Pharmacogenetics of breast cancer therapies

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Pharmacogenetics of Breast Cancer Treatments: A Sub-Saharan Africa Perspective

Breast cancer is the most frequent cause of cancer death in low- and middle-income countries, in particular among sub-Saharan African women, where response to available anticancer treatment therapy is often limited by the recurrent breast tumours and metastasis, ultimately resulting in decreased overall survival rate. This can also be attributed to African genomes that contain more variation than those from other parts of the world. The purpose of this review is to summarize published evidence on pharmacogenetic and pharmacokinetic aspects related to specific available treatments and the known genetic variabilities associated with metabolism and/or transport of breast cancer drugs, and treatment outcomes when possible. The emphasis is on the African genetic variation and focuses on the genes with the highest strength of evidence, with a close look on CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4/5, CYP19A1, UGT1A4, UGT2B7, UGT2B15, SLC22A16, SLC38A7, FcγR, DPYD, ABCB1, and SULT1A1, which are the genes known to play major roles in the metabolism and/or elimination of the respective anti-breast cancer drugs given to the patients. The genetic variability of their metabolism could be associated with different metabolic phenotypes that may cause reduced patients' adherence because of toxicity or sub-therapeutic doses. Finally, this knowledge enhances possible personalized treatment approaches, with the possibility of improving survival outcomes in patients with breast cancer.

Preliminary Pharmacogenomic-Based Predictive Models of Tamoxifen Response in Hormone-dependent Chilean Breast Cancer Patients

Tamoxifen (TAM), a selective oestrogen receptor modulator, is one of the most used treatments in oestrogen receptor-positive (ER+) early and metastatic breast cancer (BC) patients. The response to TAM has a high degree of inter-individual variability. This is mainly due to genetic variants in CYP2D6 gene, as well as other genes encoding proteins involved in the TAM pharmacokinetic and/or pharmacodynamic. Therefore, prediction of the TAM response using these genetic factors together with other non-genetic variables may be relevant to improve breast cancer treatment. Thus, in this work, we used genetic polymorphisms and clinical variables for TAM response modelling. One hundred sixty-two ER + BC patients with 2 years of TAM treatment were retrospectively recruited, and the genetic polymorphisms CYP2D6*4, CYP3A4*1B (CYP3A4*1.001), CYP3A5*3, UGT2B7*2, UGT2B15*2, SULT1A1*2, and ESRA V364E were analyzed by PCR-RFLP. Concomitantly, the therapeutic response was obtained from clinical records for association with genotypes using univariate and multivariate biostatistical models. Our results show that UGT2B15*1/*2 genotype protects against relapse (OR = 0.09; p = 0.02), CYP3A5*3/*3 genotype avoids endometrial hyperplasia (OR = 0.07; p = 0.01), SULT1A1*1/*2 genotype avoids vaginal bleeding (OR = 0.09; p = 0.03) and ESRA 364E/364E genotype increases the probability of vaginal bleeding (OR = 5.68; p = 0.02). Logistic regression models, including genomic and non-genomic variables, allowed us to obtain preliminary predictive models to explain relapse (p = 0.010), endometrial hyperplasia (p = 0.002) and vaginal bleeding (p = 0.014). Our results suggest that the response to TAM treatment in ER + BC patients might be associated with the presence of the studied genetic variants in UGT2B15, CYP3A5, SULT1A1 and ESRA genes. After clinical validation protocols, these models might be used to help to predict a percentage of BC relapse and adverse reactions, improving the individual response to TAM-based treatment.

SNPs and taxane toxicity in breast cancer patients

AIM

In order to identify genetic variants associated with taxanes toxicity, a panel with 47 SNPs in 20 genes involved in taxane pathways was designed.

PATIENTS & METHODS

Genomic DNA of 113 breast cancer patients was analyzed (70 taking docetaxel, 43 taking paclitaxel).

RESULTS

Two SNPs associated with docetaxel toxicity were identified: CYP3A4*1B with infusion-related reactions; and ERCC1 Gln504Lys with mucositis (p≤0.01). Regarding paclitaxel toxicity: CYP2C8 HapC and CYP2C8 rs1934951 were associated with anemia; and ERCC1 Gln504Lys with neuropathy (p≤0.01).

CONCLUSION

Genes involved in DNA repair mechanisms and reactive oxygen species levels influence taxane toxicity in cancer patients treated with chemotherapy schemes not containing platinum. These findings could lead to better treatment selection for breast cancer patients.

