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. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

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. APOBEC3B messenger RNA is upregulated in most primary breast tumours and breast cancer cell lines. Tumours that express high levels of APOBEC3B have twice as many mutations as those that express low levels and are more likely to have mutations in TP53. Endogenous APOBEC3B protein is predominantly nuclear and the only detectable source of DNA C-to-U editing activity in breast cancer cell-line extracts. Knockdown experiments show that endogenous APOBEC3B correlates with increased levels of genomic uracil, increased mutation frequencies, and C-to-T transitions. Furthermore, induced APOBEC3B overexpression causes cell cycle deviations, cell death, DNA fragmentation, γ-H2AX accumulation and C-to-T mutations. Our data suggest a model in which APOBEC3B-catalysed deamination provides a chronic source of DNA damage in breast cancers that could select TP53 inactivation and explain how some tumours evolve rapidly and manifest heterogeneity.

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. The overall sensitivity was 97.01%, and the overall specificity was 94.12%. The input variables to the model were the mRNA expression values of the following nine genes: ANGPT1, MED21, AOX1, PLP2, HPN, HPN-AS1, EPHA10, NKX2-3, and LRFN1. The model was validated with unknown samples, with a 10-fold cross-validation, and a leave-one-out cross-validation. We present here a genomic model, based on a whole exome and transcriptome RNA-seq analysis of biopsied prostatic tissue, that could be utilized in the diagnosis of prostate cancer.

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. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

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 = 95.31% and overall specificity = 94.00%) tumor tissue versus normal tissue. The methylation ß values of five CpG sites, corresponding to the genes LINC01091, RPS15,  SNORA10, and two unknown DNA areas in chromosome 1, provided the input to the model. The model was validated with unknown samples, as well as with a sixfold cross-validation and a leave-one-out cross-validation. This study presents a novel genomic model based on genome-wide DNA methylation analysis of biopsied prostatic tissue that could aid in the diagnosis of prostate cancer and help advance the transition to genomic medicine.

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. More specifically, of the 62 STS (short-term survivors), 61 were classified correctly (sensitivity = 98.4%); and of the 40 LTS (long-term survivors), 38 were classified correctly (specificity = 95.0%). The 5 gene expression input variables to the model were: FAM120AOS, MXI1, OCIAD2, PCDH15, and PDLIM4. Of the top 29 most significantly differentially expressed genes between STS and LTS subjects, as identified in the original study, all but one were highly significant. Furthermore, with respect to survival, the model - designed to operate at the molecular level (gene expression of tumor cells) - was also able to statistically differentiate between the two subgroups of the STS group, namely, the STS subjects with lower grade glioma and the STS subjects with glioblastoma; whereas variables either at the tissue level or at the organismal level were not able to do so. Based on these results, and taking into account that accurate clinical prognosis for short-term vs. long-term survival for glioma patients is currently nonexistent, this study provides further, independent evidence for the accuracy and the clinical utility of the model.

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. More specifically, patients with the homozygous ancestral (wild type) genotype (C/C) were 2.91 times more likely (291% increased benefit) to respond to neoadjuvant chemoradiotherapy {95% CI: [1.23, 6.89]; P=0.0150} and 3.25 times more likely (325% increased benefit) not to experience recurrence of the disease {95% CI: [1.37, 7.72]; P=0.0079} than patients with either the heterozygous (C/T) or the homozygous mutation (T/T) genotype. These results identify MTHFR as an important genetic marker and open up new, pharmacogenomic strategies in the treatment and management of rectal cancer.

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. A rigorous, multi-method bioinformatic approach was used to identify the top most differentially expressed genes as captured by mRNA sequencing of tumor tissue. Mathematical modeling was employed to develop the model, and three different and independent methods of validation were used to assess its performance. I present here a mathematical model that can identify with a high accuracy (sensitivity=92.9%, specificity=96.0%) those patients with glioma (grade II or III) who will experience short-term survival (≤ 1 year), as well as those with long-term survival (≥ 3 years), at the time of diagnosis and prior to surgery and adjuvant chemotherapy. The 5 gene input variables to the model are: FAM120AOS, PDLIM4, OCIAD2, PCDH15, and MXI1. MXI1, a transcriptional repressor, represents the top biomarker of survival and the most promising target for the development of a pharmacological treatment.

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. Remarkably, 90% of the known genes from those 36 most significant genes are associated with either inflammation or immune system activation. This suggests that chronic inflammation and immune system over-activity may underlie the aging process of the human brain, and that potential anti-inflammatory treatments targeting those genes may slow down this process and alleviate its symptoms.

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 active in the nucleus of several ovarian cancer cell lines and elicits a biochemical preference for deamination of cytosines in 5'-TC dinucleotides. Importantly, examination of whole-genome sequence from 16 ovarian cancers reveals that APOBEC3B expression correlates with total mutation load as well as elevated levels of transversion mutations. In particular, high APOBEC3B expression correlates with C-to-A and C-to-G transversion mutations within 5'-TC dinucleotide motifs in early-stage high-grade serous ovarian cancer genomes, suggesting that APOBEC3B-catalyzed genomic uracil lesions are further processed by downstream DNA "repair" enzymes including error-prone translesion polymerases. These data identify a potential role for APOBEC3B in serous ovarian cancer genomic instability.

