Practical implications of gene-expression-based assays for breast oncologists

Unraveling the future: Innovative design strategies and emerging challenges in HER2-targeted tyrosine kinase inhibitors for cancer therapy

Human epidermal growth factor receptor 2 (HER2) is a transmembrane receptor-like protein with tyrosine kinase activity that plays a vital role in processes such as cell proliferation, differentiation, and angiogenesis. The degree of malignancy of different cancers, notably breast cancer, is strongly associated with HER2 amplification, overexpression, and mutation. Currently, widely used clinical HER2 tyrosine kinase inhibitors (TKIs), such as lapatinib and neratinib, have several drawbacks, including susceptibility to drug resistance caused by HER2 mutations and adverse effects from insufficient HER2 selectivity. To address these issues, it is essential to create innovative HER2 TKIs with enhanced safety, effectiveness against mutations, and high selectivity. Typically, SPH5030 has advanced to phase I clinical trials for its strong suppression of four HER2 mutations. This review discusses the latest research progress in HER2 TKIs, with a focus on the structural optimization process and structure-activity relationship analysis. In particular, this study highlights promising design strategies to address these challenges, providing insightful information and inspiration for future development in this field.

Characterization of HER2-low breast cancer in young women with germline BRCA1/2 pathogenetic variants: Results of a large international retrospective cohort study

BACKGROUND

Breast cancer (BC) in women aged ≤40 years carrying germline pathogenetic variants (PVs) in BRCA1/2 genes is infrequent but often associated with aggressive features. Human epidermal growth factor receptor 2 (HER2)-low-expressing BC has recently emerged as a novel therapeutic target but has not been characterized in this rare patient subset.

METHODS

Women aged ≤40 years with newly diagnosed early-stage HER2-negative BC (HER2-0 and HER2-low) and germline BRCA1/2 PVs from 78 health care centers worldwide were retrospectively included. Chi-square test and Student t-test were used to describe variable distribution between HER2-0 and HER2-low. Associations with HER2-low status were assessed with logistic regression. Kaplan-Meier method and Cox regression analysis were used to assess disease-free survival (DFS) and overall survival. Statistical significance was considered for p ≤ .05.

RESULTS

Of 3547 included patients, 32.3% had HER2-low BC, representing 46.3% of hormone receptor-positive and 21.3% of triple-negative (TN) tumors. HER2-low vs. HER2-0 BC were more often of grade 1/2 (p < .001), hormone receptor-positive (p < .001), and node-positive (p = .003). BRCA2 PVs were more often associated with HER2-low than BRCA1 PVs (p < .001). HER2-low versus HER2-0 showed better DFS (hazard ratio [HR], 0.86; 95% CI, 0.76-0.97) in the overall population and more favorable DFS (HR, 0.78; 95% CI, 0.64-0.95) and overall survival (HR, 0.65; 95% CI, 0.46-0.93) in the TN subgroup. Luminal A-like tumors in HER2-low (p = .014) and TN and luminal A-like in HER2-0 (p = .019) showed the worst DFS.

CONCLUSIONS

In young patients with HER2-negative BC and germline BRCA1/2 PVs, HER2-low disease was less frequent than expected and more frequently linked to BRCA2 PVs and associated with luminal-like disease. HER2-low status was associated with a modestly improved prognosis.

Prediction of response to neoadjuvant chemotherapy by MammaTyper® across breast cancer subtypes: A retrospective cross-sectional study

BACKGROUND

Neoadjuvant chemotherapy (NACT) is widely used in the treatment of triple-negative and HER2-positive breast cancer (BC), but its use in estrogen receptor (ER) and/or progesterone receptor (PR) positive/HER2-negative BC is questioned because of the low pathologic complete response (pCR) rates. This retrospective study assessed the mRNA-based MammaTyper® assay's capability of predicting pCR with NACT, and ER, PR, Ki67, and HER2 status at immunohistochemical (IHC) through transcriptomics.

METHODS

Diagnostic biopsies from 76 BC patients treated at the Cremona Hospital between 2012-2018 were analyzed. Relative mRNA expression levels of ERBB2, ESR1, PGR, and MKI67 were measured using the MammaTyper® kit and integrated into a pCR score. Predicting capability of pCR and standard IHC biomarkers could be assessed with ROC curves in 75 and 76 patients, respectively.

RESULTS

Overall, 68.0% patients obtained a MammaTyper® high-score and 32.0% a MammaTyper® low-score. Among high-score patients, 62.7% achieved pCR, compared to 16.7% in the low-score group (p = 0.0003). The binary MammaTyper® score showed good prediction of pCR in the overall cohort (area under curve [AUC] = 0.756) and in HR+/HER2-negative cases (AUC = 0.774). In cases with residual disease, the continuous MammaTyper® score correlated moderately with residual tumor size and decrease in tumor size. MammaTyper® showed substantial agreement with IHC for ESR1/ER and ERBB2/HER2, and moderate agreement for PGR/PR and MKI67/Ki67.

CONCLUSION

Overall, MammaTyper® pCR score may serve as a standardized tool for predicting NACT response in HR+/HER2-negative BC, potentially guiding treatment strategies. Additionally, it could provide a more standardized and reproducible assessment of ER, PR, HER2, and Ki67 status.

Epigenetic Therapies in Triple-Negative Breast Cancer: Concepts, Visions, and Challenges

Breast cancer, the most frequent malignancy in women worldwide, is a molecularly and clinically very heterogeneous disease. Triple-negative breast cancer is defined by the absence of hormone receptor and growth factor receptor ERBB2/HER2 expression. It is characterized by a more aggressive course of disease and a shortage of effective therapeutic approaches. Hallmarks of cancer cells are not only genetic alterations, but also epigenetic aberrations. The most studied and best understood alterations are methylation of the DNA base cytosine and the covalent modification of histone proteins. The reversibility of these covalent modifications make them attractive targets for therapeutic intervention, as documented in numerous ongoing clinical trials. Epidrugs, targeting DNA methylation and histone modifications, might offer attractive new options in treating triple-negative breast cancer. Currently, the most promising options are combination therapies in which the epidrug increases the efficiency of immuncheckpoint inhibitors. This review focusses exclusively on DNA methylation and histone modifications. In reviewing the knowledge about epigenetic therapies in breast cancer, and especially triple-negative breast cancer, the focus is on explaining concepts and raising awareness of what is not yet known and what has to be clarified in the future.

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

All-trans-retinoic acid modulates glycolysis via H19 and telomerase: the role of mir-let-7a in estrogen receptor-positive breast cancer cells

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer in women. Treatment approaches that differ between estrogen-positive (ER+) and triple-negative BC cells (TNBCs) and may subsequently affect cancer biomarkers, such as H19 and telomerase, are an emanating delight in BC research. For instance, all-trans-Retinoic acid (ATRA) could represent a potent regulator of these oncogenes, regulating microRNAs, mostly let-7a microRNA (miR-let-7a), which targets the glycolysis pathway, mainly pyruvate kinase M2 (PKM2) and lactate dehydrogenase A (LDHA) enzymes. Here, we investigated the potential role of ATRA in H19, telomerase, miR-let-7a, and glycolytic enzymes modulation in ER + and TNBC cells.

