Progesterone Receptor Gene Variants in Metastatic Estrogen Receptor Positive Breast Cancer

A systematic review of candidate genes and their relevant pathways for metastasis among adults diagnosed with breast cancer

BACKGROUND

Presently incurable, metastatic breast cancer is estimated to occur in as many as 30% of those diagnosed with early-stage breast cancer. Timely and accurate identification of those at risk for developing metastasis using validated biomarkers has the potential to have profound impact on overall survival rates. Our primary goal was to conduct a systematic review and synthesize the existing body of scientific knowledge on the candidate genes and their respective single nucleotide polymorphisms associated with metastasis-related outcomes among patients diagnosed with breast cancer. This knowledge is critical to inform future hypothesis-driven and validation research aimed at enhancing clinical decision-making for breast cancer patients.

METHODS

Using PRISMA guidelines, literature searches were conducted on September 13th, 2023, using PubMed and Embase databases. The systematic review protocol was registered with INPLASY (DOI: https://doi.org/10.37766/inplasy2024.8.0014 ). Covidence software was used to facilitate the screening and article extraction processes. Peer-reviewed articles were selected if authors reported on single nucleotide polymorphisms directly associated with metastasis among adults diagnosed with breast cancer.

FINDINGS

We identified 451 articles after 44 duplicates were removed resulting in 407 articles to be screened for study inclusion. Three reviewers completed the article screening process which resulted in 86 articles meeting the study inclusion criteria. Sampling varied across studies with the majority utilizing a case-control design (n = 75, 87.2%), with sample sizes ranging from 23 to 1,017 participants having mean age 50.65 ± 4.50 (min-max: 20-75). The synthesis of this internationally generated evidence revealed that the scientific area on the underlying biological contributions to breast cancer metastasis remains predominantly exploratory in nature (n = 74, 86%). Of the 12 studies with reported power analyses, only 9 explicitly stated the power values which ranged from 47.88 to 99%.

DISCUSSION

Understanding the underlying biological mechanisms contributing to metastasis is a critical component for precision oncological therapeutics and treatment approaches. Current evidence investigating the contribution of SNPs to the development of metastasis is characterized by underpowered candidate gene studies. To inform individualized precision health practices and improve breast cancer survival outcomes, future hypothesis-driven research is needed to replicate these associations in larger, more diverse datasets.

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication

BACKGROUND

Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effective treatment.

OBJECTIVES

This study aims to develop an innovative artificial intelligence (AI) based model for predicting breast cancer and its various histopathological grades by integrating multiple biomarkers and subject age, thereby enhancing diagnostic accuracy and prognostication.

METHODS

A novel ensemble-based machine learning (ML) framework has been introduced that integrates three distinct biomarkers-beta-human chorionic gonadotropin (β-hCG), Programmed Cell Death Ligand 1 (PD-L1), and alpha-fetoprotein (AFP)-alongside subject age. Hyperparameter optimization was performed using the Particle Swarm Optimization (PSO) algorithm, and minority oversampling techniques were employed to mitigate overfitting. The model's performance was validated through rigorous five-fold cross-validation.

RESULTS

The proposed model demonstrated superior performance, achieving a 97.93% accuracy and a 98.06% F1-score on meticulously labeled test data across diverse age groups. Comparative analysis showed that the model outperforms state-of-the-art approaches, highlighting its robustness and generalizability.

CONCLUSION

By providing a comprehensive analysis of multiple biomarkers and effectively predicting tumor grades, this study offers a significant advancement in breast cancer screening, particularly in regions with limited medical resources. The proposed framework has the potential to reduce breast cancer mortality rates and improve early intervention and personalized treatment strategies.

The Possible Role of Progesterone Receptors in Prostate Cancer Incidences in the Iraqi Population

BACKGROUND

Prostate cancer (PCa) is one of the leading diseases causing mortality. It comes in the third rank of common cancer types. It is considered extremely a complicated cancer type since it occurs in highly steroid-responsive and dependent tissues. Many factors are considered to play an important role in the disease progression of PCa, with some functioning at the molecular level.

