Plasma Thioredoxin Reductase as a Potential Diagnostic Biomarker for Breast Cancer.

BACKGROUND: Early diagnosis of breast cancer is critical to the treatment and prognosis of breast cancer patients. Our aim is to explore more practical and effective diagnostic methods to facilitate early treatment and improve prognosis for breast cancer patients. MATERIALS AND METHODS: The Mann-Whitney U test, receiver operating characteristic curve, Youden index, Chi-square test, and Fisher's exact test were used to determine whether plasma thioredoxin reductase (TrxR) could be used for the clinical diagnosis of breast cancer. The Wilcoxon signed-rank test was used to validate the prognostic potential of plasma TrxR activity assessment. RESULTS: A total of 761 patients were included, including 537 cases of breast cancer and 224 cases of benign breast diseases. Plasma TrxR activity in the breast cancer group [8.0 (6.0, 9.45) U/mL] was significantly higher than that in the benign group [3.05 (1.20, 6.275) U/mL]. The diagnostic efficiency of TrxR for breast cancer was higher than that of other conventional breast cancer biomarkers, with an area under the curve of 0.821 (95% CI = 0.791-0.852). In addition, TrxR can be used in combination with conventional tumor markers to further improve the diagnostic efficiency. The optimal TrxR threshold for identifying benign and malignant diseases is 7.45 U/mL. We detected plasma TrxR activity and serum tumor markers before and after antitumor therapies in 333 breast cancer patients and found that their trends were basically the same, with a significant decrease in plasma TrxR activity after treatment. CONCLUSION: Plasma TrxR activity can be used as a suitable biomarker for breast cancer diagnosis and efficacy assessment.

Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study.

This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.

Development of Cyclic Peptides Targeting the Epidermal Growth Factor Receptor in Mesenchymal Triple-Negative Breast Cancer Subtype.

Triple-negative breast cancer (TNBC) is an aggressive malignancy characterized by the lack of expression of estrogen and progesterone receptors and amplification of human epidermal growth factor receptor 2 (HER2). Being the Epidermal Growth Factor Receptor (EGFR) highly expressed in mesenchymal TNBC and correlated with aggressive growth behavior, it represents an ideal target for anticancer drugs. Here, we have applied the phage display for selecting two highly specific peptide ligands for targeting the EGFR overexpressed in MDA-MB-231 cells, a human TNBC cell line. Molecular docking predicted the peptide-binding affinities and sites in the extracellular domain of EGFR. The binding of the FITC-conjugated peptides to human and murine TNBC cells was validated by flow cytometry. Confocal microscopy confirmed the peptide binding specificity to EGFR-positive MDA-MB-231 tumor xenograft tissues and their co-localization with the membrane EGFR. Further, the peptide stimulation did not affect the cell cycle of TNBC cells, which is of interest for their utility for tumor targeting. Our data indicate that these novel peptides are highly specific ligands for the EGFR overexpressed in TNBC cells, and thus they could be used in conjugation with nanoparticles for tumor-targeted delivery of anticancer drugs.

Functional Relationships between Long Non-Coding RNAs and Estrogen Receptor Alpha: A New Frontier in Hormone-Responsive Breast Cancer Management.

In the complex and articulated machinery of the human genome, less than 2% of the transcriptome encodes for proteins, while at least 75% is actively transcribed into non-coding RNAs (ncRNAs). Among the non-coding transcripts, those ≥200 nucleotides long (lncRNAs) are receiving growing attention for their involvement in human diseases, particularly cancer. Genomic studies have revealed the multiplicity of processes, including neoplastic transformation and tumor progression, in which lncRNAs are involved by regulating gene expression at epigenetic, transcriptional, and post-transcriptional levels by mechanism(s) that still need to be clarified. In breast cancer, several lncRNAs were identified and demonstrated to have either oncogenic or tumor-suppressive roles. The functional understanding of the mechanisms of lncRNA action in this disease could represent a potential for translational applications, as these molecules may serve as novel biomarkers of clinical use and potential therapeutic targets. This review highlights the relationship between lncRNAs and the principal hallmark of the luminal breast cancer phenotype, estrogen receptor α (ERα), providing an overview of new potential ways to inhibit estrogenic signaling via this nuclear receptor toward escaping resistance to endocrine therapy.

