Frederick National Lab for Cancer Research Internship Opportunities

Summer Research Projects at Frederick National Laboratory for Cancer Research

Frederick National Laboratory for Cancer Research has several summer internship opportunities available in: Cancer Biology, Cell Biology, Molecular Biology, Data Analytics/Data Science, Computer Science, Math/Stats, Pharmacology, Genomics, Bioinformatics, Biotechnology, Clinical & Translational Research, Clinical Quality, Safety, and Leadership, Health Informatics & Data Science, Health Physics, Machine Learning, Computational Chemistry, Molecular Dynamics, Microscopy, and Assay Development . The internship dates are flexible (between mid-May and mid-August) based on the student’s availability and the needs of the mentor. Internships usually last 10 weeks.

These opportunities are open to undergraduate rising seniors, master’s and PhD students (unless otherwise noted). Undergraduate rising juniors will be considered, at the discretion of the mentors.

Funds for stipends may be available through Georgetown University. More information on stipends will be provided during the application and selection period.

Below are opportunity areas for Summer 2020. You may express interest for these opportunities here, beginning November 20, 2019.

Would you like more information? Please watch the information session video below. Please contact Peter Luger directly with any questions (peter.luger@georgetown.edu)

Information Session (recorded)

Information Session Slides

Questions? Contact Peter Luger at peter.luger@georgetown.edu

Applications will be reviewed and internship interviews will be scheduled in January. Interviews can be done remotely if traveling to the lab in January is difficult. Internship matches will be made in February.

