2010 — 2014 |
Fertig, Elana Judith |
K25Activity Code Description: Undocumented code - click on the grant title for more information. |
Identifying Malignant Cell Signaling From Protein Interactions An Polyomic Data @ Johns Hopkins University
DESCRIPTION (provided by applicant): The proposed award plan combines didactic training and hands-on research to supplement Dr. Fertig's postdoctoral experience in Johns Hopkins Oncology Biostatistics with biological proficiency, complementing her mathematical background. Moreover, this training will enable Dr. Fertig to pursue pertinent research questions and fruitful, multi- disciplinary collaborations in her future career as an independent computational oncologist. The primary focus of this proposal is the development of quantitative models of the biological processes underlying the development and maintenance of tumors Mentors Michael Ochs, PhD of Oncology Biostatistics, and Joseph Califano, MD of Head and Neck Research, at Johns Hopkins will foster Dr. Fertig's proposed training and hands-on research, including providing insight to statistical methods in computational biology and to the biology and clinical treatment of head and neck cancer respectively. Dr. Fertig will apply her experience in merging dynamic models with indirect measurements from Numerical Weather Prediction to inferring relevant biological processes from patient measurements. With the support of her mentors, she will develop algorithms that infer driver processes underlying malignancies in an individual patient's tumor. In the proposed techniques, Dr. Fertig will infer transcription factor activity in head and neck cancer downstream of the malignant processes by integrating gene expression measurements with epigenetic measurements, EGFR protein-protein interaction network structure, and therapeutic strategy. By merging these diverse data sources, this tool is hypothesized to have the statistical power to accurately represent the probability of activation of specific transcription factors resulting from the modeled processes in head and neck cancer, which will be further validated through targeted cell line experiments. Thus, this research will develop tools that, when migrated to the clinic, will assist clinicians in identifying the appropriate choice of targeted therapeutics to treat an individual's cancer. PUBLIC HEALTH RELEVANCE: Project Narrative Advanced head and neck tumors have a 50% cure rate in spite of combined treatment modalities. This project will merge biological structure with head and neck tumor measurements to pinpoint specific pathways that drive individual tumor development, and thus assess personalized treatment plans for that patient. Although applied to head and neck cancers, the developed tools will have broad clinical implications.
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0.939 |
2014 — 2019 |
Fertig, Elana Judith |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Dynamical Models of Cetuximab Resistance in Hnscc Based On Serial Genomics Data @ Johns Hopkins University
DESCRIPTION (provided by applicant): Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer worldwide, with only a 50% cure rate in spite of combined treatment modalities. Therapeutic targeting of the epidermal growth factor receptor (EGFR) improves the survival in a subset of patients, although molecular predictors of sensitivity are currently elusive. Moreover, responsive patients often acquire resistance and ultimately succumb to their disease. Distinguishing the specific molecular processes that drive such therapeutic resistance amid complex cross-talk in cell signaling processes and stochastic evolutionary pressures requires dynamical models built from serial data. Therefore, in this application, we develop novel computational algorithms to infer the molecular mechanisms underlying cetuximab resistance from in vitro and in vivo model of cetuximab resistant HNSCC. Specifically, we will investigate the hypotheses that: (1) short-term time course data improve the ability of in silicon modeling techniques to infer both on- and off-target signaling responses to cetuximab; (2) combined epigenetic, post-transcriptional, and genomic changes in HNSCC cells upon chronic exposure to cetuximab result in acquired resistance; and (3) modeling inter and intra-individual heterogeneity will discern the specific cellular signaling processes that are activated to drive in vivo acquired cetuximab resistance in cell- line xenograft models of HNSCC. The results from this project will ultimately contribute to the selection of patients for cetuximab treatment and alternative molecular targets to overcome acquired cetuximab resistance. The algorithms developed will also be directly applicable to inference of molecular drivers of therapeutic resistance in additional cancers.
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0.939 |
2018 — 2020 |
Ewald, Andrew Josef (co-PI) [⬀] Fertig, Elana Judith Popel, Aleksander S. (co-PI) [⬀] Tran, Phuoc T (co-PI) [⬀] |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Integrating Bioinformatics Into Multiscale Models For Hepatocellular Carcinoma @ Johns Hopkins University
Project Summary Liver cancer is a major global health problem, responsible for the 3rd most cancer deaths worldwide. Diagnosis often occurs at late stages, at which point liver tumors have complex tumor/stroma interactions across multiple spatial and temporal scales. The resulting multiscale interactions drive tumor progression and therapeutic response. The proposed project will develop new mathematical/computational techniques to model molecular, cellular, tumor, and organ scales to elucidate the mechanisms driving liver cancer progression and to predict the response to targeted therapeutics. The investigator team is uniquely suited to develop the proposed multiscale models of hepatocellular carcinoma (HCC), the most common type of liver cancer. The expertise of the four PIs/PDs is synergistic, combining a state of the art multiscale computational models of cancer (Dr. Popel) with molecular and cellular features inferred from bioinformatics analysis (Dr. Fertig) using state of the art 3D in vitro organoid models (Dr. Ewald) and in vivo mouse models of HCC (Dr. Tran). The well-integrated experimental/computational design of the proposal will result in new algorithms for predictive computational modeling of therapeutic response in HCC. We include extensive experimental studies for model development, parameter tuning, and validation. Specific Aim 1 will infer bioinformatically the signaling pathways important in crosstalk between cancer and stromal cells, integrate models of intracellular signaling and 3D extracellular ligand transport and biochemical reactions and embed them into the cell fate decision rules of an agent-based model of cellular agents resulting in a multiscale hybrid model. The model will be parameterized with phospho- proteomic data under relevant ligand stimulations identified by the bioinformatics analysis and with growth, invasion, proteomic, and genomic data from co-cultured cancer and stromal cells and organoids; independent data will be used for model validation. We will use this model to predict outcomes in a 3D in vitro organoid model of HCC. Specific Aim 2 will extend and adapt this hybrid model to model the tumor microenvironment and to account for the drug pharmacokinetic and pharmacodynamic, the 3D geometry of the liver, molecular interactions in vivo and cellular composition inferred from bioinformatics analysis. Finally, Specific Aim 3 will develop new bioinformatics analysis algorithms to initialize the model with distribution of cellular agents and molecular states from The Cancer Genome Atlas (TCGA) genomic and proteomic data to predict the efficacy of targeted therapeutics in the diverse genetic backgrounds of human liver cancer. The project will develop innovative computational techniques to integrate features at both the molecular and cellular scales from genomics and proteomics analysis with multiscale computational models to predict therapeutic response. The resulting computational algorithms will address the IMAG cutting edge challenge of fusing data-rich and data- poor scales for predictive multiscale computational modeling of biological systems.
