-MRI radiomics and heterogeneity analysis in predicting luminal and non-luminal subtypes of breast cancer

Purpose

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and heterogeneity analysis in the differentiation of molecular subtypes of luminal and non-luminal breast cancer.

Methods

In this retrospective study, 388 female breast cancer patients (48.37 ± 9.41 years) with luminal (n = 190) and non-luminal (n = 198) molecular subtypes who received surgical treatment at Jilin Cancer Hospital were recruited from January 2019 to June 2023. All patients underwent breast MRI scan and DCE scan before surgery. The patients were then divided into a training set (n = 272) and a validation set (n = 116) in a 7:3 ratio. The three-dimensional texture feature parameters of the breast lesion areas were extracted. Four tumor heterogeneity parameters, including type I curve proportion, type II curve proportion, type III curve proportion and tumor heterogeneity values were calculated and normalized. Five machine learning (ML) models, including the logistic regression, naive Bayes algorithm (NB), k-nearest neighbor (KNN), decision tree algorithm (DT) and extreme gradient boosting (XGBoost) model were used to process the training data and were further validated. The best ML model was selected according to the performance in the validation set.

Results

In luminal subtype breast lesions, type III curve proportion and heterogeneity index were significantly lower than the corresponding parameters of the non-luminal subtype lesions both in the training set and validation set. Eight features together with four heterogeneity-related parameters with significant differences between luminal and non-luminal groups were retained as radiomics signatures for constructing the prediction model. The logistic regression ML model achieved the best performance in the validation set with the highest area under the curve value (0.93), highest accuracy (86.94%), sensitivity (87.55%) and specificity (86.25%).

Conclusion

The radiomics and heterogeneity analysis based on the DCE-MRI exhibit good application value in discriminating luminal and non-luminal subtype breast cancer. The logistic regression model demonstrates the best predictive performance among various machine learning models.

Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.

METHODS

This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.

RESULTS

Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying "ADC_wavelet-HLH_glszm_ZoneEntropy" and "DCE_wavelet-HLL_gldm_DependenceVariance" as the most significant features for the model's predictions.

CONCLUSION

The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.

CRITICAL RELEVANCE STATEMENT

An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.

A Proposal for a Modified Evaluation System of Tumor-Infiltrating Lymphocytes Using HE-Stained Sections in Breast Cancer

Tumor-infiltrating lymphocyte (TIL) scoring in tumor specimens has gained increasing attention in determining patients who are likely to benefit from immunotherapies. However, the histological evaluation methods of TILs in breast cancer remain limited. This study aimed to assess four components of lymphocytic reaction and overall lymphocytic score (L-score) used in colorectal cancer, investigate its association with clinicopathological factors, and examine the effect of TILs on postoperative mortality using 231 invasive breast cancers without neoadjuvant chemotherapy. Besides L-score, increasing modified L-score lacking peritumoral lymphocytic reaction was significantly associated with aggressive breast cancer phenotypes, including larger invasive size, higher tumor stage, higher Ki-67 labeling index, triple negative and HER2-enriched subtypes, and higher Nottingham histological grade. Importantly, modified L-score status but not L-score or TIL-Working Group (WG) score status was positively correlated with the disease-specific survival rate of the overall patients as well as the patients with luminal type or histological Grade III breast cancers. These results indicated that the modified L-score is a favorable method to comprehensively assess lymphocytic reaction to predict prognosis among patients with breast cancer, even compared with the currently used TIL-WG method, which may possess their potential integration into clinical practice.

From Breast to Eye: A Rare Case of Ocular Metastasis From Luminal Breast Cancer in a Nicaraguan Patient

Ocular metastases from breast cancer are rare but significant, with invasive lobular carcinoma (ILC) showing a higher tendency for ocular involvement than invasive ductal carcinoma (IDC). Radiotherapy is the main treatment, but the prognosis is poor. This case involves a 37-year-old woman with a history of bilateral breast cancer, initially treated with surgery, chemotherapy, and radiotherapy. She later developed pulmonary progression, bone metastases, and other complications. The patient experienced worsening vision loss, headaches, tinnitus, and systemic decline. Imaging showed brain lesions and choroidal metastasis. Extensive metastasis was confirmed, including in the pleura, liver, and bones. Ophthalmologic evaluation revealed increased intraocular pressure, requiring medical management and planned cyclophotocoagulation. Due to the advanced disease, a palliative care approach was initiated, with radiotherapy planned for ocular metastasis. This case highlights the aggressive nature of metastatic luminal B breast cancer with ocular involvement and underscores the importance of early detection and a multidisciplinary approach to patient care.

Targeting ROR2 homooligomerization disrupts ROR2-dependent signaling and suppresses stem-like cell properties of human breast adenocarcinoma

Breast cancer stem-like cells (CSCs) are enriched following treatment with chemotherapy, and posited as having a high level of plasticity and enhanced tumor-initiation capacity, which can enable cancer relapse. Here, we show that such features are shared by breast cancer (BCA) cells that express receptor tyrosine kinase-like orphan receptor (ROR2), which is expressed primarily during embryogenesis and by various cancers. We find that Wnt5a can induce ROR2 homooligomerization to activate noncanonical Wnt signaling and enhance tumor-initiation capacity of BCA cells. Molecular analysis reveals that the cysteine-rich domain and transmembrane domain are required for ROR2 homooligomerization to activate ROR2. Treatment with a newly generated monoclonal antibody (mAb) specific for ROR2 can block Wnt5a-induced ROR2 homooligomerization, ROR2-dependent noncanonical Wnt signaling, and impair the capacity of BCA patient-derived xenografts to initiate tumor in immune-deficient mice. Collectively, these results indicate that targeting ROR2 (e.g., using mAb) suppresses BCA stemness and, thereby, may prevent BCA relapse.

