All students in the MD/MS in Systems Medicine program are required to undergo a practicum as part of their degree requirements. The practicum typically takes one year to complete and is conducted in parallel with the coursework. Students will also work with a mentor. Below you will find two examples of typical practica projects as well as a list of current mentors currently working with our students. Upon completion, the practicum is expected to result in a peer-reviewed publication.
Examples of Practicum Projects
- Example 1: Microarray data identification, annotation, and quality assessment
- Example 2: Building and testing probabilistic models of breast cancer prognosis
MD/MS SYSTEMS MEDICINE: EXAMPLE PRACTICA Breast cancer prognosis.
We offer two example practica based on projects in breast cancer. Until recently, assessments of breast cancer prognosis (outcome independent of therapy) were based largely by reference to population-based data. Thus, determining an individual patient’s prognosis was challenging and often inaccurate. Several recent studies have described molecular predictors of breast cancer prognosis that should provide a more reliable and accurate assessment of an individual’s true prognosis. These have been the result of gene expression microarray studies (MammaPrint, Agendia Inc.), or studies using preselected mRNA expression values obtained by quantitative PCR (OncotypeDX; Genomic Health, Inc.). The accuracy and robustness of these measures remain uncertain and validation in large independent data sets has been difficult. Additional classifiers of prognosis continue to appear in the literature but, to date, only the MammaPrint predictor has been approved by the FDA. Over the past few years, multiple breast cancer data sets have been published that provide the potential to derive and independently validate additional classifiers, and to compare their performance against these existing models. There are several potential projects available in the context of this work, e.g., understanding data structure in the context of the clinical disease, generating clinically useful classifiers of patient prognosis, finding gene patterns that may mechanistically explain different breast cancer phenotypes.
Two such projects are hereby defined .
- Microarray data identification, annotation, and quality assessment In this practicum, students will identify appropriate breast cancer data sets, determine the nature and structure of these datasets, download and install local copies, assess the quality of the data and its annotation, and prepare consistently annotated and normalized data for subsequent analysis by others.
The data include information on (i) clinical study design (e.g., inclusion/exclusion criteria, comparability of patient populations across data sets, key clinical and associated data required for later analysis, HIPAA implications for access to clinical data); (ii) microarray experimental design, (e.g., different microarray platforms, general MIAME requirements, (iii) data quality and quantity of clinical and microarray data (e.g., are the data adequately annotated, is the correct data available to allow analysis, are there missing data and how might these preclude or limit the use of a data set). Students will learn the key clinical characteristics of breast cancer in the context of its biology, histopathology, diagnosis, and clinical course as needed to understand the nature, quality and utility of the clinical data collected, what these represent to oncologists, and which clinical data are likely to be useful for later data analysis. The end-product will be the collection and annotation of breast cancer data sets that can be used by others to extract other prognostic signatures and/or to explore the possible use of individual genes as independent biomarkers. It is likely that the students will be given a list of individual biomarkers of interest to explore towards the end of their practicum.
- Building and testing probabilistic models of breast cancer prognosis In this practicum, students will explore existing (publicly available) breast cancer data sets to find gene expression patterns associated with prognosis. Initial datasets are already available and downloaded, other data sets will eventually be available from the efforts of students working in practicum #1.
The goal will be to use one data set as a training set to find differentially expressed genes that can be used to build an in silico predictor of breast cancer prognosis. These genes will then be used as the input to several statistical models to train a predictor of prognosis (model output will be an appropriate measure of prognosis, e.g., disease recurrence or overall survival). Using one or more independent data sets also publicly available), the performance of the predictors will be evaluated and compared with the performance of known classifiers, e.g., MammaPrint. Performance will include ROC, NPV and PPV measures and the generation and interpretation of standard Kaplan-Meier plots. For data interpretation ad for model building, students will learn the key clinical characteristics of breast cancer in the context of its biology, histopathology, diagnosis, and clinical course as needed to understand the nature, quality and utility of the prognostic model.
Dr. Amrita K Cheema
Associate Professor (Research)
Director of Operations (Proteomics and Metabolomics Shared Resource)
Lombardi Comprehensive Cancer Center
GD9 Preclinical Science
Georgetown University Medical Center
Dr. Subha Madhavan
Director | Innovation Center for Biomedical Informatics | Georgetown University Medical Center
Director | Clinical Informatics | Lombardi Comprehensive Cancer Center
Director| Biomedical Informatics | Georgetown-Howard Universities CTSA
Associate Professor |Department of Oncology |Georgetown University
2115, Wisconsin Avenue, Suite 110
Joseph G. Verbalis, MD
Professor of Medicine
Chief, Endocrinology and Metabolism
Co-Director, Georgetown-Howard Universities Center for Clinical and Translational Science
Georgetown University Medical Center
4000 Reservoir Rd NW, 232 Building D
Washington, DC 20007
Paul D. Roepe
Professor of Chemistry
Professor of Biochemistry & Cell. and Mol. Biol.
Co - director, Center for Infectious Diseases
(202) 687 - 7300
Dr. Craig Thomas, Ph.D.
Chemical Biology Laboratory
Center for Cancer Research, National Cancer Institute
Building B, Room 3005, 9800 Medical Center Drive
Rockville, MD 20850-3370