METHODS

MCF-7 and MDA-MB-231 cells were treated with 5 µM ATRA and/or 100 nM fulvestrant. Then, ATRA-treated or control MCF-7 cells were transfected with either H19 or hTERT siRNA. Afterward, ATRA-treated or untreated MDA-MB-231 cells were transfected with estrogen receptor alpha ER(α) or beta ER(β) expression plasmids. RNA expression was evaluated by RT‒qPCR, and proteins were assessed by Western blot. PKM2 activity was measured using an NADH/LDH coupled enzymatic assay, and telomerase activity was evaluated with a quantitative telomeric repeat amplification protocol assay. Student's t-test or one-way ANOVA was used to analyze data from replicates.

RESULTS

Our results showed that MCF-7 cells were more responsive to ATRA than MDA-MB-231 cells. In MCF-7 cells, ATRA and/or fulvestrant decreased ER(α), H19, telomerase, PKM2, and LDHA, whereas ER(β) and miR-let-7a increased. H19 or hTERT knockdown with or without ATRA treatment showed similar results to those obtained after ATRA treatment, and a potential interconnection between H19 and hTERT was found. However, in MDA-MB-231 cells, RNA expression of the aforementioned genes was modulated after ATRA and/or fulvestrant, with no significant effect on protein and activity levels. Overexpression of ER(α) or ER(β) in MDA-MB-231 cells induced telomerase activity, PKM2 and LDHA expression, in which ATRA treatment combined with plasmid transfection decreased glycolytic enzyme expression.

CONCLUSIONS

To the best of our knowledge, our study is the first to elucidate a new potential interaction between the estrogen receptor and glycolytic enzymes in ER + BC cells through miR-let-7a.

Machine Learning-Supported Diagnosis of Small Blue Round Cell Sarcomas Using Targeted RNA Sequencing

Small blue round cell sarcomas (SBRCSs) are a heterogeneous group of tumors with overlapping morphologic features but markedly varying prognosis. They are characterized by distinct chromosomal alterations, particularly rearrangements leading to gene fusions, whose detection currently represents the most reliable diagnostic marker. Ewing sarcomas are the most common SBRCSs, defined by gene fusions involving EWSR1 and transcription factors of the ETS family, and the most frequent non-EWSR1-rearranged SBRCSs harbor a CIC rearrangement. Unfortunately, currently the identification of CIC::DUX4 translocation events, the most common CIC rearrangement, is challenging. Here, we present a machine-learning approach to support SBRCS diagnosis that relies on gene expression profiles measured via targeted sequencing. The analyses on a curated cohort of 69 soft-tissue tumors showed markedly distinct expression patterns for SBRCS subgroups. A random forest classifier trained on Ewing sarcoma and CIC-rearranged cases predicted probabilities of being CIC-rearranged >0.9 for CIC-rearranged-like sarcomas and <0.6 for other SBRCSs. Testing on a retrospective cohort of 1335 routine diagnostic cases identified 15 candidate CIC-rearranged tumors with a probability >0.75, all of which were supported by expert histopathologic reassessment. Furthermore, the multigene random forest classifier appeared advantageous over using high ETV4 expression alone, previously proposed as a surrogate to identify CIC rearrangement. Taken together, the expression-based classifier can offer valuable support for SBRCS pathologic diagnosis.

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

BACKGROUND

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment.

METHODS

We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H's (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels.

RESULTS

We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPHErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression.

CONCLUSIONS

The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

NCAPH Drives Breast Cancer Progression and Identifies a Gene Signature that Predicts Luminal A Tumor Recurrence

Despite their generally favorable prognosis, luminal A tumors paradoxically pose the highest ten-year recurrence risk among breast cancers. From those that relapse, a quarter of them do it within five years after diagnosis. Identifying such patients is crucial, as long-term relapsers could benefit from extended hormone therapy, whereas early relapsers may require aggressive treatment. In this study, we demonstrate that NCAPH plays a role in the pathogenesis of luminal A breast cancer, contributing to its adverse progression in vitro and in vivo. Furthermore, we reveal that a signature of intratumoral gene expression, associated with elevated levels of NCAPH, serves as a potential marker to identify patients facing unfavorable progression of luminal A breast cancer. Indeed, transgenic mice overexpressing NCAPH generated breast tumors with long latency, and in MMTV-NCAPH/ErbB2+ double-transgenic mice, the luminal tumors formed were more aggressive. In addition, high intratumoral levels of Ncaph were associated with worse breast cancer evolution and poor response to chemotherapy in a cohort of genetically heterogeneous transgenic mice generated by backcrossing. In this cohort of mice, we identified a series of transcripts associated with elevated intratumoral levels of NCAPH, which were linked to adverse progression of breast cancer in both mice and humans. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate regression analysis on this series of transcripts, we derived a ten-gene risk score. This score is defined by a gene signature (termed Gene Signature for Luminal A 10 or GSLA10) that correlates with unfavorable progression of luminal A breast cancer. The GSLA10 signature surpassed the Oncotype DX signature in discerning tumors with unfavorable outcomes (previously categorized as Luminal A by PAM50) across three independent human cohorts. This GSLA10 signature aids in identifying patients with Luminal A tumors displaying adverse prognosis, who could potentially benefit from personalized treatment strategies.

Hounsfield Units Predict Survival of Patients With Estrogen Receptor-Positive and Human Epithelial Growth Factor Receptor 2-Negative Breast Cancer

BACKGROUNDS

Tumor vascularity plays a fundamental role in cancer progression, including breast cancer. This study aimed to elucidate tumor vascularity and its impact on patient survival in the context of breast cancer subtypes using Hounsfield units (HU) on contrast-enhanced computed tomography (CT).

MATERIALS AND METHODS

Patients with early-stage breast cancer who completed planned treatment between 2003 and 2013 were retrospectively assessed.

RESULTS

The final cohort comprised 440 patients. Of the 440 patients, 262 had estrogen receptor (ER)-positive disease and 119 had human epidermal growth factor receptor 2 (HER2)-overexpressing disease. The tumor-to-aorta ratio of Hounsfield units (TAR) was related to significantly worse recurrence-free interval (RFI) (P < .001) and overall survival (OS) (P < .001) in patients with TAR > 0.33 for RFI and > 0.35 for OS than their counterparts. In the subgroup analysis, the survival disadvantage was limited only to patients with ER-positive and HER2-negative disease (P < .001 for both RFI and OS).

CONCLUSION

This study showed that TAR, which reflects tumor vascularity, was significantly related to patients' RFI and OS, suggesting its capacity as a feasible biomarker. This study also showed that TAR was associated with the survival in patients with ER-positive and HER2-negative disease.

Advancing Breast Cancer Heterogeneity Analysis: Insights from Genomics, Transcriptomics and Proteomics at Bulk and Single-Cell Levels

Breast cancer continues to pose a significant healthcare challenge worldwide for its inherent molecular heterogeneity. This review offers an in-depth assessment of the molecular profiling undertaken to understand this heterogeneity, focusing on multi-omics strategies applied both in traditional bulk and single-cell levels. Genomic investigations have profoundly informed our comprehension of breast cancer, enabling its categorization into six intrinsic molecular subtypes. Beyond genomics, transcriptomics has rendered deeper insights into the gene expression landscape of breast cancer cells. It has also facilitated the formulation of more precise predictive and prognostic models, thereby enriching the field of personalized medicine in breast cancer. The comparison between traditional and single-cell transcriptomics has identified unique gene expression patterns and facilitated the understanding of cell-to-cell variability. Proteomics provides further insights into breast cancer subtypes by illuminating intricate protein expression patterns and their post-translational modifications. The adoption of single-cell proteomics has been instrumental in this regard, revealing the complex dynamics of protein regulation and interaction. Despite these advancements, this review underscores the need for a holistic integration of multiple 'omics' strategies to fully decipher breast cancer heterogeneity. Such integration not only ensures a comprehensive understanding of breast cancer's molecular complexities, but also promotes the development of personalized treatment strategies.

Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer

Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.

Tumor response to neoadjuvant chemotherapy in molecular breast cancer subtypes in Medellin, Colombia. Retrospective cohort study

Objectives

To describe the frequency of clinical and pathological response in different molecular subtypes of breast cancer, in patients receiving prior neoadjuvant chemotherapy.

Materials and methods

Descriptive retrospective cohort. The study population consisted of women 18 years of age and older with a histological diagnosis of invasive breast cancer stages IIA, IIB, IIIA, IIIB and IIIC, with a classification by molecular subtypes, who had received prior neoadjuvant chemotherapy, seen at a high complexity clinic in Medellin (Colombia), between July 1, 2017, and July 30, 2019. We measured age clinical stage, histological characteristics, molecular classification, and complete clinical and pathological responses by molecular subtype. A descriptive analysis was conducted.

Results

Overall, 255 patients met the inclusion criteria. Mean age was 55.2 years; the clinical stages with the highest prevalence were IIIB (28.6 %) and IIB (26.3 %), and the most frequent by histologic grading were grades 3 (48.2 %) and 2 (37.3 %). Frequency by molecular types was as follows: luminal A (10.2 %), HER2-negative luminal B (39.6 %), triple-negative (23.1%), HER2-positive luminal B (13.7 %), and pure HER2 (13.3 %). Complete clinical response following chemotherapy, by molecular type, was as follows: luminal A (26.9 %), HER2-negative luminal B (37.6 %), HER2-positive luminal B (48.6 %), pure HER2 (41.2 %), triple-negative (45.8 %). Complete pathological response by molecular subtype was achieved in the luminal A (19.2 %), HER2-negative luminal B (32.7 %), HER2-positive luminal B (54.3 %), pure HER2 (50 %) and triple-negative (42.4 %) subtypes.

Conclusions

In clinical practice, breast cancer classification by molecular subtypes is a means to approach the assess the to neoadjuvant chemotherapy. Prospective studies are needed in the region in order to determine the ability to predict overall and disease-free survival based on the complete pathologic response.

Construction of a lncRNA-mediated ceRNA network and a genomic-clinicopathologic nomogram to predict survival for breast cancer patients

Breast cancer (BC) is the most common cancer among women and a leading cause of cancer-related deaths worldwide. The diagnosis of early patients and the prognosis of advanced patients have not improved over the past several decades. The purpose of the present study was to identify the lncRNA-related genes based on ceRNA network and construct a credible model for prognosis in BC. Based on The Cancer Genome Atlas (TCGA) database, prognosis-related differently expressed genes (DEGs) and a lncRNA-associated ceRNA regulatory network were obtained in BC. The patients were randomly divided into a training group and a testing group. A ceRNA-related prognostic model as well as a nomogram was constructed for further study. A total of 844 DElncRNAs, 206 DEmiRNAs and 3295 DEmRNAs were extracted in BC, and 12 RNAs (HOTAIR, AC055854.1, ST8SIA6-AS1, AC105999.2, hsa-miR-1258, hsa-miR-7705, hsa-miR-3662, hsa-miR-4501, CCNB1, UHRF1, SPC24 and SHCBP1) among them were recognized for the construction of a prognostic risk model. Patients were then assigned to high-risk and low-risk groups according to the risk score. The Kaplan-Meier (K-M) analysis demonstrated that the high-risk group was closely associated with poor prognosis. The predictive nomogram combined with clinical features showed performance in clinical practice. In a nutshell, our ceRNA-related gene model and the nomogram graph are accurate and reliable tools for predicting prognostic outcomes of BC patients, and may make great contributions to modern precise medicine.

Multiregional Radiomic Signatures Based on Functional Parametric Maps from DCE-MRI for Preoperative Identification of Estrogen Receptor and Progesterone Receptor Status in Breast Cancer

Radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been used for breast estrogen receptor (ER) and progesterone receptor (PR) status evaluation. However, the radiomic features of peritumoral regions were not thoroughly analyzed. This study aimed to establish and validate the multiregional radiomic signatures (RSs) for the preoperative identification of the ER and PR status in breast cancer. A total of 443 patients with breast cancer were divided into training (n = 356) and validation (n = 87) sets. Radiomic features were extracted from intra- and peritumoral regions on six functional parametric maps from DCE-MRI. A two-sample t-test, least absolute shrinkage and selection operator regression, and stepwise were used for feature selections. Three RSs for predicting the ER and PR status were constructed using a logistic regression model based on selected intratumoral, peritumoral, and combined intra- and peritumoral radiomic features. The area under the receiver operator characteristic curve (AUC) was used to assess the discriminative performance of three RSs. The AUCs of intra- and peritumoral RSs for identifying the ER status were 0.828/0.791 and 0.755/0.733 in the training and validation sets, respectively. For predicting the PR status, intra- and peritumoral RSs resulted in AUCs of 0.816/0.749 and 0.806/0.708 in the training and validation sets, respectively. Multiregional RSs achieved the best AUCs among three RSs for evaluating the ER (0.851 and 0.833) and PR (0.848 and 0.763) status. In conclusion, multiregional RSs based on functional parametric maps from DCE-MRI showed promising results for preoperatively evaluating the ER and PR status in breast cancer patients. Further studies using a larger cohort from multiple centers are necessary to confirm the reliability of the established models before clinical application.

Antitumor Potential of Sericite Treatment Mediated by Cell Cycle Arrest in Triple-Negative MDA-MB231 Breast Cancer Cells

Breast cancer is the most common cancer and the leading cause of cancer-related mortality among females worldwide. Triple-negative breast cancer (TNBC) accounts for about 10–15% of all breast cancers and is usually more aggressive and has a poorer prognosis. Sericite has been known to have antitumor and immune-stimulatory effects. Although the chemopreventive potential of sericite has been demonstrated in other cancers, its molecular pathways in TNBC still require investigation. Thus, in the present study, the antitumor mechanism of sericite against MDA-MB231 breast cancer cells was examined in vitro and in an in vivo xenograft mouse model. Sericite treatment reduced cell proliferation and cell proliferation marker proliferating cell nuclear antigen (PCNA) in MDA-MB231 cells. It also decreased the total cell number and arrested cells in the G0/G1 phase of the cell cycle with an increase in the phosphorylation of P53 and upregulation of cell cycle regulatory proteins P21 and P16. In addition, sericite treatment also induced apoptosis signaling, which was evident by the upregulation of apoptotic protein markers cleaved caspases 3 and 9. A reduction in reactive oxygen species (ROS), NADPH oxidase 4 (NOX4), p22phox, and heat shock proteins (HSPs) was also observed. Similar results were obtained in vivo with significantly reduced tumor volume in sericite-administered mice. Collectively, these findings suggest that sericite has antitumor potential based on its property to induce cell cycle arrest and apoptotic cell death and therefore could serve as a potential therapeutic agent and crucial candidate in anticancer drug development for TNBC.