Synergy of novel coumarin derivatives and tamoxifen in blocking growth and inducing apoptosis of breast cancer cells

Possible synergistic effect of tamoxifen (2 μM) and hydrazinyldiene-chroman-2,4-diones (10-100 μM) was examined with an aim to create more effective treatment for ER+ breast cancer. Anti-breast cancer effect has been evaluated on the proliferation of MCF-7 breast adenocarcinoma cells using MTT and alamarBlue assays. Cell viability was evaluated after 48h-treatment and the ICs50 of the coumarin derivatives were determined. The apoptotic effect was evaluated by detection of PARP cleavage and reduced activity of the survival kinase Akt. The results demonstrated dose-dependent activity, with a percent of growth inhibition after combination treatment being significantly higher (53% to 79%, 10 μM and 100 μM, respectively) than the one in the cell lines treated with tamoxifen (29% to 37%) and the synthesized coumarin derivatives alone (11% to 68%, 10 μM and 100 μM, respectively). The ICs50 of the synthesized compounds significantly decreased in synergy with tamoxifen (33% to 51%). Coumarin derivative having thiazole moiety with additional methyl groups attached to the carbons at positions 5 and 4 in the thiazole ring showed to be the most potent, with IC50 20 µM when administered alone and 10 µM in synergy with tamoxifen. The levels of phospho-Thr308 Akt were down-regulated by the combination treatment, pointing to tyrosine kinase phosphorylation inhibition. In conclusion, the novel coumarin derivatives enhance the activity of tamoxifen and this combination may be suitable for prevention of ER+ breast cancer or development of related compounds. Further studies are needed to elucidate precisely the type of receptor involved in the activity and the mechanism of action.

The exposure of breast cancer cells to fulvestrant and tamoxifen modulates cell migration differently

There is no doubt that there are increased benefits of hormonal therapy to breast cancer patients; however, current evidence suggests that estrogen receptor (ER) blockage using antiestrogens is associated with a small induction of invasiveness in vitro. The mechanism by which epithelial tumor cells escape from the primary tumor and colonize to a distant site is not entirely understood. This study investigates the effect of two selective antagonists of the ER, Fulvestrant (Fulv) and Tamoxifen (Tam), on the invasive ability of breast cancer cells. We found that 17β-estradiol (E₂) demonstrated a protective role regarding cell migration and invasion. Fulv did not alter this effect while Tam stimulated active cell migration according to an increase in Snail and a decrease in E-cadherin protein expression. Furthermore, both tested agents increased expression of matrix metalloproteinases (MMPs) and enhanced invasive potential of breast cancer cells. These changes were in line with focal adhesion kinase (FAK) rearrangement. Our data indicate that the anti-estrogens counteracted the protective role of E₂ concerning migration and invasion since their effect was not limited to antiproliferative events. Although Fulv caused a less aggressive result compared to Tam, the benefits of hormonal therapy concerning invasion and metastasis yet remain to be investigated.

KRAS Mouse Models: Modeling Cancer Harboring KRAS Mutations

KRAS is a potent oncogene and is mutated in about 30% of all human cancers. However, the biological context of KRAS-dependent oncogenesis is poorly understood. Genetically engineered mouse models of cancer provide invaluable tools to study the oncogenic process, and insights from KRAS-driven models have significantly increased our understanding of the genetic, cellular, and tissue contexts in which KRAS is competent for oncogenesis. Moreover, variation among tumors arising in mouse models can provide insight into the mechanisms underlying response or resistance to therapy in KRAS-dependent cancers. Hence, it is essential that models of KRAS-driven cancers accurately reflect the genetics of human tumors and recapitulate the complex tumor-stromal intercommunication that is manifest in human cancers. Here, we highlight the progress made in modeling KRAS-dependent cancers and the impact that these models have had on our understanding of cancer biology. In particular, the development of models that recapitulate the complex biology of human cancers enables translational insights into mechanisms of therapeutic intervention in KRAS-dependent cancers.

Pharmacogenomic contribution to drug response

There has been significant progress made in individualizing cancer therapy, especially for colorectal and breast cancer. This has included objective determination of aggressiveness of therapy using molecular predictors of disease recurrence (i.e., Mammaprint, OncotypeDX), identifying altered drug activation for dose modifications (i.e., DPYD, CYP2D6, UGT1A1), or variation in drug targets or components of a pharmacodynamic pathway (TYMS, EGFR, KRAS). With patient-specific molecular characteristics increasingly guiding therapy, this review provides important and timely insights on targeted therapy. Ultimately, integration of both pharmacogenomic and clinical characteristics can provide powerful predictive tools for stratifying responders from nonresponders and identifying patients at increased risk for toxicity.