Breast cancer classification and prognosis based on gene expression profiles from a population-based study

An insight into the therapeutic effects of isoliquiritigenin in breast cancer

Breast cancer ranks as the most widespread malignant condition in women, emerging as a primary contributor to mortality. The primary challenges in cancer treatments involve undesirable side effects. Therefore, exploring natural compounds as additional therapy could provide valuable insights. Isoliquiritigenin (ILN), an isoflavonoid featuring a chalcone moiety primarily sourced from Glycyrrhiza species, has garnered increasing interest in breast cancer research. This review aims to provide a comprehensive understanding of ILN's mechanisms of action in breast cancer, drawing from a range of in vitro and in vivo studies. ILN primarily acts by inhibiting angiogenesis, aromatase, inflammation, and cell proliferation, and preventing invasion and metastasis. Mechanistically, it downregulates miR-374a, phosphoinositide-3-kinase-protein kinase B/Akt, maternal embryonic leucine zipper kinase, vascular endothelial growth factor, and estrogen receptor protein levels, and causes enhancement of Wnt inhibitory factor-1, and Unc-51-like kinase 1 expression to treat breast cancer. ILN emerges as a promising natural option, offering therapeutic advantages with minimal side effects. However, it is important to note that current research on ILN is primarily limited to preclinical models, underscoring the need for further investigation to validate its potential efficacy.

TWEAK/Fn14 signalling driven super-enhancer reprogramming promotes pro-metastatic metabolic rewiring in triple-negative breast cancer

Triple Negative Breast Cancer (TNBC) is the most aggressive breast cancer subtype suffering from limited targeted treatment options. Following recent reports correlating Fibroblast growth factor-inducible 14 (Fn14) receptor overexpression in Estrogen Receptor (ER)-negative breast cancers with metastatic events, we show that Fn14 is specifically overexpressed in TNBC patients and associated with poor survival. We demonstrate that constitutive Fn14 signalling rewires the transcriptomic and epigenomic landscape of TNBC, leading to enhanced tumour growth and metastasis. We further illustrate that such mechanisms activate TNBC-specific super enhancers (SE) to drive the transcriptional activation of cancer dependency genes via chromatin looping. In particular, we uncover the SE-driven upregulation of Nicotinamide phosphoribosyltransferase (NAMPT), which promotes NAD+ and ATP metabolic reprogramming critical for filopodia formation and metastasis. Collectively, our study details the complex mechanistic link between TWEAK/Fn14 signalling and TNBC metastasis, which reveals several vulnerabilities which could be pursued for the targeted treatment of TNBC patients.

Atramacronoid A induces the PANoptosis-like cell death of human breast cancer cells through the CASP-3/PARP-GSDMD-MLKL pathways

Breast cancer is the most common malignant tumor and a major cause of mortality among women worldwide. Atramacronoid A (AM-A) is a unique natural sesquiterpene lactone isolated from the rhizome of Atractylodes macrocephala Koidz (known as Baizhu in Chinese). Our study demonstrated that AM-A triggers a specific form of cell death resembling PANoptosis-like cell death. Further analysis indicated that AM-A-induced PANoptosis-like cell death is associated with the CASP-3/PARP-GSDMD-MLKL pathways, which are mediated by mitochondrial dysfunction. These results suggest the potential of AM-A as a lead compound and offer insights for the development of therapeutic agents for breast cancer from natural products.

Surgical Tumor Resection Deregulates Hallmarks of Cancer in Resected Tissue and the Surrounding Microenvironment

Surgery exposes tumor tissue to severe hypoxia and mechanical stress leading to rapid gene expression changes in the tumor and its microenvironment, which remain poorly characterized. We biopsied tumor and adjacent normal tissues from patients with breast (n = 81) and head/neck squamous cancers (HNSC; n = 10) at the beginning (A), during (B), and end of surgery (C). Tumor/normal RNA from 46/81 patients with breast cancer was subjected to mRNA-Seq using Illumina short-read technology, and from nine patients with HNSC to whole-transcriptome microarray with Illumina BeadArray. Pathways and genes involved in 7 of 10 known cancer hallmarks, namely, tumor-promoting inflammation (TNF-A, NFK-B, IL18 pathways), activation of invasion and migration (various extracellular matrix-related pathways, cell migration), sustained proliferative signaling (K-Ras Signaling), evasion of growth suppressors (P53 signaling, regulation of cell death), deregulating cellular energetics (response to lipid, secreted factors, and adipogenesis), inducing angiogenesis (hypoxia signaling, myogenesis), and avoiding immune destruction (CTLA4 and PDL1) were significantly deregulated during surgical resection (time points A vs. B vs. C). These findings were validated using NanoString assays in independent pre/intra/post-operative breast cancer samples from 48 patients. In a comparison of gene expression data from biopsy (analogous to time point A) with surgical resection samples (analogous to time point C) from The Cancer Genome Atlas study, the top deregulated genes were the same as identified in our analysis, in five of the seven studied cancer types. This study suggests that surgical extirpation deregulates the hallmarks of cancer in primary tumors and adjacent normal tissue across different cancers.

IMPLICATIONS

Surgery deregulates hallmarks of cancer in human tissue.

Targeting Breast Cancer Using 177 Lu-Labeled Trastuzumab and Trastuzumab Fragment : First-in-Human Clinical Experience

PURPOSE

A monoclonal antibody, trastuzumab, is used for immunotherapy for HER2-expressing breast cancers. Large-sized antibodies demonstrate hepatobiliary clearance and slower pharmacokinetics. A trastuzumab fragment (Fab; 45 kDa) has been generated for theranostic use.

PATIENTS AND METHODS

Fab was generated by papain digestion. Trastuzumab and Fab have been radiolabelled with 177 Lu after being conjugated with a bifunctional chelating. The affinity and target specificity were studied in vitro. The first-in-human study was performed.

RESULTS

The bifunctional chelating agent conjugation of 1-2 molecules with trastuzumab and Fab was detected at the molar ratio 1:10 in bicarbonate buffer (0.5 M, pH 8) at 37°-40°C. However, 2-3 molecules of bifunctional chelating agent were conjugated when DMSO in PBS (0.1 M, pH 7) was used as a conjugation buffer at a molar ratio of 1:10. The radiolabelling yield of DOTA-conjugated Fab and trastuzumab at pH 5, 45°C to 50°C, with incubation time 2.5-3 hours was 80% and 41.67%, respectively. However, with DOTAGA-conjugated trastuzumab and Fab, the maximum radiolabelling yield at pH 5.5, 37°C, and at 2.5-3 hours was 80.83% and 83%, respectively. The calculated K d of DOTAGA Fab and trastuzumab with HER2-positive SKBR3 cells was 6.85 ± 0.24 × 10 -8 M and 1.71 ± 0.10 × 10 -8 M, respectively. DOTAGA-Fab and trastuzumab showed better radiolabelling yield at mild reaction conditions.177 Lu-DOTAGA-Fab demonstrated higher lesion uptake and lower liver retention as compared with 177 Lu-DOTAGA-trastuzumab. However, 177 Lu-DOTAGA-Fab as compared with 177 Lu-DOTAGA-trastuzumab showed a relatively early washout (5 days) from the lesion.

CONCLUSIONS

177 Lu-DOTAGA-Fab and trastuzumab are suitable for targeting the HER2 receptors.

Benign breast tumors may arise on different immunological backgrounds

Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.

Insulin Resistance Increases TNBC Aggressiveness and Brain Metastasis via Adipocyte-derived Exosomes

Patients with triple negative breast cancer (TNBC) and comorbid Type 2 Diabetes (T2D), characterized by insulin resistance of adipose tissue, have higher risk of metastasis and shorter survival. Adipocytes are the main non-malignant cells of the breast tumor microenvironment (TME). However, adipocyte metabolism is usually ignored in oncology and mechanisms that couple T2D to TNBC outcomes are poorly understood. Here we hypothesized that exosomes, small vesicles secreted by TME breast adipocytes, drive epithelial-to-mesenchymal transition (EMT) and metastasis in TNBC via miRNAs. Exosomes were purified from conditioned media of 3T3-L1 mature adipocytes, either insulin-sensitive (IS) or insulin-resistant (IR). Murine 4T1 cells, a TNBC model, were treated with exosomes in vitro (72h). EMT, proliferation and angiogenesis were elevated in IR vs. control and IS. Brain metastases showed more mesenchymal morphology and EMT enrichment in the IR group. MiR-145a-3p is highly differentially expressed between IS and IR, and potentially regulates metastasis.

