A New Genetic Risk Score to Predict the Outcome of Locally Advanced or Metastatic Breast Cancer Patients Treated With First-Line Exemestane: Results From a Prospective Study

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Insights on the interaction mechanism of exemestane to three digestive enzymes by multi-spectroscopy and molecular docking

Exemestane is an irreversible steroidal aromatase inhibitor, typically used to treat breast cancer. As an anti-tumor drug, exemestane has more obvious side effects on the gastrointestinal tract. The purpose of this work is to investigate the combination of exemestane with three important digestive enzymes including pepsin (Pep), trypsin (Try) and α-Chymotrypsin (α-ChT) so as to analyze the mechanism of the gastrointestinal adverse effects causing by exemestane binding. Enzyme activity experiment showed that the enzyme activity of Pep was decreased in the presence of exemestane. Fluorescence spectra revealed that exemestane formed stable complexes with digestive enzymes, and the quenching mechanism of drug-digestive enzymes interaction were all static quenching. The binding constants of Pep, Try and α-ChT at 298 K were 2.34 × 10⁵, 1.45 × 10⁵, and 2.05 × 10⁵ M^-1, respectively. Synchronous fluorescence and 3D fluorescence spectroscopy showed that the conformation of exemestane was slightly changed after combining with digestive enzymes, and non-radiative energy transfer occurred. Circular dichroism results indicated that exemestane could change the secondary structure of digestive enzymes via increase the α-helix content and decrease in the β-sheet content. Thermodynamic parameters (ΔH⁰, ΔS⁰, and ΔG⁰) revealed that exemestane interacted with α-ChT through electrostatic force, and the binding force with Pep and Try was van der Waals interactions and hydrogen, which was basically consistent with the molecular docking results.

Single nucleotide polymorphisms as the efficient prognostic markers in breast cancer

BACKGROUND

Breast cancer (BC) is known as the most common malignancy in women. Environmental and genetic factors are associated with BC progression. Genetic polymorphisms have been reported as important risk factors of BC prognosis and drug response. Main body: Therefore, in the present review, we have summarized all single nucleotide polymorphisms (SNPs) which have been significantly associated with drug response in BC patients around the world. We have also categorized the reported SNPs based on their related genes functions to clarify the molecular biology of drug responses in BC.

CONCLUSION

The majority of SNPs were reported in detoxifying enzymes, which introduced such genes as the main genetic risk factors during BC drug responses. This review paves the way for introducing a prognostic panel of SNPs for the BC patients in the world.

Polymorphism of XRCC3 in Egyptian Breast Cancer Patients

Purpose

Polymorphisms of DNA repair genes may contribute to variations in DNA repair capacity and subsequent genetic susceptibility to different cancers. In Egypt, breast cancer is the most common cancer among women, representing 18.9% of the total cancer cases. The present study assesses the correlation between X-ray repair cross-complementing group 3 (XRCC3) polymorphism with breast cancer and treatment response in Egyptian female breast cancer patients.

Patients and Methods

This pilot case–control study was conducted on 66 female breast cancer patients and 20 apparently healthy females as a control group. Tumor grading, immunohistostaining of hormone (progesterone and estrogen) receptors and human epidermal growth factor receptor 2 (HER2), and RFLP-PCR for XRCC3 (rs861539) polymorphism were performed. All breast cancer patients received a treatment protocol (after surgery) which was either chemotherapy (anthracyclines followed by paclitaxel or anthracyclines + fluorouracil) or radiotherapy, or both. Disease-free survival (DFS) and overall survival (OS) were recorded.

Results

The number of patients with a heterozygous allele (GA) was significantly higher in cases of tumor size >20 mm. The A allele was correlated with younger age at diagnosis in both chemotherapy and radiotherapy groups. Poor treatment response and higher mortality rates were significantly associated with AA and GA compared with GG alleles (normal allele). In the chemotherapy group, out of eight patients with the A allele, six showed a poor response to treatment containing fluorouracil.

Conclusion

XRCC3 rs861539 polymorphism could be associated with lower DFS and OS and poor treatment response. So, we recommend carrying out XRCC3 genotyping before starting treatment to choose the most effective treatment strategy according to XRCC3 polymorphism.

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