The molecular portraits of breast tumors are conserved across microarray platforms

Mps1 kinase functions in mitotic spindle assembly and error correction

The protein kinase Mps1 (also known as TTK) is a central component of the mitotic spindle assembly checkpoint (SAC), an essential self-monitoring system of the eukaryotic cell cycle that ensures accurate chromosome segregation by delaying the onset of anaphase until all chromosomes are properly bioriented on the mitotic spindle. Mps1 kinase is an important upstream regulator of the SAC and its recruitment to kinetochores critical for initiating SAC signaling. This review discusses the current understanding of Mps1 essential functions in the SAC, the emerging details of Mps1 role in error correction to safeguard genome stability, and the therapeutic potential of Mps1 inhibition for the treatment of cancer types associated with aberrant SAC signaling and chromosome segregation defects.

Exploration of Traditional Chinese Medicine Comprehensive Treatment of Triple Negative Breast Cancer Based on Molecular Pathological Mechanism

Triple-negative breast cancer (TNBC) is recognized as the most aggressive subtype of breast cancer and is associated with poor prognosis. Clinically, TNBC is associated with significant invasiveness, high propensity for metastasis, frequent recurrence, and unfavorable outcomes. The absence of estrogen receptors, progesterone receptors, and human epidermal growth factor receptor 2 (HER2) in TNBC renders it unresponsive to endocrine therapies and treatments that target HER2. Consequently, the current therapeutic options are primarily confined to surgical intervention, adjuvant chemotherapy, and radiotherapy. Given the considerable heterogeneity of TNBC, targeted therapies have emerged as promising avenues for treatment. Furthermore, immunotherapy has demonstrated the potential to enhance overall survival and therapeutic response in patients with TNBC. Additionally, research indicates that traditional Chinese medicine (TCM) may yield beneficial effects in the management of this cancer subtype. This review aims to consolidate recent advancements in treatment strategies for TNBC, particularly those based on molecular subtypes.

Survival benefit from abemaciclib in non-small cell lung cancer by Kirsten rat sarcoma-mutation gene expression subtype: retrospective analysis from the JUNIPER Trial

Purpose

JUNIPER, a randomized, phase III trial of patients with stage IV non-small cell lung cancer and a detectable Kristen rat sarcoma (KRAS) mutation in codons 12 or 13 whose condition progressed after platinum-based chemotherapy and up to 1 additional therapy (could include immune checkpoint inhibitors), reported prolonged progression-free survival (PFS) but not overall survival (OS) among patients who were receiving abemaciclib versus those who were receiving erlotinib. To establish whether certain patient subgroups received an OS benefit from the addition of abemaciclib to best supportive care, JUNIPER patients were retrospectively evaluated for KRAS co-mutation gene expression subtype, and abemaciclib efficacy was assessed for each patient subgroup.

Materials and methods

Of the 453 patients enrolled in the JUNIPER trial, tumor specimens for biomarker analysis were available for 148 (abemaciclib arm, n=79; erlotinib arm, n=69). Samples were profiled for gene expression and classified into 3 previously identified expression subtypes (KL, KP, and K). Tumor response, OS, and PFS were assessed within each subtype.

Results

Retrospective analyses of expression subtypes revealed an OS advantage for patients with KL subtype tumors who were receiving abemaciclib versus those with the KL subtype who were receiving erlotinib (median, 13.05 vs 5.65 months; hazard ratio, 0.25; 95% confidence interval, 0.09-0.73; P=.011). KL and KP expression subtype groups derived a PFS benefit from abemaciclib versus erlotinib (KL median, 6.64 vs 2.1 months; hazard ratio, 0.12; 95% confidence interval, 0.03-0.41; P=.001, and KP median, 5.52 vs 2.24 months; hazard ratio, 0.44; 95% confidence interval, 0.23-0.84; P=.013). Patients with K subtype tumors received no OS or PFS benefit from abemaciclib treatment.

Conclusions

Patients with KL expression subtype tumors may derive better OS and PFS from abemaciclib versus erlotinib in KRAS-mutated non-small cell lung cancer. These results should be further validated in an independent dataset.

Role of PARP Inhibitors: A New Hope for Breast Cancer Therapy

Tumors formed by the unchecked growth of breast cells are known as breast cancer. The second most frequent cancer in the world is breast cancer. It is the most common cancer among females. In 2022, 2,296,840 women were diagnosed with breast cancer. The therapy of breast cancer is evolving through the development of Poly (ADP-ribose) polymerase (PARP) inhibitors, which are offering people with specific genetic profiles new hope as research into the disease continues. It focuses on patients with BRCA1 and BRCA2 mutations. This review summarizes the most recent research on the mechanisms of action of PARP inhibitors and their implications for breast cancer therapy. We review how therapeutic applications are developing and highlight recent studies showing the effectiveness of these medicines whether used alone or in combination. Furthermore, the significance of customized therapy is highlighted in enhancing patient outcomes as we address the function of genetic testing in identifying candidates for PARP inhibition. Recommendations for future research areas to maximize the therapeutic potential of PARP inhibitors are also included, along with challenges and limits in their clinical usage. The objective of this review is to improve our comprehension of the complex interaction between breast cancer biology and PARP inhibition. This knowledge will help to guide screening approaches, improve clinical practice, and support preventive initiatives for people at risk.

PRR13 expression as a prognostic biomarker in breast cancer: correlations with immune infiltration and clinical outcomes

Introduction

Breast cancer continues to be a primary cause of cancer-related mortality among women globally. Identifying novel biomarkers is essential for enhancing patient prognosis and informing therapeutic decisions. The PRR13 gene, associated with taxol resistance and the progression of various cancers, remains under-characterized in breast cancer. This study aimed to investigate the role of PRR13 in breast cancer and its potential as a prognostic biomarker.

Methods

We performed a comparative analysis of PRR13 gene expression utilizing the TCGA database against non-cancerous tissues and employed STRING to evaluate PRR13's protein-protein interactions and associated pathways. Additionally, we investigated the relationship between PRR13 mRNA expression and immune cell infiltration in breast cancer (BRCA) using two methodologies. Furthermore, a retrospective analysis of 160 patients was conducted, wherein clinical data were collected and PRR13 expression was evaluated through immunohistochemistry and qRT-PCR to determine its association with clinicopathological features and patient survival.

Results

Analysis of the TCGA database revealed significant upregulation of PRR13 expression across 12 different cancer types, including breast cancer. High PRR13 expression was positively correlated with various immune cells, including NK cells, eosinophils, Th17 cells, and mast cells, whereas a negative correlation was observed with B cells, macrophages, and other immune subsets. Enrichment analysis of PRR13 and its 50 interacting proteins revealed significant associations with biological processes such as cell adhesion and migration, and pathways including ECMreceptor interaction and PI3K-Akt signaling. Single-cell analysis demonstrated associations between PRR13 and pathways pertinent to inflammation and apoptosis. Validation studies confirmed elevated PRR13 expression in tumor tissue compared to adjacent non-cancerous tissue. Immunohistochemistry demonstrated high PRR13 expression in 55.6% of cancer cases, particularly associated with advanced clinical stage and lymph node metastasis. Moreover, high PRR13 expression significantly correlated with shorter overall survival and served as an independent prognostic factor. Subgroup analysis underscored the prognostic significance of PRR13 in aggressive tumor subtypes, with particularly strong associations observed in T3, N1-3, and moderately to poorly differentiated tumors.