Significance

IR adipocyte exosomes modify TME, increase EMT and promote metastasis to distant organs, likely through miRNA pathways. We suggest metabolic diseases such as T2D reshape the TME, promoting metastasis and decreasing survival. Therefore, TNBC patients with T2D should be closely monitored for metastasis, with metabolic medications considered.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

PURPOSE

To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

STUDY TYPE

Prospective.

SUBJECTS

486 female breast cancer patients (training/validation/test: 64%/16%/20%).

FIELD STRENGTH/SEQUENCE

3.0 T/DW-MRI (13 b-values) and DCE-MRI (one precontrast and five postcontrast phases).

ASSESSMENT

The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel-dimensional feature-reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non-CDFR DNN (NCDFR-DNN) was built for comparative purposes. A mixture ensemble DNN (ME-DNN) integrating two CDFR-DNNs was constructed to identify subtypes on multiparametric MRI (MP-MRI) combing DW-MRI and DCE-MRI.

STATISTICAL TESTS

Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one-way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant.

RESULTS

The CDFR-DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR-DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW-MRI. Utilizing the CDFR-DNN, DW-MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE-MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME-DNN on MP-MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR-DNN and NCDFR-DNN on either DW-MRI or DCE-MRI.

DATA CONCLUSION

The CDFR-DNN enabled overall b-value DW-MRI to achieve the predictive performance comparable to DCE-MRI. MP-MRI outperformed DW-MRI and DCE-MRI in subtype prediction.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Predicting transcription factor activity using prior biological information

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Prevalent landscape of tumor genomic alterations of luminal B1 breast cancers using a comprehensive genomic profiling assay in Taiwan

BACKGROUND

The human epidermal growth factor receptor 2 (HER2) negative luminal B1 subtype of breast cancer has been reported with a poorer outcome than luminal A in recent studies. This study aimed to investigate the molecular alterations and identify potential therapeutic targets by analyzing the genetic profiling from a cohort of luminal B1 breast cancer in Taiwan.

METHODS

We enrolled patients with luminal B1 breast cancer in our study. They were classified as patients who received curative surgery and adjuvant or neoadjuvant chemotherapy as the low-risk group, and who had advanced or metastatic disease or early relapse during the follow-up time as the high-risk group. Using targeted sequencing, we evaluated genomic alterations, interpreting variants with the ESMO Scale of clinical actionability of molecular targets (ESCAT).

RESULTS

A total of 305 luminal B1 breast cancer patients underwent targeted sequencing analyses. The high-risk patients reported more actionable genes and called variants than the low-risk group (P < 0.05). PIK3CA (42%), FGFR1 (25%), and BRCA1/2 (10.5%) were the most prevalent ESCAT actionable alterations in luminal B1 breast cancer. There was no difference in the prevalence of actionable mutations between these two groups, except for ERBB2 oncogenic mutations, which were more prevalent among the high-risk than the low-risk group (P < 0.05). Alterations in PTEN, ERBB2, and BRCA1/2 were associated with disease relapse events in luminal B1 breast cancer.

CONCLUSIONS

PIK3CA, FGFR1, and BRCA1/2 were the most prevalent actionable alterations among Taiwanese luminal B1 breast cancer. Moreover, PTEN and BRCA1/2 was significantly associated with disease relapse.

ANXA2 (annexin A2) is crucial to ATG7-mediated autophagy, leading to tumor aggressiveness in triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) is associated with a poor prognosis and metastatic growth. TNBC cells frequently undergo macroautophagy/autophagy, contributing to tumor progression and chemotherapeutic resistance. ANXA2 (annexin A2), a potential therapeutic target for TNBC, has been reported to stimulate autophagy. In this study, we investigated the role of ANXA2 in autophagic processes in TNBC cells. TNBC patients exhibited high levels of ANXA2, which correlated with poor outcomes. ANXA2 increased LC3B-II levels following bafilomycin A1 treatment and enhanced autophagic flux in TNBC cells. Notably, ANXA2 upregulated the phosphorylation of HSF1 (heat shock transcription factor 1), resulting in the transcriptional activation of ATG7 (autophagy related 7). The mechanistic target of rapamycin kinase complex 2 (MTORC2) played an important role in ANXA2-mediated ATG7 transcription by HSF1. MTORC2 did not affect the mRNA level of ANXA2, but it was involved in the protein stability of ANXA2. HSPA (heat shock protein family A (Hsp70)) was a potential interacting protein with ANXA2, which may protect ANXA2 from lysosomal proteolysis. ANXA2 knockdown significantly increased sensitivity to doxorubicin, the first-line chemotherapeutic regimen for TNBC treatment, suggesting that the inhibition of autophagy by ANXA2 knockdown may overcome doxorubicin resistance. In a TNBC xenograft mouse model, we demonstrated that ANXA2 knockdown combined with doxorubicin administration significantly inhibited tumor growth compared to doxorubicin treatment alone, offering a promising avenue to enhance the effectiveness of chemotherapy. In summary, our study elucidated the molecular mechanism by which ANXA2 modulates autophagy, suggesting a potential therapeutic approach for TNBC treatment.Abbreviation: ATG: autophagy related; ChIP: chromatin-immunoprecipitation; HBSS: Hanks' balanced salt solution; HSF1: heat shock transcription factor 1; MTOR: mechanistic target of rapamycin kinase; TNBC: triple-negative breast cancer; TFEB: transcription factor EB; TFE3: transcription factor binding to IGHM enhancer 3.

Omada: robust clustering of transcriptomes through multiple testing

BACKGROUND

Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions.

FINDINGS

The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.

CONCLUSIONS

In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

Ferroptosis Inducers Upregulate PD-L1 in Recurrent Triple-Negative Breast Cancer

(1) Background: Triple-negative breast cancer (TNBC) is a distinct subgroup of breast cancer presenting a high level of recurrence, and neo-adjuvant chemotherapy is beneficial in its therapy management. Anti-PD-L1 immunotherapy improves the effect of neo-adjuvant therapy in TNBC. (2) Methods: Immune-modulation and ferroptosis-related R-packages were developed for integrative omics analyses under ferroptosis-inducer treatments: TNBC cells stimulated with ferroptosis inducers (GSE173905, GSE154425), single cell data (GSE191246) and mass spectrometry on breast cancer stem cells. Clinical association analyses were carried out with breast tumors (TCGA and METABRIC cohorts). Protein-level validation was investigated through protein atlas proteome experiments. (3) Results: Erastin/RSL3 ferroptosis inducers upregulate CD274 in TNBC cells (MDA-MB-231 and HCC38). In breast cancer, CD274 expression is associated with overall survival. Breast tumors presenting high expression of CD274 upregulated some ferroptosis drivers associated with prognosis: IDO1, IFNG and TNFAIP3. At the protein level, the induction of Cd274 and Tnfaip3 was confirmed in breast cancer stem cells under salinomycin treatment. In a 4T1 tumor treated with cyclophosphamide, the single cell expression of Cd274 was found to increase both in myeloid- and lymphoid-infiltrated cells, independently of its receptor Pdcd1. The CD274 ferroptosis-driver score computed on a breast tumor transcriptome stratified patients on their prognosis: low score was observed in the basal subgroup, with a higher level of recurrent risk scores (oncotypeDx, ggi and gene70 scores). In the METABRIC cohort, CD274, IDO1, IFNG and TNFAIP3 were found to be overexpressed in the TNBC subgroup. The CD274 ferroptosis-driver score was found to be associated with overall survival, independently of TNM classification and age diagnosis. The tumor expression of CD274, TNFAIP3, IFNG and IDO1, in a biopsy of breast ductal carcinoma, was confirmed at the protein level (4) Conclusions: Ferroptosis inducers upregulate PD-L1 in TNBC cells, known to be an effective target of immunotherapy in high-risk early TNBC patients who received neo-adjuvant therapy. Basal and TNBC tumors highly expressed CD274 and ferroptosis drivers: IFNG, TNFAIP3 and IDO1. The CD274 ferroptosis-driver score is associated with prognosis and to the risk of recurrence in breast cancer. A potential synergy of ferroptosis inducers with anti-PD-L1 immunotherapy is suggested for recurrent TNBC.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study

BACKGROUND

The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM.

