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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.

18 Search Results

246
Category:
Cancer Biology
Project:

Using genome engineering approaches to understand the genes controlling tumour growth

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

Prof. Adrian Liston

University:
Cambridge
Project Details:

Tumour growth is intimately linked to the infiltration of leukocytes (immune cells). Recruitment of suppressive leukocytes can promote angiogenesis and tissue remodelling, while repulsion of pro-inflammatory leukocytes is required to prevent tumour rejection. To date, this process has been studied in a hypothesis-directed manner, identifying a role for gene X in leukocyte subset Y. Here we will use new genome engineering approaches to simultaneously test the impact of every known migration-associated molecule in every infiltrating leukocyte subset, in order to reach a truly comprehensive understanding of the genes controlling the entry of each cell type into the tumours.

 

This project is based around the cutting-edge “Pro-code” technology. “Pro-codes” allows up to 400 lentiviruses to be built, each with a unique protein-based barcode. 400 unique CrispR guideRNAs can be built into a barcoded lentivirus library, covering every known migration-associated gene (chemokine receptors, integrins, adhesion molecules, chemotactic receptors, matrix metalloproteases, etc). Transfection of inducible Cas9-expressing bone-marrow stem cells with the ProCode library creates a mouse where the immune system is a mosaic of 400 different knockout lines. Through ultra-high parameter single cell cytometry, we can compare leukocytes that stay in circulation, migrate to healthy tissue or enter tumour tissues. Relative enrichment and depletion of each barcode in each leukocyte subset provides a comprehensive genetic map of leukocyte entry into tumours.

235
Category:
Cancer Biology
Project:

Novel approaches to cancer diagnostics in primary care

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

Prof. Fiona Walter

University:
Cambridge
Project Details:
N/A
232
Category:
Cancer Biology
Project:

Molecular and somatic genetic profiling of breast tumors in relation to etiology and survival in the Breast Cancer Association Consortium (BCAC)

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
UK Mentor:

Prof. Paul Pharoah

University:
Cambridge
Project Details:
N/A
228
Category:
Cancer Biology
Project:

Establish and implement a glioblastoma-on-a-chip model to study the effect of microenvironments on the tumor progression

Project Listed Date:
Institute or Center:
N/A
NIH Mentor:
N/A
University:
Cambridge
Project Details:
N/A
223
Category:
Cancer Biology
Project:

Radiology - Prostate Cancer Imaging

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

Multiple NIH collaborators

University:
Cambridge
Project Details:
N/A
197
Category:
Cancer Biology
Project:

Crosstalk between the tumour suppressor p53 and inflammation pathways

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

Prof. Xin Liu

University:
Oxford
Project Details:
N/A
185
Category:
Cancer Biology
Project:

What types of physical activity are associated with a lower incidence of cancer?

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
NIH Mentor:

Dr. Charles Matthews

UK Mentor:

Prof. Aiden Doherty

University:
Oxford
Project Details:

At the National Cancer Institute, we have demonstrated that higher levels of moderate to vigorous intensity physical activity are associated with a lower risk of cancer, including cancer in the breast, colon, endometrium, bladder, kidney, and stomach1. However, due to a reliance on self-reported measures of physical activity, a number of key questions remain unanswered on what overall volume of physical activity, and what types of physical activity, are associated with lower cancer risk. In addition, previous studies are observational by nature and are therefore unable to determine causality due to unmeasured or residual confounding.

At Oxford, our group has shown that wearable sensors such as wrist-worn accelerometers can be used to noninvasively measure physical activity status in large-scale biomedical studies. For example, we have measured physical activity status in 103,712 UK Biobank participants who agreed to wear a wrist-worn accelerometer for seven days2. These measurements are now actively used by health researchers worldwide to demonstrate that simple measures of overall activity are cross-sectionally associated with cancer outcomes3. However, no large study of device measured physical activity has yet taken place to assess associations with incident cancer outcomes with sufficient longitudinal follow-up. Furthermore, activity trackers often capture ~180 million data points/participant/week and therefore have the potential to identify other powerful behavioural signals to detect future cancer risk.