Discussion

In conclusion, PRR13 expression is upregulated in breast cancer tissues and may serve as a valuable prognostic indicator for breast cancer patients, potentially impacting patient survival and therapeutic strategies.

Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understand the complexity of breast cancer.ObjectiveThe new strategy laid out in this work, called "Hybrid-OmniSeq," is a deep learning-based multi-omics data analysis technology that uses molecular subtypes of breast cancer gene to increase the precision and effectiveness of breast cancer diagnosis.MethodFor preprocessing, the BC-VM procedure is utilized, and for molecular subtype analysis, the BC-MSA procedure is utilized. The implementation of Deep Neural Network (DNN) technology in conjunction with Sequential Forward Floating Selection (SFFS) and Truncated Singular Value Decomposition (TSVD) entropy enable adaptive learning from multi-omics gene data. Five machine learning classifiers are used for classification purpose. Hybrid-OmniSeq uses a variety of machine learning classifiers in a thorough analytical process to achieve remarkable diagnostic accuracy. Deep Learning-based multi-omics sequential approach was evaluated using METABRIC RNA-seq data sets of intrinsic subtypes of breast cancer.ResultsAccording to test results, Logistic Regression (LR) had ER (Estrogen Receptor) status values of 94.51%, ER status values of 96.33%, and HER2 (Human Epidermal growth factor Receptor) status values of 92.3%; Random Forest (RF) had ER status values of 93.77%, ER status values of 95.23%, and HER2 status values of 93.4%.ConclusionLR and RF increase the cancer detection accuracy for all subtypes when compared to alternative machine learning classifiers or the majority voting method, providing a comprehensive understanding of the underlying causes of breast cancer.

FAT1 functions as an oncogenic driver in triple negative breast cancer through AKT pathway-driven effects on the matrisome

FAT1 cadherin exhibits dual tumor suppressor and oncogenic roles across various cancers, but its function in breast cancer remains unclear due to conflicting reports of mutational loss and overexpression. In this study, we demonstrate that FAT1 mRNA and protein levels are reduced during mammary transformation, an effect linked to promoter methylation rather than mutational events. Subtype-specific analysis reveals that high FAT1 expression correlates with poor outcomes in basal-like/triple-negative breast cancer (TNBC), while elevated FAT1 expression in luminal A/estrogen receptor-positive breast cancers is associated with improved patient prognosis. Functional studies in TNBC models using knockdown and overexpression approaches confirm that FAT1 promotes both cell proliferation and motility. High-throughput sequencing and biochemical assessments establish strong links between FAT1 phenotypes and the activation of PI3K-AKT signaling. Additionally, FAT1 manipulation induces significant changes in matrisome-related genes, extracellular matrix components, and integrin switching. Together, these findings define an oncogenic role for FAT1 in TNBC, providing mechanistic insights into how its regulation influences AKT signaling, cell proliferation, and motility.

The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype

Current breast cancer classification methods, particularly immunohistochemistry and PAM50, face challenges in accurately characterizing the HER2-low subtype, a therapeutically relevant entity with distinct biological features. This notable gap can lead to misclassification, resulting in inappropriate treatment decisions and suboptimal patient outcomes. Leveraging RNA-seq and machine-learning algorithms, we developed the Breast Cancer Classifier (BCC), a unique transcriptomic classifier for more precise breast cancer subtyping, specifically by delineating and incorporating HER2-low as a distinct subtype. BCC also redefined the PAM50 Normal subtype into other subtypes, disputing its classification as a unique molecular group. Our statistical analysis not only confirmed the reproducibility and accuracy of BCC, but also revealed similarities in prognostic characteristics between the HER2-low and Basal subtypes. Addressing this gap in breast cancer classification is clinically significant because it not only improves treatment stratification, but also uncovers novel molecular and immunohistochemical features associated with the HER2-low and HER2-high subtypes, thereby advancing our understanding of breast cancer heterogeneity and providing guidance in precision oncology.

Unveiling the role of SYNGR4 in breast cancer development: a novel target for immunotherapy

Introduction

SYNGR4 is considered to be one of the causative genes for amyotrophic lateral sclerosis, but its role in breast cancer development has not been revealed.

Methods

The expression of SYNGR4 in a variety of malignancies including breast cancer was analyzed using Genotype Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) databases and verified by specimens collected from our center. The effect of SYNGR4 on breast cancer prognosis was analyzed using bioinformatics and possible pathways by which this molecule affects breast cancer prognosis were explored. The effect of SYNGR4 on immune infiltration of breast cancer was analyzed using GSVA, and the effects of SYNGR4 on breast cancer proliferation, migration, and tumor-associated macrophage polarization in cancer foci were verified by cellular and animal experiments, respectively.

Results

SYNGR4 is highly expressed in a variety of malignant tumors, including breast cancer, and affects the prognosis of breast cancer patients. This may be a volatile effect through Organelle fission, chromosome segregation, nuclear division, etc. SYNGR4 overexpression affects breast cancer proliferation, migration, and tumor immune infiltration, and promotes breast cancer tumor-associated macrophage polarization toward M2.

Discussion

SYNGR4 overexpression can affect the prognosis of breast cancer patients by promoting M2 polarization of tumor-associated macrophages in breast cancer, and this molecule may be a novel target for breast cancer immunotherapy.

Clinical Implications of Breast Cancer Intrinsic Subtypes

Estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers have different genomic architecture and show large-scale gene expression differences consistent with different cellular origins, which is reflected in the luminal (i.e., ER+) versus basal-like (i.e., ER-) molecular class nomenclature. These two major molecular subtypes have distinct epidemiological risk factors and different clinical behaviors. Luminal cancers can be subdivided further based on proliferative activity and ER signaling. Those with a high expression of proliferation-related genes and a low expression of ER-associated genes, called luminal B, have a high risk of early recurrence (i.e., within 5 years), derive significant benefit from adjuvant chemotherapy, and may benefit from adding immunotherapy to chemotherapy. This subset of luminal cancers is identified as the genomic high-risk ER+ cancers by the MammaPrint, Oncotype DX Recurrence Score, EndoPredict, Prosigna, and several other molecular prognostic assays. Luminal A cancers are characterized by low proliferation and high ER-related gene expression. They tend to have excellent prognosis with adjuvant endocrine therapy. Adjuvant chemotherapy may not improve their outcome further. These cancers correspond to the genomic low-risk categories. However, these cancers remain at risk for distant recurrence for extended periods of time, and over 50% of distant recurrences occur after 5 years. Basal-like cancers are uniformly highly proliferative and tend to recur within 3-5 years of diagnosis. In the absence of therapy, basal-like breast cancers have the worst survival, but these also include many highly chemotherapy-sensitive cancers. Basal-like cancers are often treated with preoperative chemotherapy combined with an immune checkpoint inhibitor which results in 60-65% pathologic complete response rates that herald excellent long-term survival. Patients with residual cancer after neoadjuvant therapy can receive additional postoperative chemotherapy that improves their survival. Currently, there is no clinically actionable molecular subclassification for basal-like cancers, although cancers with high androgen receptor (AR)-related gene expression and those with high levels of immune infiltration have better prognosis, but currently their treatment is not different from basal-like cancers in general. A clinically important, minor subset of breast cancers are characterized by frequent HER2 gene amplification and high expression of a few dozen genes, many residing on the HER2 amplicon. This is an important subset because of the highly effective HER2 targeted therapies which are synergistic with endocrine therapy and chemotherapy. The clinical behavior of HER2-enriched cancers is dominated by the underlying ER subtype. ER+/HER2-enriched cancers tend to have more indolent course and lesser chemotherapy sensitivity than their ER counterparts.

