Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis

Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC.

GABRP inhibits the progression of oesophageal cancer by regulating CFTR: Integrating bioinformatics analysis and experimental validation

Oesophageal cancer (EC) is a malignancy which accounts for a substantial number of cancer-related deaths worldwide. The molecular mechanisms underlying the pathogenesis of EC have not been fully elucidated. GSE17351 and GSE20347 data sets from the Gene Expression Omnibus (GEO) database were employed to screen differentially expressed genes (DEGs). Reverse transcription quantitative PCR (RT-qPCR) was used to examine hub gene expression. ECA-109 and TE-12 cells were transfected using the pcDNA3.1 expression vector encoding GABRP. The cell counting kit-8 (CCK-8), cell scratch and Transwell assays were performed to assess the effect of GABRP on EC cell proliferation, migration and invasion. Epithelial-mesenchymal transition (EMT)-associated protein levels were measured by Western blotting. Subsequently, CFTR was knocked down to verify whether GABRP affected biological events in EC cells by targeting CFTR. Seven hub genes were identified, including GABRP, FLG, ENAH, KLF4, CD24, ABLIM3 and ABLIM1, which all could be used as diagnostic biomarkers for EC. The RT-qPCR results indicated that the expression levels of GABRP, FLG, KLF4, CD24, ABLIM3 and ABLIM1 were downregulated, whereas the expression level of ENAH was upregulated. In vitro functional assays demonstrated that GABRP overexpression suppressed the proliferation, migration, invasion and EMT of EC cells. Mechanistically, GABRP promoted the expression of CFTR, and CFTR knockdown significantly counteracted the influence of GABRP overexpression on biological events in EC cells. Overexpression of GABRP inhibited EC progression by increasing CFTR expression, which might be a new target for EC treatment.

Predictive and prognostic biomarkers in breast tumours

In the age of precision medicine, extensive research has investigated tumour biomarkers to predict the behaviour of cancer and/or response to treatment in order to better understand the prognosis and treatment of disease. In breast cancer, significant progress has been made to categorise a common disease into subtypes defined by intrinsic tumour biology, measured by tumour biomarkers. This review encompasses the established biomarkers within breast cancer with the most up-to-date information regarding their understanding and clinical use as predictive and/or prognostic markers of breast cancer.

Surface Proteome of Extracellular Vesicles and Correlation Analysis Reveal Breast Cancer Biomarkers

Breast cancer (BC) is the second most frequently diagnosed cancer and accounts for approximately 25% of new cancer cases in Canadian women. Using biomarkers as a less-invasive BC diagnostic method is currently under investigation but is not ready for practical application in clinical settings. During the last decade, extracellular vesicles (EVs) have emerged as a promising source of biomarkers because they contain cancer-derived proteins, RNAs, and metabolites. In this study, EV proteins from small EVs (sEVs) and medium EVs (mEVs) were isolated from BC MDA-MB-231 and MCF7 and non-cancerous breast epithelial MCF10A cell lines and then analyzed by two approaches: global proteomic analysis and enrichment of EV surface proteins by Sulfo-NHS-SS-Biotin labeling. From the first approach, proteomic profiling identified 2459 proteins, which were subjected to comparative analysis and correlation network analysis. Twelve potential biomarker proteins were identified based on cell line-specific expression and filtered by their predicted co-localization with known EV marker proteins, CD63, CD9, and CD81. This approach resulted in the identification of 11 proteins, four of which were further investigated by Western blot analysis. The presence of transmembrane serine protease matriptase (ST14), claudin-3 (CLDN3), and integrin alpha-7 (ITGA7) in each cell line was validated by Western blot, revealing that ST14 and CLDN3 may be further explored as potential EV biomarkers for BC. The surface labeling approach enriched proteins that were not identified using the first approach. Ten potential BC biomarkers (Glutathione S-transferase P1 (GSTP1), Elongation factor 2 (EEF2), DEAD/H box RNA helicase (DDX10), progesterone receptor (PGR), Ras-related C3 botulinum toxin substrate 2 (RAC2), Disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), Aconitase 2 (ACO2), UTP20 small subunit processome component (UTP20), NEDD4 binding protein 2 (N4BP2), Programmed cell death 6 (PDCD6)) were selected from surface proteins commonly identified from MDA-MB-231 and MCF7, but not identified in MCF10A EVs. In total, 846 surface proteins were identified from the second approach, of which 11 were already known as BC markers. This study supports the proposition that Evs are a rich source of known and novel biomarkers that may be used for non-invasive detection of BC. Furthermore, the presented datasets could be further explored for the identification of potential biomarkers in BC.

