Polymorphism at 19q13.41 Predicts Breast Cancer Survival Specifically after Endocrine Therapy

A Genome-Wide Association Study of Endoxifen Serum Concentrations and Adjuvant Tamoxifen Efficacy in Early-Stage Breast Cancer Patients

Tamoxifen is part of the standard of care of endocrine therapy for adjuvant treatment of breast cancer. However, survival outcomes with tamoxifen are highly variable. The concentration of endoxifen, the 30-100 times more potent metabolite of tamoxifen and bioactivated by the CYP2D6 enzyme, has been described as the most relevant metabolite of tamoxifen metabolism. A genome-wide association study (GWAS) was performed with the objective to identify genetic polymorphisms associated with endoxifen serum concentration levels and clinical outcome in early-stage breast cancer patients receiving tamoxifen. A GWAS was conducted in 608 women of the CYPTAM study (NTR1509/PMID: 30120701). Germline DNA and clinical and survival characteristics were readily available. Genotyping was performed on Infinium Global Screening Array (686,082 markers) and single nucleotide polymorphism (SNP) imputation by using 1000 Genomes. Relapse-free survival during tamoxifen (RFSt) was defined the primary clinical outcome. Endoxifen serum concentration was analyzed as a continuous variable. Several genetic variants reached genome-wide significance (P value: ≤5 × 10-8). Endoxifen concentrations analysis identified 430 variants, located in TCF20 and WBP2NL genes (chromosome 22), which are in strong linkage disequilibrium with CYP2D6 variants. In the RFSt analysis, several SNP were identified (LPP gene: rs77693286, HR 18.3, 95% CI: 15.2-21.1; rs6790761, OR 18.2, 95% CI: 15.5-21.1). Endoxifen concentrations have a strong association with the chromosome 22, which contains the CYP2D6 gene.

UACA locus is associated with breast cancer chemoresistance and survival

Few germline genetic variants have been robustly linked with breast cancer outcomes. We conducted trans-ethnic meta genome-wide association study (GWAS) of overall survival (OS) in 3973 breast cancer patients from the Pathways Study, one of the largest prospective breast cancer survivor cohorts. A locus spanning the UACA gene, a key regulator of tumor suppressor Par-4, was associated with OS in patients taking Par-4 dependent chemotherapies, including anthracyclines and anti-HER2 therapy, at a genome-wide significance level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.27 \times 10^{ - 9}$$\end{document}P=1.27×10−9). This association was confirmed in meta-analysis across four independent prospective breast cancer cohorts (combined hazard ratio = 1.84, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.28 \times 10^{ - 11}$$\end{document}P=1.28×10−11). Transcriptome-wide association study revealed higher UACA gene expression was significantly associated with worse OS (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 4.68 \times 10^{ - 7}$$\end{document}P=4.68×10−7). Our study identified the UACA locus as a genetic predictor of patient outcome following treatment with anthracyclines and/or anti-HER2 therapy, which may have clinical utility in formulating appropriate treatment strategies for breast cancer patients based on their genetic makeup.

DDRS: Detection of drug response SNPs specifically in patients receiving drug treatment

Detecting SNPs associated with drug efficacy or toxicity is helpful to facilitate personalized medicine. Previous studies usually find SNPs associated with clinical outcome only in patients received a specific treatment. However, without information from patients without drug treatment, it is possible that the detected SNPs are associated with patients' clinical outcome even without drug treatment. Here we aimed to detect drug response SNPs based on data from patients with and without drug treatment through combing the cox proportional-hazards model and pairwise Kaplan-Meier survival analysis. A pipeline named Detection of Drug Response SNPs (DDRS) was built and applied to TCGA breast cancer data including 363 patients with doxorubicin treatment and 321 patients without any drug treatment. We identified 548 doxorubicin associated SNPs. Drug response score derived from these SNPs were associated with drug-resistant level (indicated by IC₅₀) of breast cancer cell lines. Enrichment analyses showed that these SNPs were enriched in active epigenetic regulation markers (e.g., H3K27ac). Compared with random genes, the cis-eQTL genes of these SNPs had a shorter protein-protein interaction distance to doxorubicin associated genes. In addition, linear discriminant analysis showed that the eQTL gene expression levels could be used to predict clinical outcome for patients with doxorubicin treatment (AUC = 0.738). Specifically, we identified rs2817101 as a drug response SNP for doxorubicin treatment. Higher expression level of its cis-eQTL gene GSTA1 is associated with poorer survival. This approach can also be applied to identify new drug associated SNPs in other cancers.

Association of germline variation with the survival of women with BRCA1/2 pathogenic variants and breast cancer

Germline genetic variation has been suggested to influence the survival of breast cancer patients independently of tumor pathology. We have studied survival associations of genetic variants in two etiologically unique groups of breast cancer patients, the carriers of germline pathogenic variants in BRCA1 or BRCA2 genes. We found that rs57025206 was significantly associated with the overall survival, predicting higher mortality of BRCA1 carrier patients with estrogen receptor-negative breast cancer, with a hazard ratio 4.37 (95% confidence interval 3.03-6.30, P = 3.1 × 10-9). Multivariable analysis adjusted for tumor characteristics suggested that rs57025206 was an independent survival marker. In addition, our exploratory analyses suggest that the associations between genetic variants and breast cancer patient survival may depend on tumor biological subgroup and clinical patient characteristics.