Tripartite motif-containing protein 50 suppresses triple-negative breast cancer progression by regulating the epithelial-mesenchymal transition

BACKGROUND AND OBJECTIVES

Tripartite motif-containing protein 50 (TRIM50) is a recently discovered E3 ubiquitin ligase that participates in tumor progression. TRIM50 is overexpressed in many cancers, although few studies focused on TRIM50's role in breast cancer.

METHODS

We overexpressed TRIM50 in triple-negative breast cancer cell lines using plasmid and found that TRIM50 upregulation markedly reduced breast cancer cell proliferation, clone formation, and migration, as well as promoted breast cancer cell apoptosis. Western blotting revealed that accumulated TRIM50 resulted in both mRNA and protein depletion of SNAI1, and partially attenuated the epithelial-mesenchymal transition (EMT) induced by SNAI1.

RESULTS

In this study, we demonstrate that TRIM50 is downregulated in human breast cancer and that its overexpression closely correlates with diminished invasion capacity in breast cancer, suggesting that TRIM50 may serve as a diagnostic marker and therapeutic target.

CONCLUSION

TRIM50 plays a key role in breast cancer proliferation and potentially serves as a prognostic and therapeutic target.

Association of Breast Cancer Subtypes and Clinicopathological Factors with Axillary Lymph Node Positivity Amongst Women with Breast Cancer in Rajasthan: An Observational Analytical Study

Prognostic factors by definition, are capable of providing information on clinical outcomes at the time of diagnosis, independent of therapy. The number of positive lymph nodes (number of ipsilateral axillary nodes with metastatic tumour deposits) is a strong and independent prognostic factor in breast cancer. In a meta-analysis (New England Journal of Medicine, 2017) of over 62,000 patients, the risk of distant recurrence over years 5 to 20 for those with T1 tumours was 13% in the absence of lymph node involvement, 20% among those with one to three involved lymph nodes, and 34% among those with four to nine involved nodes. In this study, we analyzed the association of clinicopathological factors and breast cancer subtypes with axillary lymph node (ALN) positivity in women with breast cancer in Rajasthan. A multivariate Logistic (Ordinal) Regression Model was used to predict the number of positive lymph nodes based on independent variables that showed 90% significance in bivariate analysis, such as total number of lymph nodes dissected, tumour necrosis, and lymphovascular invasion. The Wald criterion indicated that only LVI had a significant impact on the prediction (p < 0.05), while tumour necrosis and the total number of lymph nodes dissected were not significant predictors (p > 0.05). Patients with LVI had a 43.47 times higher risk of having positive lymph nodes (p < 0.05). Early prediction of lymph node metastasis through LVI testing can help in prognostication. Breast cancer subtypes should not be a criterion while deciding lymph nodal management.

Glucocorticoid receptor-mediated oncogenic activity is dependent on breast cancer subtype

Breast cancer incidence has been steadily rising and is the leading cause of cancer death in women due to its high metastatic potential. Individual breast cancer subtypes are classified by both cell type of origin and receptor expression, namely estrogen, progesterone and human epidermal growth factor receptors (ER, PR and HER2). Recently, the importance and context-dependent role of glucocorticoid receptor (GR) expression in the natural history and prognosis of breast cancer subtypes have been uncovered. In ER-positive breast cancer, GR expression is associated with a better prognosis as a result of ER-GR crosstalk. GR appears to modulate ER-mediated gene expression resulting in decreased tumor cell proliferation and a more indolent cancer phenotype. In ER-negative breast cancer, including GR-positive triple-negative breast cancer (TNBC), GR expression enhances migration, chemotherapy resistance and cell survival. In invasive lobular carcinoma, GR function is relatively understudied, and more work is required to determine whether lobular subtypes behave similarly to their invasive ductal carcinoma counterparts. Importantly, understanding GR signaling in individual breast cancer subtypes has potential clinical implications because of the recent development of highly selective GR non-steroidal ligands, which represent a therapeutic approach for modulating GR activity systemically.

MiR-338-5p, a novel metastasis-related miRNA, inhibits triple-negative breast cancer progression by targeting the ETS1/NOTCH1 axis

Breast cancer ranks as the most prevalent cancer globally, surpassing lung cancer, with recurrence/metastasis to be its main account for the cancer-related mortality. MicroRNAs (miRNAs) participate critically in various physiological and pathological processes through posttranscriptional regulation of downstream genes. Our preliminary findings identified miR-338-5p, potentially linked to metastasis in breast cancer, a previously unexplored area. Analysis of the GSE38867 dataset revealed the decreased miR-338-5p expression in metastatic breast cancer compared to normal tissues. Cellular function experiments and a xenograft tumor model demonstrated the inhibitory function of miR-338-5p on the progression of breast cancer in vitro and in vivo. Furthermore, it downregulated the expression of mesenchymal biomarkers and NOTCH1 significantly. With the predicting targets of miR-338-5p and transcription factors of the NOTCH1 gene, coupled with dual luciferase reporter assays, it is identified ETS1 as the interactor between miR-338-5p and NOTCH1. In breast cancer tissues, as well as in our xenograft tumor model, expression of ETS1 and NOTCH1 was positively correlated using immunohistochemical staining. This study reports, for the first time, on the miR-338-5p/ETS1/NOTCH1 axis and its pivotal role in breast cancer proliferation and metastasis. These findings propose a novel therapeutic strategy for breast cancer patients and lays a foundation for its clinical detection and treatment evaluation.

High Caveolin-1 mRNA expression in triple-negative breast cancer is associated with an aggressive tumor microenvironment, chemoresistance, and poor clinical outcome

BACKGROUND

Currently, there are few treatment-predictive and prognostic biomarkers in triple-negative breast cancer (TNBC). Caveolin-1 (CAV1) is linked to chemoresistance and several important processes involved in tumor progression and metastasis, such as epithelial-mesenchymal transition (EMT). Herein, we report that high CAV1 gene expression is an independent factor of poor prognosis in TNBC.

METHODS

CAV1 gene expression was compared across different molecular features (e.g., PAM50 subtypes). CAV1 expression was assessed in relation to clinical outcomes using Cox regression adjusted for clinicopathological predictors. Differential gene expression and gene set enrichment analyses were applied to compare high- and low-expressing CAV1 tumors. Tumor microenvironment composition of high- and low-expressing CAV1 tumors was estimated using ECOTYPER. Tumor tissue microarrays were used to evaluate CAV1 protein levels in stromal and malignant cells.

RESULTS

In the SCAN-B (n = 525) and GSE31519 (n = 327) cohorts, patients with CAV1-high tumors had an increased incidence of early recurrence adjusted HR 1.78 (95% CI 1.12-2.81) and 2.20 (95% CI 1.39-3.47), respectively. In further analysis, high CAV1 gene expression was associated with a molecular profile indicating altered metabolism, neovascularization, chemoresistance, EMT, suppressed immune response, and active tumor microenvironment. Protein levels of CAV1 in malignant and stromal cells were not correlated with CAV1 gene expression.