Targeting breast cancer stem cells through retinoids: A new hope for treatment

Breast cancer is a complex and diverse disease accounting for nearly 30% of all cancers diagnosed in females. But unfortunately, patients develop resistance to the existing chemotherapeutic regimen, resulting in approximately 90% treatment failure. With over half a million deaths annually, it is imperative to explore new therapeutic approaches to combat the disease. Within a breast tumor, a small sub-population of heterogeneous cells, with a unique ability of self-renew and differentiation and responsible for tumor formation, initiation, and recurrence are referred to as breast cancer stem cells (BCSCs). These BCSCs have been identified as one of the main contributors to chemoresistance in breast cancer, making them an attractive target for developing novel therapeutic strategies. These cells exhibit surface biomarkers such as CD44+, CD24-/LOW, ALDH, CD133, and CD49f phenotypes. Higher expression of CD44+ and ALDH activity has been associated with the formation of tumors in various cancers. Moreover, the abnormal regulation of signaling pathways, including Hedgehog, Notch, β-catenin, JAK/STAT, and P13K/AKT/mTOR, leads to the formation of cancer stem cells, resulting in the development of tumors. The growing drug resistance in BC is a significant challenge, highlighting the need for new therapeutic strategies to combat this dreadful disease. Retinoids, a large group of synthetic derivatives of vitamin A, have been studied as chemopreventive agents in clinical trials and have been shown to regulate various crucial biological functions including vision, development, inflammation, and metabolism. On a cellular level, the retinoid activity has been well characterized and translated and is known to induce differentiation and apoptosis, which play important roles in the outcome of the transformation of tissues into malignant. Retinoids have been investigated extensively for their use in the treatment and prevention of cancer due to their high receptor-binding affinity to directly modulate gene expression programs. Therefore, in this study, we aim to summarize the current understanding of BCSCs, their biomarkers, and the associated signaling pathways. Retinoids, such as Adapalene, a third-generation retinoid, have shown promising anti-cancer potential and may serve as therapeutic agents to target BCSCs.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Aberrant expression of bone morphogenetic proteins in the disease progression and metastasis of breast cancer

Background

Bone morphogenetic proteins (BMPs) play crucial roles in the tumorigenesis and metastasis of cancers. Controversy remains about the exact implications of BMPs and their antagonists in breast cancer (BC), due to their diverse and complex biological functions and signalling. A comprehensive study of the whole family and their signalling in breast cancer is provoked.

Methods

Aberrant expression of BMP, BMP receptors and antagonists in primary tumours in breast cancer were analysed by using TCGA-BRCA and E-MTAB-6703 cohorts. Related biomarkers including ER, HER, proliferation, invasion, angiogenesis, lymphangiogenesis and bone metastasis were involved to identify the relationship with BMPs in breast cancer.

Results

The present study showed BMP8B was significantly increased in breast tumours, while BMP6 and ACVRL1 were decreased in breast cancer tissues. The expressions of BMP2, BMP6, TGFBR1 and GREM1 were significantly correlated with BC patients' poor overall survival. Aberrant expression of BMPs, together with BMP receptors, were explored in different subtypes of breast cancer according to ER, PR and HER2 status. Furthermore, higher levels of BMP2, BMP6 and GDF5 were revealed in triple negative breast cancer (TNBC) whilst BMP4, GDF15, ACVR1B, ACVR2B and BMPR1B were relatively higher in Luminal type BC. ACVR1B and BMPR1B were positively correlated with ERα but were inversely correlated with ERβ. High expression of GDF15, BMP4 and ACVR1B were associated with poorer overall survival in HER2 positive BC. BMPs also play dual roles in tumour growth and metastasis of BC.

Conclusion

A shift pattern of BMPs was showed in different subtypes of breast cancer suggesting a subtype specific involvement. It provokes more research to shed light on the exact role of these BMPs and receptors in the disease progression and distant metastasis through a regulation of proliferation, invasion and EMT.

Sex differences in the polygenic architecture of hearing problems in adults

BACKGROUND

Hearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies.

METHODS

Leveraging the UK Biobank, the Nurses' Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors.

RESULTS

We identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes.

CONCLUSIONS

The results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.

