A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk.

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk.

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci.

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20,000 genes per sample. We were able to develop a mathematical model that classified tumor tissue versus normal tissue with a high accuracy.

Genome-Wide DNA Methylation Model for the Diagnosis of Prostate Cancer.

Prostate cancer is the most prevalent and the second most lethal malignancy among males in the United States of America. Its diagnosis is almost entirely predicated upon histopathological analysis of the biopsied tissue, and it is associated with a substantial average error. Using genome-wide DNA methylation data derived from 469 prostatic tumor tissue samples and 50 normal prostatic tissue samples and interrogating over 485 000 CpG sites per sample (spanning across gene promoters, CpG islands, shores, shelves, gene bodies, and intergenic and other areas), we were able to develop a mathematical model that classified with a high accuracy (overall sensitivity = 95.

Independent validation of a mathematical genomic model for survival of glioma patients.

An independent cohort study was conducted to validate a mathematical genomic model for survival of glioma patients that was introduced previously. Of the 102 new subjects that were employed in this study, 40 were long-term survivors (survival ≥ 3 years), and 62 were short-term survivors (survival ≤ 1 year). Utilizing the gene expression of 5 genes as captured by mRNA sequencing of primary tumor tissue, obtained from the initial biopsy during the diagnosis, and prior to the administration of any treatment, the model classified correctly all but three of the 102 subjects.

A common variant in MTHFR influences response to chemoradiotherapy and recurrence of rectal cancer.

An important determinant of the pathogenesis and prognosis of various diseases is inherited genetic variation. Single-nucleotide polymorphisms (SNPs), variations at a single base position, have been identified in both protein-coding and noncoding DNA sequences, but the vast majority of millions of those variants are far from being functionally understood. Here we show that a common variant in the gene MTHFR [rs1801133 (C>T)] not only influences response to neoadjuvant chemoradiotherapy in patients with rectal cancer, but it also influences recurrence of the disease itself.

A mathematical model for short-term vs. long-term survival in patients with glioma.

Gliomas, the most common primary brain tumors in adults, constitute clinically, histologically, and molecularly a most heterogeneous type of cancer. Owing to this, accurate clinical prognosis for short-term vs. long-term survival for patients with grade II or III glioma is currently nonexistent.

Inflammation and immune system activation in aging: a mathematical approach.

Memory and learning declines are consequences of normal aging. Since those functions are associated with the hippocampus, I analyzed the global gene expression data from post-mortem hippocampal tissue of 25 old (age ≥ 60 yrs) and 15 young (age ≤ 45 yrs) cognitively intact human subjects. By employing a rigorous, multi-method bioinformatic approach, I identified 36 genes that were the most significant in terms of differential expression; and by employing mathematical modeling, I demonstrated that 7 of the 36 genes were able to discriminate between the old and young subjects with high accuracy.

APOBEC3B upregulation and genomic mutation patterns in serous ovarian carcinoma.

Ovarian cancer is a clinically and molecularly heterogeneous disease. The driving forces behind this variability are unknown. Here, we report wide variation in the expression of the DNA cytosine deaminase APOBEC3B, with elevated expression in the majority of ovarian cancer cell lines (three SDs above the mean of normal ovarian surface epithelial cells) and high-grade primary ovarian cancers.

APOBEC3B is an enzymatic source of mutation in breast cancer.

Several mutations are required for cancer development, and genome sequencing has revealed that many cancers, including breast cancer, have somatic mutation spectra dominated by C-to-T transitions. Most of these mutations occur at hydrolytically disfavoured non-methylated cytosines throughout the genome, and are sometimes clustered. Here we show that the DNA cytosine deaminase APOBEC3B is a probable source of these mutations.

Prognosis of treatment response (pathological complete response) in breast cancer.

Pertaining to the female population in the USA, breast cancer is the leading cancer in terms of annual incidence rate and, in terms of mortality, the second most lethal cancer. There are currently no biomarkers available that can predict which breast cancer patients will respond to chemotherapy with both sensitivity and specificity > 80%, as mandated by the latest FDA requirements. In this study, we have developed a prognostic biomarker model (complex mathematical function) that-based on global gene expression analysis of tumor tissue collected during biopsy and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with breast cancer (clinical stages I-III) who will respond to the paclitaxel-fluorouracil-doxorubicin-cyclophosphamide chemotherapy and will experience pathological complete response (Responders), as well as those breast cancer patients (clinical stages I-III) who will not do so (Non-Responders).

Linear Discriminant Functions in Connection with the micro-RNA Diagnosis of Colon Cancer.

Early detection (localized stage) of colon cancer is associated with a five-year survival rate of 91%. Only 39% of colon cancers, however, are diagnosed at that early stage. Early and accurate diagnosis, therefore, constitutes a critical need and a decisive factor in the clinical treatment of colon cancer and its success.

Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer.

Following initial standard chemotherapy (platinum/taxol), more than 75% of those patients with advanced stage epithelial ovarian cancer (EOC) experience a recurrence. There are currently no accurate prognostic tests that, at the time of the diagnosis/surgery, can identify those patients with advanced stage EOC who will respond to chemotherapy. Using a novel mathematical theory, we have developed three prognostic biomarker models (complex mathematical functions) that-based on a global gene expression analysis of tumor tissue collected during surgery and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with advanced stage EOC who will respond to the standard chemotherapy [long-term survivors (>7 yrs)] and those who will not do so [short-term survivors (<3 yrs)].

Application of clustering analyses to the diagnosis of Huntington disease in mice and other diseases with well-defined group boundaries.

Nuclear magnetic resonance (NMR) spectroscopy has emerged as a technology that can provide metabolite information within organ systems in vivo. In this study, we introduced a new method of employing a clustering algorithm to develop a diagnostic model that can differentially diagnose a single unknown subject in a disease with well-defined group boundaries. We used three tests to assess the suitability and the accuracy required for diagnostic purposes of the four clustering algorithms we investigated (K-means, Fuzzy, Hierarchical, and Medoid Partitioning).

ROC-supervised principal component analysis in connection with the diagnosis of diseases.

Principal component analysis (PCA) is a data analysis method that can deal with large volumes of data. Owing to the complexity and volume of the data generated by today's advanced technologies in genomics, pro-teomics, and metabolomics, PCA has become predominant in the medical sciences. Despite its popularity, PCA leaves much to be desired in terms of accuracy and may not be suitable for certain medical applications, such as diagnostics, where accuracy is paramount.

Comparison of analytical mathematical approaches for identifying key nuclear magnetic resonance spectroscopy biomarkers in the diagnosis and assessment of clinical change of diseases.

Nuclear magnetic resonance (NMR) spectroscopy is a rapidly emerging technology that can be used to assess tissue metabolic profile in the living animal. At the present time, no approach has been developed 1) to systematically identify profiles of key chemical alterations that can be used as biomarkers to diagnose diseases and to monitor disease progression; and 2) to assess mathematically the diagnostic power of potential biomarkers. To address this issue, we have evaluated mathematical approaches that employ receiver operating characteristic (ROC) curve analysis, linear discriminant analysis, and logistic regression analysis to systematically identify key biomarkers from NMR spectra that have excellent diagnostic power and can be used accurately for disease diagnosis and monitoring.

Effects of prednisolone on skeletal muscle contractility in mdx mice.

Current treatment for Duchenne muscular dystrophy (DMD) is chronic administration of the glucocorticoid prednisolone. Prednisolone improves muscle strength in boys with DMD, but the mechanism is unknown. The purpose of this study was to determine how prednisolone improves muscle strength by examining muscle contractility in dystrophic mice over time and in conjunction with eccentric injury.