Class of 2015 Abstract / Objectives: The aim of this study was to develop a computational process capable of hypothesizing potential chemotherapeutic agents for the treatment of skin cutaneous melanoma given an annotated chemotherapy molecular target database and patient-specific genetic tumor profiles. Methods: Aberrational profiles for a total of 246 melanoma patients indexed by the Cancer Genome Atlas (TCGA) for whom complete somatic mutational, mRNA expression, and protein expression data was available were queried against an annotated targeted therapy database using Visual Basic for Applications and Python in conjunction with Microsoft Excel. Identities of positively and negatively associated therapy-profile matches were collected and ranked. Results: Subjects included in the analysis were predominantly Caucasian (93%), non-Hispanic (95.9%), female (59%), and characterized as having stage III clinical disease (37.4%). The most frequently occurring positive and negative therapy associations were determined to be 17-AAG (tanespimycin; 42.3%) and sorafenib (41.9%), respectively. Mean total therapy hypotheses per patient did not differ significantly with regard to either positive or negative associations (p=0.1951 and 0.4739 by one-way ANOVA, respectively) when stratified by clinical melanoma stage. Conclusions: The developed process does not appear to offer discernably different therapy hypotheses amongst clinical stages of cutaneous melanoma based upon genetic data alone. The therapy-matching algorithm may be useful in quickly retrieving potential therapy hypotheses based upon the genetic characteristics of one or many subjects specified by the user.

Deregulation of microRNAs (miRNAs) is implicated in tumor progression. We attempt to indentify the tumor suppressive miRNA not only down-regulated in glioblastoma multiforme (GBM) but also potent to inhibit the oncogene EZH2, and then investigate the biological function and pathophysiologic role of the candidate miRNA in GBM. In this study, we show that miRNA-138 is reduced in both GBM clinical specimens and cell lines, and is effective to inhibit EZH2 expression. Moreover, high levels of miR-138 are associated with long overall and progression-free survival of GBM patients from The Cancer Genome Atlas dataset (TCGA) data portal. Ectopic expression of miRNA-138 effectively inhibits GBM cell proliferation in vitro and tumorigenicity in vivo through inducing cell cycles G1/S arrest. Mechanism investigation reveals that miRNA-138 acquires tumor inhibition through directly targeting EZH2, CDK6, E2F2 and E2F3. Moreover, an EZH2-mediated signal loop, EZH2-CDK4/6-pRb-E2F1, is probably involved in GBM tumorigenicity, and this loop can be blocked by miRNA-138. Additionally, miRNA-138 negatively correlates to mRNA levels of EZH2 and CDK6 among GBM clinical samples from both TCGA and our small amount datasets. In conclusion, our data demonstrate a tumor suppressive role of miRNA-138 in GBM tumorigenicity, suggesting a potential application in GBM therapy.

Deregulation of microRNAs (miRNAs) is implicated in tumor progression. We attempt to indentify the tumor suppressive miRNA not only down-regulated in glioblastoma multiforme (GBM) but also potent to inhibit the oncogene EZH2, and then investigate the biological function and pathophysiologic role of the candidate miRNA in GBM. In this study, we show that miRNA-138 is reduced in both GBM clinical specimens and cell lines, and is effective to inhibit EZH2 expression. Moreover, high levels of miR-138 are associated with long overall and progression-free survival of GBM patients from The Cancer Genome Atlas dataset (TCGA) data portal. Ectopic expression of miRNA-138 effectively inhibits GBM cell proliferation in vitro and tumorigenicity in vivo through inducing cell cycles G1/S arrest. Mechanism investigation reveals that miRNA-138 acquires tumor inhibition through directly targeting EZH2, CDK6, E2F2 and E2F3. Moreover, an EZH2-mediated signal loop, EZH2-CDK4/6-pRb-E2F1, is probably involved in GBM tumorigenicity, and this loop can be blocked by miRNA-138. Additionally, miRNA-138 negatively correlates to mRNA levels of EZH2 and CDK6 among GBM clinical samples from both TCGA and our small amount datasets. In conclusion, our data demonstrate a tumor suppressive role of miRNA-138 in GBM tumorigenicity, suggesting a potential application in GBM therapy.

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Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.

Methods: We address these issues by transforming the TCGA data into the Semantic Web standard Resource Description Format (RDF), link it to relevant datasets in the Linked Open Data (LOD) cloud and further propose an efficient data distribution strategy to host the resulting 20.4 billion triples data via several SPARQL endpoints. Having the TCGA data distributed across multiple SPARQL endpoints, we enable biomedical scientists to query and retrieve information from these SPARQL endpoints by proposing a TCGA tailored federated SPARQL query processing engine named TopFed. Results: We compare TopFed with a well established federation engine FedX in terms of source selection and query execution time by using 10 different federated SPARQL queries with varying requirements. Our evaluation results show that TopFed selects on average less than half of the sources (with 100% recall) with query execution time equal to one third to that of FedX. Conclusion: With TopFed, we aim to offer biomedical scientists a single-point-of-access through which distributed TCGA data can be accessed in unison. We believe the proposed system can greatly help researchers in the biomedical domain to carry out their research effectively with TCGA as the amount and diversity of data exceeds the ability of local resources to handle its retrieval and parsing.