METHODOLOGY

After applying the exclusion criteria, 200 patients who underwent proctectomy were included in this study. Following receiving patient consent, blood samples were withdrawn from patients, DNA was extracted, and precise polymerase chain reaction (PCR) amplification was conducted using specifically designed primers. The resulting amplicons were sequenced and analyzed.

RESULTS

The progesterone receptor B (PGRB) DNA from patients showed four distinctive single-nucleotide polymorphisms (SNPs) at sites 11:101128812, 11:101128924, 11:101128949, and 11:101128986, which altered the amino acid sequences to Y>N, A>D, T>I, and C>R, respectively, compared to control. These SNPs resided in sensitive sites that either affected the control elements or promoted alterations in the protein configuration. This DNA change diminished the PGR gene function and promoted an imbalance in the encoded PGR protein structure and expression.

CONCLUSIONS

Many factors may play a role in PCa manifestation, with steroids and progesterone initially noted as factors. Many studies have dealt with the hormonal effect on PCa; however, few have ultimately determined the molecular impact on disease progression. The presence of pathogenic SNPs in the enhancing region of the gene may impact the expression level of PGR. High or low expression levels may negatively affect gene function, which can be considered a reliable factor in prostate tumorigenesis.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Enhanced eMAGE applied to identify genetic factors of nuclear hormone receptor dysfunction via combinatorial gene editing

Technologies that generate precise combinatorial genome modifications are well suited to dissect the polygenic basis of complex phenotypes and engineer synthetic genomes. Genome modifications with engineered nucleases can lead to undesirable repair outcomes through imprecise homology-directed repair, requiring non-cleavable gene editing strategies. Eukaryotic multiplex genome engineering (eMAGE) generates precise combinatorial genome modifications in Saccharomyces cerevisiae without generating DNA breaks or using engineered nucleases. Here, we systematically optimize eMAGE to achieve 90% editing frequency, reduce workflow time, and extend editing distance to 20 kb. We further engineer an inducible dominant negative mismatch repair system, allowing for high-efficiency editing via eMAGE while suppressing the elevated background mutation rate 17-fold resulting from mismatch repair inactivation. We apply these advances to construct a library of cancer-associated mutations in the ligand-binding domains of human estrogen receptor alpha and progesterone receptor to understand their impact on ligand-independent autoactivation. We validate that this yeast model captures autoactivation mutations characterized in human breast cancer models and further leads to the discovery of several previously uncharacterized autoactivating mutations. This work demonstrates the development and optimization of a cleavage-free method of genome editing well suited for applications requiring efficient multiplex editing with minimal background mutations.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

The role of the progesterone receptor PROGINS variant in the development of fibromyalgia syndrome and its psychological findings

Fibromyalgia syndrome (FMS), a chronic pain disorder of unknown etiology, is more common in women. This suggests that biological sex is important. Therefore, we performed an analysis to determine whether the progesterone receptor (P GR) gene Alu insertion (named P ROGINS) variant is associated with an increased risk of FMS in the Turkish population. A total of 288 subjects, including 138 patients diagnosed with FMS according to the 2016 American College of Rheumatology criteria and 150 healthy subjects, were evaluated. Genotyping of the P GR P ROGINS variant was determined by polymerase chain reaction (P CR) analysis. The results of the analyses were evaluated for statistical significance. There were no subjects in the control group carrying the T2 allele. The P GR P ROGINS T1/T2 genotype was more prevalent in both all patients and female patients compared to all controls and female controls (p = 0.001, p = 0.003, respectively). A statistically significant relationship was observed in both all patients and female patients when compared to the control group according to T1/T1 vs. T1/T2+T2/T2 (p < 0.000, p < 0.001, respectively). The current study suggests that the P GR Alu insertion variant T2 allele might influence FMS susceptibility in the Turkish population. Large-sample sizes and studies of different ethnicities are required to further evaluate the association between this variant and FMS.