A review of prognostic and predictive biomarkers in breast cancer

Breast cancer (BC) is a common cancer all over the world that affects women. BC is one of the leading causes of cancer mortality in women, which today has decreased with the advancement of technology and new diagnostic and therapeutic methods. BCs are histologically divided into in situ and invasive carcinoma, and both of them can be divided into ductal and lobular. The main function after the diagnosis of invasive breast cancer is which patient should use chemotherapy, which patient should receive adjuvant therapy, and which should not. If the decision is for adjuvant therapy, the next challenge is to identify the most appropriate treatment or combination of treatments for a particular patient. Addressing the first challenge can be helped by prognostic biomarkers, while addressing the second challenge can be done by predictive biomarkers. Among the molecular markers related to BC, ER, PR, HER2, and the Mib1/Ki-67 proliferation index are the most significant ones and are tightly confirmed in the standard care of all primary, recurrent, and metastatic BC patients. CEA and CA-15-3 antigens are the most valuable markers of serum tumors in BC patients. Determining the series of these markers helps monitor response to the treatment and early detection of recurrence or metastasis. miRNAs have been demonstrated to be intricate in mammary gland growth, proliferation, and formation of BC known to be incriminated in BC biology. By combining established prognostic factors with valid prognostic/predicted biomarkers, we can start the journey to personalized treatment for every recently diagnosed BC patient.

Analysis of Breast Cancer Based on the Dysregulated Network

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Multiplexed analysis of small extracellular vesicle-derived mRNAs by droplet digital PCR and machine learning improves breast cancer diagnosis

Breast cancer has become the leading cause of global cancer incidence and a serious threat to women's health. Accurate diagnosis and early treatment are of great importance to prognosis. Although clinically used diagnostic approaches can be used for cancer screening, accurate diagnosis of breast cancer is still a critical unmet need. Here, we report a 4-plex droplet digital PCR technology for simultaneous detection of four small extracellular vesicle (sEV)-derived mRNAs (PGR, ESR1, ERBB2 and GAPDH) in combination with machine learning (ML) algorithms to improve breast cancer diagnosis. We evaluate the diagnsotic results with and without the assistance of the ML models. The results indicate that ML-assisted analysis exhibits higher diagnostic performance even using a single marker for breast cancer diagnosis, and demonstrate improved diagnostic performance under the best combination of biomarkers and suitable ML diagnostic model. Therefore, multiple sEV-derived mRNAs analysis coupled with ML not only provides the best combination of markers for breast cancer diagnosis, but also significantly improves the diagnostic efficiency of breast cancer.

The anti-cancer potential of 2,4,6 tris-methyphenylamino1,3,5-triazine compound against mammary glands cancer: Via down-regulating the hormonal, inflammatory mediators, and oxidative stress

AIM OF THE STUDY

Breast cancer is caused by abnormal growth of the cells and progressed due to the over-expression of estrogen (ER) and progesterone (PR). The current study was designed to evaluate the anti-tumor activity of 2,4,6 tris-methyphenylamino1,3,5-triazine compound (MPAT) in N-nitroso, N-methyl urea (NMU)-induced mammary gland cancer.

METHODS

Molecular docking and in-vitro studies were conducted before the in-vivo analysis. Female Albino rats were divided into 5 groups (n = 6). Group I received Carboxymethylcellulose (CMC) (1 mL/100 g). Group II (diseased group) received NMU 50 mg/kg. Group III (standard group) received tamoxifen (5 mg/kg). Group IV-V received MPAT at doses of 30 and 60 mg/kg respectively. All groups received NMU intraperitoneally except the control group at 3 weeks intervals for 12 weeks. After 12 weeks of NMU dosing, MPAT was given for 15 consecutive days. Biochemical, oxidative stress markers, hormonal profile, and inflammatory mediators were analyzed.

KEY FINDINGS

MPAT showed significant interaction with the selected targets in docking studies. An over-expression of ER and PR was observed in NMU-treated rats which were restored significantly after MPAT administration. Nitrite and MDA levels were high in the diseased group and MPAT treatment attenuated the oxidative damage after treatment. Antioxidants such as superoxide dismutase (SOD), Catalase (CAT), total sulfhydryl (TSH), glutathione (GSH), and Lactate dehydrogenase (LDH) values were low in NMU-treated rats.