METHODS

The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system.

RESULTS

From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system.

CONCLUSIONS

The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images

Purpose

To evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer.

Materials and methods

A total of 158 breast cancer patients (170 lesions, median age, 50.8 ± 11.0 years), including 78 Luminal A subtype and 92 non-Luminal A subtype lesions, were retrospectively analyzed and divided into a training set (n = 100), test set (n = 45), and validation set (n = 25). Mammography (MG) and magnetic resonance imaging (MRI) images were used. Five single-mode models, i.e., MG, T2-weighted imaging (T2WI), diffusion weighting imaging (DWI), axial apparent dispersion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), were selected. The deep learning network ResNet50 was used as the basic feature extraction and classification network to construct the molecular subtype identification model. The receiver operating characteristic curve were used to evaluate the prediction efficiency of each model.

Results

The accuracy, sensitivity and specificity of a multi-modal tool for identifying Luminal A subtype were 0.711, 0.889, and 0.593, respectively, and the area under the curve (AUC) was 0.802 (95% CI, 0.657- 0.906); the accuracy, sensitivity, and AUC were higher than those of any single-modal model, but the specificity was slightly lower than that of DCE-MRI model. The AUC value of MG, T2WI, DWI, ADC, and DCE-MRI model was 0.593 (95%CI, 0.436-0.737), 0.700 (95%CI, 0.545-0.827), 0.564 (95%CI, 0.408-0.711), 0.679 (95%CI, 0.523-0.810), and 0.553 (95%CI, 0.398-0.702), respectively.

Conclusion

The combination of deep learning and multi-modal imaging is of great significance for diagnosing breast cancer subtypes and selecting personalized treatment plans for doctors.

Complete pathological response in patients with HER2 positive breast cancer treated with neoadjuvant therapy in Colombia

Introduction

Breast cancer is the most common type of cancer and the leading cause of death by cancer in women in Colombia. Approximately 15 to 20% of breast cancers overexpress HER2.

Objective

To analyze the relationship between multiple clinical and histological variables and pathological complete response in patients with HER2-positive breast cancer undergoing neoadjuvant therapy in a specialized cancer center in Colombia.

Materials and methods

We performed a retrospective analysis of non-metastatic HER2-positive breast cancer patients who received neoadjuvant therapy between 2007 and 2020 at the Instituto de Cancerología Las Americas Auna (Medellín, Colombia). Assessed parameters were tumor grade, proliferation index, estrogen receptor, progesterone receptor, HER2 status, type of neoadjuvant therapy, pathologic complete response rates, and overall survival.

Results

Variables associated with low pathologic complete response rates were tumor grades 1-2 (OR = 0.55; 95% CI = 0.37-0.81; p = 0.03), estrogen receptor positivity (OR =0.65; 95%; CI = 0.43-0.97; p=0.04), and progesterone receptor positivity (OR = 0.44; 95% CI = 0.29-0.65; p = 0.0001). HER2 strong positivity (score 3+) was associated with high pathological complete response rates (OR = 3.3; 95% CI = 1.3-8.35; p=0.013). Five-year overall survival was 91.5% (95% CI = 82.6-95.9) in patients with pathological complete response and 73.6% (95% CI = 66.4-79.6) in patients who did not achieve pathological complete response (p = 0.001). Additionally, the pathological complete response rate was three times higher in patients receiving combined neoadjuvant chemotherapy with anti-HER2 therapy than in those with chemotherapy alone (48% versus 16%).

Conclusions

In patients with HER2-positive breast cancer, tumor grade 3, estrogen receptor negativity, progesterone receptor negativity, strong HER2 positivity (score 3+), and the use of the neoadjuvant trastuzumab are associated with higher pathological complete response rates.

Adverse Crosstalk between Extracellular Matrix Remodeling and Ferroptosis in Basal Breast Cancer

(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in women and causes a high number of mortality among this population due to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy, and inhibit distant metastases, potentially impacting the tumor microenvironment. (2) Methods: Through data mining, the ferroptosis/extracellular matrix remodeling literature text-mining results were integrated into the breast cancer transcriptome cohort, taking into account patients with distant relapse-free survival (DRFS) under adjuvant therapy (anthracyclin + taxanes) with validation in an independent METABRIC cohort, along with the MDA-MB-231 and HCC338 transcriptome functional experiments with ferroptosis activations (GSE173905). (3) Results: Ferroptosis/extracellular matrix remodeling text-mining identified 910 associated genes. Univariate Cox analyses focused on breast cancer (GSE25066) selected 252 individual significant genes, of which 170 were found to have an adverse expression. Functional enrichment of these 170 adverse genes predicted basal breast cancer signatures. Through text-mining, some ferroptosis-significant adverse-selected genes shared citations in the domain of ECM remodeling, such as TNF, IL6, SET, CDKN2A, EGFR, HMGB1, KRAS, MET, LCN2, HIF1A, and TLR4. A molecular score based on the expression of the eleven genes was found predictive of the worst prognosis breast cancer at the univariate level: basal subtype, short DRFS, high-grade values 3 and 4, and estrogen and progesterone receptor negative and nodal stages 2 and 3. This eleven-gene signature was validated as regulated by ferroptosis inductors (erastin and RSL3) in the triple-negative breast cancer cellular model MDA-MB-231. (4) Conclusions: The crosstalk between ECM remodeling-ferroptosis functionalities allowed for defining a molecular score, which has been characterized as an independent adverse parameter in the prognosis of breast cancer patients. The gene signature of this molecular score has been validated to be regulated by erastin/RSL3 ferroptosis activators. This molecular score could be promising to evaluate the ECM-related impact of ferroptosis target therapies in breast cancer.

Proteomic Profile Distinguishes New Subpopulations of Breast Cancer Patients with Different Survival Outcomes

As a highly heterogeneous disease, breast cancer (BRCA) demonstrates a diverse molecular portrait. The well-established molecular classification (PAM50) relies on gene expression profiling. It insufficiently explains the observed clinical and histopathological diversity of BRCAs. This study aims to demographically and clinically characterize the six BRCA subpopulations (basal, HER2-enriched, and four luminal ones) revealed by their proteomic portraits. GMM-based high variate protein selection combined with PCA/UMAP was used for dimensionality reduction, while the k-means algorithm allowed patient clustering. The statistical analysis (log-rank and Gehan-Wilcoxon tests, hazard ratio HR as the effect size ES) showed significant differences across identified subpopulations in Disease-Specific Survival (p = 0.0160) and Progression-Free Interval (p = 0.0264). Luminal subpopulations vary in prognosis (Disease-Free Interval, p = 0.0277). The A2 subpopulation is of the poorest, comparable to the HER2-enriched subpopulation, prognoses (HR = 1.748, referenced to Luminal B, small ES), while A3 is of the best (HR = 0.250, large ES). Similar to PAM50 subtypes, no substantial dependency on demographic and clinical factors was detected across Luminal subpopulations, as measured by χ2 test and Cramér's V for ES, and ANOVA with appropriate post hocs combined with η2 or Cohen's d-type ES, respectively. Progesterone receptors can serve as the potential A2 biomarker within Luminal patients. Further investigation of molecular differences is required to examine the potential prognostic or clinical applications.