Internship Opportunities for Summer 2020

  1. Development of Assays to Assess Neutrophil Function: The Neutrophil Monitoring Lab is seeking a dedicated graduate student to assist in the development of assays to assess neutrophil function in patients with immune dysfunction. We are currently in the process of optimizing an assay to assess the staphylocidal activity of phagocytes. We are hoping to expand our capabilities by extending the studies to include gram (-) bacteria (such as E.coli). In addition, we want to develop assays to assess Neutrophil Extracellular Trap (NET) formation. Under specific conditions, neutrophils, which normally exhibit a multi-lobular nucleus, will undergo decondensation of the chromatin material and dissolution of the nuclear membrane. Eventually a DNA/histone/neutrophil granule complex is expelled from the cells as a NET that can entrap extracellular bacteria and promote the killing of the extracellular bacteria. These assays will be used to further characterize neutrophil dysfunction in patients with recurrent bacterial and fungal infections.
  2. Characterization of function of novel RNAs induced by Interleukin-27: Laboratory of Human Retrovirology and Immunoinformatics (LHRI) has been involved in translational research of HIV infected patients, to support NIAID clinical studies. Our goal is to develop novel anti-viral therapy for patients infected with multi-drug resistance (MDR) virus strains. LHRI has investigated mechanisms of (1) MDR in HIV-1 infected patients and sought novel anti-HIV protein(s) and (2) novel innate immune response to suppress MDR HIV infection. We have identified Interlukine-27(IL-27) as a novel anti-virus cytokine that inhibits infection of not only MDR HIV strains but also Hepatitis C virus, flu virus and herpes simplex virus (Blood,2007, AIDS 2008, PlosOne 2013, JEM 2013). LHRI has elucidated that Ku70, a DNA repair protein, plays a key role to induce a novel innate immune response (J.I. 2011, Nuc. Acid. Res, 2014, Sci Signal 2017) and discovered that it induces type-III Interferon. In the course of studies, we have discovered novel none-coding RNAs ( 84 of small RNA: microRNAs (miRs) (BBRC 2013, Sic. Int..J.Med.Sci 2017) and 100 of long RNAs: LncRNAs [a manuscript has been submitted]) which are differentially regulated by IL-27 and may directly or indirectly regulate virus replication. Of note, we defined that one of miRs processes an anti-cervical cancer effect (Sci. Report 2018). We propose a trainee a project related with the innate immune response via miR and LncRNA. In the project, multiple functional analysis of the RNAs will be conducted. Assays and techniques applied in this projects will be: cell culture of several cell types (primary human cells or human cell lines), virus (HIV, HSV-1, -2, KSHV, HBV, Flu) replication assay, assay of cell proliferation, cell cycle, phagocytosis, gene expression profiles, anti-tumor effect, real-time PCR, gene cloning, point mutagenesis, immune precipitation, FRET assay, FACS, AMINIS, confocal microscopic analysis, western blot, ELISA.
  3. Enrichment of Variant Information: (Project Team: 2-3 people 10 weeks) This project is geared toward enhancing the Variant Annotation and Standardization Pipeline (VarSAP). VarSAP normalizes variants to ensure a consistent terminology across different projects to assist in participant treatment assignment and overall variant information. The enhancements are aimed toward understanding more information about the variant such as variant location and gene information. This includes, but is not limited to, the exon(s) and domain(s) where the variant is located, any interesting facts about the region of the genome the variant is in and categorizing individual variants into a class of variants.
  4. AI solutions for enhanced imaging informatics leveraging data from The Cancer Imaging Archive: This project provides opportunities for students to be jointly mentored by members of BIDS and the ADRD Cancer Imaging Informatics Lab to combine FNLCR’s expertise in data science with domain expertise in cancer imaging. Analyses will be conducted on patient cohorts from The Cancer Imaging Archive (TCIA) to give students hands-on experience using real (de-identified) radiology and pathology imaging data collected from cancer care facilities all over the world. The project aims to leverage recent advances in machine learning and deep learning to deliver enhanced analytical capability to accelerate biomedical research at NCI/FNLCR. Example tasks include development of biomedical image classification and quantification pipelines using deep neural networks, as well as natural language processing (NLP) solutions for standardization of related image metadata and clinical data. Intern students will participate in essential steps in these projects including system installation, familiarization with Linux environment, basic programming using python and other scripting languages, deep neural network training and adoption using popular frameworks such as tensorflow and pytorch, and understanding of bioinformatics data formats related to radiology and digital pathology images. The possible outcomes include correlations of digital pathology and radiology images with pre-clinical and clinical outcomes, NLP pipelines for automated radiology meta-data processing, and trained deep networks and best practice of hyper parameters for deep network training. Students will also learn how to work in a cross-disciplinary, collaborative research environment that is distributed across multiple physical locations. These experiences will help students to be better-prepared and informed for college and professional life.
  5. Electron Microscopy – A very powerful technique to understand biological systems: The Center for Molecular Microscopy (CMM) offers investigators access to unique expertise and Electron Microscopy (EM) technologies that allow our collaborators to explore new avenues of research in order to enhance the knowledge of biological systems. We are mainly focusing on infectious diseases such as HIV and cancer related diseases. Our lab provides high-resolution Transmission Electron Microscopy (TEM) with the use of our 300kV Titan Krios electron microscope coupled with a direct detector k2 camera. Supporting equipment includes 2 FEI Vitrobot, as well as several FEI scopes to assess sample quality. The research goals for the student will be to be able to understand all the EM pipeline, from sample preparation to data collection and data analysis. The purpose will be for the student to become an independent user in our lab. The training plan is to become familiar with all the main procedures used in Electron Microscopy. The student will be mainly working with Htet Khant, one of my staff scientists. The student will learn how to work with viruses as well as protein-protein complexes. This will give him/her a broad spectrum of the type of samples we work with at the CMM. The student will focus mainly in the TEM technique. After this summer training, the student will be able to do some of the sample preparations and data collection independently. The student will start with some theoretical training about what is a TEM scope and after that the student will start working on the bench with Htet. At the end of his/her training, he or she will understand the main points of the EM technique.