CONCLUSION

CAV1 gene expression in TNBC is a biomarker that merits further investigation in clinical trials and as a therapeutic target.

Predicting treatment outcome using kinome activity profiling in HER2+ breast cancer biopsies

In this study, we measured the kinase activity profiles of 32 pre-treatment tumor biopsies of HER2-positive breast cancer patients. The aim of this study was to assess the prognostic potential of kinase activity levels, to identify potential mechanisms of resistance and to predict treatment success of HER2-targeted therapy combined with chemotherapy. Indeed, our system-wide kinase activity analysis allowed us to link kinase activity to treatment response. Overall, high kinase activity in the HER2-pathway was associated with good treatment outcome. We found eleven kinases differentially regulated between treatment outcome groups, including well-known players in therapy resistance, such as p38a, ERK, and FAK, and an unreported one, namely MARK1. Lastly, we defined an optimal signature of four kinases in a multiple logistic regression diagnostic test for prediction of treatment outcome (AUC = 0.926). This kinase signature showed high sensitivity and specificity, indicating its potential as predictive biomarker for treatment success of HER2-targeted therapy.

Preclinical characterization and clinical trial of CFI-400945, a polo-like kinase 4 inhibitor, in patients with relapsed/refractory acute myeloid leukemia and higher-risk myelodysplastic neoplasms

CFI-400945 is a selective oral polo-like kinase 4 (PLK4) inhibitor that regulates centriole duplication. PLK4 is aberrantly expressed in patients with acute myeloid leukemia (AML). Preclinical studies indicate that CFI-400945 has potent in vivo efficacy in hematological malignancies and xenograft models, with activity in cells harboring TP53 mutations. In this phase 1 study in very high-risk patients with relapsed/refractory AML and myelodysplastic syndrome (MDS) (NCT03187288), 13 patients were treated with CFI-400945 continuously in dose escalation from 64 mg/day to 128 mg/day. Three of the 9 efficacy evaluable AML patients achieved complete remission (CR). Two of 4 AML patients (50%) with TP53 mutations and complex monosomal karyotype achieved a CR with 1 patient proceeding to allogenic stem cell transplant. A third patient with TP53 mutated AML had a significant reduction in marrow blasts by > 50% with an improvement in neutrophil and platelet counts. Responses were observed after 1 cycle of therapy. Dose-limiting toxicity was enteritis/colitis. A monotherapy and combination therapy study with a newer crystal form of CFI-400945 in patients with AML, MDS and chronic myelomonocytic leukemia (CMML) is ongoing (NCT04730258).

Cooperative pro-tumorigenic adaptation to oncogenic RAS through epithelial-to-mesenchymal plasticity

In breast cancers, aberrant activation of the RAS/MAPK pathway is strongly associated with mesenchymal features and stemness traits, suggesting an interplay between this mitogenic signaling pathway and epithelial-to-mesenchymal plasticity (EMP). By using inducible models of human mammary epithelial cells, we demonstrate herein that the oncogenic activation of RAS promotes ZEB1-dependent EMP, which is necessary for malignant transformation. Notably, EMP is triggered by the secretion of pro-inflammatory cytokines from neighboring RAS-activated senescent cells, with a prominent role for IL-6 and IL-1α. Our data contrast with the common view of cellular senescence as a tumor-suppressive mechanism and EMP as a process promoting late stages of tumor progression in response to signals from the tumor microenvironment. We highlighted here a pro-tumorigenic cooperation of RAS-activated mammary epithelial cells, which leverages on oncogene-induced senescence and EMP to trigger cellular reprogramming and malignant transformation.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of "relative expression reversals". Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble .

LncRNA SOX9-AS1 triggers a transcriptional program involved in lipid metabolic reprogramming, cell migration and invasion in triple-negative breast cancer

At the molecular level, triple-negative breast cancer (TNBC) is frequently categorized as PAM50 basal-like subtype, but despite the advances in molecular analyses, the clinical outcome for these subtypes is uncertain. Long non-coding RNAs (lncRNAs) are master regulators of genes involved in hallmarks of cancer, which makes them suitable biomarkers for breast cancer (BRCA) diagnosis and prognosis. Here, we evaluated the regulatory role of lncRNA SOX9-AS1 in these subtypes. Using the BRCA-TCGA cohort, we observed that SOX9-AS1 was significantly overexpressed in basal-like and TNBC in comparison with other BRCA subtypes. Survival analyzes showed that SOX9-AS1 overexpression was associated with a favorable prognosis in TNBC and basal-like patients. To study the functions of SOX9-AS1, we determined the expression levels in a panel of nine BRCA cell lines finding increased levels in MDA-MB-468 and HCC1187 TNBC. Using subcellular fractionation in these cell lines, we ascertained that SOX9-AS1 was located in the cytoplasmic compartment. In addition, we performed SOX9-AS1 gene silencing using two short-harping constructs, which were transfected in both cell models and performed a genome-wide RNA-seq analysis. Data showed that 351 lncRNAs and 740 mRNAs were differentially expressed in MDA-MB-468 while 56 lncRNAs and 100 mRNAs were modulated in HCC1187 cells (Log2FC < - 1.5 and > 1.5, p.adj value < 0.05). Pathway analysis revealed that the protein-encoding genes potentially regulate lipid metabolic reprogramming, and epithelial-mesenchymal transition (EMT). Expression of lipid metabolic-related genes LIPE, REEP6, GABRE, FBP1, SCD1, UGT2B11, APOC1 was confirmed by RT-qPCR. Functional analysis demonstrated that the knockdown of SOX9-AS1 increases the triglyceride synthesis, cell migration and invasion in both two TNBC cell lines. In conclusion, high SOX9-AS1 expression predicts an improved clinical course in patients, while the loss of SOX9-AS1 expression enhances the aggressiveness of TNBC cells.

Clinicopathological analysis of 38 male patients diagnosed with breast cancer

BACKGROUND

Male breast cancer (MBC) accounts for one percent of all breast cancers. Due to the lack of awareness and routine screening programs, most patients present with systemic disease at the time of diagnosis with low overall survival.

OBJECTIVES

This study aims to investigate the prognostic factors of male breast cancer and its correlation with established prognostic parameters and patient outcomes.

METHODS

Thirty-eight male breast cancer patients are identified from the MKA Breast Cancer Clinic database, and their corresponding clinical and pathological characteristics are obtained. Cut-off values of 1% and 10% are applied to further classify ER and PR results.

RESULTS

Older men are more likely to develop MBC than younger men and are more likely to have spread to axillary lymph nodes. Invasive ductal carcinoma is a more common histologic type in MBC. All the tested patients have ER and PR positivity. Distant metastasis developed in 17/38 (44.7%) patients. Bone metastasis is seen commonly in metastatic MBC.

CONCLUSIONS

According to our cohort, MBC is seen in older males, presents in later stages, and shows hormone receptor positivity and a tendency to bone involvement. MBC is a heterogenous but distinct biological entity requiring a specific clinical and pathological approach.