Investigation of circulating miR-182-3p, miR -382-3p and miR -93, miR -142-3p involved in tamoxifen resistance and sensitivity in luminal-subtype breast cancer patients: a case-control study

Breast cancer (BC) commonly expresses estrogen receptors (ERs); hence, endocrine therapy targeting ERs is considered an effective treatment. Tamoxifen (TAM) resistance is an essential clinical complication leading to cancer progression and metastasis. This study investigated MicroRNAs (miRNAs) potentially implicated in drug resistance (miR-182-3p, miR-382-3p) or sensitivity (miR-93, miR- 142- 3p). This study aimed to provide new insights into serum microRNA expression profiles in BC. This case-control study included patients with luminal-A BC who received TAM for approximately one year. The case and control groups included 40 patients with or without local recurrence or metastasis. The expression levels of miR-182-3p, miR-382-3p, miR-93, and miR-142-3p in plasma samples were measured using real-time PCR with target-specific primers. The multivariate model of miR-93 (p = 0.0002), miR-182-3p (p = 0.0002), and miR-382-3p (p = 0.0028) demonstrated higher predictive power for TAM resistance. The only significant association was observed between miR-382-3p expression and lymphovascular invasion (LVI) (p = 0.0314). Moreover, lower expression levels of miR-93 and miR-382-3p were observed in the TAM-sensitive group compared to the TAM-resistant counterparts (p = 0.0002 and p = 0.0028, respectively). In contrast, the expression level of miR-182-3p was significantly higher in the TAM-sensitive group compared to the TAM-resistant group (p = 0.0002). receiver operating characteristic (ROC) curve analysis also indicated the expression of miR-182-3p (p < 0.001; area under curve (AUC): 0.753), miR-382-3p (p = 0.0028; AUC: 0.697), and miR-93 (p < 0.001; AUC: 0.762) as predictive markers for TAM resistance. Multivariate models based on miR-182-3p, miR-382-3p, and miR-93 can predict the response to hormone therapy. Measuring these miRNAs is also recommended for patients with luminal-subtype BC undergoing TAM therapy.

Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer

To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.

Development and Validation of a Prognostic Nomogram for HR+ HER- Breast Cancer

Purpose

We aimed to develop a nomogram to predict prognosis of HR+ HER2- breast cancer patients and guide the application of postoperative adjuvant chemotherapy.

Methods

We identified 310 eligible HR+ HER- breast cancer patients and randomly divided the database into a training group and a validation group. The endpoint was disease free survival (DFS). Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluate predictive accuracy and discriminative ability of the nomogram. We also compared the predictive accuracy and discriminative ability of our nomogram with the eighth AJCC staging system using overall data.

Results

According to the training group, platelet-to-lymphocyte ratio (PLR), tumor size, positive lymph nodes and Ki-67 index were used to construct the nomogram of DFS. The C-index of DFS was 0.708 (95% CI: 0.623-0.793) in the training group and 0.67 (95% CI: 0.544-0.796) in the validation group. The calibration curves revealed great consistencies in both groups.

Conclusion

We have developed and validated a novel and practical nomogram that can provide individual prediction of DFS for patients with HR+ HER- breast cancer. This nomogram may help clinicians in risk consulting and guiding the application of postoperative adjuvant chemotherapy.

Clustering of HR + /HER2- breast cancer in an Asian cohort is driven by immune phenotypes

Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2- breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2- breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2- samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Racial and socioeconomic disparities in triple-negative breast cancer treatment

INTRODUCTION

Triple-negative breast cancer (TNBC) continues to be a significant concern, especially among minority populations, where treatment disparities are notably pronounced. Addressing these disparities, especially among African American women and other minorities, is crucial for ensuring equitable healthcare.

AREAS COVERED

This review delves into the continuum of TNBC treatment, noting that the standard of care, previously restricted to chemotherapy, has now expanded due to emerging clinical trial results. With advances like PARP inhibitors, immunotherapy, and antibody-drug conjugates, a more personalized treatment approach is on the horizon. The review highlights innovative interventions tailored for minorities, such as utilizing technology like text messaging, smartphone apps, and targeted radio programming, coupled with church-based behavioral interventions.

EXPERT OPINION

Addressing TNBC treatment disparities demands a multifaceted approach, blending advanced medical treatments with culturally sensitive community outreach. The potential of technology, especially in the realm of promoting health awareness, is yet to be fully harnessed. As the field progresses, understanding and integrating the socio-economic, biological, and access-related challenges faced by minorities will be pivotal for achieving health equity in TNBC care.

