Molecular portraits of human breast tumours

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

Breast cancer is a multifaceted disease and a leading cause of cancer morbidity and mortality in females across the globe. In 2020 alone, 2.3 million women were diagnosed and 685,000 died of breast cancer worldwide. With the number of diagnoses projected to increase to 3 million per year by 2040 it is essential that new methods of detection and disease stratification are sought to decrease this global cancer burden. Although significant improvements have been made in breast cancer diagnosis and treatment, the prognosis of breast cancer remains poor in some patient groups (i.e. triple negative breast cancer), necessitating research into better patient stratification, diagnosis and drug discovery. The UK Biobank, a comprehensive biomedical and epidemiological database with a wide variety of multiomics data (genomics, proteomics, metabolomics) offers huge potential to uncover groundbreaking discoveries in breast cancer research leading to improved patient stratification. Combining genomic, proteomic, and metabolic profiles of breast cancer in combination with histological classification, can aid treatment decisions through accurate diagnosis and prognosis prediction of tumor behaviour. Here, we systematically reviewed PubMed publications reporting the analysis of UK Biobank data in breast cancer research. Our analysis of UK Biobank studies in the past five years identified 125 publications, of which 76 focussed on genomic data analysis. Interestingly, only two studies reported the analysis of metabolomics and proteomics data, with none performing multiomics analysis of breast cancer. A meta-analysis of the 76 publications identified 2870 genetic variants associated with breast cancer across 445 genes. Subtype analysis revealed differential genetic alteration in 13 of the 445 genes and the identification of 59 well-established breast cancer genes. in differential pathways. Pathway interaction analyses illuminated their involvement in general cancer biomolecular pathways (e.g. DNA damage repair, Gene expression). While our meta-analysis only measured genetic differences in breast cancer due to current usage of UK Biobank data, minimal multi-omics analyses have been performed and the potential for harnessing multi-omics strategies within the UK Biobank cohort holds promise for unravelling the biological signatures of distinct breast cancer subtypes further in the future.

Impact of Prosigna test on adjuvant treatment decision in lymph node-negative early breast cancer-a prospective national multicentre study (EMIT-1)

BACKGROUND

EMIT-1 is a national, observational, single-arm trial designed to assess the value of the Prosigna, Prediction Analysis of Microarray using the 50 gene classifier (PAM50)/Risk of Recurrence (ROR), test as a routine diagnostic tool, examining its impact on adjuvant treatment decisions, clinical outcomes, side-effects and cost-effectiveness. Here we present the impact on treatment decisions.

PATIENTS AND METHODS

Patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative pT1-pT2 lymph node-negative early breast cancer (EBC) were included. The Prosigna test and standard histopathology assessments were carried out. Clinicians' treatment decisions were recorded before (pre-Prosigna) and after (post-Prosigna) the Prosigna test results were disclosed.

RESULTS

Of 2217 patients included, 2178 had conclusive Prosigna results. The pre-Prosigna treatment decisions were: no systemic treatment (NT) in 27% of patients, endocrine treatment alone (ET) in 38% and chemotherapy (CT) followed by ET (CT + ET) in 35%. Post-Prosigna treatment decisions were 25% NT, 51% ET and 24% CT + ET, respectively. Adjuvant treatment changed in 28% of patients, including 21% change in CT use. Among patients assigned to CT + ET pre-Prosigna, 45% were de-escalated to ET post-Prosigna. Of patients assigned to ET, 12% were escalated to CT + ET and 8% were de-escalated to NT; of those assigned to NT, 18% were escalated to ET/CT + ET. CT was more frequently recommended for patients aged ≤50 years. In the subgroup with pT1c-pT2 G2 and intermediate Ki67 (0.5-1.5× local laboratory median Ki67 score), the pre-Prosigna CT treatment decision varied widely across hospitals (3%-51%). Post-Prosigna, the variability of CT use was markedly reduced (8%-24%). The correlation between Ki67 and ROR score within this subgroup was poor (r = 0.25-0.39). The median ROR score increased by increasing histological grade, but the ROR score ranges were wide (for G1 0-79, G2 0-90, G3 16-94).

CONCLUSION

The Prosigna test result changed adjuvant treatment decisions in all EBC clinical risk groups, markedly decreased the CT use for patients categorized as higher clinical risk pre-Prosigna and reduced treatment decision discrepancies between hospitals.

Multiomics Analysis of Interleukin-21 as a Potential Immunologic and Biomarker in Hepatocellular Carcinoma

Interleukin-21 (IL-21) is an important antitumor cytokine that contributes to the proliferation and differentiation of CD8+ T cells. It has been proven to enhance the response to immune checkpoint inhibitors (ICIs) in various solid tumors. However, its role in hepatocellular carcinoma (HCC) has not yet been clarified. In this research, we aimed to investigate the antitumor effect of IL-21 in HCC and its effect on ICI treatment. Through transcriptome sequencing analysis and immunohistochemistry validation, we found that patients with high IL-21 expression had a better prognosis. HCCs with high expression of IL-21 had higher infiltration of CD8+ T cells, increased expression of immune checkpoints, and an improved response to ICI treatment. In conclusion, IL-21 can enhance the efficacy of ICI treatment and improve the prognosis of patients by promoting the infiltration of CD8+ T cells and the expression of immune checkpoint-related genes.

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

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

IMPLICATIONS

Surgery deregulates hallmarks of cancer in human tissue.

Breast Cancer Histopathology in the Age of Molecular Oncology

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Prognostic role of SOX2 and STAT3 expression on circulating T lymphocytes and CD44+/CD24neg cells in the locally advanced and metastatic breast cancer

BACKGROUND

Breast cancer (BC) is associated with a continuous increase in incidence, with high mortality rates in several countries. CD44, STAT3, and SOX2 are related to regulating of somatic cell division, tumorigenesis, and metastasis in BC.

METHODS

A cross-sectional study was carried out at the Hospital de Cancer de Pernambuco (HCP) between 2017 and 2018. Fifty-one women with locally advanced (LA) and 14 with metastatic BC were included in the study.