SNPs in lncRNA Regions and Breast Cancer Risk

Long non-coding RNAs (lncRNAs) play crucial roles in human physiology, and have been found to be associated with various cancers. Transcribed ultraconserved regions (T-UCRs) are a subgroup of lncRNAs conserved in several species, and are often located in cancer-related regions. Breast cancer is the most common cancer in women worldwide and the leading cause of female cancer deaths. We investigated the association of genetic variants in lncRNA and T-UCR regions with breast cancer risk to uncover candidate loci for further analysis. Our focus was on low-penetrance variants that can be discovered in a large dataset. We selected 565 regions of lncRNAs and T-UCRs that are expressed in breast or breast cancer tissue, or show expression correlation to major breast cancer associated genes. We studied the association of single nucleotide polymorphisms (SNPs) in these regions with breast cancer risk in the 122970 case samples and 105974 controls of the Breast Cancer Association Consortium's genome-wide data, and also by in silico functional analyses using Integrated Expression Quantitative trait and in silico prediction of GWAS targets (INQUISIT) and expression quantitative trait loci (eQTL) analysis. The eQTL analysis was carried out using the METABRIC dataset and analyses from GTEx and ncRNA eQTL databases. We found putative breast cancer risk variants (p < 1 × 10-5) targeting the lncRNA GABPB1-AS1 in INQUISIT and eQTL analysis. In addition, putative breast cancer risk associated SNPs (p < 1 × 10-5) in the region of two T-UCRs, uc.184 and uc.313, located in protein coding genes CPEB4 and TIAL1, respectively, targeted these genes in INQUISIT and in eQTL analysis. Other non-coding regions containing SNPs with the defined p-value and highly significant false discovery rate (FDR) for breast cancer risk association were discovered that may warrant further studies. These results suggest candidate lncRNA loci for further research on breast cancer risk and the molecular mechanisms.

Mouse mammary tumor viral env sequences are not present in the human genome but are present in breast tumors and normal breast tissues

In 1936, John Joseph Bittner identified mouse mammary tumor virus (MMTV), a milk transmitted beta retrovirus, a form of single-stranded positive-sense RNA virus. A retrovirus inserts a copy of its genome into the DNA of a host cell, thus altering the cell's genome. In the current analysis, we searched for MMTV sequences within the human genome. To compare the MMTV genome to the human genome, we used BLAT, the Blast-Like Alignment Tool of the UCSC Genome Browser. BLAT can align a user sequence of 25 bases or more to the genome. 60 MMTV sequences were in the human genome. Of 56 sequences from the MMTV POL gene, 36 POL sequences were from the same part of the gene, beginning at viral nucleotide 4800 but of different lengths. 8 viral sequences began at nucleotide ∼3430 of the POL gene. Four viral sequences were from GAGdUTPase, encoded by the MMTV PRO gene. Deoxyuridine 5'-triphosphate nucleotidohydrolase (dUTPase) is an enzyme present in several major retroviral families. In MMTV dUTPase may be essential for viral replication. Since BLAT identified no MMTV envelope (env) sequence in the human genome, the env sequences from breast tumors and normal breast tissue found in other studies may have come from an MMTV infection. However, no one is certain how MMTV could enter human cells, since the cells do not have a cellular receptor for MMTV, as do mouse cells.

Deep sequencing reveals the mitochondrial DNA variation landscapes of breast-to-brain metastasis blood samples

Breast-to-brain metastasis (BBM) often represents a terminal event, due to the inability of many systemic treatments to cross the blood-brain barrier (BBB), rendering the brain a sanctuary site for tumour cells. Identifying genetic variations that can predict the patients who will develop BBM would allow targeting of adjuvant treatments to reduce risk while disease bulk is minimal. Germ-line genetic variations may contribute to whether a BBM forms by influencing the primary tumour subtype that presents, or by influencing the host response to the tumour or treatment regimen, or by facilitating transition of tumour cells across the BBB and establish a viable brain metastasis. The role of mitochondrial DNA (mtDNA) variants specifically in BBM is underexplored. Consequently, using a sensitive deep sequencing approach, we characterized the mtDNA variation landscapes of blood samples derived from 13 females who were diagnosed with early-onset breast cancer and later went on to develop BBM. We also predicted the potential pathogenic significance of variations identified in all mtDNA-encoded oxidative phosphorylation (OXPHOS) proteins using 3D protein structural mapping and analysis, to identify variations worthy of follow-up. From the 70 variations found in protein coding regions, we reveal novel links between three specific mtDNA variations and altered OXPHOS structure and function in 23% of the BBM samples. Further studies are required to confirm the origin of mtDNA variations, and whether they correlate with (1) the predicted alterations in mitochondrial function and (2) increased risk of developing breast-to-brain metastasis using a much larger cohort of samples.