Clinical Relevance of Tumour-Infiltrating Immune Cells in HER2-Negative Breast Cancer Treated with Neoadjuvant Therapy

Currently, therapy response cannot be accurately predicted in HER2-negative breast cancer (BC). Measuring stromal tumour-infiltrating lymphocytes (sTILs) and mediators of the tumour microenvironment and characterizing tumour-infiltrating immune cells (TIICs) may improve treatment response in the neoadjuvant setting. Tumour tissue and peripheral blood samples were retrospectively collected from 118 patients, and sTILs were evaluated. Circulating exosomes and myeloid-derived suppressor cells were determined by flow cytometry. TIICs markers (CD4, CD8, CD20, CD1a, and CD68) were assessed immunohistochemically. High sTILs were significantly associated with pathological complete response (pCR; p = 0.048) and event-free survival (EFS; p = 0.027). High-CD68 cells were significantly associated with pCR in triple-negative (TN, p = 0.027) and high-CD1a cells with EFS in luminal-B (p = 0.012) BC. Cluster analyses of TIICs revealed two groups of tumours (C1 and C2) that had different immune patterns and clinical outcomes. An immunoscore based on clinicopathological variables was developed to identify high risk (C1) or low-risk (C2) patients. Additionally, cluster analyses revealed two groups of tumours for both luminal-B and TNBC. Our findings support the association of sTILs with pCR and show an immunological component in a subset of patients with HER2-negative BC. Our immunoscore may be useful for future escalation or de-escalation treatments.

Circular RNAs in Breast Cancer: An Update

Breast cancer (BC), characterized by high heterogeneity, is the most commonly reported malignancy among females across the globe. Every year, many BC patients die owing to delayed diagnosis and treatment. Increasing researches have indicated that aberrantly expressed circular RNAs (circRNAs) are implicated in the tumorigenesis and progression of various tumors, including BC. Hence, this article provides a summary of the biogenesis and functions of circRNAs, as well as an examination of how circRNAs regulate the progression of BC. Moreover, circRNAs have aroused incremental attention as potential diagnostic and prognostic biomarkers for BC. Exosomes enriched with circRNAs can be secreted into the tumor microenvironment to mediate intercellular communication, affecting the progression of BC. Detecting the expression levels of exosomal circRNAs may provide reference for BC diagnosis and prognosis prediction. Illuminating insights into the earlier diagnosis and better treatment regimens of BC will be potentially available following elucidation of deeper regulatory mechanisms of circRNAs in this malignancy.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data

BACKGROUND

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

METHODS

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.

RESULTS

The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.

CONCLUSIONS

This work provides an approach to bridge single-cell level information toward population-level survival prediction.

Construction and Validation of a Novel Nomogram for Predicting the Risk of Metastasis in a Luminal B Type Invasive Ductal Carcinoma Population

BACKGROUND

Postoperative distant metastasis is the main cause of death in breast cancer patients. We aimed to construct a nomogram to predict the risk of metastasis of luminal B type invasive ductal carcinoma.

METHODS

We applied the data of 364 luminal B type breast cancer patients between 2008 and 2013. Patients were categorized into modeling group and validation group randomly (1:1). The breast cancer metastasis nomogram was developed from the logistic regression model using clinicopathological variables. The area under the receiver-operating characteristic curve (AUC) was calculated in modeling group and validation group to evaluate the predictive accuracy of the nomogram.

RESULTS

The multivariate logistic regression analysis showed that tumor size, No. of the positive level 1 axillary lymph nodes, human epidermal growth factor receptor 2 (HER2) status and Ki67 index were the independent predictors of the breast cancer metastasis. The AUC values of the modeling group and the validation group were 0.855 and 0.818, respectively. The nomogram had a well-fitted calibration curve. The positive and negative predictive values were 49.3% and 92.7% in the modeling group, and 47.9% and 91.0% in the validation group. Patients who had a score of 60 or more were thought to have a high risk of breast cancer metastasis.

CONCLUSIONS

The nomogram has a great predictive accuracy of predicting the risk of breast cancer metastasis. If patients had a score of 60 or more, necessary measures, like more standard treatment methods and higher treatment adherence of patients, are needed to take to lower the risk of metastasis and improve the prognosis.

Coexistent ARID1A-PIK3CA mutations are associated with immune-related pathways in luminal breast cancer

Up to 40% of luminal breast cancer patients carry activating mutations in the PIK3CA gene. PIK3CA mutations commonly co-occur with other mutations, but the implication of this co-occurrence may vary according to the specific genes involved. Here, we characterized a subgroup of luminal breast cancer expressing co-mutations in ARID1A and PIK3CA genes and identified their effect on important signaling pathways. Our study included 2609 primary breast cancer samples from the TCGA and METABRIC datasets that were classified based on tumor subtype and the existence of mutations in PIK3CA and ARID1A genes. Differential expression and WGCNA analyses were performed to detect molecular modules affected by the existence of the mutations. Our results reveal various evidence for the involvement of immune-related pathways in luminal tumors harboring ARID1A and PIK3CA mutations, as well as a unique Tumor-infiltrated immune cells composition. We also identified seven key hub genes in the ARID1A-PIK3CA mutated tumors associated with immune-related pathways: CTLA4, PRF1, LCK, CD3E, CD247, ZAP70, and LCP2. Collectively, these results indicate an immune system function that may contribute to tumor survival. Our data induced a hypothesis that ARID1A and PIK3CA mutations' co-occurrence might predict responses to immunotherapy in luminal BC and, if validated, could guide immunotherapy development.