RESULTS

High CD44+/CD24neg and CD44+/CD24neg/SOX2+ levels in Luminal B (LB), HER2+, and triple-negative breast cancer (TNBC) compared with controls (p < 0.05). Low CD44+/CD24negSTAT3+ levels in LB, HER2+, and TNBC compared with controls (p < 0.05). High T lymphocytes, and low STAT3 + T, and SOX2 + T levels in BC patients (p < 0.05). High SOX2 + T levels in patients with axillary lymph node-negative (N0) compared with the axillary lymph node-positives (N1 and N2 groups; p < 0.05). High SOX2 + T levels in N1 compared to N2 (p < 0.05). High T lymphocytes and low SOX2 + T levels in the LA tumor compared to metastatic disease (p = 0.0007 and p = 0.02, respectively). High CD44 + /CD24negSTAT3+, and T lymphocyte levels in TNBC patients with LA tumor compared to metastatic (p < 0.05). Low STAT3 + T levels in TBNC patients with LA tumor compared to metastatic (p = 0.0266).

CONCLUSION

SOX2 and STAT3 expression on circulating T lymphocytes and CD44 + /CD24neg cells in peripheral blood have prognostic roles in breast cancer. SOX2 and STAT3 expression are potential predictive biomarkers of disease progression in breast cancer regardless of tumor subtype.

VGLL1 stabilization of cytoplasmic TAZ promotes EGFR expression and maintains tumor initiating cells in breast cancer independent of TEAD

Vestigial-like family member 1 (VGLL1) is one of the X-linked genes whose expression is elevated in basal-like breast cancer (BLBC) because of X-chromosome isodisomy. As an approach towards understanding its function, we performed correlation study using transcript data of breast cancer patients from cBioPortal for Cancer Genomics. Our analysis identified EGFR as the most correlated transcript with VGLL1. We demonstrate that VGLL1 promotes EGFR expression and increases the frequency of breast tumor initiating cells (CD44high/+CD24low/-). These findings are crucial because an elevated EGFR expression and high frequency of CD44high/+CD24low/- cells are defining features of BLBC, and we provide a new mechanistic insight into how their expressions are controlled. Importantly, VGLL1 regulation of EGFR and CD44high/+CD24low/- population is mediated by the hippo-transducer TAZ which exerts its oncogenic roles by binding and activating TEAD transcription factors. A crucial finding is that TEAD-binding domain of TAZ is dispensable for its regulation of EGFR and CD44high/+CD24low/- cells. Instead, VGLL1 stabilization of cytoplasmic TAZ is essential for these functions. Also, we show that VGLL1/TAZ restricts the surface expression of CD24 which contributes to the increased number of CD44high/+CD24low/- cells. In addition, we observed that VGLL1 represses AXL expression and suppresses claudin-low phenotype, and that is caused by the VGLL1 mediated nuclear expulsion of TAZ. Therefore, EGFR and AXL seem to represent two different breast tumor subtypes, and their differential expressions is controlled by VGLL1.

Defining the relationship between cellular and extracellular vesicle (EV) content in breast cancer via an integrative multi-omic analysis

Much recent research has been dedicated to exploring the utility of extracellular vesicles (EVs) as circulating disease biomarkers. Underpinning this work is the assumption that the molecular cargo of EVs directly reflects the originating cell. Few attempts have been made, however, to empirically validate this on the -omic level. To this end, we have performed an integrative multi-omic analysis of a panel of breast cancer cell lines and corresponding EVs. Whole transcriptome analysis validated that the cellular transcriptome remained stable when cultured cells are transitioned to low serum or serum-free medium for EV collection. Transcriptomic profiling of the isolated EVs indicated a positive correlation between transcript levels in cells and EVs, including disease-associated transcripts. Analysis of the EV proteome verified that HER2 protein is present in EVs, however neither the estrogen (ER) nor progesterone (PR) receptor proteins are detected regardless of cellular expression. Using multivariate analysis, we derived an EV protein signature to infer cellular patterns of ER and HER2 expression, though the ER protein could not be directly detected. Integrative analyses affirmed that the EV proteome and transcriptome captured key phenotypic hallmarks of the originating cells, supporting the potential of EVs for non-invasive monitoring of breast cancers.

Cell based and In vivo systematic evaluation of some Egyptian plant extracts targeting breast cancer

The prevalence of breast cancer as a significant public health concern necessitates continued exploration of natural resources for novel anti-cancer agents is crucial.

MATERIAL AND METHODS

Anticancer activity of plant extracts on monolayer breast cancer cell line (MCF7) with lower levels of toxicity towards normal (RPE1) underwent further assessment using a three-dimensional model (3D). The extract's effects were investigated through multiple assays including apoptosis induction using quantifying cleaved cytokeratin-18 (CK18) and DNA fragmentation. Additionally, the expression of Bcl-2 and Bax was quantitative using real-time PCR. The median lethal dose (LD50) was determined by the acute oral toxicity, while biomarkers associated with tumorigenesis, metastasis, and cell death were quantified by ELISA.

RESULTS

Limoniastrum monopetalum and Bauhinia variegata exhibited the most potent antitumor efficacy among the investigated extracts. They demonstrated potent cytotoxicity against MCF7 with no significant effect on hTERT RPE-1, with an IC50 of 100 μM. The extract demonstrated effectiveness in killing cancer cells within 3D tumor-like structures, induced apoptosis through caspase-3 activation and cleavage of cytokeratin-18, up-regulated the tumor suppressor p53, down-regulated the anti-apoptotic Bcl-2 gene, and caused DNA fragmentation. Acute oral toxicity studies in mice indicated low toxicity, and in a syngeneic mouse tumor model, the extract significantly inhibited tumor growth, suggesting its potential for further development.

CONCLUSION

Limoniastrum monopetalum and Bauhinia variegata exhibited the most potent antitumor efficacy among the investigated extracts.

The impact of tumor microenvironment: unraveling the role of physical cues in breast cancer progression

Metastasis accounts for the vast majority of breast cancer-related fatalities. Although the contribution of genetic and epigenetic modifications to breast cancer progression has been widely acknowledged, emerging evidence underscores the pivotal role of physical stimuli in driving breast cancer metastasis. In this review, we summarize the changes in the mechanics of the breast cancer microenvironment and describe the various forces that impact migrating and circulating tumor cells throughout the metastatic process. We also discuss the mechanosensing and mechanotransducing molecules responsible for promoting the malignant phenotype in breast cancer cells. Gaining a comprehensive understanding of the mechanobiology of breast cancer carries substantial potential to propel progress in prognosis, diagnosis, and patient treatment.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Dipeptidyl-peptidase 9 regulates the dynamics of tumorigenesis and metastasis in breast cancer