Breast Cancer-Delivered Exosomal miRNA as Liquid Biopsy Biomarkers for Metastasis Prediction: A Focus on Translational Research with Clinical Applicability

Metastasis represents the most important cause of breast cancer-associated mortality. Even for early diagnosed stages, the risk of metastasis is significantly high and predicts a grim outcome for the patient. Nowadays, efforts are made for identifying blood-based biomarkers that could reliably distinguish patients with highly metastatic cancers in order to ensure a closer follow-up and a more personalized therapeutic method. Exosomes are nano vesicles secreted by cancer cells that can transport miRNAs, proteins, and other molecules and deliver them to recipient cells all over the body. Through this transfer, cancer cells modulate their microenvironment and facilitate the formation of the pre-metastatic niche, leading to sustained progression. Exosomal miRNAs have been extensively studied due to their promising potential as prognosis biomarkers for metastatic breast cancer. In this review, we tried to depict an overview of the existing literature regarding exosomal miRNAs that are already validated as potential biomarkers, and which could be immediately available for the clinic. Moreover, in the last section, we highlighted several miRNAs that have proven their function in preclinical studies and could be considered for clinical validation. Considering the lack of standard methods for evaluating exosomal miRNA, we also discussed the challenges and the technical aspects underlying this issue.

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.

Prognostic significance of different molecular typing methods and immune status based on RNA sequencing in HR-positive and HER2-negative early-stage breast cancer

BACKGROUND

This study was conducted to evaluate the prognostic significance of different molecular typing methods and immune status based on RNA sequencing (RNA-seq) in hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative (HR + /HER2-) early-stage breast cancer and develop a modified immunohistochemistry (IHC)-based surrogate for intrinsic subtype analysis.

METHODS

The gene expression profiles of samples from 87 HR + /HER2- early-stage breast cancer patients were evaluated using the RNA-seq of Oncotype Dx recurrence score (RS), PAM50 risk of recurrence (ROR), and immune score. Intrinsic tumor subtypes were determined using both PAM50- and IHC-based detection of estrogen receptor, progesterone receptor, Ki-67, epidermal growth factor receptor, and cytokeratins 14 and 5/6. Prognostic variables were analyzed through Cox regression analysis of disease-free survival (DFS) and distant metastasis-free survival (DMFS).

RESULTS

Survival analysis showed that ROR better predicted recurrence and distant metastasis compared to RS (for DFS: ROR, P = 0.000; RS, P = 0.027; for DMFS, ROR, P = 0.047; RS, P = 0.621). Patients with HR + /HER2- early-stage breast cancer was classified into the luminal A, luminal B, HER2-enriched, and basal-like subtypes by PAM50. Basal-like subgroups showed the shortest DFS and DMFS. A modified IHC-based surrogate for intrinsic subtype analysis improved the concordance with PAM50 from 66.7% to 73.6%, particularly for basal-like subtype identification. High level of TILs and high expression of immune genes predicted poor prognosis. Multi-factor Cox analysis showed that IHC-based basal-like markers were the only independent factors affecting DMFS.

CONCLUSIONS

Prognosis is better evaluated by PAM50 ROR in early-stage HR + /HER2- breast cancer and significantly differs among intrinsic subtypes. The modified IHC-based subtype can improve the basal-like subtype identification of PAM50. High immunity status and IHC-based basal-like markers are negative prognostic factors.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Propolin G-Suppressed Epithelial-to-Mesenchymal Transition in Triple-Negative Breast Cancer Cells via Glycogen Synthase Kinase 3β-Mediated Snail and HDAC6-Regulated Vimentin Degradation

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer with a poor prognosis. The incidence and mortality rate of TNBC are frequently found in younger women. Due to the absence of a good therapeutic strategy, effective remedies for inhibiting TNBC have been developed for improving the cure rate. Epithelial-to-mesenchymal transition (EMT) is a critical mechanism to regulate cancer cell motility and invasion. Furthermore, ectopic expression of EMT molecules correlates with the metastasis and poor prognosis of TNBC. Targeting EMT might be a strategy for the therapy and prevention of TNBC. Propolin G, an active c-prenylflavanone in Taiwanese propolis, has been shown to possess anti-cancer activity in many cancers. However, the anti-metastasis activity of propolin G on TNBC is still unclear. The present study showed that the migration and invasion activities of TNBC cells was suppressed by propolin G. Down-regulated expression of Snail and vimentin and up-regulated expression of E-cadherin were dose- and time-dependently observed in propolin G-treated MDA-MB-231 cells. Propolin G inhibited Snail and vimentin expressions via the signaling pathways associated with post-translational modification. The activation of glycogen synthase kinase 3β (GSK-3β) by propolin G resulted in increasing GSK-3β interaction with Snail. Consequently, the nuclear localization and stability of Snail was disrupted resulting in promoting the degradation. Propolin G-inhibited Snail expression and the activities of migration and invasion were reversed by GSK-3β inhibitor pretreatment. Meanwhile, the outcomes also revealed that histone deacetylase 6 (HDAC6) activity was dose-dependently suppressed by propolin G. Correspondently, the amounts of acetyl-α-tubulin, a down-stream substrate of HDAC6, were increased. Dissociation of HDAC6/Hsp90 with vimentin leading to increased vimentin acetylation and degradation was perceived in the cells with the addition of propolin G. Moreover, up-regulated expression of acetyl-α-tubulin by propolin G was attenuated by HDAC6 overexpression. On the contrary, down-regulated expression of vimentin, cell migration and invasion by propolin G were overturned by HDAC6 overexpression. Conclusively, restraint cell migration and invasion of TNBC by propolin G were activated by the expression of GSK-3β-suppressed Snail and the interruption of HDAC6-mediated vimentin protein stability. Aiming at EMT, propolin G might be a potential candidate for TNBC therapy.

Pathogenesis of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) encompasses a heterogeneous group of fundamentally different diseases with different histologic, genomic, and immunologic profiles, which are aggregated under this term because of their lack of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 expression. Massively parallel sequencing and other omics technologies have demonstrated the level of heterogeneity in TNBCs and shed light into the pathogenesis of this therapeutically challenging entity in breast cancer. In this review, we discuss the histologic and molecular classifications of TNBC, the genomic alterations these different tumor types harbor, and the potential impact of these alterations on the pathogenesis of these tumors. We also explore the role of the tumor microenvironment in the biology of TNBCs and its potential impact on therapeutic response. Dissecting the biology and understanding the therapeutic dependencies of each TNBC subtype will be essential to delivering on the promise of precision medicine for patients with triple-negative disease.

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

OBJECTIVE

Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.

METHODS

We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.

RESULTS

All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.

CONCLUSION

The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

Subclonal heterogeneity and evolution in breast cancer

Subclonal heterogeneity and evolution are characteristics of breast cancer that play a fundamental role in tumour development, progression and resistance to current therapies. In this review, we focus on the recent advances in understanding the epigenetic and transcriptomic changes that occur within breast cancer and their importance in terms of cancer development, progression and therapy resistance with a particular focus on alterations at the single-cell level. Furthermore, we highlight the utility of using single-cell tracing and molecular barcoding methodologies in preclinical models to assess disease evolution and response to therapy. We discuss how the integration of single-cell profiling from patient samples can be used in conjunction with results from preclinical models to untangle the complexities of this disease and identify biomarkers of disease progression, including measures of intra-tumour heterogeneity themselves, and how enhancing this understanding has the potential to uncover new targetable vulnerabilities in breast cancer.

