Plasma cell-free tumor DNA, PIK3CA and TP53 mutations predicted inferior endocrine-based treatment outcome in endocrine receptor-positive metastatic breast cancer.

PURPOSE: How to factor both tumor burden and oncogenic genomic mutations as variables to predict the outcome of endocrine-based therapy (ET) in ER-positive/HER2-negative metastatic breast cancer patients (MBC) remains to be explored. METHOD: Blood samples prospectively collected from 163 ER-positive/HER2-negative female MBC patients, before ET, were used for cell-free tumor DNA (cfDNA) analysis. cfDNA was subjected to next-generation sequencing (NGS) to interrogate oncogenic PIK3CA hotspot and TP53 DNA-binding domain (DBD) mutations, including single nucleotide variants (SNVs) or small insertions and deletions (InDels). The variant calling threshold was set at 0.5%. Progression-free survival (PFS) was measured from the start of the ET treatment to the time of disease progression of the same treatment regimen. RESULTS: Overall, the median PFS was 8.3 months (95% CI 5.7-11.1 months). The median cfDNA was 38.5 ng (range 4.4-1935 ng). The proportion of patients with PIK3CA and TP53 alterations were 25.1 and 15.3%, respectively. Patients with high total cfDNA (HR 1.74, p = 0.003), PIK3CA mutation (HR 1.74, p = 0.007), and TP53 mutation (HR 1.64, p = 0.047) in liquid biopsy conferred worse outcome after ET. Even for patients with low tumor burden, the detrimental effect of PIK3CA or TP53 mutation remained significant (p < 0.001). For patients with either PIK3CA (p < 0.001) or TP53 mutation (p = 0.004), there was significant positive correlation between allele frequency (AF) and total cfDNA. CONCLUSION: After adjustment of cfDNA level, PIK3CA and TP53 mutations observed in liquid biopsy exerted detrimental effects on the outcome of ET-based regimens. The AF of PIK3CA or TP53 may be a surrogate marker for PFS.

Propensity score analysis of the prognostic value of genomic assays for breast cancer in diverse populations using the National Cancer Data Base.

BACKGROUND: Genomic assays such as Oncotype Dx (ODX) and MammaPrint are used for risk-adapted treatment decisions among patients with early breast cancer. However, to the authors' knowledge, concordance between genomic assays is modest. Using real-world data, the authors performed a comparative analysis of ODX and MammaPrint. METHODS: A cohort of women diagnosed with early-stage, hormone receptor-positive breast cancer who received ODX or MammaPrint was established using the National Cancer Data Base (NCDB) for 2010 through 2016. Using the propensity score matching method, 2 groups of patients with similar clinical and demographic characteristics were defined: one group received ODX and the other received MammaPrint. The authors examined the association between use of the ODX or MammaPrint assays and overall survival using Cox models. RESULTS: Of the 451,693 eligible patients, approximately 45.3% received ODX and 1.8% received MammaPrint testing. The use of ODX increased from 36.1% in 2010 to 49.9% in 2016, whereas use of MammaPrint increased from 0.5% in 2010 to 3.3% in 2016. The authors matched 5042 patients who received ODX with 5042 patients who received MammaPrint. The 5-year risks of death for the MammaPrint low-risk group and the ODX low-risk group were 3.4% and 4.7%, respectively. The prognostic value of MammaPrint was similar to that of ODX; the C-index was 0.614 (95% confidence interval, 0.572-0.657) for MammaPrint and 0.581 (95% confidence interval, 0.530-0.631) for ODX. There was a difference in the performance of the ODX assay observed across racial and/or ethnic groups (P < .001), with a slightly better performance noted among white compared with African American and Hispanic individuals. CONCLUSIONS: Both the ODX and MammaPrint tests are good at identifying low-risk individuals who could be spared chemotherapy. The suboptimal performance of ODX in ethnic minority individuals deserves further investigation.

Neoadjuvant endocrine therapy for luminal breast tumors: State of the art, challenges and future perspectives.

Neoadjuvant endocrine treatment (NET) associates to satisfactory rates of breast conservative surgery and conversions from inoperable to operable hormone receptor-positive (HR+)/HER2-negative breast cancer (BC), with less toxicities than neoadjuvant chemotherapy (NACT) and similar outcomes. Hence, it has been proposed as a logical alternative to NACT in patients with HR+/HER2- BC candidate to a neoadjuvant approach. Nevertheless, potential barriers to the widespread use of NET include the heterogeneous nature of patient response coupled with the long duration needed to achieve a clinical response. However, interest in NET has significantly increased in the last decade, owing to more in-depth investigation of several biomarkers for a more adequate patient selection and on-treatment benefit monitoring, such as PEPI score, Ki67 and genomic assays. This review is intended to describe the state-of-the-art regarding NET, its future perspectives and potential integration with molecular biomarkers for the optimal selection of patients, regimen and duration of (neo)adjuvant treatments.