Meta-analysis of three genome-wide association studies identifies two loci that predict survival and treatment outcome in breast cancer

The majority of breast cancers are driven by the female hormone oestrogen via oestrogen receptor (ER) alpha. ER-positive patients are commonly treated with adjuvant endocrine therapy, however, resistance is a common occurrence and aside from ER-status, no unequivocal predictive biomarkers are currently in clinical use. In this study, we aimed to identify constitutional genetic variants influencing breast cancer survival among ER-positive patients and specifically, among endocrine-treated patients. We conducted a meta-analysis of three genome-wide association studies comprising in total 3,136 patients with ER-positive breast cancer of which 2,751 had received adjuvant endocrine therapy. We identified a novel locus (rs992531 at 8p21.2) associated with reduced survival among the patients with ER-positive breast cancer (P = 3.77 × 10-8). Another locus (rs7701292 at 5q21.3) was associated with reduced survival among the endocrine-treated patients (P = 2.13 × 10-8). Interaction analysis indicated that the survival association of rs7701292 is treatment-specific and independent of conventional prognostic markers. In silico functional studies suggest plausible biological mechanisms for the observed survival associations and a functional link between the putative target genes of the rs992531 and rs7701292 (RHOBTB2 and RAB9P1, respectively). We further explored the genetic interaction between rs992531 and rs7701292 and found a significant, treatment-specific interactive effect on survival among ER-positive, endocrine-treated patients (hazard ratio = 6.97; 95% confidence interval, 1.79-27.08, Pinteraction= 0.036). This is the first study to identify a genetic interaction that specifically predicts treatment outcome. These findings may provide predictive biomarkers based on germ line genotype informing more personalized treatment selection.

Breast cancer: The translation of big genomic data to cancer precision medicine

Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single-gene to whole-genome screening by using genome-wide association study and next-generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high-throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative "-omics" data to refine the current medical practice to be applied in the near future.

Application of pharmacometrics and quantitative systems pharmacology to cancer therapy: The example of luminal a breast cancer

Breast cancer (BC) is the most common cancer in women, and the second most frequent cause of cancer-related deaths in women worldwide. It is a heterogeneous disease composed of multiple subtypes with distinct morphologies and clinical implications. Quantitative systems pharmacology (QSP) is an emerging discipline bridging systems biology with pharmacokinetics (PK) and pharmacodynamics (PD) leveraging the systematic understanding of drugs' efficacy and toxicity. Despite numerous challenges in applying computational methodologies for QSP and mechanism-based PK/PD models to biological, physiological, and pharmacological data, bridging these disciplines has the potential to enhance our understanding of complex disease systems such as BC. In QSP/PK/PD models, various sources of data are combined including large, multi-scale experimental data such as -omics (i.e. genomics, transcriptomics, proteomics, and metabolomics), biomarkers (circulating and bound), PK, and PD endpoints. This offers a means for a translational application from pre-clinical mathematical models to patients, bridging the bench to bedside paradigm. Not only can these models be applied to inform and advance BC drug development, but they also could aid in optimizing combination therapies and rational dosing regimens for BC patients. Here, we review the current literature pertaining to the application of QSP and pharmacometrics-based pharmacotherapy in BC including bottom-up and top-down modeling approaches. Bottom-up modeling approaches employ mechanistic signal transduction pathways to predict the behavior of a biological system. The ones that are addressed in this review include signal transduction and homeostatic feedback modeling approaches. Alternatively, top-down modeling techniques are bioinformatics reconstruction techniques that infer static connections between molecules that make up a biological network and include (1) Bayesian networks, (2) co-expression networks, and (3) module-based approaches. This review also addresses novel techniques which utilize the principles of systems biology, synthetic lethality and tumor priming, both of which are discussed in relationship to novel drug targets and existing BC therapies. By utilizing QSP approaches, clinicians may develop a platform for improved dose individualization for subpopulation of BC patients, strengthen rationale in treatment designs, and explore mechanism elucidation for improving future treatments in BC medicine.

Potential prognostic value of clinical characteristics, hormone status and major depressive disorder in breast cancer

AIM

To identify independent factors predicting overall survival (OS) of breast cancer (BC) patients.

PATIENTS & METHODS

Two hundred and eighty one women with BC were recruited and clinical characteristics including lymphovascular invasion, clinical stage of Tumor Node Metastasis and positive axillary lymph nodes were documented; immunohistochemistry/fluorescence in situ hybridization was used to examine the expression of estrogen receptor, progesterone receptor, HER2 and Ki-67; major depressive disorder was assessed with Diagnostic and Statistical Manual of Mental Disorders V.

RESULTS

Multivariable analyses indicated that in BC patients, lymphovascular invasion, Tumor Node Metastasis, pN, Ki-67 and major depressive disorder were significantly negatively correlated with OS; estrogen receptor was significantly positively associated with OS.

CONCLUSION

Early diagnostic approaches and effective psychologic intervention are indispensable for BC patients.