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Research Opportunities

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Prospective Students

The goal of the NIH Oxford-Cambridge (OxCam) Scholars Program is to create, foster, and advance unique and collaborative research opportunities between NIH laboratories and laboratories at the University of Oxford or the University of Cambridge. Each OxCam Scholar develops a collaborative research project that will constitute his/her doctoral training. Each Scholar also select two mentors – one at the NIH and one in the UK – who work together to guide the Scholar throughout the research endeavor.

Students may select from two categories of projects: Self-designed or Prearranged. OxCam Scholars may create a self-designed project, which enables students to develop a collaborative project tailored to his/her specific scientific interests by selecting one NIH mentor and one UK mentor with expertise in the desired research area(s). Alternatively, students may select a prearranged project provided by NIH and/or UK Investigator(s) willing to mentor an OxCam Scholar in their lab.

Self-designed Projects: Students may create a novel (or de novo) project based on their unique research interests. Students have the freedom to contact any PI at NIH or at Oxford or Cambridge to build a collaboration from scratch. The NIH Intramural Research Program (IRP) represents a community of approximately 1,200 tenured and tenure-track investigators providing a wealth of opportunity to explore a wide variety of research interests. Students may visit https://irp.nih.gov to identify NIH PIs performing research in the area of interest. For additional tips on choosing a mentor, please visit our Training Plan.

Prearranged Projects: Investigators at NIH or at Oxford or Cambridge have voluntarily offered collaborative project ideas for NIH OxCam Scholars. These projects are provided below and categorized by research area, NIH Institute/Center, and University. In some cases, a full collaboration with two mentors is already in place. In other instances, only one PI is identified, which allows the student to select a second mentor to complete the collaboration. Please note that prearranged project offerings are continuously updated throughout the year and are subject to change.

6 Search Results

464
Category:
Computational Biology
Project:

Computational modelling in large scale imaging datasets to understand hypertensive disease progression after pregnancy

Project Listed Date:
Institute or Center:
N/A
NIH Mentor:
N/A
University:
Oxford
Project Details:

Our research group aims to understand hypertensive disease progression of women and their children following pregnancy complications, such as hypertensive pregnancy and preterm birth, to identify optimal approaches to reduce long term risk. This includes development of new clinical tools to identify, track, and slow the disease progression as well as novel interventions.

This project will apply computational modelling and machine learning to large scale imaging datasets to study disease progression related to a hypertensive pregnancy across multiple modalities and organs. The insights into key structural and functional changes at the organ-level that describe stages of disease will be used to identify potential intervention targets.

Furthermore, we will use imaging data collected within our ongoing clinical trials to help us understand how interventions modify the underlying disease development and how this could be incorporated in clinical practice to transform long-term patient outcomes after a hypertensive pregnancy.

455
Category:
Computational Biology
Project:

Understanding the clonality of drug resistance in cancer

Project Listed Date:
Institute or Center:
N/A
NIH Mentor:
N/A
UK Mentor:

Prof. Adam Cribbs

University:
Oxford
Project Details:

We are interested in understanding the clonality of drug resistance in cancer (primarily in a haematological cancer called Multiple Myeloma (MM)). To achieve our goals, we have developed state of the art long-read single-cell sequencing approaches (termed scCOLOR-seq) that allow us to measure single clones within patient samples. This method allows for the simultaneous measurement of gene expression, exon mutations, exon SNPs and translocations. We apply this technology and develop cutting edge computational analysis solutions to understand the relationship between clonality and drug resistance in oncology. Furthermore, our lab is also working as part of the Human Cell Atlas (HCA) project and we have several international collaborations with immunologists, cancer biologists to support our work.

The aim of this project is to develop computational analysis strategies aimed at better defining specific MM clones within patients that are resistant to first line therapeutics. The work will involve combining long-read (Oxford Nanopore Technology) multi-modal datasets and performing machine learning approaches to better define high risk patients. This work is important to better understand drug resistance mechanisms in MM and identify patients that may respond less well to therapy.

432
Category:
Computational Biology
Project:

Artificial intelligence in diagnostic prostate MRI to improve outcomes

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
University:
Cambridge
Project Details:

There has been increasing interest in applying computational methods in medicine, to make sense of cancer’s ‘big data’ problem by exploiting recent advances in data-processing and machine learning to capture and integrate clinical, genomic, and image data collated from hundreds of cancer patients in real-time. Such methods can be applied to digital clinical images to extract image information about patterns of pixels that are not perceivable to the human eye, allowing characterisation of tumour.  Prostate cancer is the 2nd commonest male cancer worldwide, and MRI is the diagnostic tool of choice, however, MRI can miss 10% of significant tumours and leads to unnecessary (invasive) biopsy in around 1/3rd patients who do not have cancer.  

We will use a prototype AI system (Pi) developed with Lucida Medical on retrospective data, in a prospective clinical study. We plan to link histological data to imaging features derived from MRI (including texture analysis) to identify predictors of lesion aggressiveness and need for sampling, using biopsy cores and surgical specimens from the prospective cohort. Further work will link biopsy tissue to MRI data to identify radiogenomic markers of disease aggressiveness. The project presents an opportunity for AI to answer key clinical questions at the intersection of interpretation, imaging and biopsy.  

The project will involve working with
an established interdisciplinary programme of researchers and help in the assessment of cross-cutting “multi-omic” approaches to cancer assessment, involving integration of advanced image analysis, transcriptomic, genomic, tissue, and patient outcomes to inform the design of diagnostic strategies.

348
Category:
Computational Biology
Project:

Identifying germline and somatic genetic interactions that are relevant to patient outcomes in cancer

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
UK Mentor:
N/A
University:
N/A
Project Details:

Since 2018, several studies have reported “germline-somatic interactions” across cancer types. However, such studies demonstrate extensive heterogeneity in defining the germline and somatic features involved or the specific mechanism of interaction (including predicted protein changes, cell lines/CRISPR screens, and immune system-associated phenotypes), and effectively none evaluate results clinically. We hypothesize that pan-cancer or breast-cancer-specific germline-somatic interactions will demonstrate associations with therapy in the metastatic setting. To test this, we will compile reported (a) germline-somatic variant interactions from papers and (b) datasets with individual-level treatment information and test the associations between these interactions and treatment response in metastatic breast cancer.

343
Category:
Computational Biology
Project:

Computational investigation of tumor microenvironment

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
UK Mentor:
N/A
University:
N/A
Project Details:

We are interested in a variety of topics related to “Deciphering cellular heterogeneity in tumor microenvironment using single cell data”, “Functional characterization of tumor infiltrating T cells”, “Links between embryonic development and cancer”, “Functional characterization of non-coding somatic mutations”, and “Methods for single cell omics”. These projects involve non-trivial  methods development as well as sophisticated data analysis but the focus is always on the biological question. Our lab is involved in several collaborations with immunologists and cancer biologists.

149
Category:
Computational Biology
Project:

Combined Computational-Experimental Approaches to Predict Acute Systemic Toxicity.

Project Listed Date:
Institute or Center:
National Institute of Environmental Health Sciences (NIEHS)
NIH Mentor:

Dr. Scott Auerbach,
Dr. Nicole Kleinstreuer,
& Dr. Nisha Sipes

University:
Cambridge
Project Details:
N/A
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