Machine learning methods can help maximise the utility of data from wearable sensors. These methods attempt to automatically detect patterns in data and then use those uncovered patterns to predict future data. Our group has demonstrated the utility of supervised machine learning to identify sleep and functional physical activity behaviours from raw accelerometer data4. However, there is a broad concern around the lack of reproducibility of machine learning models in health data science5. It is therefore important to carefully consider how to promote robust machine learning findings and reject irreproducible ones, to ensure credibility and trustworthiness.

This DPhil project therefore proposes to use the world’s largest available datasets to investigate what types of physical activity are associated with a lower incidence of cancer. Working with colleagues at the University of Oxford and the National Cancer Institute, you will have the opportunity to address the following important questions:

1. What behavioural measurements of physical activity status can be reliably ascertained from accelerometer datasets?
You will have the opportunity to develop reproducible machine learning skills to develop methods to identify physical activity behaviours from raw accelerometer datasets. Specifically, you will develop semi-supervised machine learning methods which seek to combine supervised methods (good quality labels, small datasets) with unsupervised methods (no labels but large datasets which are less prone to sampling bias). This will involve use of the largest available accelerometer datasets with reference measurements for physical activity behaviours in free-living environments (using wearable cameras)6.

2. What physical activity behaviours are associated with incident cancer events?
Here, you will have the opportunity to develop new skills in epidemiological data analysis. You will have the opportunity to use the UK Biobank dataset which has collected wrist worn accelerometer data from 103,712 participants2. This dataset includes information on participants’ first hospital admission or death from cancer, identified from linkages to the national death index, Hospital Episode Statistics, and cancer registries.

3. Are physical activity behaviours potentially causally associated with cancer?
You will have the opportunity to develop genetic epidemiology skills by implementing two-sample Mendelian Randomization7 to assess potential causal effects of accelerometer measured physical activity and cancer. For cancer outcomes, summary genetic association data will be obtained from existing collaborators from International cancer consortia.

Candidates should have a BSc, or ideally MSc, in a discipline with a substantive epidemiological, computational, or quantitative component. We very much welcome prospective candidates to directly contact us to further develop this proposal.

166
Category:
Cancer Biology
Project:

Identifying Regulators of Cancer Stem Cells in Pancreatic Cancer

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
NIH Mentor:

Dr. Udo Rudloff

UK Mentor:

Prof. Siim Pauklin

University:
Oxford
Project Details:

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies in human due to its late detection, highly metastatic characteristics, and poor responsiveness to current therapeutics. Pancreatic tumorigenesis involves a dedifferentiation process of cellular identity and the acquisition of a stem cell-like state of a subpopulation of cells known as cancer stem cells (CSCs). These cells are exceptionally important due to their higher therapeutic resistance and phenotypic plasticity that allows CSCs to metastasize and give rise to tumours. Currently, it remains largely unclear, which molecular markers and protein machineries control the stem cell-like identity of pancreatic CSCs. This knowledge would be valuable for earlier cancer detection and for developing more efficient pancreatic cancer therapeutics in the future.


The research objective of the project is to identify and characterize novel transcriptional regulators which govern gene expression of pancreatic cancer cells, particularly stem cell-like characteristics CSCs. The project will apply a broad range of cutting-edge research techniques such as 2D and 3D human cell culture systems, co-cultures of different cell types, next-generation single cell sequencing (scRNA-seq, scATAC-seq) of tumoural subpopulations in genetically engineered murine models (GEMMs) of pancreas cancer, functional studies (CRISPR/Cas9-mediated gene editing, tumour sphere assays), mechanistic studies (confocal microscopy, flow cytometry, cell sorting, CyTOF, western blotting), patient samples and mouse in vivo studies.


Collectively, this project will provide key insights to the signalling pathways and molecular mechanisms essential for the formation and maintenance of pancreatic CSCs, helping to better understand the tumorigenic process, and to uncover novel ways for diagnosing and treating this lethal cancer.

165
Category:
Cancer Biology
Project:

Understanding combination cytotoxic chemotherapy in Acute Myeloid Leukaemia

Project Listed Date:
Institute or Center:
National Heart, Lung, and Blood Institute (NHLBI)
NIH Mentor:

Dr. Chris Hourigan

UK Mentor:

Prof. Paresh Vyas

University:
Oxford
Project Details:

Acute Myeloid Leukaemia (AML) is the most common, aggressive human leukemia. Within the whole group of AML patients there is a subset of patients, typically younger (less than 65 years of age) who receive intensive conventional combination cytotoxic chemotherapy (anthracyclines and nucleoside analogues), who have a higher cure rate (~65%). Despite these cytotoxic drugs being in routine clinical use since the 1970’s, the field surprisingly still does not understand why these patients are cured. Conventional wisdom is that these patients are cured, because intensive combination cytotoxic chemotherapy kills all AML cells. However, this has never been rigorously proven and alternative hypotheses have not been tested.