Impact of molecular subtype and race on HR+, HER2- breast cancer survival

PURPOSE

There is an urgent need to understand the biological factors contributing to the racial survival disparity among women with hormone receptor-positive (HR+), HER2- breast cancer. In this study, we examined the impact of PAM50 subtype on 10-year mortality rate in women with HR+, HER2- breast cancer by race.

METHODS

Women with localized, HR+, HER2- breast cancer diagnosed between 2002 and 2012 from two population-based cohorts were evaluated. Archival tumors were obtained and classified by PAM50 into four molecular subtypes (i.e., luminal A, luminal B, HER2-enriched, and basal-like). The molecular subtypes within HR+, HER2- breast cancers and corresponding 10-year mortality rate were compared between Black and Non-Hispanic White (NHW) women using Cox proportional hazard ratios and survival analysis, adjusting for covariates.

RESULTS

In this study, 318 women with localized, HR+, HER2- breast cancer were included-227 Black (71%) and 91 NHW (29%). Young Black women (age ≤ 50) had the highest proportion of HR+, non-luminal A tumors (47%), compared to young NHW (10%), older Black women (31%), and older NHW (30%). Overall, women with HR+, non-luminal A subtypes had a higher 10-year mortality rate compared to HR+, luminal A subtypes after adjustment for age, stage, and income (HR 4.21 for Blacks, 95% CI 1.74-10.18 and HR 3.44 for NHW, 95% CI 1.31-9.03). Among HR+, non-luminal A subtypes there was, however, no significant racial difference in 10-yr mortality observed (Black vs. NHW: HR 1.23, 95% CI 0.58-2.58).

CONCLUSION

Molecular subtype classification highlights racial disparities in PAM50 subtype distribution among women with HR+, HER2- breast cancer. Among women with HR+, HER2- breast cancer, racial survival disparities are ameliorated after adjusting for molecular subtype.

HER2 Signaling and Breast Cancer Stem Cells: The Bridge behind HER2-Positive Breast Cancer Aggressiveness and Therapy Refractoriness

Simple Summary

Breast cancer (BC) is not a single disease, but a group of different tumors, and altered HER2 expression defines a particularly aggressive subtype. Although HER2 pharmacological inhibition has dramatically improved the prognosis of HER2-positive BC patients, there is still an urgent need for improved knowledge of HER2 biology and mechanisms underlying HER2-driven aggressiveness and drug susceptibility. Emerging data suggest that the clinical efficacy of molecularly targeted therapies is related to their ability to target breast cancer stem cells (BCSCs), a population that is not only self-sustaining and able to differentiate into distinct lineages, but also contributes to tumor growth, aggressiveness, metastasis and treatment resistance. The aim of this review is to provide an overview of how the full-length HER2 receptor, the d16HER2 splice variant and the truncated p95HER2 variants are involved in the regulation and maintenance of BCSCs.

Abstract

HER2 overexpression/amplification occurs in 15–20% of breast cancers (BCs) and identifies a highly aggressive BC subtype. Recent clinical progress has increased the cure rates of limited-stage HER2-positive BC and significantly prolonged overall survival in patients with advanced disease; however, drug resistance and tumor recurrence remain major concerns. Therefore, there is an urgent need to increase knowledge regarding HER2 biology and implement available treatments. Cancer stem cells (CSCs) represent a subset of malignant cells capable of unlimited self-renewal and differentiation and are mainly considered to contribute to tumor onset, aggressiveness, metastasis, and treatment resistance. Seminal studies have highlighted the key role of altered HER2 signaling in the maintenance/enrichment of breast CSCs (BCSCs) and elucidated its bidirectional communication with stemness-related pathways, such as the Notch and Wingless/β-catenin cascades. d16HER2, a splice variant of full-length HER2 mRNA, has been identified as one of the most oncogenic HER2 isoform significantly implicated in tumorigenesis, epithelial-mesenchymal transition (EMT)/stemness and the response to targeted therapy. In addition, expression of a heterogeneous collection of HER2 truncated carboxy-terminal fragments (CTFs), collectively known as p95HER2, identifies a peculiar subgroup of HER2-positive BC with poor prognosis, with the p95HER2 variants being able to regulate CSC features. This review provides a comprehensive overview of the current evidence regarding HER2-/d16HER2-/p95HER2-positive BCSCs in the context of the signaling pathways governing their properties and describes the future prospects for targeting these components to achieve long-lasting tumor control.

A novel immune subtype classification of ER-positive, PR-negative and HER2-negative breast cancer based on the genomic and transcriptomic landscape

BACKGROUND

The diversity and plasticity behind ER+/PR-/HER2- breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes.

METHODS

Based on the immune-related gene expression profiles of 411 ER+/PR-/HER2- breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient.

RESULTS

Our analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum.

CONCLUSION

Overall, this study revealed five heterogeneous immune subtypes among ER+/PR-/HER2- breast cancer, also provided important implications for clinical translations.

Dissecting the biological heterogeneity of HER2-positive breast cancer

HER2-positive (HER2+) breast cancer (BC) is a heterogenous and multifaceted disease, with interesting therapeutic implications. First, all intrinsic molecular subtypes can be identified in HER2+ tumors, with the HER2-enriched being the most frequent. Such subtypes do not differ much from their counterparts in HER2-negative disease, apart for the high expression of genes in/near the HER2 amplicon on chromosome 17. Intrinsic subtyping, along with the quantification of ERBB2 mRNA levels, is associated with higher rates of pathologic complete response across neoadjuvant trials of dual HER2 blockade and might help select patients for de-escalation and escalation treatment strategies. Secondly, HER2+ tumors have a broad range of DNA alterations. ERBB2 mutations and alterations in the PI3K/Akt/mTOR pathway are among the most frequent and might predict benefit from potent pan-HER, PI3K and mTOR inhibitors. Moreover, HER2+ tumors are usually infiltrated by lymphocytes. These tumor infiltrating-lymphocytes (TILs) predict response to neoadjuvant anti-HER2-based treatment and exert a prognostic role. PD-L1, detected in ∼42 % of HER2+ BC, might also be useful to define patients responding to novel anti-PD1/PD-L1 immunotherapies. New multiparametric clinicopathologic and genomic tools accounting for this complexity, such as HER2DX, are under development to define more tailored treatment approaches. Finally, HER2-targeted antibody-drug conjugates (ADC) such as trastuzumab deruxtecan might be active in tumors with low expression of HER2. Overall, there is a need to molecularly characterize and develop novel targeted therapies for HER2+ disease.

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Almost 50 % of HER2+ breast cancer (BC) are molecularly HER2-Enriched (HER2-E).

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Most relevant mutations are found in ERBB2 (∼4 %) and PI3K/AKT/mTOR pathway (>30 %).

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Tumor infiltrating lymphocytes are frequent, predictive and prognostic in HER2+ BC.

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HER2 heterogeneity and HER2 low status are gaining therapeutic relevance.