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. In this study, using supervised linear discriminant analysis, we have developed three diagnostic biomarker models that-based on global micro-RNA expression analysis of colonic tissue collected during surgery-can discriminate with a perfect accuracy between subjects with colon cancer (stages II-IV) and normal healthy subjects. We developed our three diagnostic biomarker models with 57 subjects [40 with colon cancer (stages II-IV) and 17 normal], and we validated them with 39 unknown (new and different) subjects [28 with colon cancer (stages II-IV) and 11 normal]. For all three diagnostic models, both the overall sensitivity and specificity were 100%. The nine most significant micro-RNAs identified, which comprise the input variables to the three linear discriminant functions, are associated with genes that regulate oncogenesis, and they play a paramount role in the development of colon cancer, as evidenced in the tumor tissue itself. This could have a significant impact in the fight against this disease, in that it may lead to the development of an early serum or blood diagnostic test based on the detection of those nine key micro-RNAs.

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). Most importantly, both the application and the accuracy of our breast cancer prognostic biomarker model are independent of the status of the hormone receptors ER, PR, and HER2, as well as of the ethnicity and age of the subjects. We developed our prognostic biomarker model with 50 subjects [10 responders (R) and 40 non-responders (NR)], and we validated it with 43 unknown (new and different) subjects [10 responders (R) and 33 non-responders (NR)]. All 93 subjects were recruited at five different clinical centers around the world. The overall sensitivity and specificity of our prognostic biomarker model were 90.0% and 91.8%, respectively. The nine most significant genes identified, which comprise the input variables to the mathematical function, are involved in regulation of transcription; cell proliferation, invasion, and migration; oncogenesis; suppression of immune response; and drug resistance and cancer recurrence.

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). To accomplish this goal, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice and that of wild type (WT) mice using high field in vivo proton NMR spectroscopy (9.4T). We tested all four clustering algorithms (1) with the original R6/2 HD mice and WT mice, (2) with unknown mice, whose status had been determined via genotyping, and (3) with the ability to separate the original R6/2 mice into the two age subgroups (8 and 12 weeks old). Only our diagnostic models that employed ROC-supervised Fuzzy, unsupervised Fuzzy, and ROC-supervised K-means Clustering passed all three stringent tests with 100% accuracy, indicating that they may be used for diagnostic purposes.

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. In this study, we introduced a new PCA method, one that is carefully supervised by receiver operating characteristic (ROC) curve analysis. In order to assess its performance with respect to its ability to render an accurate differential diagnosis, and to compare its performance with that of standard PCA, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice, as well as that of wild type (WT) mice, using high field in vivo proton nuclear magnetic resonance (NMR) spectroscopy (9.4-Tesla). We tested both the standard PCA and our ROC-supervised PCA (using in each case both the covariance and the correlation matrix), 1) with the original R6/2 HD mice and WT mice, 2) with unknown mice, whose status had been determined via genotyping, and 3) with the ability to separate the original R6/2 mice into the two age subgroups (8 and 12 wks old). Only our ROC-supervised PCA (both with the covariance and the correlation matrix) passed all tests with a total accuracy of 100%; thus, providing evidence that it may be used for diagnostic purposes.

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)]. Our three prognostic biomarker models were developed with 34 subjects and validated with 20 unknown (new and different) subjects. Both the overall biomarker model sensitivity and specificity ranged from 95.83% to 100.00%. The 12 most significant genes identified, which are also the input variables to the three mathematical functions, constitute three distinct gene networks with the following functions: 1) production of cytoskeletal components, 2) cell proliferation, and 3) cell energy production. The first gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a significant impact in the discovery of new, more effective pharmacological treatments that may significantly extend the survival of patients with advanced stage EOC.

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. To validate our mathematical approaches, we studied the striatal concentrations of 17 metabolites of 13 R6/2 transgenic mice with Huntington's disease, as well as those of 17 wild-type (WT) mice, which were obtained via in vivo proton NMR spectroscopy (9.4 Tesla). We developed diagnostic biomarker models and clinical change assessment models based on our three aforementioned mathematical approaches, and we tested all of them, first, with the 30 original mice and, then, with 31 unknown mice. Their prediction results were compared with genotyping-the gold standard. All models correctly diagnosed all of the 30 original mice (17 WT and 13 R6/2) and all of the 31 unknown mice (20 WT and 11 R6/2), with a positive likelihood ratio approximating infinity [1/0 (→ ∞)], and with a negative likelihood ratio equal to zero [0/1 = 0].

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. Mdx mice began receiving prednisolone (n = 23) or placebo (n = 16) at 5 weeks of age. Eight weeks of prednisolone increased specific force of the extensor digitorum longus muscle 26%, but other parameters of contractility were not affected. Prednisolone also improved the histological appearance of muscle by decreasing the number of centrally nucleated fibers. Prednisolone treatment did not affect force loss during eccentric contractions or recovery of force following injury. These data are of clinical relevance, because the increase in muscle strength in boys with DMD taking prednisolone does not appear to occur via the same mechanism in dystrophic mice.