Progesterone receptor isoform ratios influence the transcriptional activity of progestins via the progesterone receptor

Progestins (synthetic progestogens) are progesterone receptor (PR) ligands used globally by women in both hormonal contraception and menopausal hormone therapy. Although four generations of unique progestins have been developed, studies seldom distinguish between the activities of progestins via the two functionally distinct PR isoforms, PR-A and PR-B. Moreover, not much is known about the action of progestins in breast cancer tumors where PR-A is mostly overexpressed relative to PR-B. Understanding progestin action in breast cancer is crucial since the clinical use of some progestins has been associated with an increased risk of developing breast cancer. This study directly compared the agonist activities of selected progestins from all four generations for transactivation and transrepression via either PR-A or PR-B, and when PR-A and PR-B were co-expressed at ratios comparable to those detected in breast cancer tumors. Comparative dose-response analysis showed that earlier generation progestins mostly displayed similar efficacies for transactivation on a minimal progesterone response element via the PR isoforms, while most of the 4th generation progestins, similar to the natural progestogen, progesterone (P4), were more efficacious via PR-B. Most of the progestogens were however more potent via PR-A. We are the first to show that the efficacies of the selected progestogens via the individual PR isoforms were generally decreased when PR-A and PR-B were co-expressed, irrespective of the ratio of PR-A:PR-B. While the potencies of most progestogens via PR-B were enhanced when the ratio of PR-A relative to PR-B was increased, those via PR-A were minimally influenced. This study is also the first to report that all progestogens evaluated, except 1st generation medroxyprogesterone acetate and 4th generation drospirenone, displayed similar agonist activity for transrepression via PR-A and PR-B on a minimal nuclear factor kappa B containing promoter. Moreover, we showed that the progestogen activity for transrepression was significantly increased when PR-A and PR-B were co-expressed. Taken together, our results highlight that PR agonists (progestogens) do not always display the same activity via PR-A and PR-B, or when PR-A and PR-B are co-expressed at ratios mimicking those found in breast cancer tumors. These results suggest that biological responses are progestogen- and PR isoform-dependent and may differ in target tissues expressing varying PR-A:PR-B ratios.

Progesterone Receptor Gene Polymorphisms and Breast Cancer Risk

The objective of this systematic review was to investigate the association between polymorphisms in the progesterone receptor gene (PGR) and breast cancer risk. A search of PubMed, Scopus, and Web of Science databases was performed in November 2021. Study characteristics, minor allele frequencies, genotype frequencies, and odds ratios were extracted. Forty studies met the eligibility criteria and included 75 032 cases and 89 425 controls. Of the 84 PGR polymorphisms reported, 7 variants were associated with breast cancer risk in at least 1 study. These polymorphisms included an Alu insertion (intron 7) and rs1042838 (Val660Leu), also known as PROGINS. Other variants found to be associated with breast cancer risk included rs3740753 (Ser344Thr), rs10895068 (+331G/A), rs590688 (intron 2), rs1824128 (intron 3), and rs10895054 (intron 6). Increased risk of breast cancer was associated with rs1042838 (Val660Leu) in 2 studies, rs1824128 (intron 3) in 1 study, and rs10895054 (intron 6) in 1 study. The variant rs3740753 (Ser344Thr) was associated with decreased risk of breast cancer in 1 study. Mixed results were reported for rs590688 (intron 2), rs10895068 (+331G/A), and the Alu insertion. In a pooled analysis, the Alu insertion, rs1042838 (Val660Leu), rs3740753 (Ser344Thr), and rs10895068 (+331G/A) were not associated with breast cancer risk. Factors reported to contribute to differences in breast cancer risk associated with PGR polymorphisms included age, ethnicity, obesity, and postmenopausal hormone therapy use. PGR polymorphisms may have a small contribution to breast cancer risk in certain populations, but this is not conclusive with studies finding no association in larger, mixed populations.