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New treatments need to consider HER2+ molecular and microenvironmental complexity.

CHIP/STUB1 Ubiquitin Ligase Functions as a Negative Regulator of ErbB2 by Promoting Its Early Post-Biosynthesis Degradation

Simple Summary

Overexpressed ErbB2/HER2 receptor drives up to a quarter of breast cancers. One aspect of ErbB2 biology that is poorly understood is how it reaches the cell surface following biosynthesis in the endoplasmic reticulum (ER). Here, the authors show that the CHIP (C-terminus of HSC70-Interacting protein)/STUB1 (STIP1-homologous U-Box containing protein 1) protein targets the newly synthesized ErbB2 for ubiquitin/proteasome-dependent degradation in the ER and Golgi, identifying a novel mechanism that negatively regulates cell surface expression of ErbB2. These findings provide one explanation for frequent loss of CHIP expression is ErbB2-overexpressing breast cancers. The authors further show that ErbB2-overexpressing breast cancer cells with low CHIP expression exhibit higher ER stress inducibility, and ER stress-inducing anticancer drug Bortezomib synergizes with ErbB2-targeted humanized antibody Trastuzumab to inhibit cancer cell proliferation. These new insights suggest that reduced CHIP expression may specify ErbB2-overexpressing breast cancers suitable for combined treatment with Trastuzumab and ER stress inducing agents.

Abstract

Overexpression of the epidermal growth factor receptor (EGFR) family member ErbB2 (HER2) drives oncogenesis in up to 25% of invasive breast cancers. ErbB2 expression at the cell surface is required for oncogenesis but mechanisms that ensure the optimal cell surface display of overexpressed ErbB2 following its biosynthesis in the endoplasmic reticulum are poorly understood. ErbB2 is dependent on continuous association with HSP90 molecular chaperone for its stability and function as an oncogenic driver. Here, we use knockdown and overexpression studies to show that the HSP90/HSC70-interacting negative co-chaperone CHIP (C-terminus of HSC70-Interacting protein)/STUB1 (STIP1-homologous U-Box containing protein 1) targets the newly synthesized, HSP90/HSC70-associated, ErbB2 for ubiquitin/proteasome-dependent degradation in the endoplasmic reticulum and Golgi, thus identifying a novel mechanism that negatively regulates cell surface ErbB2 levels in breast cancer cells, consistent with frequent loss of CHIP expression previously reported in ErbB2-overexpressing breast cancers. ErbB2-overexpressing breast cancer cells with low CHIP expression exhibited higher endoplasmic reticulum stress inducibility. Accordingly, the endoplasmic reticulum stress-inducing anticancer drug Bortezomib combined with ErbB2-targeted humanized antibody Trastuzumab showed synergistic inhibition of ErbB2-overexpressing breast cancer cell proliferation. Our findings reveal new insights into mechanisms that control the surface expression of overexpressed ErbB2 and suggest that reduced CHIP expression may specify ErbB2-overexpressing breast cancers suitable for combined treatment with Trastuzumab and ER stress inducing agents.

Steroid Ligands, the Forgotten Triggers of Nuclear Receptor Action; Implications for Acquired Resistance to Endocrine Therapy

PURPOSE

There is strong epidemiologic evidence indicating that estrogens may not be the sole steroid drivers of breast cancer. We hypothesize that abundant adrenal androgenic steroid precursors, acting via the androgen receptor (AR), promote an endocrine-resistant breast cancer phenotype.

EXPERIMENTAL DESIGN

AR was evaluated in a primary breast cancer tissue microarray (n = 844). Androstenedione (4AD) levels were evaluated in serum samples (n = 42) from hormone receptor-positive, postmenopausal breast cancer. Levels of androgens, progesterone, and estradiol were quantified using LC/MS-MS in serum from age- and grade-matched recurrent and nonrecurrent patients (n = 6) before and after aromatase inhibitor (AI) therapy (>12 months). AR and estrogen receptor (ER) signaling pathway activities were analyzed in two independent AI-treated cohorts.

RESULTS

AR protein expression was associated with favorable progression-free survival in the total population (Wilcoxon, P < 0.001). Pretherapy serum samples from breast cancer patients showed decreasing levels of 4AD with age only in the nonrecurrent group (P < 0.05). LC/MS-MS analysis of an AI-sensitive and AI-resistant cohort demonstrated the ability to detect altered levels of steroids in serum of patients before and after AI therapy. Transcriptional analysis showed an increased ratio of AR:ER signaling pathway activities in patients failing AI therapy (t test P < 0.05); furthermore, 4AD mediated gene changes associated with acquired AI resistance.

CONCLUSIONS

This study highlights the importance of examining the therapeutic consequences of the steroid microenvironment and demonstrable receptor activation using indicative gene expression signatures.

Comparative survival analysis of multiparametric tests-when molecular tests disagree-A TEAM Pathology study

Multiparametric assays for risk stratification are widely used in the management of both node negative and node positive hormone receptor positive invasive breast cancer. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. The TEAM pathology study consists of 3284 postmenopausal ER+ve breast cancers treated with endocrine therapy Using genes comprising the following multi-parametric tests OncotypeDx®, Prosigna™ and MammaPrint® signatures were trained to recapitulate true assay results. Patients were then classified into risk groups and survival assessed. Whilst likelihood χ2 ratios suggested limited value for combining tests, Kaplan-Meier and LogRank tests within risk groups suggested combinations of tests provided statistically significant stratification of potential clinical value. Paradoxically whilst Prosigna-trained results stratified Oncotype-trained subgroups across low and intermediate risk categories, only intermediate risk Prosigna-trained cases were further stratified by Oncotype-trained results. Both Oncotype-trained and Prosigna-trained results further stratified MammaPrint-trained low risk cases, and MammaPrint-trained results also stratified Oncotype-trained low and intermediate risk groups but not Prosigna-trained results. Comparisons between existing multiparametric tests are challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. Detailed analysis of the TEAM pathology study suggests a complex inter-relationship between test results in the same patient cohorts which requires careful evaluation regarding test utility. Further prognostic improvement appears both desirable and achievable.

Validation of the Skåne University Hospital nomogram for the preoperative prediction of a disease-free axilla in patients with breast cancer

BACKGROUND

Axillary staging via sentinel lymph node biopsy (SLNB) is performed for clinically node-negative (N0) breast cancer patients. The Skåne University Hospital (SUS) nomogram was developed to assess the possibility of omitting SLNB for patients with a low risk of nodal metastasis. Area under the receiver operating characteristic curve (AUC) was 0.74. The aim was to validate the SUS nomogram using only routinely collected data from the Swedish National Quality Registry for Breast Cancer at two breast cancer centres during different time periods.

METHOD

This retrospective study included patients with primary breast cancer who were treated at centres in Lund and Malmö during 2008-2013. Clinicopathological predictors in the SUS nomogram were age, mode of detection, tumour size, multifocality, lymphovascular invasion and surrogate molecular subtype. Multiple imputation was used for missing data. Validation performance was assessed using AUC and calibration.

RESULTS

The study included 2939 patients (1318 patients treated in Lund and 1621 treated in Malmö). Node-positive disease was detected in 1008 patients. The overall validation AUC was 0.74 (Lund cohort AUC: 0.75, Malmö cohort AUC: 0.73), and the calibration was satisfactory. Accepting a false-negative rate of 5 per cent for predicting N0, a possible SLNB reduction rate of 15 per cent was obtained in the overall cohort.

