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 (9.4 Tesla). 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 wks 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.

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.

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.

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.

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, proteomics, 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.

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-wks of age. Eight wks of prednisolone increased specific force of the EDL 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.