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer.

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Breast cancer: molecular mechanisms of underlying resistance and therapeutic approaches.

Breast cancer (BC) affects over 250,000 women in the US each year. Drug-resistant cancer cells are responsible for most breast cancer fatalities. Scientists are developing novel chemotherapeutic drugs and targeted therapy combinations to overcome cancer cell resistance. Combining drugs can reduce the chances of a tumor developing resistance to treatment. Clinical research has shown that combination chemotherapy enhances or improves survival, depending on the patient's response to treatment. Combination therapy is a highly successful supplemental cancer treatment. This review sheds light on intrinsic resistance to BC drugs and the importance of combination therapy for BC treatment. In addition to recurrence and metastasis of BC, the article discussed biomarkers for BC.

Chemometric Analysis of Urinary Volatile Organic Compounds to Monitor the Efficacy of Pitavastatin Treatments on Mammary Tumor Progression over Time.

Volatile organic compounds (VOCs) in urine are potential biomarkers of breast cancer. Previously, our group has investigated breast cancer through analysis of VOCs in mouse urine and identified a panel of VOCs with the ability to monitor tumor progression. However, an unanswered question is whether VOCs can be exploited similarly to monitor the efficacy of antitumor treatments over time. Herein, subsets of tumor-bearing mice were treated with pitavastatin at high (8 mg/kg) and low (4 mg/kg) concentrations, and urine was analyzed through solid-phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Previous investigations using X-ray and micro-CT analysis indicated pitavastatin administered at 8 mg/kg had a protective effect against mammary tumors, whereas 4 mg/kg treatments did not inhibit tumor-induced damage. VOCs from mice treated with pitavastatin were compared to the previously analyzed healthy controls and tumor-bearing mice using chemometric analyses, which revealed that mice treated with pitavastatin at high concentrations were significantly different than tumor-bearing untreated mice in the direction of healthy controls. Mice treated with low concentrations demonstrated significant differences relative to healthy controls and were reflective of tumor-bearing untreated mice. These results show that urinary VOCs can accurately and noninvasively predict the efficacy of pitavastatin treatments over time.

Early Imaging and Molecular Changes with Neoadjuvant Bevacizumab in Stage II/III Breast Cancer.

This prospective, phase II study evaluated novel biomarkers as predictors of response to bevacizumab in patients with breast cancer (BC), using serial imaging methods and gene expression analysis. Patients with primary stage II/III BC received bevacizumab 15 mg/kg (cycle 1; C1), then four cycles of neoadjuvant docetaxel doxorubicin, and bevacizumab every 3 weeks (C2-C5). Tumour proliferation and hypoxic status were evaluated using 18F-fluoro-3'-deoxy-3'-L-fluorothymidine (FLT)- and 18F-fluoromisonidazole (FMISO)-positron emission tomography (PET) at baseline, and during C1 and C5. Pre- and post-bevacizumab vascular changes were evaluated using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Molecular biomarkers were assessed using microarray analysis. A total of 70 patients were assessed for treatment efficacy. Significant decreases from baseline in tumour proliferation (FLT-PET), vascularity, and perfusion (DCE-MRI) were observed during C1 (p ≤ 0.001), independent of tumour subtype. Bevacizumab treatment did not affect hypoxic tumour status (FMISO-PET). Significant changes in the expression of 28 genes were observed after C1. Changes in vascular endothelial growth factor receptor (VEGFR)-2p levels were observed in 65 patients, with a > 20% decrease in VEGFR-2p observed in 13/65. Serial imaging techniques and molecular gene profiling identified several potentially predictive biomarkers that may predict response to neoadjuvant bevacizumab therapy in BC patients.

Tracking the Progression of Triple Negative Mammary Tumors over Time by Chemometric Analysis of Urinary Volatile Organic Compounds.