A Clinical Model for the Differentiation of Suicidality: Protocol for a Usability Study of the Proposed Model

BACKGROUND

Even though various types of suicidality are observed in clinical practice, suicidality is still considered a uniform concept. To distinguish different types of suicidality and consequently improve detection and management of suicidality, we developed a clinical differentiation model for suicidality. We believe that the model allows for a more targeted assessment of suicidal conditions and improves the use of evidence-based treatment strategies. The differentiation model is based on the experience with suicidality that we have encountered in clinical practice. This model distinguishes 4 subtypes of entrapment leading to suicidality. The earliest description of this model and a proposal for usability research has been previously presented in a book chapter.

OBJECTIVE

In this study, we present the most recent version of the 4-type differentiation model of suicidality and a protocol for a study into the usability of the proposed model.

METHODS

The 4-type differentiation model of suicidality distinguishes the following subtypes: (1) perceptual disintegration, (2) primary depressive cognition, (3) psychosocial turmoil, and (4) inadequate coping or communication. We plan to test the usability of the 4 subtypes in a pilot study of 25 cases, and subsequently, we will include 75 cases in a follow-up study. We looked at the case notes of 100 anonymized patients with suicidality who presented to mental health care emergency service in The Hague International Center. The summary and conclusions of the letters sent to the patients' general practitioners after suicide risk assessment will be independently rated by 3 psychiatrists and 3 nurse-scientists for absolute and dimensional scores. The Suicidality Differentiation version 2 (SUICIDI-II) instrument, developed for this study, is used for rating all the cases. Intraclass correlation coefficients for absolute and dimensional scores will be calculated to examine type agreement between raters to examine the usability of the model and the feasibility of the SUICIDI-II instrument.

RESULTS

We consider the model tentatively valid if the intraclass correlation coefficients are ≥0.70. Subsequently, if the model turns out to be valid, we plan to rate 75 other cases in a follow-up study, according to a similar or adjusted procedure. Study results are expected to be published by the end of 2023.

CONCLUSIONS

The theoretical roots of the differentiation model stem from classic and contemporary theoretical models of suicidality and from our clinical practice experiences with suicidal behaviors. We believe that this model can be used to adjust the diagnosis, management, treatment, and research of suicidality, in addition to distinguishing different dynamics between practitioners and patients with suicidality and their families.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45438.

Involvement of the kynurenine pathway in breast cancer: updates on clinical research and trials

Breast cancer (BrCa) is the leading cause of cancer incidence and mortality in women worldwide. While BrCa treatment has been shown to be highly successful if detected at an early stage, there are few effective strategies to treat metastatic tumours. Hence, metastasis remains the main cause in most of BrCa deaths, highlighting the need for new approaches in this group of patients. Immunotherapy has been gaining attention as a new treatment for BrCa metastasis and the kynurenine pathway (KP) has been suggested as one of the potential targets. The KP is the major biochemical pathway in tryptophan (TRP) metabolism, catabolising TRP to nicotinamide adenine dinucleotide (NAD+). The KP has been reported to be elevated under inflammatory conditions such as cancers and that its activity suppresses immune surveillance. Dysregulation of the KP has previously been reported implicated in BrCa. This review aims to discuss and provide an update on the current mechanisms involved in KP-mediated immune suppression and cancer growth. Furthermore, we also provide a summary on 58 studies about the involvement of the KP and BrCa and five clinical trials targeting KP enzymes and their outcome.

Association of Proliferative Indices With Various Grades of Breast Carcinoma

AIMS AND OBJECTIVES

Various grades of breast carcinoma and proliferative indices used as nuclear protein Ki-67 and argyrophilic nucleolar organizer regions (AgNOR) are being compared to each other.

MATERIALS AND METHOD

In this observational cross-sectional investigation, 42 breast biopsies from questionable breast areas were collected and preserved in formalin and paraffin before the tissue blocks were made. A thorough medical history regarding the breast tumor and thorough physical examination results were recorded. Two sections were produced, one stained with an immunohistochemical marker called Ki-67 and the other with a unique stain called AgNOR.

RESULTS

Grade I in Nottingham was found to be highest in subjects with Ki-67 1%, grade II in subjects with Ki-67 1-10%, and grade III in subjects with Ki-67>10%. Therefore, a higher Ki-67 score and a higher Nottingham grade were more closely associated. The mean AgNOR score was determined to be highest in Nottingham grade III and lowest in Nottingham grade I. In contrast to grade I and grade II of carcinoma (CA) breast, where there was no statistically significant association between Ki-67 and AgNOR, grade III of CA breast showed a statistically significant link between Ki-67 and AgNOR.

CONCLUSION

Proliferation has been identified as a distinctive feature of cancer and as a key factor in the prognosis of the disease.

Serum-derived extracellular vesicles from breast cancer patients contribute to differential regulation of T-cell-mediated immune-escape mechanisms in breast cancer subtypes

Background

Intracellular communication within the tumour is complex and extracellular vesicles (EVs) have been identified as major contributing factors for the cell-to-cell communication in the local and distant tumour environments. Here, we examine the differential effects of breast cancer (BC) subtype-specific patient serum and cell-line derived EVs in the regulation of T cell mediated immune responses.

Methods

Ultracentrifugation was used to isolate EVs from sera of 63 BC patients, 15 healthy volunteers and 4 human breast cancer cell lines. Longitudinal blood draws for EV isolation for patients on neoadjuvant chemotherapy was also performed. Characterization of EVs was performed by Nanoparticle Tracking Analysis (NTA), transmission electron microscopy (TEM) and immunoblotting. CD63 staining was performed on a tissue microarray of 218 BC patients. In-house bioinformatics algorithms were utilized for the computation of EV associated expression scores within The Cancer Genome Atlas (TCGA) and correlated with tumour infiltrating lymphocyte (TIL) scores. In vitro stimulation of PBMCs with EVs from serum and cell-line derived EVs was performed and changes in the immune phenotypes characterized by flow cytometry. Cytokine profiles were assessed using a 105-plex immunoassay or IL10 ELISA.

Results

Patients with triple negative breast cancers (TNBCs) exhibited the lowest number of EVs in the sera; whilst the highest was detected in ER+HER2+ cancers; reflected also in the higher level of CD63+ vesicles found within the ER+HER2+ local tumour microenvironment. Transcriptomic analysis of the TCGA data identified that samples assigned with lower EV scores had significantly higher abundance of CD4+ memory activated T cells, T follicular cells and CD8 T cells, plasma, and memory B cells; whilst samples with high EV scores were more enriched for anti-inflammatory M2 macrophages and mast cells. A negative correlation between EV expression scores and stromal TIL counts was also observed. In vitro experiments confirmed that circulating EVs within breast cancer subtypes have functionally differing immunomodulatory capabilities, with EVs from patients with the most aggressive breast cancer subtype (TNBCs) demonstrating the most immune-suppressive phenotype (decreased CD3+HLA-DR+ but increased CD3+PD-L1 T cells, increased CD4+CD127-CD25hi T regulatory cells with associated increase in IL10 cytokine production). In depth assessment of the cytokine modulation triggered by the serum/cell line derived exosomes confirmed differential inflammatory cytokine profiles across differing breast cancer subtypes. Studies using the MDA-231 TNBC breast cancer cell-line derived EVs provided further support that TNBC EVs induced the most immunosuppressive response within PBMCs.

Discussion

Our study supports further investigations into how tumour derived EVs are a mechanism that cancers can exploit to promote immune suppression; and breast cancer subtypes produce EVs with differing immunomodulatory capabilities. Understanding the intracellular/extracellular pathways implicated in alteration from active to suppressed immune state may provide a promising way forward for restoring immune competence in specific breast cancer patient populations.