SIGNIFICANCE

This study concluded that MPAT can be used as an anticancer agent due to its significant effects on down-regulating the hormonal profile and oxidative stress markers.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background:

With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim:

The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods:

Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results:

It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion:

Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review

Simple Summary

Breast cancer is the most common cancer among women. It is estimated that 2.3 million new cases of BC are diagnosed globally each year. Based on mRNA gene expression levels, BC can be divided into molecular subtypes that provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. This review addresses the overview on the BC epidemiology, risk factors, classification with an emphasis on molecular types, prognostic biomarkers, as well as possible treatment modalities.

Abstract

Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020. Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50. Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences.

Transcriptome Analysis Reveals Key Genes and Pathways Associated with Mummify Piglets

China is the country with the largest pork consumption in the world. However, the incidence of high mummify piglets (3-5%) is one of the important factors that cause the slow improvement of pig reproductive capacity, and the genetic mechanism is still unclear. This study aimed to identify candidate genes related to high mummify piglets. RNA-seq technology was used to comparative transcriptome profiling of blood from high piglets mummified and healthy sow at different stages of pregnancy (35d, 56d, 77d and 98d). A total of 137 to 420 DEGs were detected in each stage. Seven differentially expressed genes were significantly differentially expressed at various stages. IL-9R, TLR8, ABLIM3, FSH-α, ASCC1, PRKCZ, and GCK may play an important role in course of mummify piglets. The differential genes we identified between the groups were mainly enriched in immune and inflammation regulation, and others were mainly enriched in reproduction. Considering the function of candidate genes, IL-9R and TLR8 were suggested as the most promising candidate genes involved in mummify piglet traits. We speculate that during pregnancy, it may be the combined effects of the above-mentioned inflammation, immune response, and reproduction-related signal pathways that affect the occurrence of mummifying piglets, and further affect pig reproduction.

A variable selection approach for highly correlated predictors in high-dimensional genomic data

MOTIVATION

In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings.

RESULTS

We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also illustrated on publicly available gene expression data in breast cancer.

AVAILABILITY

Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network (CRAN).

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Unfavorable Prognostic Effects of the Stem Cell Pluripotency Factor Sox2 in Feline Invasive Mammary Carcinomas

Background: Sex-determining Region Y (SRY)-box transcription factor-2 (Sox2) belongs to the "Yamanaka's factors," necessary and sufficient to convert somatic cells into pluripotent stem cells. In breast cancers, Sox2 expression has been associated with poor prognosis, and resistance to therapy. The aims of this study were to determine the frequency of Sox2 positivity in feline invasive mammary carcinomas (FMCs), its relationships with other clinical-pathologic variables, and with patient outcomes. Materials and Methods: This study relies on a previously described retrospective cohort of 180 FMCs, diagnosed in female cats treated by mastectomy alone, with 2-year follow-up. Sox2 (clone SP76), Estrogen Receptor alpha (ER), Progesterone Receptor (PR), Ki-67, Human Epidermal growth factor Receptor 2 (HER2), Androgen Receptor (AR), Bcl-2, Forkhead box protein A1 (FOXA1), basal markers and FoxP3-positive regulatory T cells (Tregs) were detected by automated immunohistochemistry. Sox2 expression was quantitated as an index (percentage of neoplastic cells demonstrating a positive nuclear signal). The FMCs were considered Sox2-positive at threshold >42%. Results: Sox2 was not expressed in the normal mammary gland or in mammary hyperplasia without atypia, but was occasionally detected in atypical hyperplasia. In FMCs, the mean Sox2 index was 38 ± 30%, and 79/180 FMCs (44%) were Sox2-positive. Sox2 expression was associated with older age at diagnosis, lymphovascular invasion, high Ki-67 proliferation indexes, low PR and FOXA1 expression, and increased numbers of tumor-associated Tregs, but was not significantly associated with the clinical stage, histological types, and histological grade. By multivariate survival analysis, Sox2 was associated with poor cancer-specific survival (Hazard Ratio = 1.48, 95% confidence interval 1.04-2.11, p = 0.0292), independently of the pathologic tumor size, pathologic nodal stage, distant metastasis, and AR expression. A rare subgroup of FMCs characterized by an AR+Sox2-phenotype (19/180 cases, 11%) was associated with very favorable outcomes. Conclusion: Sox2 expression was associated with poor cancer-specific survival of female cats with invasive mammary carcinomas, as previously reported in human breast cancer, but was more commonly expressed in cats than reported in breast cancers. Sox2 showed complementarity with AR in FMC prognostication.