Proteomics on malignant pleural effusions reveals ERα loss in metastatic breast cancer associates with SGK1-NDRG1 deregulation

Breast cancer (BCa) is a highly heterogeneous disease, with hormone receptor status being a key factor in patient prognostication and treatment decision-making. The majority of primary tumours are positive for oestrogen receptor alpha (ERα), which plays a key role in tumorigenesis and disease progression, and represents the major target for treatment of BCa. However, around one-third of patients with ERα-positive BCa relapse and progress into the metastatic stage, with 20% of metastatic cases characterised by loss of ERα expression after endocrine treatment, known as ERα-conversion. It remains unclear whether ERα-converted cancers are biologically similar to bona fide ERα-negative disease and which signalling cascades compensate for ERα loss and drive tumour progression. To better understand the biological changes that occur in metastatic BCa upon ERα loss, we performed (phospho)proteomics analysis of 47 malignant pleural effusions derived from 37 BCa patients, comparing ERα-positive, ERα-converted and ERα-negative cases. Our data revealed that the loss of ERα-dependency in this metastatic site leads to only a partial switch to an ERα-negative molecular phenotype, with preservation of a luminal-like proteomic landscape. Furthermore, we found evidence for decreased activity of several key kinases, including serum/glucocorticoid regulated kinase 1 (SGK1), in ERα-converted metastases. Loss of SGK1 substrate phosphorylation may compensate for the loss of ERα-dependency in advanced disease and exposes a potential therapeutic vulnerability that may be exploited in treating these patients.

Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification

Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.

Immunohistochemistry-derived subtypes of breast cancer distribution in four regions of Ethiopia

Purpose

Different biological characteristics, therapeutic responses, and disease-specific outcomes are associated with different molecular subtypes of breast cancer (BC). Although there have been different studies on BC in the Ethiopian capital city of Addis Ababa, there have been few studies in other parts of the nation, and none have evaluated biological characteristics in other locations in the context of the extensive ethnic and genetic diversity found in Ethiopia. This study was carried out to evaluate the distribution of immunohistochemistry (IHC) subtypes of BCs throughout four Ethiopian regions.

Methods

A total of 227 formalin-fixed paraffin-embedded (FFPE) tissue blocks were collected from tertiary hospitals in four Ethiopian regions between 2015 and 2021. The IHC staining was performed for subtyping, ER, PR, HER2, and Ki-67 proliferation markers.

Results

The mean age at diagnosis was 43.9 years. The percentage of ER and PR-negative tumors were 48.3% and 53.2%, respectively. The IHC subtypes showed the following distribution: 33.1% triple-negative breast cancer (TNBC), 27.6% luminal B, 25.2% luminal A, and 14.1% HER2 enriched. In multiple logistic regression analysis, grade III and HER2 positivity were associated with larger tumor size, and also originating from Jimma compared to Mekele.

Conclusion

Patients with ER-negative, PR-negative, and TNBC were found in 48.3%, 53.2%, and 33.1% of cases, respectively, showing that half the patients could potentially benefit from endocrine treatment. A considerably high prevalence of TNBC was reported in our study, demanding additional research that includes genetic predisposition factors. Additionally, aggressive tumors were found in a high percentage of younger age groups, which must be considered when planning personalized treatment strategies.

Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer

Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%-40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

CSPG4 as a target for the specific killing of triple-negative breast cancer cells by a recombinant SNAP-tag-based antibody-auristatin F drug conjugate

PURPOSE

Triple-negative breast cancer (TNBC) is phenotypic of breast tumors lacking expression of the estrogen receptor (ER), the progesterone receptor (PgR), and the human epidermal growth factor receptor 2 (HER2). The paucity of well-defined molecular targets in TNBC, coupled with the increasing burden of breast cancer-related mortality, emphasizes the need to develop targeted diagnostics and therapeutics. While antibody-drug conjugates (ADCs) have emerged as revolutionary tools in the selective delivery of drugs to malignant cells, their widespread clinical use has been hampered by traditional strategies which often give rise to heterogeneous mixtures of ADC products.

METHODS

Utilizing SNAP-tag technology as a cutting-edge site-specific conjugation method, a chondroitin sulfate proteoglycan 4 (CSPG4)-targeting ADC was engineered, encompassing a single-chain antibody fragment (scFv) conjugated to auristatin F (AURIF) via a click chemistry strategy.

RESULTS

After showcasing the self-labeling potential of the SNAP-tag component, surface binding and internalization of the fluorescently labeled product were demonstrated on CSPG4-positive TNBC cell lines through confocal microscopy and flow cytometry. The cell-killing ability of the novel AURIF-based recombinant ADC was illustrated by the induction of a 50% reduction in cell viability at nanomolar to micromolar concentrations on target cell lines.

CONCLUSION

This research underscores the applicability of SNAP-tag in the unambiguous generation of homogeneous and pharmaceutically relevant immunoconjugates that could potentially be instrumental in the management of a daunting disease like TNBC.

CSRP2 promotes cell stemness in head and neck squamous cell carcinoma

BACKGROUND

Cysteine-rich protein 2 (CSRP2) is discovered as oncogene. The study aims to investigate the clinical significance and potential mechanism of CSRP2 in head and neck squamous cell carcinoma (HNSCC).

METHODS

CSRP2 expression was explored by immunohistochemistry tissue microarrays and Western blotting in HNSCC. The effect of CSRP2 on the cancer stemness and epithelial-to-mesenchymal transition (EMT) of HNSCC cells was investigated by sphere formation, wound healing, and transwell assays. The vitro and vivo experiments revealed that CSRP2 modulated cancer stemness and EMT phenotypes in HNSCC.

RESULTS

CSRP2 was overexpressed in HNSCC patients and presented poor prognosis. CSRP2 knockdown inhibited the migration and invasion ability of the HNSCC cells. And CSRP2 expression was closely associated with CSCs markers, EMT-transcription factor, new oncoprotein, and immune checkpoint.

CONCLUSION

The overexpression of CSRP2 indicates poor prognosis and plays a key role in maintaining the cancer cell stemness and EMT.

Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype

Breast cancer is a heterogeneous disease consisting of a diverse set of genomic mutations and clinical characteristics. The molecular subtypes of breast cancer are closely tied to prognosis and therapeutic treatment options. We investigate using deep graph learning on a collection of patient factors from multiple diagnostic disciplines to better represent breast cancer patient information and predict molecular subtype. Our method models breast cancer patient data into a multi-relational directed graph with extracted feature embeddings to directly represent patient information and diagnostic test results. We develop a radiographic image feature extraction pipeline to produce vector representation of breast cancer tumors in DCE-MRI and an autoencoder-based genomic variant embedding method to map variant assay results to a low-dimensional latent space. We leverage related-domain transfer learning to train and evaluate a Relational Graph Convolutional Network to predict the probabilities of molecular subtypes for individual breast cancer patient graphs. Our work found that utilizing information from multiple multimodal diagnostic disciplines improved the model's prediction results and produced more distinct learned feature representations for breast cancer patients. This research demonstrates the capabilities of graph neural networks and deep learning feature representation to perform multimodal data fusion and representation in the breast cancer domain.

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.