Drug repurposing for Basal breast cancer subpopulations using modular network signatures

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

Seeking a better understanding of the non-receptor tyrosine kinase, SRMS

SRMS (Src-Related kinase lacking C-terminal regulatory tyrosine and N-terminal Myristoylation Sites) is a non-receptor tyrosine kinase first reported in a 1994 screen for genes regulating murine neural precursor cells. SRMS, pronounced "Shrims", lacks the C-terminal regulatory tyrosine critical for the regulation of the enzymatic activity of Src-family kinases (SFKs). Another remarkable characteristic of SRMS is its localization into distinct SRMS cytoplasmic punctae (SCPs) or GREL (Goel Raghuveera-Erique Lukong) bodies, a pattern not observed in the SFKs. This unique subcellular localization of SRMS could dictate its cellular targets, proteome, and potentially, substrates. However, the function of SRMS is still relatively unknown. Further, how is its activity regulated and by what cellular targets? Studies have emerged highlighting the potential role of SRMS in autophagy and in regulating the activation of BRK/PTK6. Potential novel cellular substrates have also been identified, including DOK1, vimentin, Sam68, FBKP51, and OTUB1. Recent studies have also demonstrated the potential role of the kinase in various cancers, including gastric and colorectal cancers and platinum resistance in ovarian cancer. This review discusses the advancements made in SRMS-related biology to date and the path to understanding the cellular and physiological significance of the kinase.

Differences in Molecular Subtype Reference Standards Impact AI-based Breast Cancer Classification with Dynamic Contrast-enhanced MRI

Background Breast cancer tumors can be identified as different luminal molecular subtypes depending on either immunohistochemical (IHC) staining or St Gallen criteria that includes Ki-67. Purpose To characterize molecular subtypes and understand the impact of disagreement among IHC and St Gallen molecular subtype reference standards on artificial intelligence classification of luminal A and luminal B tumors with use of radiomic features extracted from dynamic contrast-enhanced (DCE) MRI scans. Materials and Methods In this retrospective study, 28 radiomic features previously extracted from DCE-MRI scans of breast tumors imaged between February 2015 and October 2017 were examined in the following groups: (a) tumors classified as luminal A by both reference standards ("agreement"), (b) tumors classified as luminal A by IHC and luminal B by St Gallen ("disagreement"), and (c) tumors classified as luminal B by both ("agreement"). Luminal A or luminal B tumor classification with use of radiomic features was conducted with use of three sets: (a) IHC molecular subtyping, (b) St Gallen molecular subtyping, and (c) agreement tumors. The Kruskal-Wallis test was followed by the Mann-Whitney U test to determine pair-wise differences of radiomic features among agreement and disagreement tumors. Fivefold cross-validation with use of stepwise feature selection and linear discriminant analysis classified tumors in each set, with performance measured with use of area under the receiver operating characteristic curve (AUC). Results A total of 877 breast cancer tumors from 872 women (mean age, 48 years [range, 19-75 years]) were analyzed. Six features (sphericity, irregularity, surface area to volume ratio, variance of radial gradient histogram, sum average, volume of most enhancing voxels) were different (P ≤ .001) among agreement and disagreement tumors. AUC (median, 0.74 [95% CI: 0.68, 0.80]) was higher than when using tumors subtyped by either reference standard (IHC, 0.66 [0.60, 0.71], P = .003; St Gallen, 0.62 [0.58, 0.67], P = .001). Conclusion Differences in reference standards can hinder artificial intelligence classification performance of luminal molecular subtypes with dynamic contrast-enhanced MRI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Molecular targets that sensitize cancer to radiation killing: From the bench to the bedside

Radiotherapy is a standard cytotoxic therapy against solid cancers. It uses ionizing radiation to kill tumor cells through damage to DNA, either directly or indirectly. Radioresistance is often associated with dysregulated DNA damage repair processes. Most radiosensitizers enhance radiation-mediated DNA damage and reduce the rate of DNA repair ultimately leading to accumulation of DNA damages, cell-cycle arrest, and cell death. Recently, agents targeting key signals in DNA damage response such as DNA repair pathways and cell-cycle have been developed. This new class of molecularly targeted radiosensitizing agents is being evaluated in preclinical and clinical studies to monitor their activity in potentiating radiation cytotoxicity of tumors and reducing normal tissue toxicity. The molecular pathways of DNA damage response are reviewed with a focus on the repair mechanisms, therapeutic targets under current clinical evaluation including ATM, ATR, CDK1, CDK4/6, CHK1, DNA-PKcs, PARP-1, Wee1, & MPS1/TTK and potential new targets (BUB1, and DNA LIG4) for radiation sensitization.

Comparing the Biology of Young versus Old Age Estrogen-Receptor-Positive Breast Cancer through Gene and Protein Expression Analyses

Background: Breast cancer developed at a young age (≤45 years) is hypothesized to have unique biology; however, findings in this field are controversial. Methods: We compared the whole transcriptomic profile of young vs. old-age breast cancer using DNA microarray. RNA was extracted from 13 fresh estrogen receptor (ER)-positive primary breast cancer tissues of untreated patients (7 = young age ≤45 years and 6 = old age ≥55 years). In silico validation for the differentially expressed genes (DEGs) by young-age patients was conducted using The Cancer Genome Atlas (TCGA) database. Next, we analyzed the protein expression encoded by two of the significantly down-regulated genes by young-age patients, Glycine N-acyltransferase-like 1 (GLYATL-1) and Ran-binding protein 3 like (RANBP3L), using immunohistochemical analysis in an independent cohort of 56 and 74 ER-positive pre-therapeutic primary breast cancer tissues, respectively. Results: 12 genes were significantly differentially expressed by young-age breast cancers (fold change >2 or <2- with FDR p-value < 0.05). TCGA data confirmed the differential expression of six genes. Protein expression analysis of GLYATL-1 and RANBP3L did not show heterogeneous expression between young and old-age breast cancer tissues. Loss of expression of GLYATL-1 was significantly (p-value 0.005) associated with positive lymph node status. Higher expression of RANBP3L was significantly associated with breast cancers with lower histopathological grades (p-value 0.038). Conclusions: At the transcriptomic level, breast cancer developed in young and old age patients seems homogenous. The variation in the transcriptomic profiles can be attributed to the other clinicopathological characteristics rather than the age of the patient.

Statistical Methods for Integrative Clustering of Multi-omics Data

Cancers are heterogeneous diseases caused by accumulated mutations or abnormal alterations at multi-levels of biological processes including genomics, epigenomics, transcriptomics, and proteomics. There is a great clinical interest in identifying cancer molecular subtypes for disease prognosis and personalized medicine. Integrative clustering is a powerful unsupervised learning method that has been increasingly used to identify cancer molecular subtypes using multi-omics data including somatic mutations, DNA copy numbers, DNA methylation, and gene expression. Integrative clustering methods are generally classified into model-based or nonparametric approaches. In this chapter, we will give an overview of the frequently used model-based methods, including iCluster, iClusterPlus, and iClusterBayes, and the nonparametric method, integrative nonnegative matrix factorization (intNMF). We will use the integrative analyses of uveal melanoma and lower-grade glioma to illustrate these representative methods. Finally, we will discuss the strengths and limitations of these representative methods and give suggestions for performing integrative analyses of cancer multi-omics data in practice.

OSNA Total Tumor Load for the Prediction of Axillary Involvement in Breast Cancer Patients: Should We use Different Thresholds According to the Intrinsic Molecular Subtype? MOTTO Study

Aims

To assess the impact of the molecular subtype (MS) on the total number of CK19 mRNA copies in all positive SLN (TTL) threshold, to predict non-SLN affectation, and to compare 5 years progression-free survival (PFS) according to the risk of recurrence (ROR) group by PAM50.

Methods

Cohort with infiltrating breast cancer with intra-operative metastatic SLN detected by one-step nucleic acid amplification (OSNA) assay who underwent subsequent ALND. Logistic regression was used to assess a possible interaction between TTL and MS(Triple Negative, Her-2-Enriched, Luminal A, or Luminal B), or hormone receptors (HR: positive or negative) by immunohistochemistry (IMH). Cox regression was used to compare PFS and OS in the 3 ROR groups (high, medium, or low).

Results

TTL was predictive of non-SLN affectation in both univariate (OR [95% CI]: 1.72 [1.43, 2.05], P < .001) and multivariate (1.55 [95% CI: 1.04, 2.32], P = .030) models, but MS-IMH or HR-IMH, and their interactions with TTL were not (best multivariate model: HR + main effect OR 1.16 [95% CI: 0.18, 7.64], P = .874; interaction OR: 1.04 [0.7, 1.55], P = .835; univariate model: HR + main effect OR: 1.44 [95% CI: 0.85, 2.44], P = .180). PFS was lower in the high-risk ROR group (81.1%) than in the low-risk group (93.9%) (HR: 3.68 [95 CI: 1.70, 7.94], P < .001).

