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[This corrects the article DOI: 10.1371/journal.pone.0199012.].
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Most breast cancer deaths are caused by metastasis and treatment options beyond radiation and cytotoxic drugs, which have severe side effects, and hormonal treatments, which are or become ineffective for many patients, are urgently needed. This study reanalyzed existing data from three genome-wide association studies (GWAS) using a novel computational biostatistics approach (muGWAS), which had been validated in studies of 600-2000 subjects in epilepsy and autism. MuGWAS jointly analyzes several neighboring single nucleotide polymorphisms while incorporating knowledge about genetics of heritable diseases into the statistical method and about GWAS into the rules for determining adaptive genome-wide significance. Results from three independent GWAS of 1000-2000 subjects each, which were made available under the National Institute of Health's "Up For A Challenge" (U4C) project, not only confirmed cell-cycle control and receptor/AKT signaling, but, for the first time in breast cancer GWAS, also consistently identified many genes involved in endo-/exocytosis (EEC), most of which had already been observed in functional and expression studies of breast cancer. In particular, the findings include genes that translocate (ATP8A1, ATP8B1, ANO4, ABCA1) and metabolize (AGPAT3, AGPAT4, DGKQ, LPPR1) phospholipids entering the phosphatidylinositol cycle, which controls EEC. These novel findings suggest scavenging phospholipids as a novel intervention to control local spread of cancer, packaging of exosomes (which prepare distant microenvironment for organ-specific metastases), and endocytosis of ß1 integrins (which are required for spread of metastatic phenotype and mesenchymal migration of tumor cells). Beta-cyclodextrins (ßCD) have already been shown to be effective in in vitro and animal studies of breast cancer, but exhibits cholesterol-related ototoxicity. The smaller alpha-cyclodextrins (αCD) also scavenges phospholipids, but cannot fit cholesterol. An in-vitro study presented here confirms hydroxypropyl (HP)-αCD to be twice as effective as HPßCD against migration of human cells of both receptor negative and estrogen-receptor positive breast cancer. If the previous successful animal studies with ßCDs are replicated with the safer and more effective αCDs, clinical trials of adjuvant treatment with αCDs are warranted. Ultimately, all breast cancer are expected to benefit from treatment with HPαCD, but women with triple-negative breast cancer (TNBC) will benefit most, because they have fewer treatment options and their cancer advances more aggressively.
Asunto(s)
Neoplasias de la Mama/tratamiento farmacológico , Endocitosis/genética , Neoplasias de la Mama Triple Negativas/tratamiento farmacológico , alfa-Ciclodextrinas/administración & dosificación , 2-Hidroxipropil-beta-Ciclodextrina/uso terapéutico , Transportador 1 de Casete de Unión a ATP/genética , Aciltransferasas/genética , Adenosina Trifosfatasas/genética , Neoplasias de la Mama/genética , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/patología , Femenino , Estudio de Asociación del Genoma Completo , Humanos , Proteínas de Transferencia de Fosfolípidos/genética , Monoéster Fosfórico Hidrolasas/genética , Polimorfismo de Nucleótido Simple/genética , Neoplasias de la Mama Triple Negativas/genética , Neoplasias de la Mama Triple Negativas/metabolismo , Neoplasias de la Mama Triple Negativas/patología , alfa-Ciclodextrinas/metabolismoRESUMEN
BACKGROUND: Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts. METHODS: Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence). RESULTS: By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes. CONCLUSION: With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.