Previous studies have shown that volatile organic compounds (VOCs) are potential biomarkers of breast cancer. An unanswered question is how urinary VOCs change over time as tumors progress. To explore this, BALB/c mice were injected with 4T1.2 triple negative murine tumor cells in the tibia. This typically causes tumor progression and osteolysis in 1-2 weeks. Samples were collected prior to tumor injection and from days 2-19. Samples were analyzed by headspace solid phase microextraction coupled to gas chromatography-mass spectrometry. Univariate analysis identified VOCs that were biomarkers for breast cancer; some of these varied significantly over time and others did not. Principal component analysis was used to distinguish Cancer (all Weeks) from Control and Cancer Week 1 from Cancer Week 3 with over 90% accuracy. Forward feature selection and linear discriminant analysis identified a unique panel that could identify tumor presence with 94% accuracy and distinguish progression (Cancer Week 1 from Cancer Week 3) with 97% accuracy. Principal component regression analysis also demonstrated that a VOC panel could predict number of days since tumor injection (R2 = 0.71 and adjusted R2 = 0.63). VOC biomarkers identified by these analyses were associated with metabolic pathways relevant to breast cancer.

Biosensor-based diagnostic approaches for various cellular biomarkers of breast cancer: A comprehensive review.

Breast cancer is the most commonly occurring cancer among women which leads to thousands of deaths worldwide. The chances of survival are more if the breast cancer is diagnosed at early stage. At present, mammography, magnetic resonance imaging, ultrasound and tissue biopsies are the main diagnostic techniques available for the detection of breast cancer. However, despite of offering promising results, requirement of expensive setup, skilled supervision, expert analysis, invasive procedure (biopsy) and low capacity of multiplexing are the main limitations of these diagnostic techniques. Due to high cost, these screening tests are out of reach of people belonging to low socioeconomic groups and this poses serious health burden to the society. Recently, biosensor-based diagnostic technology for early detection of various types of cancers and other non-oncological disorders have gained considerable attention because of their several advantageous features over existing diagnostic technologies such as high throughput, noninvasive nature, cost effectiveness, easy interpretable results and capacity for multiplexing. Further, biosensors can be designed for biomarkers which are confined to particular type of cancer. In this review, we have discussed about various genomic, transcriptomic, proteomic and metabolomic biomarkers associated with breast cancer, various biosensors-based diagnostic approaches designed for detection of specific biomarkers associated with breast cancer are also described. Further, this review throws insight on various biomarkers linked with breast cancer which can be effectively exploited to develop new diagnostic technology. The assessment of these biomarkers associated with BC using biosensors in large population are cost-effective, non-invasive and high throughput. They help in risk assessment of disease at very initial stage even in backward areas and also help to lower the disease burden of society and economic cost of treatment for a common man. This review would provide new avenues for the development of biosensor based diagnostic technology for the detection of biomarkers associated with breast cancer.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis.

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and ß-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

Breast Cancer Derived Extracellular Vesicles in Bone Metastasis Induction and Their Clinical Implications as Biomarkers.

Cancer incidence and mortality are rapidly growing worldwide. The main risk factors for cancer can be associated with aging as well as the growth of the population and socioeconomic condition. Breast cancer, a crucial public health problem, is the second cause of death among women. About 70% of patients with advanced breast cancer have bone metastases. In bone metastasis, cancer cells and osteoclasts form a vicious cycle: cancer cells promote osteoclast differentiation and activation that, in turn, induce cancer cell seeding and proliferation in the bone. Growing evidence shows that extracellular vesicles (EVs) play a key role in carcinogenesis, proliferation, pre-metastatic niche formation, angiogenesis, metastasis, and chemoresistance in several tumors, such as breast, lung, prostate, and liver cancer. Here, we discuss the role of EVs released by breast cancer cells, focusing on bone metastasis induction and their clinical implications as biomarkers.

pH-responsive allochroic nanoparticles for the multicolor detection of breast cancer biomarkers.

Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) are the three crucial biomarkers for the clinical diagnosis of breast cancer. Sensitive and precise detection of ER, PR, and HER2 is of great significance for the diagnosis of breast cancer. Herein, through a simple solvent-induced self-assembly process, the self-carried allochroic nanoparticles were prepared by using some hydrophobic pH indicator molecules for allochroic NPs-linked immunosorbent assay (named ALISA) of ER, PR, and HER2, respectively. Meanwhile, the introduction of bovine serum albumin (BSA) and antibody (Ab) enhanced the dispersity of the self-assembled nanoparticles as signal tags. Since the ultra-high loading and high-efficiency release of pH indicators, the ALISA exhibitssatisfactory selectivity and sensitivity, which demonstrated the great potential in the early diagnosis and postoperative monitoring of breast cancers. Furthermore, the smartphone was introduced to combine with the ALISA for point-of-care testing, indicating the high feasibility in practical applications.

