Genetic Studies Identify Critical Biomarkers and Refine the Classification of Malignant Gliomas

Gliomagenesis is driven by a complex network of genetic alterations and while the glioma genome has been a focus of investigation for many years; critical gaps in our knowledge of this disease remain. The identification of novel molecular biomarkers remains a focus of the greater cancer community as a method to improve the consistency and accuracy of pathological diagnosis. In addition, novel molecular biomarkers are drastically needed for the identification of targets that may ultimately result in novel therapeutics aimed at improving glioma treatment. Through the identification of new biomarkers, laboratories will focus future studies on the molecular mechanisms that underlie glioma development. Here, we report a series of genomic analyses identifying novel molecular biomarkers in multiple histopathological subtypes of glioma and refine the classification of malignant gliomas. We have completed a large scale analysis of the WHO grade II-III astrocytoma exome and report frequent mutations in the chromatin modifier, alpha thalassemia mental retardation x-linked (ATRX), isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), and mutations in tumor protein 53 (TP53) as the most frequent genetic mutations in low grade astrocytomas. Furthermore, by analyzing the status of recurrently mutated genes in 363 brain tumors, we establish that highly recurrent gene mutational signatures are an effective tool in stratifying homogeneous patient populations into distinct groups with varying outcomes, thereby capable of predicting prognosis. Next, we have established mutations in the promoter of telomerase reverse transcriptase (TERT) as a frequent genetic event in gliomas and in tissues with low rates of self renewal. We identify TERT promoter mutations as the most frequently mutated gene in primary glioblastoma. Additionally, we show that TERT promoter mutations in combination with IDH1 and IDH2 mutations are able to delineate distinct clinical tumor cohorts and are capable of predicting median overall survival more effectively than standard histopathological diagnosis alone. Taken together, these data advance our understanding of the genetic alterations that underlie the transformation of glial cells into neoplasms and we provide novel genetic biomarkers and multi – gene mutational signatures that can be utilized to refine the classification of malignant gliomas and provide opportunity for improved diagnosis.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Evolution of the sex-related locus and genomic features shared in microsporidia and fungi.

BACKGROUND: Microsporidia are obligate intracellular, eukaryotic pathogens that infect a wide range of animals from nematodes to humans, and in some cases, protists. The preponderance of evidence as to the origin of the microsporidia reveals a close relationship with the fungi, either within the kingdom or as a sister group to it. Recent phylogenetic studies and gene order analysis suggest that microsporidia share a particularly close evolutionary relationship with the zygomycetes. METHODOLOGY/PRINCIPAL FINDINGS: Here we expanded this analysis and also examined a putative sex-locus for variability between microsporidian populations. Whole genome inspection reveals a unique syntenic gene pair (RPS9-RPL21) present in the vast majority of fungi and the microsporidians but not in other eukaryotic lineages. Two other unique gene fusions (glutamyl-prolyl tRNA synthetase and ubiquitin-ribosomal subunit S30) that are present in metazoans, choanoflagellates, and filasterean opisthokonts are unfused in the fungi and microsporidians. One locus previously found to be conserved in many microsporidian genomes is similar to the sex locus of zygomycetes in gene order and architecture. Both sex-related and sex loci harbor TPT, HMG, and RNA helicase genes forming a syntenic gene cluster. We sequenced and analyzed the sex-related locus in 11 different Encephalitozoon cuniculi isolates and the sibling species E. intestinalis (3 isolates) and E. hellem (1 isolate). There was no evidence for an idiomorphic sex-related locus in this Encephalitozoon species sample. According to sequence-based phylogenetic analyses, the TPT and RNA helicase genes flanking the HMG genes are paralogous rather than orthologous between zygomycetes and microsporidians. CONCLUSION/SIGNIFICANCE: The unique genomic hallmarks between microsporidia and fungi are independent of sequence based phylogenetic comparisons and further contribute to define the borders of the fungal kingdom and support the classification of microsporidia as unusual derived fungi. And the sex/sex-related loci appear to have been subject to frequent gene conversion and translocations in microsporidia and zygomycetes.

Long-term dynamics of death rates of emphysema, asthma, and pneumonia and improving air quality.

BACKGROUND: The respiratory tract is a major target of exposure to air pollutants, and respiratory diseases are associated with both short- and long-term exposures. We hypothesized that improved air quality in North Carolina was associated with reduced rates of death from respiratory diseases in local populations. MATERIALS AND METHODS: We analyzed the trends of emphysema, asthma, and pneumonia mortality and changes of the levels of ozone, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matters (PM2.5 and PM10) using monthly data measurements from air-monitoring stations in North Carolina in 1993-2010. The log-linear model was used to evaluate associations between air-pollutant levels and age-adjusted death rates (per 100,000 of population) calculated for 5-year age-groups and for standard 2000 North Carolina population. The studied associations were adjusted by age group-specific smoking prevalence and seasonal fluctuations of disease-specific respiratory deaths. RESULTS: Decline in emphysema deaths was associated with decreasing levels of SO2 and CO in the air, decline in asthma deaths-with lower SO2, CO, and PM10 levels, and decline in pneumonia deaths-with lower levels of SO2. Sensitivity analyses were performed to study potential effects of the change from International Classification of Diseases (ICD)-9 to ICD-10 codes, the effects of air pollutants on mortality during summer and winter, the impact of approach when only the underlying causes of deaths were used, and when mortality and air-quality data were analyzed on the county level. In each case, the results of sensitivity analyses demonstrated stability. The importance of analysis of pneumonia as an underlying cause of death was also highlighted. CONCLUSION: Significant associations were observed between decreasing death rates of emphysema, asthma, and pneumonia and decreases in levels of ambient air pollutants in North Carolina.

Validation of ICDPIC software injury severity scores using a large regional trauma registry.

BACKGROUND: Administrative or quality improvement registries may or may not contain the elements needed for investigations by trauma researchers. International Classification of Diseases Program for Injury Categorisation (ICDPIC), a statistical program available through Stata, is a powerful tool that can extract injury severity scores from ICD-9-CM codes. We conducted a validation study for use of the ICDPIC in trauma research. METHODS: We conducted a retrospective cohort validation study of 40,418 patients with injury using a large regional trauma registry. ICDPIC-generated AIS scores for each body region were compared with trauma registry AIS scores (gold standard) in adult and paediatric populations. A separate analysis was conducted among patients with traumatic brain injury (TBI) comparing the ICDPIC tool with ICD-9-CM embedded severity codes. Performance in characterising overall injury severity, by the ISS, was also assessed. RESULTS: The ICDPIC tool generated substantial correlations in thoracic and abdominal trauma (weighted κ 0.87-0.92), and in head and neck trauma (weighted κ 0.76-0.83). The ICDPIC tool captured TBI severity better than ICD-9-CM code embedded severity and offered the advantage of generating a severity value for every patient (rather than having missing data). Its ability to produce an accurate severity score was consistent within each body region as well as overall. CONCLUSIONS: The ICDPIC tool performs well in classifying injury severity and is superior to ICD-9-CM embedded severity for TBI. Use of ICDPIC demonstrates substantial efficiency and may be a preferred tool in determining injury severity for large trauma datasets, provided researchers understand its limitations and take caution when examining smaller trauma datasets.