ZNF703 mRNA-Targeting Antisense Oligonucleotide Blocks Cell Proliferation and Induces Apoptosis in Breast Cancer Cell Lines

The luminal B molecular subtype of breast cancers (BC) accounts for more than a third of BCs and is associated with aggressive clinical behavior and poor prognosis. The use of endocrine therapy in BC treatment has significantly contributed to the decrease in the number of deaths in recent years. However, most BC patients with prolonged exposure to estrogen receptor (ER) selective modulators such as tamoxifen develop resistance and become non-responsive over time. Recent studies have implicated overexpression of the ZNF703 gene in BC resistance to endocrine drugs, thereby highlighting ZNF703 inhibition as an attractive modality in BC treatment, especially luminal B BCs. However, there is no known inhibitor of ZNF703 due to its nuclear association and non-enzymatic activity. Here, we have developed an antisense oligonucleotide (ASO) against ZNF703 mRNA and shown that it downregulates ZNF703 protein expression. ZNF703 inhibition decreased cell proliferation and induced apoptosis. Combined with cisplatin, the anti-cancer effects of ZNF703-ASO9 were improved. Moreover, our work shows that ASO technology may be used to increase the number of targetable cancer genes.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Actin cytoskeleton depolymerization increases matrix metalloproteinase gene expression in breast cancer cells by promoting translocation of cysteine-rich protein 2 to the nucleus

The actin cytoskeleton plays a critical role in cancer cell invasion and metastasis; however, the coordination of its multiple functions remains unclear. Actin dynamics in the cytoplasm control the formation of invadopodia, which are membrane protrusions that facilitate cancer cell invasion by focusing the secretion of extracellular matrix-degrading enzymes, including matrix metalloproteinases (MMPs). In this study, we investigated the nuclear role of cysteine-rich protein 2 (CRP2), a two LIM domain-containing F-actin-binding protein that we previously identified as a cytoskeletal component of invadopodia, in breast cancer cells. We found that F-actin depolymerization stimulates the translocation of CRP2 into the nucleus, resulting in an increase in the transcript levels of pro-invasive and pro-metastatic genes, including several members of the MMP gene family. We demonstrate that in the nucleus, CRP2 interacts with the transcription factor serum response factor (SRF), which is crucial for the expression of MMP-9 and MMP-13. Our data suggest that CRP2 and SRF cooperate to modulate of MMP expression levels. Furthermore, Kaplan-Meier analysis revealed a significant association between high-level expression of SRF and shorter overall survival and distant metastasis-free survival in breast cancer patients with a high CRP2 expression profile. Our findings suggest a model in which CRP2 mediates the coordination of cytoplasmic and nuclear processes driven by actin dynamics, ultimately resulting in the induction of invasive and metastatic behavior in breast cancer cells.

The changes of subtype markers between first and second primary breast cancers

BACKGROUND

Previous studies investigated the changes of subtype markers [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2)] in several clinical settings, but not for second primary breast cancer (SPBC) after first primary breast cancer (FPBC).

METHODS

A total of 15,390 patients with SPBC were preliminarily selected from the Surveillance, Epidemiology, and End Results Program, and 3777 patients with complete information on three subtype markers in both FPBC and SPBC were included in the final analyses. The changes of subtype markers and their prognostic implications and potential influential factors were well investigated.

RESULTS

The overall change rates of ER, PR, and HER2 between FPBC and SPBC were 23.0% (867/3777), 35.0% (1322/3777), and 18.3% (691/3777), respectively. Gains of ER, PR, and HER2 after negative index markers were 48.7% (364/748), 37.9% (418/1103), and 11.5% (370/3211), while losses of markers after positive index markers were 16.6% (503/3029), 33.8%(904/2674), and 56.7%(321/566). Loss of ER was significantly associated with increased mortality (18.1% vs. 7.9%, p < 0.001), while gain of ER was significantly associated with decreased mortality (11.5% vs. 23.2%, p < 0.001). Similar results were observed for changes of PR status. However, loss of HER2 was significantly associated with decreased mortality (8.7% vs. 16.3%, p = 0.014), and no significant association was observed between the gain of HER2 and the prognosis of SPBC. Multivariate competing risk analyses showed similar results. HER2 status in FPBC, chemotherapy, and radiotherapy was significantly associated with changes of ER/PR (all p < 0.05), and no available therapies associated with HER2 change.

CONCLUSION

The changes of subtype markers are observed in a considerable proportion of patients and has statistically significant prognostic implications. Biopsies should be taken as a routine procedure for better therapy management.

Altered Expression of CYSLTR1 is Associated With Adverse Clinical Outcome in Triple Negative Breast Tumors: An In Silico Approach

OBJECTIVE

Triple negative breast cancer (TNBC) has high relapse rates due to dysregulated inflammatory signaling pathways and significant changes in the tumor microenvironment, probably influencing the failure of several therapies. The Cysteinyl Leukotriene Receptor 1 (CYSLTR1), a leukotriene modulator of inflammation, has been shown to play an important role in cancer pathogenesis and survival but few studies have been reported on its role in breast cancer.

MATERIALS AND METHODS

The present work was conducted using publicly available platforms that have omics data to assess the clinical potential of CYSLTR1 expression and its prognostic validation in large cohorts of samples from breast cancer patients. Web platforms containing clinical information, RNA-seq and protein data were selected to perform in silico analyses of the potential marker CYLSTR1. Added together, the platforms included modules for correlation, expression, prognosis, drug interactions, and construction of gene networks.

RESULTS

Kaplan-Meier curves revealed that reduced levels of CYSLTR1 corresponded to an unfavorable outcome for overall survival (p<0.005) as well as relapse-free survival (p<0.001) in the basal subtype. Additionally, CYSLTR1 was downregulated in breast tumor samples compared to adjacent healthy tissue (p<0.01) and the basal subtype exhibited the lowest expression of CYSLTR1 relative to the other subtypes (p<0.0001). Furthermore, gene networking analysis showed strong associations of CYSLTR1 with two protein-coding genes (P2RY10 and XCR1) when tested on a TNBC dataset.

CONCLUSION

Our data highlighted the relevance of CYSLTR1 since it may play an important role in TNBC therapy. However, further in vitro and in vivo studies should be directed towards validating our findings in an effort to improve our understanding of TNBC pathology.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

BACKGROUND

Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).

METHODS

A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.

RESULTS

We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).

CONCLUSIONS

These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

[Triple-negative breast cancer : Classification, current concepts, and therapy-related factors]

Triple-negative breast cancer (TNBC) accounts for about 10% of all breast cancer cases and is defined by the lack of expression of estrogen and progesterone receptors and the lack of overexpression or amplification of HER2. It differs with regard to the younger age of the patients, an increased association with a mutation of BRCA1 and a mostly low differentiation from hormone receptor-positive breast cancer. The spectrum of triple-negative breast cancer shows considerable heterogeneity both at the morphological and at the molecular level. It includes most commonly TNBC of no special type, with and without basal phenotype, triple-negative metaplastic breast carcinomas, triple-negative breast carcinomas with apocrine differentiation and rare triple-negative tumor types. At the gene-expression level, TNBC most commonly is associated with a basal phenotype, with rarer molecular variants of TNBC involving the Claudin-low, molecular apocrine types, and other rarer subtypes. Therefore, a critical use of the term TNBC, considering the histopathological tumor differentiation, is recommended.