The quest for improving the management of breast cancer by functional imaging: The discovery and development of 16α-[18F]fluoroestradiol (FES), a PET radiotracer for the estrogen receptor, a historical review

INTRODUCTION

16α-[18F]Fluoroestradiol (FES), a PET radiotracer for the estrogen receptor (ER) in breast cancer, was the first receptor-targeted PET radiotracer for oncology and is continuing to prove its value in clinical research, antiestrogen development, and breast cancer care. The story of its conception, design, evaluation and use in clinical studies parallels the evolution of the whole field of receptor-targeted radiotracers, one greatly influenced by the research and intellectual contributions of William C. Eckelman.

METHODS AND RESULTS

The development of methods for efficient production of fluorine-18, for conversion of [18F]fluoride ion into chemically reactive form, and for its rapid and efficient incorporation into suitable estrogen precursor molecules at high molar activity, were all methodological underpinnings required for the preparation of FES. FES binds to ER with very high affinity, and its in vivo uptake by ER-dependent target tissues in animal models was efficient and selective, findings that preceded its use for PET imaging in patients with breast cancer.

ADVANCES IN KNOWLEDGE AND IMPLICATIONS FOR PATIENT CARE

Comparisons between ER levels measured by FES-PET imaging of breast tumors with tissue-specimen ER quantification by IHC and other methods show that imaging provided improved prediction of benefit from endocrine therapies. Serial imaging of ER by FES-PET, before and after dosing patients with antiestrogens, is used to determine the efficacious dose for established antiestrogens and to facilitate clinical development of new ER antagonists. Beyond FES imaging, PET-based hormone challenge tests, which evaluate the functional status of ER by monitoring rapid changes in tumor metabolic or transcriptional activity after a brief estrogen challenge, provide highly sensitive and selective predictions of whether or not there will be a favorable response to endocrine therapies. There is sufficient interest in the clinical applications of FES that FDA approval is being sought for its wider use in breast cancer.

CONCLUSIONS

FES was the first PET probe for a receptor in cancer, and its development and clinical applications in breast cancer parallel the conceptual evolution of the whole field of receptor-binding radiotracers.

PET Imaging Agents (FES, FFNP, and FDHT) for Estrogen, Androgen, and Progesterone Receptors to Improve Management of Breast and Prostate Cancers by Functional Imaging

Many breast and prostate cancers are driven by the action of steroid hormones on their cognate receptors in primary tumors and in metastases, and endocrine therapies that inhibit hormone production or block the action of these receptors provide clinical benefit to many but not all of these cancer patients. Because it is difficult to predict which individuals will be helped by endocrine therapies and which will not, positron emission tomography (PET) imaging of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer, and androgen receptor (AR) in prostate cancer can provide useful, often functional, information on the likelihood of endocrine therapy response in individual patients. This review covers our development of three PET imaging agents, 16α-[18F]fluoroestradiol (FES) for ER, 21-[18F]fluoro-furanyl-nor-progesterone (FFNP) for PgR, and 16β-[18F]fluoro-5α-dihydrotestosterone (FDHT) for AR, and the evolution of their clinical use. For these agents, the pathway from concept through development tracks with an emerging understanding of critical performance criteria that is needed for successful PET imaging of these low-abundance receptor targets. Progress in the ongoing evaluation of what they can add to the clinical management of breast and prostate cancers reflects our increased understanding of these diseases and of optimal strategies for predicting the success of clinical endocrine therapies.

G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer

The vast majority of breast cancer death is a result of metastasis. Thus, accurate identification of patients who are likely to have metastasis is expected to improve survival. The G2M checkpoint plays a critical role in cell cycle. We hypothesized that breast cancer tumors with high activity of G2M pathway genes are more aggressive and likely to metastasize. To test this, we used the single-sample gene set variation analysis method to calculate the score for the Hallmark G2M checkpoint pathway using gene expression data of a total of 4626 samples from 12 human breast cancer cohorts. As expected, a high G2M pathway score correlated with enriched tumor expression of other cell proliferation-related gene sets. The score was significantly associated with clinical aggressive features of tumors and patient survival in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Interestingly, a high G2M score of metastasis tumors was also significantly associated with worse survival. In primary as well as metastasis tumors with high scores, the infiltration of both pro- and anti-cancerous immune cells increased. Tumor G2M score was also associated with treatment response to systemic chemotherapy in ER-positive/HER2-negative cancer, and was predictive of response to cyclin-dependent kinase inhibition therapy.