The cytosolic dipeptidyl-aminopeptidase 9 (DPP9) cleaves protein N-termini post-proline or -alanine. Our analysis of DPP9 mRNA expression from the TCGA 'breast cancer' data set revealed that low/intermediate DPP9 levels are associated with poor overall survival of breast cancer patients. To unravel the impact of DPP9 on breast cancer development and progression, the transgenic MMTV-PyMT mouse model of metastasizing breast cancer was used. In addition, tissue- and time-controlled genetic deletion of DPP9 by the Cre-loxP recombination system was done. Despite a delay of tumor onset, a higher number of lung metastases were measured in DPP9-deficient mice compared to controls. In human mammary epithelial cells with oncogenic RAS pathway activation, DPP9 deficiency delayed tumorigenic transformation and accelerated TGF-β1 induced epithelial-to-mesenchymal transition (EMT) of spheroids. For further analysis of the mechanism, primary breast tumor cells were isolated from the MMTV-PyMT model. DPP9 deficiency in these cells caused cancer cell migration and invasion accompanied by EMT. In absence of DPP9, the EMT transcription factor ZEB1 was stabilized due to insufficient degradation by the proteasome. In summary, low expression of DPP9 appears to decelerate mammary tumorigenesis but favors EMT and metastasis, which establishes DPP9 as a novel dynamic regulator of breast cancer initiation and progression.

Searching for the "Holy Grail" of breast cancer recurrence risk: a narrative review of the hunt for a better biomarker and the promise of circulating tumor DNA (ctDNA)

BACKGROUND

This paper is a narrative review of a major clinical challenge at the heart of breast cancer care: determining which patients are at risk of recurrence, which require systemic therapy, and which remain at risk in the survivorship phase of care despite initial therapy.

METHODS

We review the literature on prognostic and predictive biomarkers in breast cancer with a focus on detection of minimal residual disease.

RESULTS

While we have many tools to estimate and refine risk that are used to individualize local and systemic therapy, we know that we continue to over treat many patients and undertreat others. Many patients also experience what is, at least in hindsight, needless fear of recurrence. In this review, we frame this dilemma for the practicing breast oncologist and discuss the search for what we term the "holy grail" of breast cancer evaluation: the ideal biomarker of residual distant disease. We review the history of attempts to address this problem and the up-to-date science on biomarkers, circulating tumor cells and circulating tumor DNA (ctDNA).

CONCLUSION

This review suggests that the emerging promise of ctDNA may help resolve a crticical dilemma at the heart of breast cancer care, and improve prognostication, treatment selection, and outcomes for patients with breast cancer.

Priming with oncolytic adenovirus followed by anti-PD-1 and paclitaxel treatment leads to improved anti-cancer efficacy in the 3D TNBC model

Triple-negative breast cancer (TNBC) is considered one of the most incurable malignancies due to its clinical characteristics, including high invasiveness, high metastatic potential, proneness to relapse, and poor prognosis. Therefore, it remains a critical unmet medical need. On the other hand, poor delivery efficiency continues to reduce the efficacy of anti-cancer therapeutics developed against solid tumours using various strategies, such as genetically engineered oncolytic vectors used as nanocarriers. The study was designed to evaluate the anti-tumour efficacy of a novel combinatorial therapy based on oncolytic adenovirus AdV5/3-D24-ICOSL-CD40L with an anti-PD-1 (pembrolizumab) and paclitaxel (PTX). Here, we first tested the antineoplastic effect in two-dimensional (2D) and three-dimensional (3D) breast cancer models in MDA-MB-231, MDA-MB-468 and MCF-7 cells. Then, to further evaluate the efficacy of combinatorial therapy, including immunological aspects, we established a three-dimensional (3D) co-culture model based on MDA-MB-231 cells with peripheral blood mononuclear cells (PBMCs) to create an integrated system that more closely mimics the complexity of the tumour microenvironment and interacts with the immune system. Treatment with OV as a priming agent, followed by pembrolizumab and then paclitaxel, was the most effective in reducing the tumour volume in TNBC co-cultured spheroids. Further, T-cell phenotyping analyses revealed significantly increased infiltration of CD8+, CD4+ T and Tregs cells. Moreover, the observed anti-tumour effects positively correlated with the level of CD4+ T cell infiltrates, suggesting the development of anti-cancer immunity. Our study demonstrated that combining different immunotherapeutic agents (virus, pembrolizumab) with PTX reduced the tumour volume of the TNBC co-cultured spheroids compared to relevant controls. Importantly, sequential administration of the investigational agents (priming with the vector) further enhanced the anti-cancer efficacy in 3D culture over other groups tested. Taken together, these results support further evaluation of the virus in combination with anti-PD-1 and PTX for the treatment of triple-negative breast cancer patients. Importantly, further studies with in vivo models should be conducted to better understand the translational aspects of tested therapy.

Molecular Pathology of Breast Tumors: Diagnostic and Actionable Genetic Alterations

Breast cancer is a heterogenous disease with various histologic subtypes, molecular profiles, behaviors, and response to therapy. After the histologic assessment and diagnosis of an invasive breast carcinoma, the use of biomarkers, multigene expression assays and mutation profiling may be used. With improved molecular assays, the identification of somatic genetic alterations in key oncogenes and tumor suppressor genes are playing an increasingly important role in many areas of breast cancer care. This review summarizes the most clinically significant somatic alterations in breast tumors and how this information is used to facilitate diagnosis, provide potential treatment options, and identify mechanisms of resistance.

Downregulated CDH3 is correlated with a better prognosis for LUAD and decreases proliferation and migration of lung cancer cells

BACKGROUND

CDH3 is a glycoprotein with a single-span transmembrane domain that mediates cell-to-cell adhesion. Abnormal expression of CDH3 is associated with a poor prognosis in patients with breast, thyroid, colorectal carcinomas and glioblastoma. Soluble CDH3 in pleural effusions can be used as a marker for real-time monitoring of resistance to first- and second-generation EGFR-TKIs. The CDH3 mechanism underlying lung adenocarcinomas (LUADs) has not been established.

OBJECTIVE

This study analyzed the correlation between CDH3 expression and lung cancer prognosis and the effect of down-regulation CDH3 expression on the proliferation and migration of lung cancer cells.

METHODS

CDH3 expression was studied using the Oncomine, TIMER, PanglaoDB, and GEPIA databases. The effect of CDH3 on clinical prognosis was assessed with GEPIA, the PrognoScan database, and Kaplan-Meier plotter. The relationship between CDH3 to immune infiltrating cells was explored using TIMER and TISIDB. The function of CDH3 in lung cancer cell lines was determined by CCK-8 and wound healing assays in vitro. Furthermore, RNA sequencing was used to identify key signaling pathways and differentially-expressed genes.