Conclusions

our results do not provide evidence to support the utilization of subtype-specific thresholds for TTL values to make therapeutic decisions on the axilla. The ROR group was predictive of 5 years-PFS.

The Value of Semiquantitative Parameters Derived from 18F-FDG PET/CT for Predicting Response to Neoadjuvant Chemotherapy in a Cohort of Patients with Different Molecular Subtypes of Breast Cancer

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is a strong prognostic factor in breast cancer (BC). The aim of this study was to investigate whether semiquantitative parameters derived from baseline [¹⁸F]Fluorodeoxyglucose ([¹⁸F]FDG) positron emission computed tomography/computed tomography (PET/CT) could predict pCR after NAC and survival outcomes in patients affected by different molecular subtypes of BC. We retrospectively retrieved patients from the databases of two Italian hospitals (Centre A: University Hospital of Ferrara; Centre B: University of Padua) meeting the following inclusion criteria: (1) diagnosis of BC; (2) history of NAC; (3) baseline [¹⁸F]FDG PET/CT performed before the first cycle of NAC; (4) available follow-up data (response after NAC and survival information). For each [¹⁸F]FDG PET/CT scan, semiquantitative parameters (SUVmax, SUVmean, MTV and TLG) related to the primary tumor (B), to the reference lesion for both axillary (N) and distant lymph node (DN), and to the whole-body burden of disease (WB) were evaluated. Patients enrolled were 133: 34 from centre A and 99 from centre B. Patients' molecular subtypes were: 9 luminal A, 49 luminal B, 33 luminal B + HER-2, 10 HER-2 enriched, and 32 triple negative (TNBC). Luminal A and HER-2 enriched BC patients were excluded from the analysis due to the small sample size. pCR after NAC was achieved in 47 patients (41.2%). [¹⁸F]FDG PET/CT detected the primary tumor in 98.3% of patients and lymph node metastases were more frequently detected in Luminal B subgroup. Among Luminal B patients, median SUVmean_B values were significantly higher (p = 0.027) in responders (7.06 ± 5.9) vs. non-responders (4.4 ± 2.1) to NAC. Luminal B + HER-2 non-responders showed a statistically significantly higher median MTV_B (7.3 ± 4.2 cm³ vs. 3.5 ± 2.5 cm³; p = 0.003) and TLG_B (36.5 ± 24.9 vs. 18.9 ± 17.7; p = 0.025) than responders at baseline [¹⁸F]FDG PET/CT. None of the semiquantitative parameters predicted pCR after NAC in TNBC patients. However, among TNBC patients who achieved pCR after NAC, 4 volumetric parameters (MTV_B, TLG_B, MTV_WB and TLG_WB) were significantly higher in patients dead at follow-up. If confirmed in further studies, these results could open up a widespread use of [¹⁸F]FDG PET/CT as a baseline predictor of response to NAC in luminal B and luminal B + HER-2 patients and as a prognostic tool in TNBC.

Reduced expression of FRG1 facilitates breast cancer progression via GM-CSF/MEK-ERK axis by abating FRG1 mediated transcriptional repression of GM-CSF

Multiple molecular subtypes and distinct clinical outcomes in breast cancer, necessitate specific therapy. Moreover, despite the improvements in breast cancer therapy, it remains the fifth cause of cancer-related deaths, indicating the involvement of unknown genes. To identify novel contributors and molecular subtype independent therapeutic options, we report reduced expression of FRG1 in breast cancer patients, which regulates GM-CSF expression via direct binding to its promoter. Reduction in FRG1 expression enhanced EMT and increased cell proliferation, migration, and invasion, in breast cancer cell lines. Loss of FRG1 increased GM-CSF levels which activated MEK/ERK axis and prevented apoptosis by inhibiting p53 in an ERK-dependent manner. FRG1 depletion in the mouse model increased tumor volume, phospho-ERK, and EMT marker levels. The therapeutic potential of anti-GM-CSF therapy was evident by reduced tumor size, when tumors with decreased FRG1 were treated with anti-GM-CSF mAb. We found an inverse expression pattern of FRG1 and phospho-ERK levels in breast cancer patient tissues, corroborating the in vitro and mouse model-based findings. Our findings first time elucidate the role of FRG1 as a metastatic suppressor of breast cancer by regulating the GM-CSF/MEK-ERK axis.

Impact of preoperative magnetic resonance imaging on surgery and eligibility for intraoperative radiotherapy in early breast cancer

We looked at the usefulness of magnetic resonance imaging (MRI) in decision-making and surgical management of patients selected for intraoperative radiotherapy (IORT). We also compared lesion size measurements in different modalities (ultrasound (US), mammogram (MMG), MRI) against pathological size as the gold standard. 63 patients eligible for IORT based on clinical and imaging criteria over a 34-month period were enrolled. All had MMG and US, while 42 had additional preoperative MRI for locoregional preoperative staging. Imaging findings and pathological size concordances were analysed across the three modalities. MRI changed the surgical management of 5 patients (11.9%) whereby breast-conserving surgery (BCS) and IORT was cancelled due to detection of satellite lesion, tumor size exceeding 30mm and detection of axillary nodal metastases. Ten of 42 patients (23.8%) who underwent preoperative MRI were subjected to additional external beam radiotherapy (EBRT); 7 due to lymphovascular invasion (LVI), 2 due to involved margins, and 1 due to axillary lymph node metastatic carcinoma detected in the surgical specimen. Five of 21 (23.8%) patients without prior MRI were subjected to additional EBRT post-surgery; 3 had LVI and 2 had involved margins. The rest underwent BCS and IORT as planned. MRI and MMG show better imaging-pathological size correlation. Significant increase in the mean 'waiting time' were seen in the MRI group (34.1 days) compared to the conventional imaging group (24.4 days). MRI is a useful adjunct to conventional imaging and impacts decision making in IORT. It is also the best imaging modality to determine the actual tumour size.

Chromatin Variants Reveal the Genetic Determinants of Oncogenesis in Breast Cancer

Breast cancer presents as multiple distinct disease entities. Each tumor harbors diverse cell populations defining a phenotypic heterogeneity that impinges on our ability to treat patients. To date, efforts mainly focused on genetic variants to find drivers of inter- and intratumor phenotypic heterogeneity. However, these efforts have failed to fully capture the genetic basis of breast cancer. Through recent technological and analytical approaches, the genetic basis of phenotypes can now be decoded by characterizing chromatin variants. These variants correspond to polymorphisms in chromatin states at DNA sequences that serve a distinct role across cell populations. Here, we review the function and causes of chromatin variants as they relate to breast cancer inter- and intratumor heterogeneity and how they can guide the development of treatment alternatives to fulfill the goal of precision cancer medicine.

Annexin A2 (AnxA2) association with the clinicopathological data in different breast cancer subtypes: A possible role for AnxA2 in tumor heterogeneity and cancer progression

BACKGROUND

Breast cancer is a highly heterogeneous type of neoplasia with molecular and biochemical alterations in the ductal epithelium. AnxA2 has a diverse functions and through intracellular interaction with other molecules promotes carcinogenesis.

AIMS

To study the possible involvement of AnxA2 in breast cancer heterogeneity and cancer progression.

PATIENTS AND METHODS

Tumor tissue and serum were obtained from different breast cancer subtypes. Tumor tissues were processed for histopathological studies. AnxA2 levels were assessed in the tissues by H scoring and in the serum by ELISA. AnxA2 levels were correlated with HER2 and Ki67 and with clinicopathological data. Normal breast tissues and serum from healthy subjects were used as controls.

RESULTS

AnxA2 showed a peculiar distribution in tumor tissues and nearby interstitial tissues. Pattern of expressions varied in different subtypes with the highest expression in triple negative subtype. Tissue and serum AnxA2 showed significant co-upregulations in breast cancer. Moreover, they showed positive correlations with HER2 and Ki67 and associations with clinicopathological data including cancer staging and lymph node metastasis.