Cancer stem cell-immune cell crosstalk in breast tumor microenvironment: a determinant of therapeutic facet

Breast cancer (BC) is globally one of the leading killers among women. Within a breast tumor, a minor population of transformed cells accountable for drug resistance, survival, and metastasis is known as breast cancer stem cells (BCSCs). Several experimental lines of evidence have indicated that BCSCs influence the functionality of immune cells. They evade immune surveillance by altering the characteristics of immune cells and modulate the tumor landscape to an immune-suppressive type. They are proficient in switching from a quiescent phase (slowly cycling) to an actively proliferating phenotype with a high degree of plasticity. This review confers the relevance and impact of crosstalk between immune cells and BCSCs as a fate determinant for BC prognosis. It also focuses on current strategies for targeting these aberrant BCSCs that could open avenues for the treatment of breast carcinoma.

Working tables on Hormone Receptor positive (HR+), Human Epidermal growth factor Receptor 2 negative (HER2-) early stage breast cancer: Defining high risk of recurrence

Hormone-receptor positive (HR+), Human-Epidermal-growth Factor negative (HER2-) breast cancer, including the Luminal A and the Luminal B subtypes, is the most common in women diagnosed with early-stage BC. Despite the advances in screening, surgery and therapies, recurrence still occurs. Therefore, it is important to identify early those factors that significantly impact the recurrence risk. Based on current evidence and their professional expertise, a Panel of oncologists discussed the definition of high risk of recurrence in early breast cancer. Histological grade, nodal involvement, genomic score, histological grade, tumor size, and Ki-67 proliferation index were rated as the most important factors to define the high risk in patients with early breast cancer. All these factors should be considered comprehensively to tailor the choice of treatment to the peculiar characteristics of each patient.

Tailoring Potential Natural Compounds for the Treatment of Luminal Breast Cancer

Breast cancer (BC) is the most diagnosed cancer worldwide, mainly affecting the epithelial cells from the mammary glands. When it expresses the estrogen receptor (ER), the tumor is called luminal BC, which is eligible for endocrine therapy with hormone signaling blockade. Hormone therapy is essential for the survival of patients, but therapeutic resistance has been shown to be worrying, significantly compromising the prognosis. In this context, the need to explore new compounds emerges, especially compounds of plant origin, since they are biologically active and particularly promising. Natural products are being continuously screened for treating cancer due to their chemical diversity, reduced toxicity, lower side effects, and low price. This review summarizes natural compounds for the treatment of luminal BC, emphasizing the activities of these compounds in ER-positive cells. Moreover, their potential as an alternative to endocrine resistance is explored, opening new opportunities for the design of optimized therapies.

Immune system and tumor microenvironment in early-stage breast cancer: different mechanisms for early recurrence after mastectomy and chemotherapy on ductal and lobular types

Background: The most common type of breast cancer is the ductal type (IDC), followed by lobular type (ILC). Surgery is the main therapy for early-stage breast cancer. Adjuvant chemotherapy might be given to those at high risk of recurrence. Recurrence is still possible after mastectomy and chemotherapy and most often occurs in the first two years. We aimed to determine the mechanisms in early local recurrence in both types. Methods: We used an observational method with a cross-sectional study design. The samples were patients with early-stage IDC and ILC, who underwent modified radical mastectomy (MRM) and got adjuvant chemotherapy with taxan and anthracycline base, and experienced recurrence in the first two years after surgery. The materials in this study were paraffin blocks from surgical specimens; we examined vimentin, α-SMA and MMP1, PDGF and CD95 by immunohistochemistry (IHC). Data analysis was done using OpenEpi 3.0.1 and EZR. We used pathway analysis with linear regression. Results: There were 25 samples with local recurrence and 25 samples without recurrence in the ductal type group. The lobular type group consisted of six subjects without recurrence and seven with recurrence. There were significant differences in the expression of vimentin (p=0.000 and 0.021, respectively), PDGF (p=0.000 and 0.002) and CD95 (p=0.000 and 0.045) in ductal and lobular cancer types, respectively. MMP1 (p=0.000) and α-SMA (p=0.000) only showed a significant difference in the ductal type. The pathway analysis showed that in the ductal type, the mechanism of recurrence was enabled by two factors: α-SMA and CD95. Meanwhile, for the lobular type, the recurrence mechanism was through the CD95 pathway. Conclusions: Local recurrence in early-stage IDC and ILC had different mechanisms.  These findings are expected to make cancer treatment in both types more focused and efficient.

Clinical and pathological factors and outcome of central nervous system metastasis in breast cancer

Background

In Switzerland, approximately 6000 new breast cancer cases and 1300 deaths are reported annually. Brain metastasis from breast cancer (BMBC) has a major effect on prognosis. This study aimed to identify prognostic factors for overall survival (OS) in a cohort of Swiss patients with BMBC. This study evaluated the prognosis on older BMBC, which has not been completely addressed in the literature.

Methods

We performed a retrospective chart review analysis with the primary endpoint of OS after a diagnosis of BMBC. The study population was divided into 2 groups based on an OS cut-off value of 12 months after diagnosis. Univariate and multivariate analyses of several risk factors, including age, were performed. To evaluate differences in OS according to age, we performed a secondary analysis to examine the prognostic value of clinical symptoms, metastatic pattern, and lymph node involvement in an older (≥65 years) vs. younger (<65 years) cohort.

