Phylogenetic simulation of promoter evolution: estimation and modeling of binding site turnover events and assessment of their impact on alignment tools

Phylogenetic simulation of promoter evolution were used to analyze functional site turnover in regulatory sequences

Utilization of Genomic Signatures to Identify Phenotype-Specific Drugs

Genetic and genomic studies highlight the substantial complexity and heterogeneity of human cancers and emphasize the general lack of therapeutics that can match this complexity. With the goal of expanding opportunities for drug discovery, we describe an approach that makes use of a phenotype-based screen combined with the use of multiple cancer cell lines. In particular, we have used the NCI-60 cancer cell line panel that includes drug sensitivity measures for over 40,000 compounds assayed on 59 independent cells lines. Targets are cancer-relevant phenotypes represented as gene expression signatures that are used to identify cells within the NCI-60 panel reflecting the signature phenotype and then connect to compounds that are selectively active against those cells. As a proof-of-concept, we show that this strategy effectively identifies compounds with selectivity to the RAS or PI3K pathways. We have then extended this strategy to identify compounds that have activity towards cells exhibiting the basal phenotype of breast cancer, a clinically-important breast cancer characterized as ER-, PR-, and Her2- that lacks viable therapeutic options. One of these compounds, Simvastatin, has previously been shown to inhibit breast cancer cell growth in vitro and importantly, has been associated with a reduction in ER-, PR- breast cancer in a clinical study. We suggest that this approach provides a novel strategy towards identification of therapeutic agents based on clinically relevant phenotypes that can augment the conventional strategies of target-based screens

Origin of bistability underlying mammalian cell cycle entry

Mammalian cell cycle entry is controlled at the restriction point by a bistable and resettable switch, which is shown to emerge from a minimal gene circuit containing a mutual-inhibition feedback loop between Rb and E2F modules, coupled with a feed-forward loop between Myc and E2F modules

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

BACKGROUND: The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. RESULTS: In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. CONCLUSION: Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression

A Role for E2F Activities in Determining the Fate of Myc-Induced Lymphomagenesis

The phenotypic heterogeneity that characterizes human cancers reflects the enormous genetic complexity of the oncogenic process. This complexity can also be seen in mouse models where it is frequently observed that in addition to the initiating genetic alteration, the resulting tumor harbors additional, somatically acquired mutations that affect the tumor phenotype. To investigate the role of genetic interactions in the development of tumors, we have made use of the Eμ-myc model of pre-B and B cell lymphoma. Since various studies point to a functional interaction between Myc and the Rb/E2F pathway, we have investigated the role of E2F activities in the process of Myc-induced lymphomagenesis. Whereas the absence of E2F1 and E2F3 function has no impact on Myc-mediated tumor development, the absence of E2F2 substantially accelerates the time of tumor onset. Conversely, tumor development is delayed by the absence of E2F4. The enhanced early onset of tumors seen in the absence of E2F2 coincides with an expansion of immature B lineage cells that are likely to be the target for Myc oncogenesis. In contrast, the absence of E2F4 mutes the response of the lineage to Myc and there is no expansion of immature B lineage cells. We also find that distinct types of tumors emerge from the Eμ-myc mice, distinguished by different patterns of gene expression, and that the relative proportions of these tumor types are affected by the absence of either E2F2 or E2F4. From these results, we conclude that there are several populations of tumors that arise from the Eμ-myc model, reflecting distinct populations of cells that are susceptible to Myc-mediated oncogenesis and that the proportion of these cell populations is affected by the presence or absence of E2F activities

The Grizzly, May 3, 1985

Reimert Hall Will Welcome Girls in the Fall • Fraternities Are Still Alive at Ursinus • Ursinus Applicants Improve • Letters: Greek Week Disappointing; Radio Offers Thanks • Drinking Age of 21 Should Not Be a Standard • Profile: Dr. Coggers Says Farewell • Greek Week\u27s Final Results • Lacrosse Looks to Repeat Division III Title • Successful Year for Lacrosse Club • Gasser Retires • Sally Grim Shines As Star Pitcher • Griffin Worth Far More than Gold • Trackmen Head to MAC\u27s • Stormy Baver is Pilot Behind the Plate • Golf Team Optimistic • Visit the Writing Center • 1985 Baseball Wraps it Up • 1985 Lacrosse Stats • St. Joseph\u27s M.B.A Courses Offered at Ursinus • Open Dialog On Intervention • Area Residents Share College Memories • Shorts: Faculty Members to Retire; Open Dialog; Color Analysis Held on Campus; Evening Concert Announced; Voices ; Art Show • Dead Kennedys • WVOU Conducts Survey • Luau on Sat. • Weekend Highlights • It Will Be a Fantasy Weekendhttps://digitalcommons.ursinus.edu/grizzlynews/1142/thumbnail.jp

An Integrated Approach to the Prediction of Chemotherapeutic Response in Patients with Breast Cancer

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities

Stochastic E2F Activation and Reconciliation of Phenomenological Cell-Cycle Models

A new, stochastic model of entry into the mammalian cell cycle provides a mechanistic understanding of the temporal variability observed across populations of cells and reconciles previously proposed phenomenological cell-cycle models