RESULTS

LUAD tissues had higher CDH3 expression compared with normal tissues and were associated with worse overall survival in patients with LUAD. CDH3 expression had positive associations with infiltration of CD4 + T cells, Tregs and exhausted T cells, but negative associations with infiltration of B cells in patients with LUAD. CCK-8 and wound healing assays revealed that downregulation of CDH3 inhibited the proliferation and migration of cells. KEGG analysis revealed that the TGF-beta signaling pathways were demonstrated to be enriched pathways for genes negatively regulated by knockdown of CDH3.

CONCLUSION

CDH3 expression affects proliferation and migration of lung cancer cells and might serve as a potential prognostic marker in LUAD patients.

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity

PURPOSE

Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC.

METHODS

We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo.

RESULTS

OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors.

CONCLUSIONS

The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Exploring novel protein-based biomarkers for advancing breast cancer diagnosis: A review

This review provides a contemporary examination of the evolving landscape of breast cancer (BC) diagnosis, focusing on the pivotal role of novel protein-based biomarkers. The overview begins by elucidating the multifaceted nature of BC, exploring its prevalence, subtypes, and clinical complexities. A critical emphasis is placed on the transformative impact of proteomics, dissecting the proteome to unravel the molecular intricacies of BC. Navigating through various sources of samples crucial for biomarker investigations, the review underscores the significance of robust sample processing methods and their validation in ensuring reliable outcomes. The central theme of the review revolves around the identification and evaluation of novel protein-based biomarkers. Cutting-edge discoveries are summarised, shedding light on emerging biomarkers poised for clinical application. Nevertheless, the review candidly addresses the challenges inherent in biomarker discovery, including issues of standardisation, reproducibility, and the complex heterogeneity of BC. The future direction section envisions innovative strategies and technologies to overcome existing challenges. In conclusion, the review summarises the current state of BC biomarker research, offering insights into the intricacies of proteomic investigations. As precision medicine gains momentum, the integration of novel protein-based biomarkers emerges as a promising avenue for enhancing the accuracy and efficacy of BC diagnosis. This review serves as a compass for researchers and clinicians navigating the evolving landscape of BC biomarker discovery, guiding them toward transformative advancements in diagnostic precision and personalised patient care.

Identification of modules and key genes associated with breast cancer subtypes through network analysis

Breast cancer is the most common malignancy in women around the world. Intratumor and intertumoral heterogeneity persist in mammary tumors. Therefore, the identification of biomarkers is essential for the treatment of this malignancy. This study analyzed 28,143 genes expressed in 49 breast cancer cell lines using a Weighted Gene Co-expression Network Analysis to determine specific target proteins for Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes. Sixty-five modules were identified, of which five were characterized as having a high correlation with breast cancer subtypes. Genes overexpressed in the tumor were found to participate in the following mechanisms: regulation of the apoptotic process, transcriptional regulation, angiogenesis, signaling, and cellular survival. In particular, we identified the following genes, considered as hubs: IFIT3, an inhibitor of viral and cellular processes; ETS1, a transcription factor involved in cell death and tumorigenesis; ENSG00000259723 lncRNA, expressed in cancers; AL033519.3, a hypothetical gene; and TMEM86A, important for regulating keratinocyte membrane properties, considered as a key in Basal A, Basal B, Luminal A, Luminal B, and HER2 ampl breast cancer subtypes, respectively. The modules and genes identified in this work can be used to identify possible biomarkers or therapeutic targets in different breast cancer subtypes.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Breast cancer in northern Peru: molecular subtypes and HER2 low

This study aimed to understand the immunohistochemical profile of breast cancer and to identify the HER2 low subgroup in the northern macro-region of Peru. A cross-sectional study was conducted in 1176 patients from the Regional Institute of Neoplastic Diseases Northern Peru, from January 2016 to December 2023. We analyzed the data (age, histological type, grade and complementary results), with frequencies and percentages. The profile corresponded to: luminal B (45.6%); luminal A (24.7%); triple negative (18.2%); and HER2 positive non luminal (11.5%). In addition, 215 patients presented HER2 low (25.1% of those previously considered negative). This study provides evidence that the subtyping of breast cancer has changed, being luminal B the most frequent. It is essential to involve health policies to acquire targeted therapies considering HER2 low patients. Motivation for the study. Molecular classification of breast cancer allows the use of targeted treatments. Information on this profile in the northern macroregion of Peru is unknown. In addition, new therapies have appeared for a subgroup of patients. Main findings. In this study, the most frequent molecular subtypes were: luminal B, luminal A, triple negative and non-luminal HER2. Also, 18.3% of patients had low HER2 expression. Implications. Health policies should be aligned with scientific advances, to guarantee targeted therapies and to update the information in health manuals or protocols.

Age-related clusters and favorable immune phenotypes in breast cancer of the young

Breast cancer (BC) patients aged below 40 years at diagnosis experience aggressive disease and poorer survival compared to women diagnosed at 40-49 years, but the age-related biology is described to little extent. Here, we explored transcriptional alterations in BC to gain better understanding of age-related tumor biology. We studied a subset of the Bergen in-house cohort (n=127, age range 26-49) and used the NanoString Breast Cancer 360 expression panel on formalin-fixed paraffin-embedded breast cancer tissue, and publicly available global breast cancer mRNA expression data (n=204, age range 22-49), to explore differentially expressed genes between the young (age <40) and older (age 40-49) patients. Unsupervised hierarchical clustering was applied to identify gene expression-based patient clusters. We applied established computational approaches to define the PAM50 subtypes, Risk of recurrence scores (ROR), and Risk groups, and to infer the proportions of 22 immune cell types from bulk gene expression profiles of patients aged <50 at breast cancer diagnosis. Differentially expressed genes and gene sets were investigated using OncoEnrichR and g:Profiler to describe functional profiles and pathway enrichment. We identified four age-related patient clusters presenting distinct characteristics of PAM50 subtypes and ROR profiles, which demonstrated independent prognostic value when adjusted for traditional clinico-pathologic variables and the known molecular subtypes. Our findings showed better survival than expected in the basal-enriched cluster 2, and in triple negative and basal-like BC. Deconvolution analyses of immunophenotypes indicated higher levels of M0 and M1 macrophages than M2 macrophages in subsets of young BC. Our approach identifies age-based patient clusters with distinct clinico-pathologic profiles, to a large extent overlapping with the PAM50 subtypes, although with independent prognostic value in multivariate survival analyses. The patient clusters provided new insight in the immune cell distribution across tumor subtypes, potentially contributing to survival differences between the clusters and the molecular subtypes and indicating age-related mechanisms improving outcome. Our study confirms the applicability of ROR as a valid prognosticator also in a young breast cancer cohort.

Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification

The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

Extracellular vesicle-mediated drug delivery in breast cancer theranostics

Breast cancer (BC) continues to be a significant global challenge due to drug resistance and severe side effects. The increasing prevalence is alarming, requiring new therapeutic approaches to address these challenges. At this point, Extracellular vesicles (EVs), specifically small endosome-released nanometer-sized EVs (SEVs) or exosomes, have been explored by literature as potential theranostics. Therefore, this review aims to highlight the therapeutic potential of exosomes in BC, focusing on their advantages in drug delivery and their ability to mitigate metastasis. Following the review, we identified exosomes' potential in combination therapies, serving as miRNA carriers and contributing to improved anti-tumor effects. This is evident in clinical trials investigating exosomes in BC, which have shown their ability to boost chemotherapy efficacy by delivering drugs like paclitaxel (PTX) and doxorubicin (DOX). However, the translation of EVs into BC therapy is hindered by various challenges. These challenges include the heterogeneity of EVs, the selection of the appropriate parent cell, the loading procedures, and determining the optimal administration routes. Despite the promising therapeutic potential of EVs, these obstacles must be addressed to realize their benefits in BC treatment.

A CARM1 Inhibitor Potently Suppresses Breast Cancer Both In Vitro and In Vivo

CARM1, belonging to the protein arginine methyltransferase (PRMT) family, is intricately associated with the progression of cancer and is viewed as a promising target for both cancer diagnosis and therapy. However, the number of specific and potent CARM1 inhibitors is limited. We herein discovered a CARM1 inhibitor, iCARM1, that showed better specificity and activity toward CARM1 compared to the known CARM1 inhibitors, EZM2302 and TP-064. Similar to CARM1 knockdown, iCARM1 suppressed the expression of oncogenic estrogen/ERα-target genes, whereas activated type I interferon (IFN) and IFN-induced genes (ISGs) in breast cancer cells. Consequently, iCARM1 potently suppressed breast cancer cell growth both in vitro and in vivo. The combination of iCARM1 with either endocrine therapy drugs or etoposide demonstrated synergistic effects in inhibiting the growth of breast tumors. In summary, targeting CARM1 by iCARM1 effectively suppresses breast tumor growth, offering a promising therapeutic approach for managing breast cancers in clinical settings.

Autoantibodies against Endophilin A2 as a novel biomarker are beneficial to early diagnosis of breast cancer

BACKGROUND

Due to the lack of early symptoms, breast cancer is frequently overlooked, leading to distant metastases and multi-organ lesions that directly threaten patients' lives. We have identified a novel tumor marker, antibodies to endophilin A2 (EA2), to improve early diagnosis of breast cancer.

METHODS

Antibody levels of EA2 were analyzed in sera of patients with cancers of different origins and stages by indirect enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy and reference range were determined by the area under the receiver operating curve and distribution curve. The levels of EA2 antigen in sera were determined by sandwich ELISA.

RESULTS

The levels of antibodies against EA2 were higher in sera of patients with breast cancer (P < 0.0001), liver cancer (P = 0.0005), gastric cancer (P = 0.0026), and colon cancer (P = 0.0349) than those in healthy controls, but not in patients with rectal cancer (P = 0.1151), leukemia (P = 0.7508), or lung cancer (P = 0.2247). The highest diagnostic value was for breast cancer, particularly in early cases (AUC = 0.8014) and those with distant metastases (AUC = 0.7885). The titers of EA2 antibodies in sera were correlated with levels of EA2 antigen in breast cancer patients.

CONCLUSION

Antibodies to EA2 are novel blood biomarkers for early diagnosis of breast cancer that warrants further study in larger-scale cohort studies.

Association Between Neighborhood Opportunity, Allostatic Load, and All-Cause Mortality in Patients With Breast Cancer

PURPOSE

Adverse neighborhood contextual factors may affect breast cancer outcomes through environmental, psychosocial, and biological pathways. The objective of this study is to examine the relationship between allostatic load (AL), neighborhood opportunity, and all-cause mortality among patients with breast cancer.

METHODS

Women age 18 years and older with newly diagnosed stage I-III breast cancer who received surgical treatment between January 1, 2012, and December 31, 2020, at a National Cancer Institute Comprehensive Cancer Center were identified. Neighborhood opportunity was operationalized using the 2014-2018 Ohio Opportunity Index (OOI), a composite measure derived from neighborhood level transportation, education, employment, health, housing, crime, and environment. Logistic and Cox regression models tested associations between the OOI, AL, and all-cause mortality.

RESULTS

The study cohort included 4,089 patients. Residence in neighborhoods with low OOI was associated with high AL (adjusted odds ratio, 1.21 [95% CI, 1.05 to 1.40]). On adjusted analysis, low OOI was associated with greater risk of all-cause mortality (adjusted hazard ratio [aHR], 1.45 [95% CI, 1.11 to 1.89]). Relative to the highest (99th percentile) level of opportunity, risk of all-cause mortality steeply increased up to the 70th percentile, at which point the rate of increase plateaued. There was no interaction between the composite OOI and AL on all-cause mortality (P = .12). However, there was a higher mortality risk among patients with high AL residing in lower-opportunity environments (aHR, 1.96), but not in higher-opportunity environments (aHR, 1.02; P interaction = .02).