Results

From 1989 to 2019, 55 patients were identified as having BMBC, among whom 47 patients were confirmed to be dead. The median patient age was 58 years (range 25-83 years). Comorbidities were present in 45 (81.8%) patients. The median survival in the OS <12 and OS ≥12 months groups was 4.3 and 30.7 months, respectively (p<0.001). Multivariate analysis revealed no significant differences in terms of comorbidities, medication use, M-stage, and symptomatology between the 2 groups. Additionally, there was no significant difference in OS in the 2 subgroups of patients aged <65 and ≥65 years.

Discussion

We concluded that age should not be a decisive factor in therapy planning for advanced breast cancer patients with BMBC.

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction.

STATEMENT OF TRANSLATIONAL RELEVANCE

Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

The prognostic value of tumour-infiltrating lymphocytes, programmed cell death protein-1 and programmed cell death ligand-1 in Stage I-III triple-negative breast cancer

BACKGROUND

Tumour-infiltrating lymphocytes (TILs) represent a robust biological prognostic biomarker in triple-negative breast cancer (TNBC); however, the contribution of different subsets of immune cells is unclear. We investigated the prognostic value of immune markers, including stromal TILs (sTILs), CD8+T and FOPX3+T cells, PD-1 and PD-L1 in non-metastatic TNBC.

METHODS

In total, 259 patients with Stage I-III TNBC were reviewed. The density of sTILs along with the presence of total (t), stromal (s), and intratumoral (i) CD8+T cells and FOPX3+T cells were evaluated by haematoxylin and eosin and immunohistochemical staining. Immunohistochemical staining of PD-1, PD-L1 was also conducted.

RESULTS

All immune markers were positively correlated with each other (P < 0.05). In the multivariate analysis, sTILs (P = 0.046), tCD8+T cells (P = 0.024), iCD8+T cells (P = 0.050) and PD-1 (P = 0.039) were identified as independent prognostic factors for disease-free survival (DFS). Further analysis showed that tCD8+T cells (P = 0.026), iCD8+T cells (P = 0.017) and PD-1 (P = 0.037) increased the prognostic value for DFS beyond that of the classic clinicopathological factors and sTILs.

CONCLUSIONS

In addition to sTILs, inclusion of tCD8+T, iCD8+T cells, or PD-1 may further refine the prognostic model for non-metastatic TNBC beyond that including classical factors alone.

Positive p53 Expression Is Associated with Primary Endocrine Therapy Resistance in Locally Advanced Stage Luminal B HER2-Negative Breast Cancer Patients: A Cross-Sectional Study in Indonesia

Luminal B HER2-negative breast cancer (BC) is the most common type in Indonesian BC patients, and frequently manifests with locally advanced staging. Recurrence often occurs within two years of the endocrine therapy course (primary endocrine therapy (ET) resistance). p53 mutation often exists in luminal B HER2-negative BC, but its application as an ET resistance predictor in those populations is still limited. The primary purpose of this research is to evaluate p53 expression and its association with primary ET resistance in luminal B HER2-negative BC. This cross-sectional study compiled 67 luminal B HER2-negative patients' clinical data during their pre-treatment period until they completed a two-year course of endocrine therapy. They were divided into two groups: 29 patients with primary ET resistance and 38 without primary ET resistance. Pre-treatment paraffin blocks from each patient were retrieved, and the p53 expression difference between the two groups was analyzed. Positive p53 expression was significantly higher in patients with primary ET resistance [odds ratio (OR) of 11.78 (95% CI: 3.72-37.37, p-value < 0.0001)]. We conclude that p53 expression could be a beneficial marker for primary ET resistance in locally advanced luminal B HER2-negative BC.

Fifteen-year survival and conditional survival of women with breast cancer in Osaka, Japan: A population-based study

BACKGROUND

In recent years, the survival of patients with breast cancer has improved. However, few published studies have a longer than 10-year follow-up. Conditional relative survival (CRS), which is relative survival (RS) of patients who have survived beyond a certain period after diagnosis, is useful for assessing excess mortality among long-term survivors compared with the general population.

METHODS

This was a retrospective observational cohort study. Population-based cancer registry data in Osaka, Japan were used to determine 15-year RS and 5-year CRS of women with breast cancer diagnosed between 2001 and 2002 and followed up for at least 15 years. Fifteen-year RS and age-standardized RS (ASR) were calculated by Ederer II and cohort methods. Five-year CRS according to age group and extent of disease (localized, regional, and distant) was estimated for every year from diagnosis to 10 years.

RESULTS

In the cohort of 4006 patients, the ASR declined progressively, the 5-year ASR being 85.8%, 10-year ASR 77.3%, and 15-year ASR 71.6%. The overall 5-year CRS exceeded 90% at 5 years after diagnosis, reflecting a small excess mortality compared with the general population. The 5-year CRS of patients with regional and distant disease did not reach 90% within 10 years of follow-up (89.4% for regional and 72.9% for distant disease 10 years after diagnosis), indicating that these patients had substantial excess mortality.

CONCLUSION

Long-term survival data can help cancer survivors plan their lives and receive better medical care and support.