Receptor discordance and phenotype change in metastatic breast cancer.

BACKGROUND: changes may occur in tumor phenotype and receptor status during the progression of breast cancer. Discordance between primary and metastases has implications for further treatment and prognosis. METHODS: 185 patients confirmed breast cancer metastasis were retrospectively analyzed during 1999-2019. All the pathological assessments of receptors and phenotypes of both primaries and metastases were recorded. RESULTS: rates of receptor discordance were 18.65%, 30.57%, and 16.06% for ER, PR, and HER2, respectively and 31.62% for phenotype change. Patients with ER discordance experienced a worse OS and PMS, and those with ER loss had worse PMS compared with ER positive concordance. Patients with PR discordance experienced poorer OS and loss of PR positivity also had decreased OS and PMS when comparing with PR positive concordance. There was also significantly poorer PMS of hormon receptor (HR) discordance than HR positive concordance. In phenotype change, the luminal A type concordance group showed better PMS result. CONCLUSIONS: this study demonstrated that discordance in subtype and receptor status between primary and metastatic lesions ultimately affects the survival and has a potential impact on treatment options.

Distinct Microbial Signatures Associated With Different Breast Cancer Types.

A dysbiotic microbiome can potentially contribute to the pathogenesis of many different diseases including cancer. Breast cancer is the second leading cause of cancer death in women. Thus, we investigated the diversity of the microbiome in the four major types of breast cancer: endocrine receptor (ER) positive, triple positive, Her2 positive and triple negative breast cancers. Using a whole genome and transcriptome amplification and a pan-pathogen microarray (PathoChip) strategy, we detected unique and common viral, bacterial, fungal and parasitic signatures for each of the breast cancer types. These were validated by PCR and Sanger sequencing. Hierarchical cluster analysis of the breast cancer samples, based on their detected microbial signatures, showed distinct patterns for the triple negative and triple positive samples, while the ER positive and Her2 positive samples shared similar microbial signatures. These signatures, unique or common to the different breast cancer types, provide a new line of investigation to gain further insights into prognosis, treatment strategies and clinical outcome, as well as better understanding of the role of the micro-organisms in the development and progression of breast cancer.

A big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction.

CONTEXT: Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task. AIMS: In this study, we investigate and explore big data technologies and machine learning approaches to do an efficient chemical data mining for endocrine receptor disruption prediction and virtual compound screening. The power of artificial neural network (ANN) in predicting chemicals' activity toward androgen receptor (AR) and estrogen receptor (ER) and thereby classifying into human endocrine disruptor or nondisruptor is investigated. SUBJECTS AND METHODS: Molecules are collected along with their Inhibitory Concentration (IC 50) values toward AR and ER. Training and test datasets are created with active and inactive classes of molecules. Molecular fingerprints of Electro Topological State (E-State) are generated for describing every compound. ANN machine learning model is created using Apache Spark and implemented in Hadoop big data environment. Test chemical's structural similarity toward active class of training compounds is estimated and combined with ANN model for improving prediction accuracy. RESULTS: AR and ER predictive models applied on corresponding test datasets gave 86.31% and 89.57% accuracies, respectively, in correctly classifying molecules as disruptor or nondisruptor. Molecular fragments and functional groups are ranked based on their importance in forming ANN model and influence toward the AR and ER disruption behavior. Training molecules that are specific to the test molecules' endocrine disruption prediction are retrieved based on the structural similarity values. CONCLUSIONS: The current study demonstrates a new approach of chemical endocrine receptor disruption prediction combining ANN machine learning method and molecular similarity in a big data environment. This method of predictive modeling can be further tested with more receptors and hormones and predictive power can be examined.

Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery.

INTRODUCTION: Androgen receptor (AR) is a ligand-dependent transcription factor and a member of the nuclear receptor superfamily. It plays a vital role in male sexual development and regulates gene expression in various tissues, including prostate. Androgens are compounds that exert their biological effects via interaction with AR. Binding of androgens to AR initiates conformational changes in AR that affect binding of co-regulator proteins and DNA. AR agonists and antagonists are widely used in a variety of clinical applications (i.e. hypogonadism and prostate cancer therapy). AREAS COVERED: This review provides a close look at structures of AR-ligand complexes and mutations in the receptor that have been revealed, discusses current challenges in the field, and sheds light on future directions. EXPERT OPINION: AR is one of the primary targets for the treatment of prostate cancer, as AR antagonists inhibit prostate cancer growth. However, these drugs are not effective for long-term treatment and lead to castration-resistant prostate cancer. The structures of AR-ligand complexes are an invaluable scientific asset that enhances our understanding of biological functions and mechanisms of androgenic and anti-androgenic chemicals as well as promotes the discovery of superior drug candidates.