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy associated with molecular heterogeneity, locoregional spread, resistance to therapy and relapse after initial treatment. Increasing evidence suggests that master developmental pathways with key roles in adult tissue homeostasis, including Hippo and Wnt/β-catenin signaling, are dysregulated in the initiation and progression of HNSCC. However, a comprehensive investigation into the crosstalk between these pathways is currently lacking, and may prove crucial to the discovery of novel targets for HNSCC therapy. More recent evidence points to the tumor microenvironment, mainly comprising cancer-associated fibroblasts (CAFs), as capable of influencing tumor cell behavior and promoting invasive HNSCC phenotypes. Nonetheless, current methods to screen for CAF markers in tumors are restricted to targeted immunostaining experiments with limited success and robustness across tissue types. The Cancer Genome Atlas network has generated multi-tiered molecular profiles for over 10,000 tumors spanning more than two dozen different cancer types, providing an unprecedented opportunity for the application and development of integrative methods aimed at the in silico interrogation of experimentally-derived signatures. These multi-omic profiles further enable one to link genomic anomalies, including somatic mutations and DNA copy number alterations, with phenotypic effects driven by pathogenic pathway activity. Effectively querying this vast amount of information to help elucidate subsets of functionally and clinically-relevant oncogenic drivers, however, remains an ongoing challenge. To address these issues, I first investigate the effects of oncogenic pathway perturbation in HNSCC using experimental models coupled with in vitro genome-wide transcriptional profiling. Next, I describe a new computational approach for the unbiased identification of CAF markers in HNSCC solely using bulk tumor RNA-sequencing information. Lastly, I have developed Candidate Driver Analysis or CaDrA - a statistical framework that allows one to query genetic and epigenetic alterations for candidate drivers of signature activity within a given disease context. Collectively, this work offers new perspectives on the molecular cues underlying HNSCC development, while simultaneously highlighting the power of integrative genomics methods capable of accelerating the discovery of novel targets for cancer diagnosis and therapy.

No description available.

Gene expression regulation encompasses a wide range of mechanisms that govern cellular processes. Among these, post-transcriptional regulation, including translation control, plays a pivotal role in ensuring precise protein synthesis, timing, and quantity. Perturbations of mechanisms such as RNA modifications, and interactions between RNA-binding proteins (RBPs) and specific RNA motifs, can lead to dysregulation of essential cellular processes. These alterations contribute to the development of various disorders, including cancer, neurodegenerative diseases, and metabolic disorders. Many publicly available datasets and studies offer opportunities to investigate the link between alterations in these mechanisms and disease manifestations. However, the limited availability of datasets for certain conditions or notable inconsistencies among reported associations prevent complete understanding of the underlying processes. Therefore, extending the investigations to encompass a diverse range of genes and/or diseases will enhance our comprehension of these intricate regulatory and disease mechanisms, aiding in the identification of potential therapeutic targets and innovative interventions to mitigate pathological conditions. In particular, we focused on three separate aspects involved in gene expression regulation: RNA modifications, RBPs interactions with RNA secondary structures, and the Kozak consensus sequence as a translational modulator. Each part uncovers essential mechanisms that govern post-transcriptional control of gene expression, shedding light on their roles in cellular processes and disease contexts. At first, we performed a comprehensive exploration of 15 RBPs involved in the regulation of the N6-methyladenosine (m6A) methylation. Leveraging data from The Cancer Genome Atlas (TCGA), we conducted a pan-cancer analysis across 31 tumor types to uncover the distribution of alterations of these factors, and we developed a user-friendly web application to enable users to conduct similar analyses. Additionally, we performed a parallel analysis focused on neuroblastoma, using data from publicly available and in-house datasets. These investigations unveil the potential impact of a subset of m6A factors on cancer development and progression. While in the first case, VIRMA and YTHDF reader proteins, emerged as the most frequently altered genes with significant pan-cancer prognostic implications, in the context of neuroblastoma, the writer METTL14 and the reader ALKBH5, showed the most prominent roles. Subsequently, our focus shifted to a distinct subset of RBPs capable of interacting with RNA secondary structures, particularly with RNA G-quadruplexes (RG4s). We established a comprehensive database cataloging RBPs with potential RG4-binding capabilities. This resource represents a valuable tool for researchers aiming to explore the intricate interplays between RBPs and RG4s, and their putative implications in diverse biological processes and diseases. Finally, attention was directed to the Kozak sequence, a pivotal determinant of the regulation of translation initiation. Exploiting the power of base editors, we developed a method to optimize translation initiation by modifying the Kozak sequence. This strategy offers promise in addressing haploinsufficiency-related disorders, where enhancing the functional protein is essential. Overall, these findings present opportunities for the identification of potential therapeutic targets and precision medicine strategies to alleviate a spectrum of pathological conditions, thus fostering advancements in the field of molecular biology and disease management.