Identification of circRNA-miRNA-mRNA network in luminal breast cancers by integrated analysis of microarray datasets

Introduction: Circular RNAs (circRNAs) regulatory network is important in human cancer. We, therefore, mapped the regulatory networks driven by circRNA in luminal-subtype breast cancer. Methods: Breast cancer-related microarray datasets from GEO database were analyzed for the differentially expressed circRNAs, miRNAs, and mRNAs. The potential downstream RNAs were collected using Circular RNA Interactome or Targetscan database. Protein-protein interaction (PPI) analysis was performed for the filtered genes to identify hub genes. The functions were annotated by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. CircRNA-miRNA-mRNA networks were mapped using Cytoscape software. Hsa_circ_0086735-miR-1296-5p-STAT1 axis was used for verification. The expression levels of hsa_circ_0086735, miR-1296-5p, and STAT1 mRNA were confirmed by qRT-PCR in luminal-subtype tissues and cell lines. The interactions among them were verified by Luciferase reporter assay and RNA pull-down assay. Cell proliferation and apoptosis were assayed. Overall and distant metastasis-free survival was analyzed. Results: A total of 70 genes were finally targeted and enriched in multi-process and multi-pathway. Networks containing 96 circRNA-miRNA-mRNA axes were constructed. Hsa_circ_0086735 and STAT1 mRNA was upregulated in luminal breast cancer, while miR-1296-5p was downregulated. Hsa_circ_0086735-miR-1296-5p-STAT1 axis promotes breast cancer progression and contributes to tamoxifen resistance. High hsa_circ_0086735 was associated with poor overall and distant metastasis-free survival. Discussion: This study identified the hsa_circ_0086735-miR-1296-5p-STAT1 as an important regulatory axis in luminal-subtype breast cancer, aiding to determine potential therapeutic targets.

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

Background

The goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.

Methods

One hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.

Results

Multivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78-0.93) and the testing set (AUC, 0.80; 95% CI, 0.65-0.95).

Conclusion

The DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

The Variety of 3D Breast Cancer Models for the Study of Tumor Physiology and Drug Screening

Breast cancer is the most common cancer in women and responsible for multiple deaths worldwide. 3D cancer models enable a better representation of tumor physiology than the conventional 2D cultures. This review summarizes the important components of physiologically relevant 3D models and describes the spectrum of 3D breast cancer models, e.g., spheroids, organoids, breast cancer on a chip and bioprinted tissues. The generation of spheroids is relatively standardized and easy to perform. Microfluidic systems allow control over the environment and the inclusion of sensors and can be combined with spheroids or bioprinted models. The strength of bioprinting relies on the spatial control of the cells and the modulation of the extracellular matrix. Except for the predominant use of breast cancer cell lines, the models differ in stromal cell composition, matrices and fluid flow. Organoids are most appropriate for personalized treatment, but all technologies can mimic most aspects of breast cancer physiology. Fetal bovine serum as a culture supplement and Matrigel as a scaffold limit the reproducibility and standardization of the listed 3D models. The integration of adipocytes is needed because they possess an important role in breast cancer.

Identification of prognostic cancer-associated fibroblast markers in luminal breast cancer using weighted gene co-expression network analysis

Background

Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression and are known to mediate endocrine and chemotherapy resistance through paracrine signaling. Additionally, they directly influence the expression and growth dependence of ER in Luminal breast cancer (LBC). This study aims to investigate stromal CAF-related factors and develop a CAF-related classifier to predict the prognosis and therapeutic outcomes in LBC.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to obtain mRNA expression and clinical information from 694 and 101 LBC samples, respectively. CAF infiltrations were determined by estimating the proportion of immune and cancer cells (EPIC) method, while stromal scores were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify stromal CAF-related genes. A CAF risk signature was developed through univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. The Spearman test was used to evaluate the correlation between CAF risk score, CAF markers, and CAF infiltrations estimated through EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The TIDE algorithm was further utilized to assess the response to immunotherapy. Additionally, Gene set enrichment analysis (GSEA) was applied to elucidate the molecular mechanisms underlying the findings.

Results

We constructed a 5-gene prognostic model consisting of RIN2, THBS1, IL1R1, RAB31, and COL11A1 for CAF. Using the median CAF risk score as the cutoff, we classified LBC patients into high- and low-CAF-risk groups and found that those in the high-risk group had a significantly worse prognosis. Spearman correlation analyses demonstrated a strong positive correlation between the CAF risk score and stromal and CAF infiltrations, with the five model genes showing positive correlations with CAF markers. In addition, the TIDE analysis revealed that high-CAF-risk patients were less likely to respond to immunotherapy. Gene set enrichment analysis (GSEA) identified significant enrichment of ECM receptor interaction, regulation of actin cytoskeleton, epithelial-mesenchymal transition (EMT), and TGF-β signaling pathway gene sets in the high-CAF-risk group patients.

Conclusion

The five-gene prognostic CAF signature presented in this study was not only reliable for predicting prognosis in LBC patients, but it was also effective in estimating clinical immunotherapy response. These findings have significant clinical implications, as the signature may guide tailored anti-CAF therapy in combination with immunotherapy for LBC patients.