CONCLUSION

Lower neighborhood opportunity was associated with higher AL and greater risk of all-cause mortality among patients with breast cancer. Additionally, environmental factors and AL interacted to influence all-cause mortality. Future studies should focus on interventions at the neighborhood and individual level to address socioeconomically based disparities in breast cancer.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

A PET-Surrogate Signature for the Interrogation of the Metabolic Status of Breast Cancers

Metabolic alterations in cancers can be exploited for diagnostic, prognostic, and therapeutic purposes. This is exemplified by 18F-fluorodeoxyglucose (FDG)-positron emission tomography (FDG-PET), an imaging tool that relies on enhanced glucose uptake by tumors for diagnosis and staging. By performing transcriptomic analysis of breast cancer (BC) samples from patients stratified by FDG-PET, a 54-gene signature (PETsign) is identified that recapitulates FDG uptake. PETsign is independently prognostic of clinical outcome in luminal BCs, the most common and heterogeneous BC molecular subtype, which requires improved stratification criteria to guide therapeutic decision-making. The prognostic power of PETsign is stable across independent BC cohorts and disease stages including the earliest BC stage, arguing that PETsign is an ab initio metabolic signature. Transcriptomic and metabolomic analysis of BC cells reveals that PETsign predicts enhanced glycolytic dependence and reduced reliance on fatty acid oxidation. Moreover, coamplification of PETsign genes occurs frequently in BC arguing for their causal role in pathogenesis. CXCL8 and EGFR signaling pathways feature strongly in PETsign, and their activation in BC cells causes a shift toward a glycolytic phenotype. Thus, PETsign serves as a molecular surrogate for FDG-PET that could inform clinical management strategies for BC patients.

Benign breast tumors may arise on different immunological backgrounds

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

The evolving landscape of metastatic HER2-positive, hormone receptor-positive Breast Cancer

Therapeutic agents targeting Human Epidermal Growth Factor Receptor 2 (HER2) demonstrated to positively impact the prognosis of HER2-positive breast cancer. HER2-positive breast cancer can present either as hormone receptor-negative or positive, defining Triple-positive breast cancer (TPBC). TPBC demonstrate unique gene expression profiles, showing reduced HER2-driven gene expression, as recapitulated by a higher proportion of Luminal-type intrinsic subtypes. The different molecular landscape of TPBC dictates distinctive clinical features, including reduced chemotherapy sensitivity, different patterns of recurrence, and better overall prognosis. Cross-talk between HER2 and hormone receptor signaling seems to be critical to determine resistance to HER2-directed agents. Accordingly, superior outcomes have been achieved with the use of endocrine therapy, representing the first subtype-specific pharmacological intervention unique to this subgroup. Additional targeted agents capable to tackle resistance mechanisms to anti-HER2, hormone agents, or both might further improve the efficacy of treatments, such as PI3K/AKT/mTOR inhibitors, particularly in a biomarker-enriched setting, and CDK4/6-inhibitors, with preliminary data suggesting a role of PAM50 subtyping to predict higher benefits in luminal tumors. Finally, the distinct biology of triple-positive tumors may yield the rationale for considering combinations within antibody-drug conjugate regimens. Accordingly, in this review, we summarized the current evidence and rationale for considering TPBC as a different entity, in which distinct therapeutical approaches leveraging on the different biological profile of TPBC may result in superior anticancer regimens and improved patient-centric outcomes.

Landscape of HER2-low breast cancer: Insights from a six-year study on prevalence and clinicopathological characteristics

Human epidermal growth factor receptor 2 (HER2)-low breast cancer has emerged as a subtype of breast cancer, defined by HER2 1+/2+ in immunohistochemistry (IHC) and absence of ERBB2 gene amplification on fluorescence in situ hybridization (FISH). Recent trials showed marked response of HER2-low breast cancer to novel anti-HER2 antibody-drug-conjugates. Data on characteristics of HER2-low breast cancer subtype is limited. Real-world data from the Anatomic Pathology Department of Hotel-Dieu de France, spanning 2017-2023, was retrospectively collected. HER2-positive patients were excluded to compare HER2-low to HER2-zero breast cancer subtypes. Clinicopathological characteristics between the groups were compared using a Chi-Squared test. Out of 1195 patients, we observed 341 (28.5 %) HER2-low breast cancers cases. HER2-positive breast cancer cases (n = 178; 14.9 %) were excluded. There was no significant difference in age and sex between HER2-low and HER2-zero group (p = 0.33 and 0.79, respectively). HER2-low breast cancer was associated with positive estrogen receptor status and positive progesterone receptor status (p < 0.001 and p = 0.01, respectively). Ductal adenocarcinomas were more commonly observed in HER2-low group (p < 0.001). When stratified by hormone (HR) status, 87.4 % of patients had HR-positive status and 12.6 % were HR-negative. Among the HR-negative group, HER2-low tumors tended to show lower proliferation index compared to HER2-zero tumors (25%vs.10 %, p = 0.04). This study showed that HER2-low is distinct from HER2-zero and is common among patients with breast cancer. Clinicopathological features such as histological type differ between HER2-zero and HER2-low breast cancer. Within HR-negative breast cancer, those with low HER2 expression exhibit a less aggressive profile compared to HER2-zero tumors.

Effects of Garlic on Breast Tumor Cells with a Triple Negative Phenotype: Peculiar Subtype-Dependent Down-Modulation of Akt Signaling

Breast cancer includes tumor subgroups with morphological, molecular, and clinical differences. Intrinsic heterogeneity especially characterizes breast tumors with a triple negative phenotype, often leading to the failure of even the most advanced therapeutic strategies. To improve breast cancer treatment, the use of natural agents to integrate conventional therapies is the subject of ever-increasing attention. In this context, garlic (Allium sativum) shows anti-cancerous potential, interfering with the proliferation, motility, and malignant progression of both non-invasive and invasive breast tumor cells. As heterogeneity could be at the basis of variable effects, the main objective of our study was to evaluate the anti-tumoral activity of a garlic extract in breast cancer cells with a triple negative phenotype. Established triple negative breast cancer (TNBC) cell lines from patient-derived xenografts (PDXs) were used, revealing subtype-dependent effects on morphology, cell cycle, and invasive potential, correlated with the peculiar down-modulation of Akt signaling, a crucial regulator in solid tumors. Our results first demonstrate that the effects of garlic on TNBC breast cancer are not unique and suggest that only more precise knowledge of the mechanisms activated by this natural compound in each tumor will allow for the inclusion of garlic in personalized therapeutic approaches to breast cancer.

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Effects of hormone receptor status on patient clinic and survival in HER2 positive breast cancer

BACKGROUND

In the current study, the effect of hormone receptor (HR) status on clinical and survival in early-stage human epidermal growth factor receptor 2 (HER2)-positive breast cancer was investigated.

METHODS

Two hundred ninety-one patients with HER2- positive were examined in two categories as HR-positive and HR-negative.