This proposal will test if in patients who are cured, compared to those who are not, if eradication of all AML cells, could result from:
1. Increased killing of AML from cytotoxic chemotherapy.
2. An autologous innate and, or, acquired immune anti-AML cell response.
3. A combination of (1) and (2).

Specific Aims:

Using patient samples from cured patients and patients who relapse we will:
1. Contrast amount of AML cells left after treatment (measurable residual disease, MRD), in bone marrow (BM) samples.
2. If residual disease is detected in samples, characterise the single cell (sc) clonal architecture, epigenome and transcriptome and determine the leukemic stem cell content of the residual AML.
3. Perform an unbiased sc transcriptomic analysis of innate and acquired immune cells in BM, and peripheral blood (PB).
4. Test functional differences in comparable immune cells.

162
Category:
Cancer Biology
Project:

Identifying sub-populations of cells critical for cancer disease progression

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

Tissue multiplexing is a new imaging method that allows to visualise a large number of protein targets in tissues. This exciting new technology allows for new approaches to phenotyping cells and to decode more complex patters of communication between different tissue compartments. The goal of this project is to develop the required image analysis and inference methods using advanced machine learning and AI in 2D and 3D. As a result, you will be advancing our understanding of the tumour environment and find novel ways of identifying sub-populations of cells that play a critical role in disease progression.

 

You will be working side by side with world leading cancer researchers at NIH NCI and the University of Oxford. At both sites you will have access to unique patient cohorts. Together with David Wink and Stephen Lockett (both NCI) you will be working on aspects if breast cancer. In Oxford, Richard Bryant and Ian Mills will lead on work in prostate cancer, which is the commonest non-cutaneous cancer in men, and often progresses to incurable metastatic disease. Your work will also be supported by expert pathologists and you will be working towards improving current practice in cellular pathology.

 

The broader group has already established a very active collaboration and you will be expected to work in both locations. In Oxford, you will be embedded in the Quantitative Biomedical Image Analysis group led by Prof. Rittscher. Part of your role will be to accelerate the exchange of technology and software between the two locations. This project provides a unique opportunity to study mechanisms that are common to different cancer types.

160
Category:
Cancer Biology
Project:

Exploring mechanisms underlying heterogeneity of response in personalized cancer immunotherapy by using machine-learning techniques

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

Dr. Hashem Koohy

University:
Oxford
Project Details:

T cell recognition of a cognate peptide-MHC (pMHC) complex presented by infected/malignant antigen-presenting cells are of utmost importance for mediating a robust and long-term immune response. The recognition is mediated by specific molecular interactions between heterodimeric T Cell Receptors (TCRs) and pMHC ligands and instructs the nature of ensuing adaptive immune response. A better understanding of TCR:pMHC interaction would allow further harnessing of the adaptive T cell immunity and may lead to the development of  vaccines and therapeutics  both in the context of personalized cancer immunotherapies and infectious diseases such as COVID19. The research interests in the Koohy group are focused on the development of machine-learning and Bayesian statistical models to help us better understand two key components of this interaction: A) architecture of the immune repertoire and its dynamics upon exposure to antigens, B) processing and presentation of antigens by MHC molecules to their cognate T cells.

 

Cancer is usually characterized by accumulation of genetic alterations. Tumour-specific somatic mutations may generate small mutated proteins known as neoantigens that are presented on the surface of cancer cell as ‘cancerous flags’ in association with class I and II HLA molecules.  Neoantigens can be recognized by autologous T cells as foreign and therefore are considered as targets for improved cancer vaccines and adaptive T cell therapies. Almost similar mechanisms are applied to infectious diseases with the difference that the immunogenic epitopes on the surface of infected cells originate from invasive pathogens. Prediction of both immunogenic viral epitopes and cancer neoantigens has been at the centre of extensive research around the globe over the past couple of decades but remains unsolved.   We have been developing various statistical models such as Bayesian Hidden Markov Models to predict immunogenic epitopes that can be used as targets for vaccines for personalized cancer immunotherapy as well as infectious diseases such as COVID1,2.