Paradigm change in urological, gynaecological and breast cancer management: A new practice-changing data from ASCO 2022 annual meeting

Changing medical practice has been a constant process based on many scientific findings for years. In the oncology community, the American Society of Clinical Oncology (ASCO), one of the major annual conferences, presented the latest updates on practice-changing in cancer. At the recent meeting of 2022, held this year on June 2-6, researchers presented essential findings in urological, gynecological, and breast cancer management. In urological cancer, olaparib + abiraterone was demonstrated thru the PROpel trial to benefit in first-line treatment mCRPC regardless of the HRR stratification factor, along with the adjuvant therapy everolimus, for fully resected RCC in the EVEREST trial. In gynecological cancer, tisotumab vedotin demonstrated a potential role in improving clinical outcomes in 1st line r/mCC thru InnovaTV-205. In breast cancer, trastuzumab deruxtecan showed significant benefit for redefined human epidermal growth factors receptor 2 status in HER2 low BC patients, where current targeted-HER2 therapies are ineffective in the DESTINY-BREAST 04 study. The use of Immuno-based combinations in the medical management of TNBC patients has been supported thru several recent studies, showing positive results and outcomes, as demonstrated by the expert's opinions in Rizzo et al. research papers. In this article, we resumed the different renowned and what we considered intriguing to review studies presented during these three long sessions at the ASCO 2022 meeting.

SETD7 Expression Is Associated with Breast Cancer Survival Outcomes for Specific Molecular Subtypes: A Systematic Analysis of Publicly Available Datasets

SETD7 is a lysine N-methyltransferase that targets many proteins important in breast cancer (BC). However, its role and clinical significance remain unclear. Here, we used online tools and multiple public datasets to explore the predictive potential of SETD7 expression (high or low quartile) considering BC subtype, grade, stage, and therapy. We also investigated overrepresented biological processes associated with its expression using TCGA-BRCA data. SETD7 expression was highest in the Her2 (ERBB2)-enriched molecular subtype and lowest in the basal-like subtype. For the basal-like subtype specifically, higher SETD7 was consistently correlated with worse recurrence-free survival (p < 0.009). High SETD7-expressing tumours further exhibited a higher rate of ERBB2 mutation (20% vs. 5%) along with a poorer response to anti-Her2 therapy. Overall, high SETD7-expressing tumours showed higher stromal and lower immune scores. This was specifically related to higher counts of cancer-associated fibroblasts and endothelial cells, but lower B and T cell signatures, especially in the luminal A subtype. Genes significantly associated with SETD7 expression were accordingly overrepresented in immune response processes, with distinct subtype characteristics. We conclude that the prognostic value of SETD7 depends on the BC subtype and that SETD7 may be further explored as a potential treatment-predictive marker for immune checkpoint inhibitors.

Prognostic value of tumor-infiltrating lymphocytes in DCIS: a meta-analysis

Background

Tumor infiltrating lymphocytes (TILs) have been shown to be associated with the prognosis of breast ductal carcinoma in situ (DCIS). In this systematic review and meta-analysis, we investigated the role of TILs and TIL subsets in predicting the recurrence risk of DCIS.

Method

PubMed, Medline, Web of Science, Embase and Cochrane were searched to identify publications investigating the prognostic role of TILs in DCIS. After study screening, data extraction and risk of bias assessment, a meta-analysis was performed to assess the association between TILs (total TILs, CD4+, CD8+, FOXP3+, PD-L1+ TILs) and the risk of DCIS recurrence.

Results

A pooled analysis indicated that dense stromal TILs in DCIS were associated with a higher recurrence risk (HR 2.11 (95% CI 1.35–3.28)). Subgroup analysis showed that touching TILs (HR 4.73 (95% CI 2.28–9.80)) was more precise than the TIL ratio (HR 1.49 (95% CI 1.11–1.99)) in estimating DCIS recurrence risk. Moreover, the prognostic value of TILs seemed more suitable for patients who are diagnosed with DCIS and then undergo surgery (HR 2.77, (95% CI 1.26–6.07)) or surgery accompanied by radiotherapy (HR 2.26, (95% CI 1.29–3.95)), than for patients who receive comprehensive adjuvant therapies (HR 1.16, (95% CI 1.35–3.28)). Among subsets of TILs, dense stromal PD-L1+ TILs were valuable in predicting higher recurrence risk of DCIS.

Conclusion

This systematic review and meta-analysis suggested a non-favorable prognosis of TILs and stromal PD-L1+ TILs in DCIS and indicated an appropriate assessment method for TILs and an eligible population.

The Granger Causal Effects of Canady Helios Cold Plasma on the Inhibition of Breast Cancer Cell Proliferation

Cold atmospheric plasma (CAP) has become a promising tool for modern medicine. With its recent applications in oncology, regenerative medicine, and immunotherapy, CAP can be used for a myriad of different clinical treatments. When using CAP specifically for the treatment of tumors, it is known to elicit an oxidative response within malignant cancer cells, inducing cell cycle arrest and apoptosis. In this study, data of intracellular reactive oxygen species (ROS), caspase activity, Ki-67 expression, and cell cycle activity in the G1 phase were acquired to determine the causal relationships these intermediates have with cell proliferation and death after Canady Helios Cold Plasma (CHCP) treatment. The data were derived from four different subtypes of breast cancer cell lines: BT-474, MCF-7, MDA-MB-231, and SK-BR-3. Data transformation techniques were conducted on the time-series data for the input into the causal model code. The models were created on the basis of Granger causality principles. Our results demonstrated that there was a Granger causal relationship among all potentially causal variables (ROS, caspase, Ki-67, and G1 activity) and cell proliferation after 5 min CHCP treatment; however, not all variables were causal for the 3 min models. This same pattern did not exist for cell death models, which tested all potentially causal variables (ROS, Ki-67, and G1 activity) vs. caspase activity. All models were validated through a variety of statistical tests and forecasting accuracy metrics. A pseudo data set with defined causal links was also created to test R’s ability in picking up known causal relationships. These models, while nonexhaustive, elucidated the effects cold plasma has on cell activity regulators. Research in causal modeling is needed to help verify the exact mechanism of cold plasma for the ultimate optimization of its application in the treatment of cancers.