CONCLUSION

The SUS nomogram provided acceptable power for predicting a disease-free axilla in the validation cohort. This tool may assist surgeons in identifying and counselling patients with a low risk of nodal metastasis on the omission of SLNB staging.

Role of immune regulatory cells in breast cancer: Foe or friend?

Breast cancer (BC) is the most common cancer among women between the ages of 20 and 50, affecting more than 2.1 million people and causing the annual death of more than 627,000 women worldwide. Based on the available knowledge, the immune system and its components are involved in the pathogenesis of several malignancies, including BC. Cancer immunobiology suggests that immune cells can play a dual role and induce anti-tumor or immunosuppressive responses, depending on the tumor microenvironment (TME) signals. The most important effector immune cells with anti-tumor properties are natural killer (NK) cells, B, and T lymphocytes. On the other hand, immune and non-immune cells with regulatory/inhibitory phenotype, including regulatory T cells (Tregs), regulatory B cells (Bregs), tolerogenic dendritic cells (tDCs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), myeloid-derived suppressor cells (MDSCs), mesenchymal stem cells (MSCs), and regulatory natural killer cells (NKregs), can promote the growth and development of tumor cells by inhibiting anti-tumor responses, inducing angiogenesis and metastasis, as well as the expression of inhibitory molecules and suppressor mediators of the immune system. However, due to the complexity of the interaction and the modification in the immune cells' phenotype and the networking of the immune responses, the exact mechanism of action of the immunosuppressive and regulatory cells is not yet fully understood. This review article reviews the immune responses involved in BC as well as the role of regulatory and inhibitory cells in the pathogenesis of the disease. Finally, therapeutic approaches based on inhibition of immunosuppressive responses derived from regulatory cells are discussed.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer

Novel antibody-drug conjugates against HER2 are showing high activity in HER2-negative breast cancer (BC) with low HER2 expression (i.e., 1+ or 2+ and lack of ERBB2 amplification). However, the clinical and molecular features of HER2-low BC are yet to be elucidated. Here, we collected retrospective clinicopathological and PAM50 data from 3,689 patients with HER2-negative disease and made the following observations. First, the proportion of HER2-low was higher in HR-positive disease (65.4%) than triple-negative BC (TNBC, 36.6%). Second, within HR-positive disease, ERBB2 and luminal-related genes were more expressed in HER2-low than HER2 0. In contrast, no gene was found differentially expressed in TNBC according to HER2 expression. Third, within HER2-low, ERBB2 levels were higher in HR-positive disease than TNBC. Fourth, HER2-low was not associated with overall survival in HR-positive disease and TNBC. Finally, the reproducibility of HER2-low among pathologists was suboptimal. This study emphasizes the large biological heterogeneity of HER2-low BC, and the need to implement reproducible and sensitive assays to measure low HER2 expression.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX^® (Genomic Health.), Prosigna^™ (NanoString Technologies, Inc.), MammaPrint^® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational “modelling” of test results using published algorithms and secondly a “training” approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a “modelling” approach without reference to real world test results. Simultaneously we show that a “training” approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results.

Overestimation of Late Distant Recurrences in High-Risk Patients With ER-Positive Breast Cancer: Validity and Accuracy of the CTS5 Risk Score in the TEAM and IDEAL Trials

PURPOSE

Most distant recurrences (DRs) in women with hormone receptor-positive breast cancer occur after 5 years from diagnosis. The Clinical Treatment Score post-5 years (CTS5) estimates DRs after 5 years of adjuvant endocrine therapy (AET). The aim of this study was to externally validate the CTS5 as a prognostic/predictive tool.

METHODS

The CTS5 categorizes patients who have been disease free for 5 years into low, intermediate, and high risk and calculates an absolute risk for developing DRs between 5 and 10 years. Discrimination and calibration were assessed using data from the TEAM and IDEAL trials. The predictive value of the CTS5 was tested with data from the IDEAL trial.

RESULTS

A total of 5,895 patients from the TEAM trial and 1,591 patients from the IDEAL trial were included. When assessing the CTS5 discrimination, significantly more DRs were found at 10 years after diagnosis in the CTS5 high- and intermediate-risk groups than in the low-risk group (hazard ratio, 5.7 [95% CI, 3.6 to 8.8] and 2.8 [95% CI, 1.7 to 4.4], respectively). In low- and intermediate-risk patients, the CTS5-predicted DR rates were higher, although not statistically significantly so, than observed rates. However, in high-risk patients, the CTS5-predicted DR rates were significantly higher than observed rates (29% v 19%, respectively; P < .001). The CTS5 was not predictive for extended AET duration.

CONCLUSION

The CTS5 score as applied to patients treated in the TEAM and IDEAL cohorts discriminates between risk categories but overestimates the risk of late DRs in high-risk patients. Therefore, the numerical risk assessment from the CTS5 calculator in its current form should be interpreted with caution when used in daily clinical practice, particularly in high-risk patients.

Notch activation in the mouse mammary luminal lineage leads to ductal hyperplasia and altered partitioning of luminal cell subtypes

Hyperactivated Notch signalling has been implicated in breast cancer, but how elevated levels of Notch signalling contribute to mammary dysplasia and tumorigenesis is not fully understood. In this study, we express an activated form of Notch1 in the mouse mammary luminal lineage and analyse the consequences for tumour formation and the transcriptomic landscape in the luminal lineage. Simultaneous conditional activation of a Notch1 intracellular domain (Notch1 ICD) and EGFP in the luminal lineage was achieved by removal of a stop cassette by CRE-recombinase expression from the whey acidic protein (WAP) promoter. Mice in which Notch1 ICD was activated in the luminal lineage (WAP-CRE;R26-N1ICD mice) exhibit ductal hyperplasia after lactation with an increase in branching frequency and in the number of side-branch ends in the ductal tree. A subset of the mice developed mammary tumours and the majority of the tumour cells expressed EGFP (as a proxy for Notch1 ICD), indicating that the tumours originate from the Notch1 ICD-expressing cells. Single-cell transcriptome analysis of the EGFP-positive mammary cells identified six subtypes of luminal cells. The same six subtypes were found in control mice (WAP-CRE;R26-tdTomato mice expressing the tdTomato reporter from WAP-CRE-mediated activation), but the proportion of cells in the various subtypes differed between the WAP-CRE;R26-N1ICD and control WAP-CRE;R26-tdTomato mice. In conclusion, we show that Notch1 ICD expression in the luminal lineage produces a ductal hyperplasia and branching phenotype accompanied by altered luminal cell subtype partitioning.