CONCLUSION

For the best of our knowledge this is the first study showing correlation between AnxA2, the proposed prognostic marker and the well-established tumor markers; HER2 and Ki67. AnxA2 might contribute to breast cancer heterogeneity and is associated with poor prognosis. AnxA2 might be a prognostic marker and an additional marker for breast cancer grading and clinical staging. Interestingly, tissue and serum AnxA2 showed a strong correlation. Thus, assessing serum AnxA2 can be a noninvasive prognostic tool.

Aurora Kinase A and Bcl-xL Inhibition Suppresses Metastasis in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a heterogeneous disease that accounts for 10–15% of all breast cancer cases. Within TNBC, the treatment of basal B is the most challenging due to its highly invasive potential, and thus treatments to suppress metastasis formation in this subgroup are urgently needed. However, the mechanisms underlying the metastatic ability of TNBC remain unclear. In the present study, we investigated the role of Aurora A and Bcl-xL in regulating basal B cell invasion. We found gene amplification and elevated protein expression in the basal B cells, which also showed increased invasiveness in vitro, compared to basal A cells. Chemical inhibition of Aurora A with alisertib and siRNA-mediated knockdown of BCL2L1 decreased the number of invading cells compared to non-treated cells in basal B cell lines. The analysis of the correlation between AURKA and BCL2L1 expression in TNBC and patient survival revealed significantly decreased relapse-free survival (n = 534, p = 0.012) and distant metastasis-free survival (n = 424, p = 0.017) in patients with primary tumors exhibiting a high combined expression of AURKA and BCL2L1. Together, our findings suggest that high levels of Aurora A and Bcl-xL promote metastasis, and inhibition of these proteins may suppress metastasis and improve patient survival in basal B TNBC.

The Molecular Predictive and Prognostic Biomarkers in Metastatic Breast Cancer: The Contribution of Molecular Profiling

The past decade was marked by several important studies deciphering the molecular landscape of metastatic breast cancer. Although the initial goal of these studies was to find driver oncogenic events to explain cancer progression and metastatic spreading, they have also permitted the identification of several molecular alterations associated with treatment response or resistance. Herein, we review validated (PI3KCA, ESR1, MSI, NTRK translocation) and emergent molecular biomarkers (ERBB2, AKT, PTEN, HRR gene, CD274 amplification RB1, NF1, mutational process) in metastatic breast cancer, on the bases of the largest molecular profiling studies. These biomarkers will be classed according the level of evidence and, if possible, the ESCAT (ESMO) classification. Finally, we will provide some perspective on development in clinical practice for the main biomarkers.

Clustering Molecular Subtypes in Breast Cancer, Immunohistochemical Parameters and Risk of Axillary Nodal Involvement

(1) Background: To establish similarities in the risk of axillary lymph node metastasis between different groups of women with breast cancer according to immunohistochemical (IHC) parameters. (2) Methods: Data was collected retrospectively, from 2000 to 2013, of 1058 node-positive breast tumours. All patients were divided according to the St Gallen 2013 criteria and IHC features. The proportion of axillary involvement (pN > pN0; pN > pN1mi; pN > pN1) was calculated for each group. Similarities in axillary nodal dissemination were explored by cluster analysis and association between IHC and risk of axillary disease was studied with multivariate analysis. (3) Results: Among clinico-pathological surrogates of intrinsic subtypes, axillary involvement was more frequent in Luminal-B like HER2 negative (45.8%) and less frequent in Luminal-B HER2 positive (33.8%; p = 0.044). Axillary macroscopic involvement was more frequent in Luminal-B like HER2 negative (37.9%) and HER2 positive (37.8%) and less frequent in Luminal-B HER2 positive (25.5%) and Luminal-A like (25.6%; p = 0.002). Axillary involvement ≥pN2 was significantly less frequent in Luminal-A like (7.4%; p < 0.001). Luminal-A with Luminal-B HER2 positive, and triple-negative with Erb-B2 overexpressing tumours were clustered together regarding any axillary involvement, macroscopic disease or ≥pN2. Among the defined subgroups, axillary metastases were more frequent when Ki67 was higher. In a multivariate analysis, Ki67>14% were associated with a risk of axillary metastases (HR: 1.31; 95% CI, 1.51−6.80; p < 0.037). (4) Conclusions: there are two lymphatic drainage pathways of the breast according to the expression of hormone receptor-related genes. Positive-ER tumors are associated with lower axillary involvement and negative-ER tumors and Ki67 > 14% with higher nodal involvement.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

Association of Obesity and Diabetes With the Incidence of Breast Cancer in Louisiana

INTRODUCTION

Breast cancer is a heterogeneous disease, consisting of multiple molecular subtypes. Obesity has been associated with an increased risk for postmenopausal breast cancer, but few studies have examined breast cancer subtypes separately. Obesity is often complicated by type 2 diabetes, but the possible association of diabetes with specific breast cancer subtypes remains poorly understood.

METHODS

In this retrospective case-control study, Louisiana Tumor Registry records of primary invasive breast cancer diagnosed in 2010-2015 were linked to electronic health records in the Louisiana Public Health Institute's Research Action for Health Network. Controls were selected from Research Action for Health Network and matched to cases by age and race. Conditional logistic regression was used to identify metabolic risk factors. Data analysis was conducted in 2020‒2021.

RESULTS

There was a significant association between diabetes and breast cancer for Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2‒positive subtypes. In multiple logistic regression, including both obesity status and diabetes as independent risk factors, Luminal A breast cancer was also associated with overweight status. Diabetes was associated with increased risk for Luminal A and Triple-Negative Breast Cancer in subgroup analyses, including women aged ≥50 years, Black women, and White women.

CONCLUSIONS

Although research has identified obesity and diabetes as risk factors for breast cancer, these results underscore that comorbid risk is complex and may differ by molecular subtype. There was a significant association between diabetes and the incidence of Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2‒positive breast cancer in Louisiana.

The crucial role of epigenetic regulation in breast cancer anti-estrogen resistance: Current findings and future perspectives

Breast cancer (BC) cell de-sensitization to Tamoxifen (TAM) or other selective estrogen receptor (ER) modulators (SERM) is a complex process associated with BC heterogeneity and the transformation of ER signalling. The most influential resistance-related mechanisms include modifications in ER expression and gene regulation patterns. During TAM/SERM treatment, epigenetic mechanisms can effectively silence ER expression and facilitate the development of endocrine resistance. ER status is efficiently regulated by specific epigenetic tools including hypermethylation of CpG islands within ER promoters, increased histone deacetylase activity in the ER promoter, and/or translational repression by miRNAs. Over-methylation of the ER α gene (ESR1) promoter by DNA methyltransferases was associated with poor prognosis and indicated the development of resistance. Moreover, BC progression and spreading were marked by transformed chromatin remodelling, post-translational histone modifications, and expression of specific miRNAs and/or long non-coding RNAs. Therefore, targeted inhibition of histone acetyltransferases (e.g. MYST3), deacetylases (e.g. HDAC1), and/or demethylases (e.g. lysine-specific demethylase LSD1) was shown to recover and increase BC sensitivity to anti-estrogens. Indicated as a powerful molecular instrument, the administration of epigenetic drugs can regain ER expression along with the activation of tumour suppressor genes, which can in turn prevent selection of resistant cells and cancer stem cell survival. This review examines recent advances in the epigenetic regulation of endocrine drug resistance and evaluates novel anti-resistance strategies. Underlying molecular mechanisms of epigenetic regulation will be discussed, emphasising the utilization of epigenetic enzymes and their inhibitors to re-program irresponsive BCs.