Core promoter in TNBC is highly mutated with rich ethnic signature

The core promoter plays an essential role in regulating transcription initiation by controlling the interaction between transcriptional factors and sequence motifs in the core promoter. Although mutation in core promoter sequences is expected to cause abnormal gene expression leading to pathogenic consequences, limited supporting evidence showed the involvement of core promoter mutation in diseases. Our previous study showed that the core promoter is highly polymorphic in worldwide human ethnic populations in reflecting human history and adaptation. Our recent characterization of the core promoter in triple-negative breast cancer (TNBC), a subtype of breast cancer, in a Chinese TNBC cohort revealed the wide presence of core promoter mutation in TNBC. In the current study, we analyzed the core promoter in a Thai TNBC cohort. We also observed rich core promoter mutation in the Thai TNBC patients. We compared the core promoter mutations between Chinese and Thai TNBC cohorts. We observed substantial differences of core promoter mutation in TNBC between the two cohorts, as reflected by the mutation spectrum, mutation-effected gene and functional category, and altered gene expression. Our study confirmed that the core promoter in TNBC is highly mutable, and is highly ethnic-specific.

Protein disulfide isomerase A3 activity promotes extracellular accumulation of proteins relevant to basal breast cancer outcomes in human MDA-MB-A231 breast cancer cells

Metastasis and recurrence of breast cancer remain major causes of patient mortality, and there is an ongoing need to identify new therapeutic targets relevant to tumor invasion. Protein disulfide isomerase A3 (PDIA3) is a disulfide oxidoreductase and isomerase of the endoplasmic reticulum that has known extracellular substrates and has been correlated with aggressive breast cancers. We show that either prior PDIA3 inhibition by the disulfide isomerase inhibitor 16F16 or depletion of heparin-binding proteins strongly reduces the activity of conditioned medium (CM) of MDA-MB-231 human breast cancer cells to support promigratory cell spreading and F-actin organization by newly adherent MDA-MB-231 cells. Quantitative proteomics to investigate effects of 16F16 inhibition on heparin-binding proteins in the CM of MDA-MB-231 cells identified 80 proteins reproducibly decreased at least twofold (at q ≤ 0.05) after 16F16 treatment. By Gene Ontology analysis, many of these have roles in extracellular matrix (ECM) structure and function and cell adhesion; ribosomal proteins that also correlate with extracellular vesicles were also identified. Protein-protein interaction analysis showed that many of the extracellular proteins have known network interactions with each other. The predominant types of disulfide-bonded domains in the extracellular proteins contained β-hairpin folds, with the knottin fold the most common. From human breast cancer data sets, the extracellular proteins were found to correlate specifically with the basal subtype of breast cancer and their high expression in tumors correlated with reduced distant metastasis-free survival. These data provide new evidence that PDIA3 may be a relevant therapeutic target to alter properties of the ECM-associated microenvironment in basal breast cancer.

Role of DEK in carcinogenesis, diagnosis, prognosis, and therapeutic outcome of breast cancer: An evidence-based clinical review

Breast cancer is a significantly burdening women's cancer with limited diagnostic modalities. DEK is a novel biomarker overexpressed in breast cancers, currently exhaustively researched for its diagnosis and prognosis. Search for relevant meta-analyses, cohorts, and experimental studies in the last fifteen years was done in five large scientific databases. Non-English, non-full text articles or unrelated studies were excluded. Thirteen articles discussed the potential of DEK to estimate breast cancer characteristics, treatment outcomes, and prognosis. This proto-oncogene plays a role in breast carcinogenesis, increasing tumour proliferation and invasion, preventing apoptosis, and creating an immunodeficient tumour milieu with M2 tumour-associated macrophages. DEK is also associated with worse clinicopathological features and survival in breast cancer patients. Using a Kaplan-Meier plotter data analysis, DEK expression predicts worse overall survival (HR 1.24, 95%CI: 1.01-1.52, p = 0.039), comparable to other biomarkers. DEK is a promising novel biomarker requiring further research to determine its bedside applications.

Long term trends of breast cancer incidence according to proliferation status

BACKGROUND

Long-term breast cancer incidence trends according to proliferation status are poorly described. We studied time-trends in breast cancer incidence, using mitotic count and Ki-67 as markers of proliferation.

METHODS

Among 83,298 Norwegian women followed for breast cancer occurrence 1961-2012, 2995 incident breast cancers were diagnosed. Ki-67 was assessed using immunohistochemistry on tissue microarrays and mitoses were counted on whole sections. We compared incidence rates according to proliferation status among women born 1886-1928 and 1929-1977, estimating age-specific incidence rate ratios. We performed multiple imputations to account for unknown proliferation status. Mean values of Ki-67 and mitotic counts were calculated, according to age and birth year. We performed separate incidence analyses for HER2⁺ and triple negative breast cancers.

RESULTS

Among women aged 40-69 years, incidence rates of tumours with low-proliferative activity were higher among those born in 1929 or later, compared to before 1929, according to Ki-67 and mitotic count. Incidence rates of tumours with high-proliferative activity were also higher in women born in 1929 or later compared to before 1929 according to Ki-67, but not according to mitotic count. Mean values of Ki-67 and mitotic count varied according to age and birth year. In subtype-specific analyses we found an increase of high-proliferative HER2⁺ tumours according to Ki-67 in women born in 1929 or later, compared to before 1929.

CONCLUSIONS

There has been a temporal increase in both low- and high-proliferative breast cancers.

Multivalent state transitions shape the intratumoral composition of small cell lung carcinoma

Studies to date have not resolved how diverse transcriptional programs contribute to the intratumoral heterogeneity of small cell lung carcinoma (SCLC), an aggressive tumor associated with a dismal prognosis. Here, we identify distinct and commutable transcriptional states that confer discrete functional attributes in individual SCLC tumors. We combine an integrative approach comprising the transcriptomes of 52,975 single cells, high-resolution measurement of cell state dynamics at the single-cell level, and functional and correlative studies using treatment naïve xenografts with associated clinical outcomes. We show that individual SCLC tumors contain distinctive proportions of stable cellular states that are governed by bidirectional cell state transitions. Using drugs that target the epigenome, we reconfigure tumor state composition in part by altering individual state transition rates. Our results reveal new insights into how single-cell transition behaviors promote cell state equilibrium in SCLC and suggest that facile plasticity underlies its resistance to therapy and lethality.

Harnessing the cyclization strategy for new drug discovery

The design of new ligands with high affinity and specificity against the targets of interest has been a central focus in drug discovery. As one of the most commonly used methods in drug discovery, the cyclization represents a feasible strategy to identify new lead compounds by increasing structural novelty, scaffold diversity and complexity. Such strategy could also be potentially used for the follow-on drug discovery without patent infringement. In recent years, the cyclization strategy has witnessed great success in the discovery of new lead compounds against different targets for treating various diseases. Herein, we first briefly summarize the use of the cyclization strategy in the discovery of new small-molecule lead compounds, including the proteolysis targeting chimeras (PROTAC) molecules. Particularly, we focus on four main strategies including fused ring cyclization, chain cyclization, spirocyclization and macrocyclization and highlight the use of the cyclization strategy in lead generation. Finally, the challenges including the synthetic intractability, relatively poor pharmacokinetics (PK) profiles and the absence of the structural information for rational structure-based cyclization are also briefly discussed. We hope this review, not exhaustive, could provide a timely overview on the cyclization strategy for the discovery of new lead compounds.