Integration of differential expression and network structure for 'omics data analysis

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

Long noncoding RNA HOTAIR regulates the stemness of breast cancer cells via activation of the NF-κB signaling pathway

Breast cancer is the most prevalent cancer among women, and it is characterized by a high rate of tumor development and heterogeneity. Breast cancer stem cells (CSCs) may well contribute to these pathological properties, but the mechanisms underlying their self-renewal and maintenance are still elusive. Here, we found that the long noncoding RNA HOTAIR is highly expressed in breast CSCs. HOTAIR is required for breast CSC self-renewal and tumor propagation. Mechanistically, we demonstrate that HOTAIR recruits the PRC2 protein complex to the promoter of IκBα to inhibit its expression, leading to activation of the NF-κB signaling pathway. The activated NF-κB signaling promotes downstream c-Myc and Cyclin D1 expression. Furthermore, our analysis of clinical samples from the GEPIA database indicated that the IκBα level, as well as the survival rate of patients, with high HOTAIR expression was significantly lower than that of patients with relatively low HOTAIR expression. Our data suggest that HOTAIR-mediated NF-κB signaling primes breast CSC self-renewal and tumor propagation. In sum, we have identified HOTAIR-based NF-κB signaling regulatory circuit that promotes tumorigenic activity in breast CSCs, further indicating that HOTAIR could be a promising target for clinical treatment of breast cancers.

Long non-coding RNAs affecting cell metabolism in cancer

Metabolic reprogramming is commonly recognized as one important hallmark of cancers. Cancer cells present significant alteration of glucose metabolism, oxidative phosphorylation, and lipid metabolism. Recent findings demonstrated that long non-coding RNAs control cancer development and progression by modulating cell metabolism. Here, we give an overview of breast cancer metabolic reprogramming and the role of long non-coding RNAs in driving cancer-specific metabolic alteration.

Current Molecular Combination Therapies Used for the Treatment of Breast Cancer

Breast cancer is the second leading cause of death for women worldwide. While monotherapy (single agent) treatments have been used for many years, they are not always effective, and many patients relapse after initial treatment. Moreover, in some patients the response to therapy becomes weaker, or resistance to monotherapy develops over time. This is especially problematic for metastatic breast cancer or triple-negative breast cancer. Recently, combination therapies (in which two or more drugs are used to target two or more pathways) have emerged as promising new treatment options. Combination therapies are often more effective than monotherapies and demonstrate lower levels of toxicity during long-term treatment. In this review, we provide a comprehensive overview of current combination therapies, including molecular-targeted therapy, hormone therapy, immunotherapy, and chemotherapy. We also describe the molecular basis of breast cancer and the various treatment options for different breast cancer subtypes. While combination therapies are promising, we also discuss some of the challenges. Despite these challenges, the use of innovative combination therapy holds great promise compared with traditional monotherapies. In addition, the use of multidisciplinary technologies (such as nanotechnology and computer technology) has the potential to optimize combination therapies even further.

TAp63 regulates bone remodeling by modulating the expression of TNFRSF11B/Osteoprotegerin

ABBREVIATIONS

MSC, mesenchymal stem cells; OPG, osteoprotegerin; RUNX2, Run-trelated transcription factor 2.

Global mapping of cancers: The Cancer Genome Atlas and beyond

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole-genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan-Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine-learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning.

Lost but Not Least-Novel Insights into Progesterone Receptor Loss in Estrogen Receptor-Positive Breast Cancer

Estrogen receptor α (ERα) and progesterone receptor (PgR) are crucial prognostic and predictive biomarkers that are usually co-expressed in breast cancer (BC). However, 12-24% of BCs present ERα(+)/PgR(-) phenotype at immunohistochemical evaluation. In fact, BC may either show primary PgR(-) status (in chemonaïve tumor sample), lose PgR expression during neoadjuvant treatment, or acquire PgR(-) phenotype in local relapse or metastasis. The loss of PgR expression in ERα(+) breast cancer may signify resistance to endocrine therapy and poorer outcomes. On the other hand, ERα(+)/PgR(-) BCs may have a better response to neoadjuvant chemotherapy than double-positive tumors. Loss of PgR expression may be a result of pre-transcriptional alterations (copy number loss, mutation, epigenetic modifications), decreased transcription of the PGR gene (e.g., by microRNAs), and post-translational modifications (e.g., phosphorylation, sumoylation). Various processes involved in the down-regulation of PgR have distinct consequences on the biology of cancer cells. Occasionally, negative PgR status detected by immunohistochemical analysis is paradoxically associated with enhanced transcriptional activity of PgR that might be inhibited by antiprogestin treatment. Identification of the mechanism of PgR loss in each patient seems challenging, yet it may provide important information on the biology of the tumor and predict its responsiveness to the therapy.