RESULTS

Of these, 197 (68%) were HR-positive and 94 (32%) were HR-negative with a mean follow-up period of 68 ± 2.7 months. The groups were found to be similar in terms of age, menopausal status, comorbidity, pathologic type, stage, T stage, N stage, lymphovascular invasion, presence and percentage of intraductal component, multicentricity/focality and extracapsular invasion. Family history (P = 0.038), stage 2 tumor rate (P < 0.001), and perineural invasion (P = 0.005) were significantly higher in the HR-positive group. In the HR-negative group, mean Ki-67 value (P = 0.014), stage 3 tumor rate (P < 0.001), tumor necrosis (P = 0.004) and strong (3+) HER2 staining on immunohistochemical staining (P = 0.003) were higher. The incidence of relapse and metastasis, and the localization of metastasis were similar in both patient groups. The rate of locoregional relapse during the first 2 years was higher in the HR-negative patients than in the HR-positive patients (P = 0.023). Overall survival (OS) and disease-free survival (DFS) did not differ between the groups in univariate analysis. However, HR status was determined as an independent prognostic factor (HR: 2.11, 95% CI: 1.17-3.79; P = 0.012) for OS was not found to be significant for DFS in multivariate analysis.

CONCLUSION

Both clinicopathologic features and OS outcomes of HR-negative patients were worse than those of HR-positive patients.

Overcoming retinoic acid resistance in HER2-enriched breast cancers: role of MYC

HER2-enriched (HER2+) breast cancers express high levels of the growth-promoting HER2 protein. Although these cancers are treated with the HER2-targeted drug, trastuzumab, resistance to treatment is common. Retinoic acid (RA) is an anti-cancer agent that has been successfully used for the treatment of leukemia and holds promise for the treatment of solid cancers, including breast cancer. The HER2 gene is frequently co-amplified with RARA, a key determinant of RA sensitivity in breast cancers. It seems surprising, therefore, that HER2+ breast cancers are refractory to RA treatment. Here, we show that MYC mediates RA resistance by suppressing the expression of cellular retinoic acid binding protein 2 (CRABP2), resulting in RARα inactivation. CRABP2 is an intracellular RA transporter that delivers RA to the nuclear receptor RARα for its activation. Our results indicate that response to RA is enhanced by MYC depletion in HER2+ breast cancer cells and that RA treatment enhances trastuzumab responsiveness. Our findings support the use of RA and trastuzumab for the treatment of subsets of patients with breast cancers that are HER2-RARα co-amplified and have low levels of MYC.

CD44v5 domain regulates crosstalk between TNBC cells and tumor-associated macrophages by enhancing the IL-4R/STAT3 axis

Triple-negative breast cancer (TNBC) has greater infiltration of M2-like macrophages (TAMs), which enhances cancer cell invasion and leads to a poor prognosis. TNBC progression is mediated by both tumor cells and the tumor microenvironment (TME). Here we elucidate the mechanism of the interaction between TNBC cells and TAMs. In this study, we confirmed that CD44v5 is highly expressed in TNBC, which drives TNBC cell metastasis and promotes TAM polarization by co-localizing with IL4Rα and inhibiting its internalization and degradation, thereby promoting activation of the STAT3/IL6 signaling axis. At the same time, TAMs also facilitate TNBC cell metastasis by secreting IL-4, IL-6, and other cytokines, in which the IL-4/IL-4R/STAT3/IL-6 signaling axis plays the same role for TNBC cells responding to TAMs. Moreover, we found that the above progress could be suppressed when the CD44v5 domain was blocked. We demonstrated that the CD44v5/IL-4R/STAT3/IL-6 signaling pathway plays a key role in TNBC cell metastasis, and in TNBC cells inducing TAM polarization and responding to TAMs, promoting metastasis. Collectively, we suggest that the CD44v5 domain may be a promising target for regulating the TME of TNBC as well as treating TNBC.

Spatial Biology of Breast Cancer

Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.

Diffsig: Associating Risk Factors with Mutational Signatures

BACKGROUND

Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors.

METHODS

Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations.

RESULTS

We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts.

CONCLUSIONS

Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures.

IMPACT

We expect Diffsig to provide more robust associations of risk factors with signatures to lead to better understanding of the tumor development process and improved models of tumorigenesis.

Influence of tumor microenvironment on the different breast cancer subtypes and applied therapies

Despite the significant improvements made in breast cancer therapy during the last decades, this disease still has increasing incidence and mortality rates. Different targets involved in general processes, like cell proliferation and survival, have become alternative therapeutic options for this disease, with some of them already used in clinic, like the CDK4/6 inhibitors for luminal A tumors treatment. Nevertheless, there is a demand for novel therapeutic strategies focused not only on tumor cells, but also on their microenvironment. Tumor microenvironment (TME) is a very complex and dynamic system that, more than surrounding and supporting tumor cells, actively participates in tumor development and progression. During the last decades, it has become clear that the cellular and acellular components of TME differ between the various breast cancer subtypes and shape the differences regarding their severity and prognosis. The pivotal role of the TME in controlling tumor growth and influencing responses to therapy represents a potential source for novel targets and therapeutic strategies. In this review, we present a description of the multiple therapeutic options used for different breast cancer subtypes, as well as the influence that the TME may exert on the development of the disease and on the response to the distinct therapies, which in some cases may explain their failure by the occurrence of relapses and resistance. Furthermore, the ongoing studies focused on the use of TME components for developing potential cancer treatments are described.

Critical role of miR-21/exosomal miR-21 in autophagy pathway

Activation of autophagy, a process of cellular stress response, leads to the breakdown of proteins, organelles, and other parts of the cell in lysosomes, and can be linked to several ailments, such as cancer, neurological diseases, and rare hereditary syndromes. Thus, its regulation is very carefully monitored. Transcriptional and post-translational mechanisms domestically or in whole organisms utilized to control the autophagic activity, have been heavily researched. In modern times, microRNAs (miRNAs) are being considered to have a part in post-translational orchestration of the autophagic activity, with miR-21 as one of the best studied miRNAs, it is often more than expressed in cancer cells. This regulatory RNA is thought to play a major role in a plethora of processes and illnesses including growth, cancer, cardiovascular disease, and inflammation. Different studies have suggested that a few autophagy-oriented genes, such as PTEN, Rab11a, Atg12, SIPA1L2, and ATG5, are all targeted by miR-21, indicating its essential role in the regulation.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Activation of Bivalent Gene POU4F1 Promotes and Maintains Basal-like Breast Cancer

Basal-like breast cancer (BLBC) is the most aggressive molecular subtype of breast cancer with worse prognosis and fewer treatment options. The underlying mechanisms upon BLBC transcriptional dysregulation and its upstream transcription factors (TFs) remain unclear. Here, among the hyperactive candidate TFs of BLBC identified by bioinformatic analysis, POU4F1 is uniquely upregulated in BLBC and is associated with poor prognosis. POU4F1 is necessary for the tumor growth and malignant phenotypes of BLBC through regulating G1/S transition by direct binding at the promoter of CDK2 and CCND1. More importantly, POU4F1 maintains BLBC identity by repressing ERα expression through CDK2-mediated EZH2 phosphorylation and subsequent H3K27me3 modification in ESR1 promoter. Knocking out POU4F1 in BLBC cells reactivates functional ERα expression, rendering BLBC sensitive to tamoxifen treatment. In-depth epigenetic analysis reveals that the subtype-specific re-configuration and activation of the bivalent chromatin in the POU4F1 promoter contributes to its unique expression in BLBC, which is maintained by DNA demethylase TET1. Together, these results reveal a subtype-specific epigenetically activated TF with critical role in promoting and maintaining BLBC, suggesting that POU4F1 is a potential therapeutic target for BLBC.