 

Over the past decade we have witnessed unprecedented achievements on various cancer immunotherapies in which patients’ own immune system is modulated to find and kill cancer cells. This is evident by the 2018 Nobel Prize for development of Immune Checkpoint Blocked ICB that has greatly improved patients care. However, not all patients respond the same way, besides, some patients develop immune related adverse events such as checkpoint colitis. 
Multiple factors affect immune response to treatment including mutation burden rate, cytotoxic T cell infiltration, antigen processing and presentation defects, mutation-driven clonal signature and the composition of intestinal microbiota. Owing to advances in high throughput sequencing technologies, in particular recent single cell advancements, these features can now be measured from patients’ samples at single cell level at multiple time points including before, during and after the treatment.  We take readouts of these experiments in the form of high throughput sequencing data including genomics, transcriptomics, T cell receptor repertoire, and epigenomics data to train  statistical and machine learning models to study the mechanisms underlying heterogeneity of the response.

132
Category:
Cancer Biology
Project:

Understanding the mechanisms of tumorigenesis in individuals with predisposition to neuroendocrine tumor syndromes

Project Listed Date:
Institute or Center:
National Institute of Child Health and Human Development (NICHD)
NIH Mentor:

Dr. Karel Pacak

UK Mentor:
N/A
University:
N/A
Project Details:

Undertake genomic and epigenomic studies into the mechanisms of tumorigenesis in individuals with inherited predisposition to neuroendocrine tumor syndromes, especially pheochromocytoma/paraganglioma associated with mutations in the Krebs cycle. Such discoveries can lead to understanding of developmental and other mechanisms in these tumors related to the same syndrome but behaving in a different way and occurring in different tissue of origin. Such data can be paramount to study novel therapeutic approaches for these tumors based on the discovery on novel tumor-specific targets as well as biomarkers.

109
Category:
Cancer Biology
Project:

The role or tumor suppressor, adenomatous polyposis coli (APC) inactivation in colorectal cancer

Project Listed Date:
Institute or Center:
National Heart, Lung, and Blood Institute (NHLBI)
NIH Mentor:

Dr. John Hammer

University:
Cambridge
Project Details:

The tumor suppressor adenomatous polyposis coli (APC) is at the nexus of cellular homeostasis, controlling microtubule and actin dynamics and regulating the Wnt pathway, a cell-to-cell communication system that specifies stem cell identity. Mutational inactivation of APC in colorectal epithelial stem cells drives malignant transformation. Colorectal cancer is the second leading cause of cancer-related deaths in the Western world, and greater than 90% of all cases harbor oncogenic mutations in APC. The molecular contribution that APC inactivation makes to the development of lethal metastatic colorectal cancer is unclear. We hypothesize a direct connection between APC inactivation, the aberrant transmission of chromosomes in dividing cancer cells (i.e. aneuploidy), and a defect in the extrusion of aneuploid cells from the epithelium leading the dissemination by metastasis of the primary gut tumor.

 

The research interests of the Hammer lab (NIH) and the de la Roche lab (Cambridge) intersect with a goal of understanding the fundamental basis by which cells regulate their organization and shape. This project will exploit the power of the intestinal organoid system, an in vitro model of intestinal epithelia that recapitulates the morphology, cellular complexity and organization of the gut epithelium in vivo. One major goal of this project is to use genetically engineered organoids (GEOs) to determine how oncogenic mutations in APC contribute to the generation of aneuploid cells and the subsequent development of colorectal cancer. Overall, this project offers numerous exciting training opportunities, including CRISPR/Cas9-based genetic engineering of organoids and state-of-the-art super-resolution imaging technologies, and focuses on biological questions that have major implications for human health.

96
Category:
Cancer Biology
Project:

Comprehensive quantitative assessment of tissue biopsies in 3D

Project Listed Date:
Institute or Center:
National Cancer Institute (NCI)
NIH Mentor:

Dr. David Wink

University:
Oxford
Project Details:
N/A
95
Category:
Cancer Biology
Project:

Genetic and functional association of a novel human interferon, IFN-λ4, with human infections and cancer.

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