Recent advances in cancer immunotherapy

Cancer immunotherapy represents a major advance in the cure of cancer following the dramatic advancements in the development and refinement of chemotherapies and radiotherapies. In the recent decades, together with the development of early diagnostic techniques, immunotherapy has significantly contributed to improving the survival of cancer patients. The immune-checkpoint blockade agents have been proven effective in a significant fraction of standard therapy refractory patients. Importantly, recent advances are providing alternative immunotherapeutic tools that could help overcome their limitations. In this mini review, we provide an overview on the main steps of the discovery of classic immune-checkpoint blockade agents and summarise the most recent development of novel immunotherapeutic strategies, such as tumour antigens, bispecific antibodies and TCR-engineered T cells.

Molecular Evaluation of PROGINS Mutation in Progesterone Receptor Gene and Determination of its Frequency, Distribution Pattern and Association with Breast Cancer Susceptibility in Saudi Arabia

AIMS

Experimental and clinical evidence demonstrate that progesterone hormone and its nuclear receptor, the Progesterone Receptor (PR), play critical role in controlling mammary gland tumorigenesis and breast cancer development. Hormonal therapy (Tomaxifen) is the frontline treatment for hormone-dependent breast cancers. Progesterone hormone induces its action on the target cells by binding with its Progesterone receptor (PgR) therefore any genetic variations, which might induce alienation in the progesterone receptor, can result in an increased susceptibility of gynecological cancers. Alu insertion (PROGINS) mutation in PgR gene is reported to be associated with an increased risk of ovarian cancer and a decreased risk of breast cancer. However, its association with breast cancer risk remains inconclusive. Therefore, we investigated the association of PROGINS allele and its link with breast cancer risk.

METHODS

This case control study was performed on 200 subjects in which 100 were breast cancer cases and 100 gender matched healthy controls.The mutation was detected by using mutation specific PCR and results were confirmed by direct Sanger sequencing.

RESULTS

A clinically significant difference was reported in genotype distribution of PROGINs allele among the cases and gender-matched healthy controls (P<0. 032). Genotype frequencies of A1/A1, A1/A2, A2/A2 reported in cases was 81%, 19% (18% & 1%) and in matched healthy controls were 93%, 7% (6% & 1%). The higher frequency of PROGINs allele (19%) was observed in cases than the healthy controls (7%). The findings indicated that PgR variants (CC vs CT) increased the risk of Breast cancer in codominant inheritance model with OR= 3.44, 95% CI =1. 30-9.09, P<0.021) whereas nonsignificant association was found for CC vs TT genotypes with OR=1.14, 95% CI=0.07-18.658, P=0. 92. However, subgroup analysis revealed that CT + TT vs CC genotype increased the risk of breast cancer in dominant inheritance model tested OR = 3. 11, 95% CI = (1.24-7.79), P = 0.015). A nonsignificant association for PgR (CC+CT) vs TT) genotypes were reported in breast cancer OR = 1. 0, 95% CI= (0. 061-16.21), P=1) in recessive inheritance model tested. However, analysis with clinicalpathological variables revealed that the PROGINs allele is significantly associated with the distant metastasis and advanced stage of the disease.

CONCLUSION

The mutation specific PCR was successfully developed as an alternative to Sanger sequencing for the cost-effective detection for PROGINS allele of progesterone receptor gene. A clinically significant correlation of PROGINs allele was reported with the distant metastasis and advanced stage of the disease. Taken together, these results demonstrated that PROGINS variant is associated with an increased susceptibility to Breast cancer, providing novel insights into the genetic etiology and underlying biology of Breast carcinogenesis. Further studies with large sample sizes are required to validate our findings.

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.