Radiation therapy for triple-negative breast cancer: from molecular insights to clinical perspectives

INTRODUCTION

Triple-negative breast cancer (TNBC) lacks three common receptors, making traditional treatments less effective. This review highlights the importance of radiotherapy and emerging therapeutic strategies to enhance treatment outcomes in TNBC.

AREAS COVERED

We conducted a literature search on PubMed for publications from 2000 to 2023 to discuss the critical role of radiotherapy in managing TNBC, emphasizing its applications from locoregional control to improving survival rates. The review explores molecular mechanisms underlying TNBC's radiotherapy response, including DNA damage repair and apoptosis, with a focus on BRCA1/2 mutations and Poly (ADP-ribose) polymerase (PARP) inhibition. We summarize preclinical and clinical research on radiosensitization strategies, from gene-targeted therapies to immunotherapy combinations, and the impact of post-mastectomy radiation therapy on locoregional control. The potential of personalized treatment approaches, integrating molecular profiling, targeted radiosensitizers, and the synergistic effects of radiotherapy with immunotherapy, is also discussed.

EXPERT OPINION

Future TNBC treatment strategies should focus on precision medicine, integrating immunotherapy, developing novel radiosensitizers, and targeting biological pathways to overcome radioresistance. The integration of radiomics and artificial intelligence offers promising avenues for enhancing treatment personalization and efficacy, aiming to improve patient outcomes in TNBC.

Real-World Data Analysis of CDK4/6 Inhibitor Therapy-A Patient-Centric Single Center Study

BACKGROUND

The quest to comprehend the real-world efficacy of CDK4/6 inhibitors (CDKis) in breast cancer continues, as patient responses vary significantly.

METHODS

This single-center retrospective study evaluated CDKi use outside the trial condition from November 2016 to May 2020. Progression-free survival (PFS), time-to-treatment failure (TTF), short-term and prolonged treatment benefit (≥4 and ≥10 months), as well as prognostic and predictive markers were assessed with Kaplan-Meier and multivariate regression analyses.

RESULTS

Out of 86 identified patients, 58 (67.4%) had treatment failure of which 40 (46.5%) were due to progression. Median PFS and TTF were 12 and 8.5 months, respectively. A total of 57 (66.3%) and 42 (48.8%) patients experienced short-term and prolonged treatment benefit. Independent, significant predictors for PFS were progesterone receptor expression (HR: 0.88), multiple metastatic sites (HR: 2.56), and hepatic metastasis (HR: 2.01). Significant predictors for TTF were PR expression (HR: 0.86), multiple sites (HR: 3.29), adverse events (HR: 2.35), and diabetes (HR: 2.88). Aside from tumor biology and adverse events, treatment modifications like pausing and switching of CDKi were predictive for short-term (OR: 6.73) and prolonged (OR: 14.27) therapeutic benefit, respectively.

CONCLUSIONS

These findings emphasize the importance of tailored treatment strategies, highlighting the role of PR expression, metastatic burden, and therapeutic adjustments in optimizing patient outcomes in real-world breast cancer management.

Epigenetic mechanisms of cancer progression and therapy resistance in estrogen-receptor (ER+) breast cancer

Estrogen receptor-positive (ER+) breast cancer is the most frequent breast cancer subtype. Agents targeting the ER signaling pathway have been successful in reducing mortality from breast cancer for decades. However, mechanisms of resistance to these treatments arise, especially in the metastatic setting. Recently, it has been recognized that epigenetic dysregulation is a common feature that facilitates the acquisition of cancer hallmarks across cancer types, including ER+ breast cancer. Alterations in epigenetic regulators and transcription factors (TF) coupled with changes to the chromatin landscape have been found to orchestrate breast oncogenesis, metastasis, and the development of a resistant phenotype. Here, we review recent advances in our understanding of how the epigenome dictates breast cancer tumorigenesis and resistance to targeted therapies and discuss novel therapeutic interventions for overcoming resistance.

Molecular Imaging of Steroid Receptors in Breast Cancer

ABSTRACT

Steroid receptors regulate gene expression for many important physiologic functions and pathologic processes. Receptors for estrogen, progesterone, and androgen have been extensively studied in breast cancer, and their expression provides prognostic information as well as targets for therapy. Noninvasive imaging utilizing positron emission tomography and radiolabeled ligands targeting these receptors can provide valuable insight into predicting treatment efficacy, staging whole-body disease burden, and identifying heterogeneity in receptor expression across different metastatic sites. This review provides an overview of steroid receptor imaging with a focus on breast cancer and radioligands for estrogen, progesterone, and androgen receptors.

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward

We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.

Cutting-edge approaches for targeted drug delivery in breast cancer: beyond conventional therapies

Breast cancer is a global health challenge with staggering statistics underscoring its pervasive impact. The burden of this disease is measured in terms of its prevalence and the challenges it poses to healthcare systems, necessitating a closer look at its epidemiology and impact. Current breast cancer treatments, including surgery, chemotherapy, radiation therapy, and targeted therapies, have made significant strides in improving patient outcomes. However, they are not without limitations, often leading to adverse effects and the development of drug resistance. This comprehensive review delves into the complex landscape of breast cancer, including its incidence, current treatment modalities, and the inherent limitations of existing therapeutic approaches. It also sheds light on the promising role of nanotechnology, encompassing both inorganic and organic nanoparticles equipped with the ability to selectively deliver therapeutic agents to tumor sites, in the battle against breast cancer. The review also addresses the emerging therapies, their associated challenges, and the future prospects of targeted drug delivery in breast cancer management.