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0.939 |
2020 — 2021 |
Fertig, Elana Judith |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Single-Cell and Imaging Data Integration Software to Spatially Resolve the Tumor Microenvironment @ Johns Hopkins University
Project Summary The tumor microenvironment (TME) plays a critical role in cancer progression and therapeutic response. New single cell and imaging technologies provide unprecedented measurements of cell type composition, subtypes of common cell types in disparate states, interactions between neighboring cells, and T cell function. However, each of these components of the TME are captured by disparate measurement technologies. Both new computational methods and software for multi-platform data integration are essential to characterize the TME. Therefore, we propose a unified R/Bioconductor package TMEMap for multi-platform single cell data integration. Aim 1 will integrate single cell RNA-sequencing and single cell TCR-sequencing data to distinguish T cell function in distinct T cell subtypes and states. Aim 2 will integrate combined single cell RNA-sequencing and protein from CITE-seq with imaging proteomics for digital pathology to map cellular interactions in the TME. Aim 3 will further the disseminate this software with through GenePattern Notebook. This workflow will be developed in collaboration with clinical investigators to ensure usability and interpretability of the visualization methods using clinical biospecimens from synergistic studies. Altogether, this software will provide a strong foundation for future work embedding these methods in a database for multi-platform single cell data to automatically perform comprehensive TME characterization in large scale NCI profiling efforts such as the Human Tumor Atlas Network.
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0.939 |
2021 |
Fertig, Elana Judith |
P01Activity Code Description: For the support of a broadly based, multidisciplinary, often long-term research program which has a specific major objective or a basic theme. A program project generally involves the organized efforts of relatively large groups, members of which are conducting research projects designed to elucidate the various aspects or components of this objective. Each research project is usually under the leadership of an established investigator. The grant can provide support for certain basic resources used by these groups in the program, including clinical components, the sharing of which facilitates the total research effort. A program project is directed toward a range of problems having a central research focus, in contrast to the usually narrower thrust of the traditional research project. Each project supported through this mechanism should contribute or be directly related to the common theme of the total research effort. These scientifically meritorious projects should demonstrate an essential element of unity and interdependence, i.e., a system of research activities and projects directed toward a well-defined research program goal. |
Core C: Genomics and Bioinformatics @ Johns Hopkins University
PROJECT SUMMARY The Genomics and Bioinformatics Core (Core C) will provide state-of-the-art sequencing technologies to enable immunogenomic and epigenomic measurements and corresponding bioinformatics analyses for the proposed P01 Program Projects. This Core will deploy a wide range of bulk and single cell next generation sequencing technologies optimized for the preclinical and clinical biospecimens in the Projects. Technologies supported by this Core include: whole exome sequencing for neoantigen prediction, bulk and single cell RNA-sequencing for inference of molecular and cellular networks, bulk and single cell T cell receptor sequencing to query T cell clones and their associated function, and ATAC-seq and ChIP-seq technologies to query epigenetic and tran- scriptional regulation. These technologies are complemented by optimized bioinformatics pipelines and compu- tational methods tailored to immunogenomics developed by the Core and Program Investigators. Bioinformatics methods are tailored to customized analyses of the datasets generated in Program Projects, and proteomics measurements from the Digital Pathology and Mass Cytometry Core (Core D) to support TME characterization. This Genomics and Bioinformatics Core will perform further integrative analyses of biospecimens of the thera- peutic regimens employed across Projects in this Program and promote Program Synergy. The computational methods employed in this Core will summarize the high-dimensional data to provide low dimensional summaries to the Biostatistics and Clinical Trials Core (Core B) to identify molecular and cellular determinants of immuno- therapy response and resistance to support the Projects in defining new therapeutic strategies for precision immunotherapy. The Core Co-Leaders combine expertise in genomics technologies (Dr. Yegnasubramanian) and bioinformatics (Dr. Fertig). In addition to the Program-specific goals, this Core further leverages the existing infrastructure in the NCI-designated Johns Hopkins University SKCCC Core Grant-supported Experimental and Computational Genomics Core (ECGC; Co-Director Yegnasubramanian; Co-Investigator Fertig). Both Leaders, Fertig and Yegnasubramanian, have an established track record in Genomics and Bioinformatics in collaboration with one another and Program Investigators ideally suited to enable the profiling and bioinformatics analysis proposed for the Program in this Core.
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0.939 |