The Transcriptomic Portrait of Locally Advanced Breast Cancer and Its Prognostic Value in a Multi-Country Cohort of Latin American Patients

PURPOSES

Most molecular-based published studies on breast cancer do not adequately represent the unique and diverse genetic admixture of the Latin American population. Searching for similarities and differences in molecular pathways associated with these tumors and evaluating its impact on prognosis may help to select better therapeutic approaches.

PATIENTS AND METHODS

We collected clinical, pathological, and transcriptomic data of a multi-country Latin American cohort of 1,071 stage II-III breast cancer patients of the Molecular Profile of Breast Cancer Study (MPBCS) cohort. The 5-year prognostic ability of intrinsic (transcriptomic-based) PAM50 and immunohistochemical classifications, both at the cancer-specific (OSC) and disease-free survival (DFS) stages, was compared. Pathway analyses (GSEA, GSVA and MetaCore) were performed to explore differences among intrinsic subtypes.

RESULTS

PAM50 classification of the MPBCS cohort defined 42·6% of tumors as LumA, 21·3% as LumB, 13·3% as HER2E and 16·6% as Basal. Both OSC and DFS for LumA tumors were significantly better than for other subtypes, while Basal tumors had the worst prognosis. While the prognostic power of traditional subtypes calculated with hormone receptors (HR), HER2 and Ki67 determinations showed an acceptable performance, PAM50-derived risk of recurrence best discriminated low, intermediate and high-risk groups. Transcriptomic pathway analysis showed high proliferation (i.e. cell cycle control and DNA damage repair) associated with LumB, HER2E and Basal tumors, and a strong dependency on the estrogen pathway for LumA. Terms related to both innate and adaptive immune responses were seen predominantly upregulated in Basal tumors, and, to a lesser extent, in HER2E, with respect to LumA and B tumors.

CONCLUSIONS

This is the first study that assesses molecular features at the transcriptomic level in a multicountry Latin American breast cancer patient cohort. Hormone-related and proliferation pathways that predominate in PAM50 and other breast cancer molecular classifications are also the main tumor-driving mechanisms in this cohort and have prognostic power. The immune-related features seen in the most aggressive subtypes may pave the way for therapeutic approaches not yet disseminated in Latin America.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov (Identifier: NCT02326857).

Circulating microRNAs as indicators in the prediction of neoadjuvant chemotherapy response in luminal B breast cancer

PURPOSE

Circulating microRNAs (miRNAs) have been indicated as predictive biomarker for the response to neoadjuvant chemotherapy (NAC) and the prognosis of breast cancer (BC); however, to date the conclusions have been controversial. The biological characteristics of BC were affected by molecular subtypes. Hence, we aimed to investigate the predictive effect of miRNAs on NAC response in luminal B BC patients.

METHODS

Thirty-seven luminal B BC patient under NAC were prospectively enrolled in this study. Based on their clinical, pathological, and comprehensive response, the patients were defined as responder or non-responders, respectively. Circulating miRNAs were isolated from blood samples before and at the middle of NAC, and candidate miRNAs (miR-34a-5p, miR-125b-5p, miR-210, miR-222, miR-375, miR-718, miR-4516, and let-7g) were analyzed by quantitative real-time polymerase chain reaction (PCR). In addition, the association between miRNAs and disease-free survival (DFS) was examined.

RESULTS

miR-718, miR-4516, miR-210, and miR-125b-5p were found to be specific miRNAs associated with chemo-sensitivity of luminal B HER2 negative patients (n = 24). In the luminal B HER2 positive cohort (n = 13), dynamics of miR-222 and let-7g correlated with pathological response. Treatment-induced increase in miR-34a-5p in the responders except who reached pathologic complete response (pCR) was consistently identified across all luminal B patients and its two subgroups. Finally, after adjustments for Neo-Bioscore, patients with increased levels of miR-125b-5p during NAC had a worse DFS than those with decreased levels (HR = 5.86, 95% CI = 1.39-24.62, p = 0.016).

CONCLUSION

Specific circulating miRNAs were identified as predictive markers for NAC response and prognosis in luminal B BC. The underlying mechanism needs further studies.

A review of immune checkpoint blockade in breast cancer

In the recent years characterized by the cancer immunotherapy revolution, attention has turned to how to potentially boost and/or generate an efficient anti-tumor immune response in breast cancer (BC). Clinical activity of immune checkpoint blockade (ICB) targeting PD-1 or PD-L1 in BC has been more evident in the triple negative subtype and in earlier lines of the treatment. Remarkably, some responders to single agent ICB have achieved durable responses with metastatic disease, possibly as a result of treatment-induced immunological memory. However, most BC are immunologically quiescent and current research efforts developing ICB combinations are attempting to convert "cold" into "hot" tumors by manipulating the tumor microenvironment, expanding anti-tumor T cells improving efficient antigen presentation, and suppressing pro-tumor inhibitory cells. The aim of this review is to summarize existing data on the efficacy of immune checkpoint blockers as single agents and combination strategies in all BC subtypes, highlighting the BC subgroups that benefit most from ICB.