Combinatorial immunotherapies overcome MYC-driven immune evasion in triple negative breast cancer

Few patients with triple negative breast cancer (TNBC) benefit from immune checkpoint inhibitors with complete and durable remissions being quite rare. Oncogenes can regulate tumor immune infiltration, however whether oncogenes dictate diminished response to immunotherapy and whether these effects are reversible remains poorly understood. Here, we report that TNBCs with elevated MYC expression are resistant to immune checkpoint inhibitor therapy. Using mouse models and patient data, we show that MYC signaling is associated with low tumor cell PD-L1, low overall immune cell infiltration, and low tumor cell MHC-I expression. Restoring interferon signaling in the tumor increases MHC-I expression. By combining a TLR9 agonist and an agonistic antibody against OX40 with anti-PD-L1, mice experience tumor regression and are protected from new TNBC tumor outgrowth. Our findings demonstrate that MYC-dependent immune evasion is reversible and druggable, and when strategically targeted, may improve outcomes for patients treated with immune checkpoint inhibitors.

ENDORSE: a prognostic model for endocrine therapy in estrogen-receptor-positive breast cancers

Advanced and metastatic estrogen receptor-positive (ER+ ) breast cancers are often endocrine resistant. However, endocrine therapy remains the primary treatment for all advanced ER+ breast cancers. Treatment options that may benefit resistant cancers, such as add-on drugs that target resistance pathways or switching to chemotherapy, are only available after progression on endocrine therapy. Here we developed an endocrine therapy prognostic model for early and advanced ER+ breast cancers. The endocrine resistance (ENDORSE) model is composed of two components, each based on the empirical cumulative distribution function of ranked expression of gene signatures. These signatures include a feature set associated with long-term survival outcomes on endocrine therapy selected using lasso-regularized Cox regression and a pathway-based curated set of genes expressed in response to estrogen. We extensively validated ENDORSE in multiple ER+ clinical trial datasets and demonstrated superior and consistent performance of the model over clinical covariates, proliferation markers, and multiple published signatures. Finally, genomic and pathway analyses in patient data revealed possible mechanisms that may help develop rational stratification strategies for endocrine-resistant ER+ breast cancer patients.

Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors

Heterogeneity of cancer means many tumorigenic genes are only aberrantly expressed in a subset of patients and thus follow a bimodal distribution, having two modes of expression within a single population. Traditional statistical techniques that compare sample means between cancer patients and healthy controls fail to detect bimodally expressed genes. We utilize a mixture modeling approach to identify bimodal microRNA (miRNA) across cancers, find consistent sources of heterogeneity, and identify potential oncogenic miRNA that may be used to guide personalized therapies. Pathway analysis was conducted using target genes of the bimodal miRNA to identify potential functional implications in cancer. In vivo overexpression experiments were conducted to elucidate the clinical importance of bimodal miRNA in chemotherapy treatments. In nine types of cancer, tumors consistently displayed greater bimodality than normal tissue. Specifically, in liver and lung cancers, high expression of miR-105 and miR-767 was indicative of poor prognosis. Functional pathway analysis identified target genes of miR-105 and miR-767 enriched in the phosphoinositide-3-kinase (PI3K) pathway, and analysis of over 200 cancer drugs in vitro showed that drugs targeting the same pathway had greater efficacy in cell lines with high miR-105 and miR-767 levels. Overexpression of the two miRNA facilitated response to PI3K inhibitor treatment. We demonstrate that while cancer is marked by considerable genetic heterogeneity, there is between-cancer concordance regarding the particular miRNA that are more variable. Bimodal miRNA are ideal biomarkers that can be used to stratify patients for prognosis and drug response in certain types of cancer.

Bimodal genes can be defined as those having two modes of expression within the same population. A variety of statistical methodologies have been employed to assess bimodal gene expression, but current methods and their applications have been limited. Given the advances in next-generation sequencing as well as the extensive regulatory role of miRNA, assessing bimodality in miRNA-seq data can greatly broaden our understanding of factors underlying tumor progression. The goal of the current study was to utilize a novel mixture modeling approach to identify bimodal miRNA and then demonstrate their importance in cancer by evaluating their ability to predict overall survival and drug response. Our results showed that high levels of bimodal miRNA expression was characteristic of cancer. Additionally, several bimodal miRNA were common to multiple cancer types, suggesting that certain miRNA consistently account for tumor heterogeneity and may be involved in general oncogenic processes. Our study points to the potential of bimodal miRNA to facilitate precise prognostic evaluation and effective treatment strategies.

Fucoxanthin Is a Potential Therapeutic Agent for the Treatment of Breast Cancer

Breast cancer (BC) is one of the most common cancers diagnosed and the leading cause of cancer-related death in women. Although there are first-line treatments for BC, drug resistances and adverse events have been reported. Given the incidence of BC keeps increasing, seeking novel therapeutics is urgently needed. Fucoxanthin (Fx) is a dietary carotenoid commonly found in seaweeds and diatoms. Both in vitro and in vivo studies show that Fx and its deacetylated metabolite fucoxanthinol (Fxol) inhibit and prevent BC growth. The NF-κB signaling pathway is considered the major pathway contributing to the anti-proliferation, anti-angiogenesis and pro-apoptotic effects of Fx and Fxol. Other signaling molecules such as MAPK, MMP2/9, CYP and ROS are also involved in the anti-cancer effects by regulating the tumor microenvironment, cancer metastasis, carcinogen metabolism and oxidation. Besides, Fx also possesses anti-obesity effects by regulating UCP1 levels and lipid metabolism, which may help to reduce BC risk. More importantly, mounting evidence demonstrates that Fx overcomes drug resistance. This review aims to give an updated summary of the anti-cancer effects of Fx and summarize the underlying mechanisms of action, which will provide novel strategies for the development of Fx as an anti-cancer therapeutic agent.

Metabolomics and EMT Markers of Breast Cancer: A Crosstalk and Future Perspective

Cancer cells undergo transient EMT and MET phenomena or vice versa, along with the parallel interplay of various markers, often correlated as the determining factor in decoding metabolic profiling of breast cancers. Moreover, various cancer signaling pathways and metabolic changes occurring in breast cancer cells modulate the expression of such markers to varying extents. The existing research completed so far considers the expression of such markers as determinants regulating the invasiveness and survival of breast cancer cells. Therefore, this manuscript is crosstalk among the expression levels of such markers and their correlation in regulating the aggressiveness and invasiveness of breast cancer. We also attempted to cover the possible EMT-based metabolic targets to retard migration and invasion of breast cancer.

Wnt Signaling in the Breast: From Development to Disease

The Wnt cascade is a primordial developmental signaling pathway that plays a myriad of essential functions throughout development and adult homeostasis in virtually all animal species. Aberrant Wnt activity is implicated in embryonic and tissue morphogenesis defects, and several diseases, most notably cancer. The role of Wnt signaling in mammary gland development and breast cancer initiation, maintenance, and progression is far from being completely understood and is rather shrouded in controversy. In this review, we dissect the fundamental role of Wnt signaling in mammary gland development and adult homeostasis and explore how defects in its tightly regulated and intricated molecular network are interlinked with cancer, with a focus on the breast.

Regression-based heterogeneity analysis to identify overlapping subgroup structure in high-dimensional data

Heterogeneity is a hallmark of complex diseases. Regression-based heterogeneity analysis, which is directly concerned with outcome-feature relationships, has led to a deeper understanding of disease biology. Such an analysis identifies the underlying subgroup structure and estimates the subgroup-specific regression coefficients. However, most of the existing regression-based heterogeneity analyses can only address disjoint subgroups; that is, each sample is assigned to only one subgroup. In reality, some samples have multiple labels, for example, many genes have several biological functions, and some cells of pure cell types transition into other types over time, which suggest that their outcome-feature relationships (regression coefficients) can be a mixture of relationships in more than one subgroups, and as a result, the disjoint subgrouping results can be unsatisfactory. To this end, we develop a novel approach to regression-based heterogeneity analysis, which takes into account possible overlaps between subgroups and high data dimensions. A subgroup membership vector is introduced for each sample, which is combined with a loss function. Considering the lack of information arising from small sample sizes, an l 2 $l_2$ norm penalty is developed for each membership vector to encourage similarity in its elements. A sparse penalization is also applied for regularized estimation and feature selection. Extensive simulations demonstrate its superiority over direct competitors. The analysis of Cancer Cell Line Encyclopedia data and lung cancer data from The Cancer Genome Atlas show that the proposed approach can identify an overlapping subgroup structure with favorable performance in prediction and stability.