Recent nanotechnological interventions targeting PI3K/Akt/mTOR pathway: A focus on breast cancer.

Breast cancer is the major cause of deaths in women worldwide. Detection and treatment of breast cancer at earlier stages of the disease has shown encouraging results. Modern genomic technologies facilitated several therapeutic options however the diagnosis of the disease at an advanced stage claim more deaths. Therefore more research directed towards genomics and proteomics into this area may lead to novel biomarkers thereby enhancing the survival rates in breast cancer patients. Phosphoinositide-3-kinase/Akt/mammalian target of rapamycin (PI3K/Akt/mTOR) signaling pathway was shown to be hyperactivated in most of the breast carcinomas resulting in excessive growth, proliferation, and tumor development. Development of nanotechnology has provided many interesting avenues to target the PI3K/Akt/mTOR pathway both at the pre-clinical and clinical stages. Therefore, the current review summarizes the underlying mechanism and the importance of targeting PI3K/Akt/mTOR pathway, novel biomarkers and use of nanotechnological interventions in breast cancer.

HLJ1 (DNAJB4) Gene Is a Novel Biomarker Candidate in Breast Cancer.

Breast cancer is the most common cancer type and cause of cancer-related mortality among women worldwide. New biomarker discovery is crucial for diagnostic innovation and personalized medicine in breast cancer. Heat shock proteins (HSPs) have been increasingly reported as biomarkers and potential drug targets for cancers. HLJ1 (DNAJB4) belongs to the DNAJ (HSP40) family of HSPs and is regarded as a tumor suppressor gene in lung, colon, and gastric cancers. However, the role of the HLJ1 gene in breast cancer is currently unknown. We evaluated the role of the HLJ1 gene in breast cancer progression by analyzing its in vitro and in vivo expression and its genetic/epigenetic alterations. HLJ1 expression was found to be reduced or lost in breast cancer cell lines (SK-BR-3, MDA-MB-231, ZR-75-1) compared with the nontumorigenic mammary epithelial cell line (MCF 10A). In a clinical context for breast cancer progression, the HLJ1 expression was significantly less frequent in invasive breast carcinoma samples (n = 230) compared with normal breast tissue (n = 100), benign neoplasia (n = 53), and ductal carcinoma in situ (n = 21). In methylation analyses by the combined bisulfite restriction analysis assay, the CpG island located in the 5'-flanking region of the HLJ1 gene was found to be methylated in breast cancer cell lines. HLJ1 expression was restored in the ZR-75-1 cell line by DNA demethylating agent 5-Aza-2'-deoxycytidine (5-AzadC) and histone deacetylase inhibitor trichostatin A. These new observations support the idea that HLJ1 is a tumor suppressor candidate and potential biomarker for breast cancer. Epigenomic mechanisms such as CpG methylation and histone deacetylation might contribute to downregulation of HLJ1 expression. We call for future functional, epigenomic, and clinical studies to ascertain the contribution of HLJ1 to breast cancer pathogenesis and, importantly, evaluate its potential for biomarker development in support of personalized medicine diagnostic innovation in clinical oncology.

New generation of electrochemical immunoassay based on polymeric nanoparticles for early detection of breast cancer.

Screening and early diagnosis are the key factors for the reduction of mortality rate and treatment cost of cancer. Therefore, sensitive and selective methods that can reveal the low abundance of cancer biomarkers in a biological sample are always desired. Here, we report the development of a novel electrochemical biosensor for early detection of breast cancer by using bioconjugated self-assembled pH-responsive polymeric micelles. The micelles were loaded with ferrocene molecules as "tracers" to specifically target cell surface-associated epithelial mucin (MUC1), a biomarker for breast and other solid carcinoma. The synthesis of target-specific, ferrocene-loaded polymeric micelles was confirmed, and the resulting sensor was capable of detecting the presence of MUC1 in a sample containing about 10 cells/mL. Such a high sensitivity was achieved by maximizing the loading capacity of ferrocene inside the polymeric micelles. Every single event of binding between the antibody and antigen was represented by the signal of hundreds of thousands of ferrocene molecules that were released from the polymeric micelles. This resulted in a significant increase in the intensity of the ferrocene signal detected by cyclic voltammetry.