Nuclear PDCD4 Expression Defines a Subset of Luminal B-Like Breast Cancers with Good Prognosis

The hormone receptor-positive (estrogen and/or progesterone receptor (PR)-positive) and HER2-negative breast cancer (BC) subtype is a biologically heterogeneous entity that includes luminal A-like (LumA-like) and luminal B-like (LumB-like) subtypes. Decreased PR levels is a distinctive biological feature of LumB-like tumors. These tumors also show reduced sensitivity to endocrine therapies and poorer prognosis than LumA-like tumors. Identification of biomarkers to accurately predict disease relapse in these subtypes is crucial in order to select effective therapies. We identified the tumor suppressor PDCD4 (programmed cell death 4), located in the nucleus (NPDCD4), as an independent prognostic factor of good clinical outcome in LumA-like and LumB-like subtypes. NPDCD4-positive LumB-like tumors presented overall and disease-free survival rates comparable to those of NPDCD4-positive LumA-like tumors, indicating that NPDCD4 improves the outcome of LumB-like patients. In contrast, NPDCD4 loss increased the risk of disease recurrence and death in LumB-like compared with LumA-like tumors. This, along with our results showing that LumB-like tumors present lower NPDCD4 positivity than LumA-like tumors, suggests that NPDCD4 loss contributes to endocrine therapy resistance in LumB-like BCs. We also revealed that PR induces PDCD4 transcription in LumB-like BC, providing a mechanistic explanation to the low PDCD4 levels in LumB-like BCs lacking PR. Finally, PDCD4 silencing enhanced BC cell survival in a patient-derived explant model of LumB-like disease. Our discoveries highlight NPDCD4 as a novel biomarker in LumA- and LumB-like subtypes, which could be included in the panel of immunohistochemical markers used in the clinic to accurately predict the prognosis of LumB-like tumors.

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.

Adjuvant chemotherapy could not bring survival benefit to HR-positive, HER2-negative, pT1b-c/N0-1/M0 invasive lobular carcinoma of the breast: a propensity score matching study based on SEER database

BACKGROUND

The benefit of adjuvant chemotherapy in invasive lobular carcinoma (ILC) is still unclear. The objective of the current study was to elucidate the effectiveness of adjuvant chemotherapy in hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, pT1b-c/N0-1/M0 ILC.

METHODS

Based on Surveillance, Epidemiology, and End-Results (SEER) database, we identified original 12,334 HR-positive, HER2-negative, pT1b-c/N0-1/M0 ILC patients, who were then divided into adjuvant chemotherapy group and control group. End-points were overall survival (OS) and breast cancer-specific mortality (BCSM). Aiming to minimize the selection bias of baseline characteristics, Propensity Score Matching (PSM) method was used.

RESULTS

In a total of 12,334 patients with HR-positive, HER2-negative, pT1b-c/N0-1/M0 ILC, 1785 patients (14.5%) were allocated into adjuvant chemotherapy group and 10,549 (85.5%) into control group. Used PSM, the 1785 patients in adjuvant chemotherapy group matched to the 1785 patients in control group. By Kaplan-Meier survival analyses, we observed no beneficial effect of adjuvant chemotherapy on OS in both original samples (P = 0.639) and matched samples (P = 0.962), however, ineffective or even contrary results of adjuvant chemotherapy on BCSM both in original samples (P = 0.001) and in matched samples (P = 0.002). In both original and matched multivariate Cox models, we observed ineffectiveness of adjuvant chemotherapy on OS (hazard ratio (HR) for overall survival = 0.82, 95% confidence interval (CI) [0.62-1.09]; P = 0.172 and HR = 0.90, 95%CI [0.65-1.26]; P = 0.553, respectively), unexpectedly promoting effect of adjuvant chemotherapy on BCSM (HR = 2.33, 95%CI [1.47-3.67]; P = 0.001 and HR = 2.41, 95%CI [1.32-4.39]; P = 0.004, respectively). Standard surgery was beneficial to the survival of patients. Lymph node metastasis was detrimental to survival and radiotherapy brought survival benefit in original samples, but two issues had unobvious effect in matched samples.

CONCLUSION

In this study, adjuvant chemotherapy did not improve survival for patients with HR-positive, HER2-negative pT1b-c/N0-1/M0 ILC.

Trefoil factor 1 (TFF1) is a potential prognostic biomarker with functional significance in breast cancers

Breast cancer (BC) is the most common cancer in women and the second leading cause of their cancer death. Establishing an accurate BC prognosis is very difficult because of its heterogeneity. Elevated TFF1 levels in serum were associated with development of BC, TFF1 expression was upregulated in BC compared to the healthy breast tissue. The aim of this study was to investigate the function of TFF1 in BCs, and to assess whether serum TFF1 could be used in formulating a prognosis for BC patients. In silico analyses were carried out to determine the expression of TFF1 mRNA in different types of BC and the association between TFF1 expression and survival of BC patients. Expression of TFF1 protein was checked in 52 paraffin-embedded tissues of BCs by immunochemistry, and serum concentration of TFF1 in 70 BC patients and 32 healthy controls was measured by ELISA. Functional activities of TFF1 in BC cells were determined by CCK-8 assay, colony formation, BrdU-DNA synthesis, and assays for migration and invasion. Results showed that expression of TFF1 mRNA was correlated with expression of biomarkers of luminal cancers including ESR1, GATA3, FOXA1, MYB and XBP1. In addition, patients with ER⁺BC had higher expression of TFF1 than those with ER^- (p < 0.05). There was also lower expression of TFF1 in triple-negative breast cancer (TNBC) than in non-TNBC (p < 0.05), which corresponds with the level of serum TFF1 in TNBC patients, compared with non-TNBC patients (p < 0.001). Furthermore, expression of TFF1 was associated with tumor size (p = 0.002), nodal status (p < 0.001), histological grade (p < 0.001), ER status (p = 0.012), PR status (p < 0.001) and HER2 (p < 0.001), while serum TFF1 was only statistically different among BC with ER⁺, PR ⁺ and HER2⁺ (p = 0.04139, 0.0018, 0.0004). Elevated TFF1 expression correlated with increased overall survival of BC patients (p = 0.00068). Finally, TFF1 was found to inhibit the cell growth, colony formation, migration and invasion of BC cells in vitro. All these results suggest that expression of TFF1 was related to ER status of BC and that expression of TFF1 was lower in TNBC than in non-TNBC. TFF1 was found to inhibit proliferation, migration and invasion of BC cells in vitro. Expression of TFF1 was associated with clinical characters of patients with BC. Serum TFF1 could be used to predict prognosis of patients with BC, especially non-TNBC.

Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

Deciphering HER2 Breast Cancer Disease: Biological and Clinical Implications

The main obstacle for designing effective treatment approaches in breast cancer is the extensive and the characteristic heterogeneity of this tumor. The vast majority of critical genomic changes occurs during breast cancer progression, creating a significant variability within primary tumors as well as between the primary breast cancer and their metastases, a hypothesis have already demonstrated in retrospective studies (1). A clear example of this is the HER2-positive breast cancer. In these tumors, we can find all of the transcriptional subtypes of breast cancer, even the basal like or luminal A subtypes. Although the HER2-enriched is the most representative transcriptional subtype in the HER2-positive breast cancer, we can find it too in breast cancers with HER2-negative status. This intrinsic subtype shows a high expression of the HER2 and is associated with proliferation-related genes clusters, among other features. Therefore, two hypotheses can be suggested. First, the HER2 amplification can be a well-defined driver event present in all of the intrinsic subtypes, and not a subtype marker isolated. Secondly, HER2-enriched subtype can have a distinctive transcriptional landscape independent of HER2 amplification. In this review, we present an extensive revision about the last highlights and advances in clinical and genomic settings of the HER2-positive breast cancer and the HER2-enriched subtype, in an attempt to improving the knowledge of the underlying biology of both entities and to explaining the intrinsic heterogeneity of HER2-positive breast cancers.