  6. Exploiting RAS flexibility for ligand discovery: RAS is a very plastic protein and, by examining the publicly available RAS structures, we can obtain a deeper understanding of RAS flexibility to exploit for ligand (drug) discovery. Through this project, the student will learn about computational methods including hierarchical clustering and molecular docking methods (by following tutorials and reading key papers) and scientific computing skills (running programs using the Linux command line and writing Python and Shell scripts).
  7. Exploiting a non-functional RAS dimer for ligand discovery: The small molecule BI-2852 appears to induce a non-functional dimer of KRAS as we note in a Letter to the Editor (Tran et al., PNAS, submitted), which is a hitherto unprecedented and promising direction for targeting RAS. Through this project, the student will learn about computational methods including molecular docking methods and molecular dynamics simulations (by following tutorials and reading key papers) and scientific computing skills (running programs using the Linux command line and writing Python and Shell scripts).
  8. Segmentation of nanoscale features in electron microscopic images of cells and tissue: At the Center for Molecular Microscopy (CMM), we develop and use cutting-edge electron microscopic (EM) technologies to image cell and tissue samples at high-resolution and in 3-D. Segmentation, i.e. the extraction of features of interest, from these large and information-rich image datasets reveals exciting new biology. One of our group’s aims is to streamline manual and semi-automated segmentation of EM datasets to easily visualize biological structures in 3-D; we also currently use these segmentations to train neural networks (NN), with a “human-in-the-loop” corrective step. Ultimately, we hope to generate an artificial intelligence (AI) based algorithm that will automatically recognize specific cellular features in health and disease. In this summer internship, the student will be trained on segmentation software to generate “ground truth” segmentation as well as display final results of statistical analyses. Based on expertise and experience, the student may be tasked with some simple scripting to help train and analyze outputs from the NN. There will be no wet lab work in this training, however, the student will be in the midst of an exciting cross-disciplinary laboratory, with many opportunities to be learn from a wide variety of unique experimental approaches.
  9. Associate Drug Information to Variant/Variant Class or Gene. This project is geared towards identifying a possible correlation between drugs and different variants. The team will be responsible for identifying drug information available from different resources (i.e. DrugBank, PubChem, ChEMBL, DGIdb, etc.) and determining at what level the drug interaction can be associated to molecular data. If there is an association, then classifying and explaining what that association is.
  10. AI solutions for digital pathology and radiomics for cancer research. The project provides opportunities for students to work with FNLCR ABCS staff members and FNLCR investigators on bioinformatics and machine learning projects such as data mining and image segmentation via deep neural networks. These tasks aim to leverage recent advances in machine learning and deep learning to enhance ABCS analytical capability to accelerate biomedical research at NCI and FNLCR. Intern students will participate in essential steps in these projects, including but not limited to system installation, familiarization with Linux environment, basic programming using python and other scripting languages, deep neural network training and adoption using popular frameworks such as tensorflow and pytorch, and understanding of bioinformatics data formats such as radiology and digital pathology images. These experiences will help intern students to be better-prepared and informed for college school and professional life. The training plan includes image annotation creation under the guidance of ABCS staff members, domain experts such as pathologists, and NCI/FNLCR collaborators using common imaging tools such as ImageJ, ITK-snap, 3D Slicer, Gimp, etc.; deep network training for classification, correlation, and semantic segmentation to assist ABCS staff members to develop deep learning based image quantification pipelines. The possible outcomes include image annotations and correlations of digital pathology and radiology images and pre-clinical and clinical outcomes and trained deep networks and best practice of hyper parameters for deep network training.
  11. Clinical monitoring research program rotation. The focus of the CMRPD mentoring opportunity will be clinical research operations, project/program management, and leadership development. CMRPD will take one undergrad (rising junior or rising senior) or grad-level student from May to August for 40-hours/week over a 10- week period. The student will rotate through each of the CMRPD groups over the course of the 10 weeks, including: Clinical Project Management; Clinical Trials Management; Clinical Safety; Regulatory Affairs; Learning & Professional Development; NIH Clinical Team; Protocol Navigation/Development (day or half-day rotation with NIMH and NHLBI PN teams); Administration/Financial Management/IT; One week with Leidos Biomedical Research (operator of FNLCR) Administration/Project Management Office.
    • Student can attend (audit) the MHA508 Leadership Assessment & Development graduate class at the Mount St. Mary’s-Frederick campus in May-June (Monday nights from 6-9:30 PM).  These hours could be used to flex towards 40-hour/week requirement above. This course is not required for the internship.   
    • Course Description MHA 508 – Leadership Assessment and Development: This course provides an overview and analysis of past and contemporary leadership strategies. Leadership skills are developed through organizational efficiencies and appropriate oral and written communications techniques. Leaders must demonstrate the importance of resolving issues in the health care organization, the community-at-large, and the whole of society.