Quantitative proteomics of breast tumors: Tissue quality assessment to clinical biomarkers.

Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM) is not only a proven tool for clinical chemistry, but also a versatile method to enhance the capability to quantify biomarkers for tumor biology research. As the treatment of cancer continues to evolve, the ability to assess multiple biomarkers to assign cancer phenotypes based on the genetic background and the signaling of the individual tumor becomes paramount to our ability to treat the patient. In breast cancer, the American Society of Clinical Oncology has defined biomarkers for patient assessment to guide selection of therapy: estrogen receptor, progesterone receptor, and the HER2/Neu receptor tyrosine kinase; therefore, these proteins were selected for LC-SRM assay development. Detailed molecular characterization of these proteins is necessary for patient treatment, so expression and phosphorylation assays have been developed and applied. In addition, other LC-SRM assays were developed to further evaluate tumor biology (e.g. Ki-67 for proliferation and vimentin for tumor aggressiveness related to the epithelial-to-mesenchymal transition). These measurements combined with biomarkers for tissue quality and histological content are implemented in a three-tier multiplexed assay platform, which is translated from cell line models into frozen tumor tissues banked from breast cancer patients.

Prolactin-Inducible Protein: From Breast Cancer Biomarker to Immune Modulator-Novel Insights from Knockout Mice.

The propensity for breast cancers to elicit immune responses in patients is well established. The accumulation of tumor infiltrating lymphocytes within the primary breast tumor has been linked to better prognosis and better response to therapy. The prolactin-inducible protein (PIP) is a 15 kD protein that is expressed under physiological conditions of the breast and is regarded as a marker of mammary differentiation. While highly expressed under pathological conditions of the mammary gland, including breast cancers, PIP is expressed in very few other cancers. Although the function of PIP is not well elucidated, numerous studies suggest that its primary role may be related to host defense and immune modulation. However, evidence to show a direct link between PIP and the immune response has been lacking. In this review, we discuss our recent work with Pip-deficient mice, linking PIP not only to a role in innate immunity but for the first time, providing evidence for a role in cell-mediated immunity. These functional studies in Pip null mice lend new insight into the role of PIP in immunity and suggest that PIP may play a similar immune-regulatory role in breast cancer.

Phase I biomarker modulation study of atorvastatin in women at increased risk for breast cancer.

Selective estrogen receptor modulators (SERMs), tamoxifen, and raloxifene that reduce the risk of breast cancer are limited to only estrogen receptor-positive (ER(+)) breast cancer. In addition, patient acceptance of SERMs is low due to toxicity and intolerability. New agents with improved toxicity profile that reduce risk of ER-negative breast cancer are urgently needed. Observational studies show that statins can reduce breast cancer incidence and recurrence. The objective of this prospective short-term prevention study was to evaluate the effect of a lipophilic statin, atorvastatin, on biomarkers in breast tissue and serum of women at increased risk. Eligible participants included women with previous history of carcinoma in situ, or atypical hyperplasia, or 5 year breast cancer projected Gail risk >1.67 %, or lifetime breast cancer risk >20 % calculated by models including Claus, Tyrer-Cuzick, Boadicea, or BRCAPRO. Patients underwent baseline fine needle aspiration (FNA) of the breast, blood collection for biomarker analysis, and were randomized to either no treatment or atorvastatin at 10, 20, or 40 mg/day dose for 3 months. At 3 months, blood collection and breast FNA were repeated. Biomarkers included C-reactive protein (CRP), lipid profile, atorvastatin, and its metabolites, Ki-67, bcl-2, EGFR, and pEGFR. Baseline genotype for 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMG-CoAR) was also measured. Among 60 patients evaluated, a significant reduction in serum CRP, cholesterol and low-density lipoprotein (LDL), and increase in atorvastatin metabolites in serum and breast FNAs was demonstrated. No changes were observed in other tissue biomarkers. This study shows that atorvastatin and its metabolites are detectable in breast samples and may lower serum CRP among women without hyperlipidemia.