ERK MAP Kinase Signaling Regulates RAR Signaling to Confer Retinoid Resistance on Breast Cancer Cells

Retinoic acid (RA) and its synthetic derivatives, retinoids, have been established as promising anticancer agents based on their ability to regulate cell proliferation and survival. Clinical trials, however, have revealed that cancer cells often acquire resistance to retinoid therapy. Therefore, elucidation of underlying mechanisms of retinoid resistance has been considered key to developing more effective use of retinoids in cancer treatment. In this study, we show that constitutive activation of ERK MAP kinase signaling, which is often caused by oncogenic mutations in RAS or RAF genes, suppresses RA receptor (RAR) signaling in breast cancer cells. We show that activation of the ERK pathway suppresses, whereas its inhibition promotes, RA-induced transcriptional activation of RAR and the resultant upregulation of RAR-target genes in breast cancer cells. Importantly, ERK inhibition potentiates the tumor-suppressive activity of RA in breast cancer cells. Moreover, we also reveal that suppression of RAR signaling and activation of ERK signaling are associated with poor prognoses in breast cancer patients and represent hallmarks of specific subtypes of breast cancers, such as basal-like, HER2-enriched and luminal B. These results indicate that ERK-dependent suppression of RAR activity underlies retinoid resistance and is associated with cancer subtypes and patient prognosis in breast cancers.

Expression and Signaling Pathways of Nerve Growth Factor (NGF) and Pro-NGF in Breast Cancer: A Systematic Review

Breast cancer represents the most common type of cancer and is the leading cause of death due to cancer among women. Thus, the prevention and early diagnosis of breast cancer is of primary urgency, as well as the development of new treatments able to improve its prognosis. Nerve Growth Factor (NGF) is a neurotrophic factor involved in the regulation of neuronal functions through the binding of the Tropomyosin receptor kinase A (TrkA) and the Nerve Growth Factor receptor or Pan-Neurotrophin Receptor 75 (NGFR/p75NTR). In addition, its precursor (pro-NGF) can extert biological activity by forming a trimeric complex with NGFR/p75NTR and sortilin, or by binding to TrkA receptors with low affinity. Several examples of in vitro and in vivo evidence show that NGF is both synthesized and released by breast cancer cells, and has mitogen, antiapoptotic and angiogenic effects on these cells through the activation of different signaling cascades that involve TrkA and NGFR/p75NTR receptors. Conversely, pro-NGF signaling has been related to breast cancer invasion and metastasis. Other studies suggested that NGF and its receptors could represent a good diagnostic and prognostic tool, as well as promising therapeutic targets for breast cancer. In this paper, we comprehensively summarize and systematically review the current experimental evidence on this topic. INPLASY ID: INPLASY2022100017.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Can EGFR be a therapeutic target in breast cancer?

Epidermal growth factor receptor (EGFR) is highly expressed in certain cancer types and is involved in regulating the biological characteristics of cancer progression, including proliferation, metastasis, and drug resistance. Various medicines targeting EGFR have been developed and approved for several cancer types, such as lung and colon cancer. To date, however, EGFR inhibitors have not achieved satisfactory clinical results in breast cancer, which continues to be the most serious malignant tumor type in females. Therefore, clarifying the underlying mechanisms related to the ineffectiveness of EGFR inhibitors in breast cancer and developing new EGFR-targeted strategies (e.g., combination therapy) remain critical challenges. Various studies have demonstrated aberrant expression and maintenance of EGFR levels in breast cancer. In this review, we summarize the regulatory mechanisms underlying EGFR protein expression in breast cancer cells, including EGFR mutations, amplification, endocytic dysfunction, recycling acceleration, and degradation disorders. We also discuss potential therapeutic strategies that act directly or indirectly on EGFR, including reducing EGFR protein expression, treating the target protein to mediate precise clearance, and inhibiting non-EGFR signaling pathways. This review should provide new therapeutic perspectives for breast cancer patients with high EGFR expression.

Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications

As the field of translational ‘omics has progressed, refined classifiers at both genomic and proteomic levels have emerged to decipher the heterogeneity of breast cancer in a clinically-applicable way. The integration of ‘omics knowledge at the DNA, RNA and protein levels is further expanding biologic understanding of breast cancer and opportunities for customized treatment, a particularly pressing need in clinically triple negative tumors. For this group of aggressive breast cancers, work from multiple groups has now validated at least four major biologically and clinically distinct omics-based subtypes. While to date most clinical trial designs have considered triple negative breast cancers as a single group, with an expanding arsenal of targeted therapies applicable to distinct biological pathways, survival benefits may be best realized by designing and analyzing clinical trials in the context of major molecular subtypes. While RNA-based classifiers are the most developed, proteomic classifiers proposed for triple negative breast cancer based on new technologies have the potential to more directly identify the most clinically-relevant biomarkers and therapeutic targets. Phospho-proteomic data further identify targetable signalling pathways in a unique subtype-specific manner. Single cell profiling of the tumor microenvironment represents a promising way to allow a better characterization of the heterogeneity of triple negative breast cancer which could be integrated in a spatially resolved context to build an ecosystem-based patient classification. Multi-omic data further allows in silico analysis of genetic and pharmacologic screens to map therapeutic vulnerabilities in a subtype-specific context. This review describes current knowledge about molecular subtyping of triple negative breast cancer, recent advances in omics-based genomics and proteomics diagnostics addressing the diversity of this disease, key advances made through single cell analysis approaches, and developments in treatments including targeted therapeutics being tested in major clinical trials.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq

Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.

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.

Glycosylation defects, offset by PEPCK-M, drive entosis in breast carcinoma cells

On glucose restriction, epithelial cells can undergo entosis, a cell-in-cell cannibalistic process, to allow considerable withstanding to this metabolic stress. Thus, we hypothesized that reduced protein glycosylation might participate in the activation of this cell survival pathway. Glucose deprivation promoted entosis in an MCF7 breast carcinoma model, as evaluated by direct inspection under the microscope, or revealed by a shift to apoptosis + necrosis in cells undergoing entosis treated with a Rho-GTPase kinase inhibitor (ROCKi). In this context, curbing protein glycosylation defects with N-acetyl-glucosamine partially rescued entosis, whereas limiting glycosylation in the presence of glucose with tunicamycin or NGI-1, but not with other unrelated ER-stress inducers such as thapsigargin or amino-acid limitation, stimulated entosis. Mitochondrial phosphoenolpyruvate carboxykinase (PEPCK-M; PCK2) is upregulated by glucose deprivation, thereby enhancing cell survival. Therefore, we presumed that PEPCK-M could play a role in this process by offsetting key metabolites into glycosyl moieties using alternative substrates. PEPCK-M inhibition using iPEPCK-2 promoted entosis in the absence of glucose, whereas its overexpression inhibited entosis. PEPCK-M inhibition had a direct role on total protein glycosylation as determined by Concanavalin A binding, and the specific ratio of fully glycosylated LAMP1 or E-cadherin. The content of metabolites, and the fluxes from ¹³C-glutamine label into glycolytic intermediates up to glucose-6-phosphate, and ribose- and ribulose-5-phosphate, was dependent on PEPCK-M content as measured by GC/MS. All in all, we demonstrate for the first time that protein glycosylation defects precede and initiate the entosis process and implicates PEPCK-M in this survival program to dampen the consequences of glucose deprivation. These results have broad implications to our understanding of tumor metabolism and treatment strategies.