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Figure 1. Multi-modal integration of bone marrow biopsy features and radiology imaging will be combined to define a new 'bone pathology disease space'. This will allow individual patients to be contextualised to larger patient cohorts presenting with overlapping diseases and help to inform improved diagnosis and prognostication.

ABOUT THE RESEARCH

Lytic bone lesions frequently present as skeletal fractures and are a major cause of mortality and morbidity worldwide in patients with a range of neoplastic and non-neoplastic diseases. Improved methods for detecting early pathological changes in bone that are predictive of skeletal fractures are highly attractive, particularly when integrated into exiting diagnostic pathways. Using routinely prepared diagnostic bone marrow biopsies taken from patients investigated for blood diseases (including cancers such as multiple myeloma), we will employ newly developed approaches to identify the cellular and stromal / matrix features associated with skeletal disease and fractures. These insights will be combined with high resolution polarising-light microscopy and radiological imaging (including PET-CT and MRI) to develop an integrated, multi-model approach  to the evaluation of fracture risk in patients (Figure 1).  

This project builds upon our group’s extensive expertise in the computational analysis of tissue biopsies taken from patients investigated for blood cancer. Our overarching aim is to improve the accuracy and consistency of tissue diagnosis using quantitative and objective features that are beyond the assessment of pathologists using conventional light microscopy. As part of an established collaborative network in Oxford involving Cellular PathologyHaematologyand Engineering we have already successfully demonstrated the potential of image analysis / machine learning (ML) to improve the accuracy and prognostic significance of tissue features extracted from bone marrow biopsy specimens in myeloproliferative neoplasms (MPN), a type of blood cancer. We now seek to apply these insights into other neoplastic and non-neoplastic diseases affecting haematological tissues and bone.

As a DPhil student within our group you will gain experience in the assessment of bone marrow morphological features of diverse pathological processes and develop expertise in diagnostic sample curation and annotation. In close collaboration with Prof. Jens Rittscher’s group (Institute of Biomedical Engineering [IBME], Oxford) you will develop advanced computational analytical methods and machine learning (ML) approaches to analyse tissue and radiological images. This will be integrated with hypothesis-driven application of polarising light microscopy (Prof. Martin Booth, Engineering Science, Oxford) and tissue immunofluorescent multiplexing (Translational Histopathology Lab, Oxford). 

Additional supervision may be provided by Professor Jens Rittscher (Department of Engineering Science) and Professor Martin Booth (Department of Engineering Science).

TRAINING OPPORTUNITIES

In addition to the generic training opportunities offered by the MRC Weatherall Institute of Molecular Medicine (WIMM) and Medical Sciences Division (see below), DPhil students joining our group will be trained in a wide range of tissue diagnostic and analytical techniques including conventional microscopy, immunohistochemistry (IHC) / immunofluorescence (IF) and polarising light microscopy. This will involve supervised training in the use of specialist software (e.g. HALO) and incorporate methodologies designed to analyse complex multiplexed IF tissue data from large sample cohorts. With the support of the IBME, students will also develop novel approaches for the integration of this tissue-based data with radiological imaging (PET-CT & MRI) and genetic data as part of a multi-modality project spanning multiple research themes and clinical / academic  departments within the University and NHS Foundation Trust. Finally, all DPhil students joining our group will participate fully in the Prof. Rittscher’s successful student training programme at the IBME, incorporating weekly lab meetings. The research groups of Prof. Royston, Prof. Rittscher and Prof. Booth have a strong track record of training students and post-doctoral staff and are experienced and successful collaborators.

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

 As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

 

PUBLICATIONS

1

Ryou H, Sirinukunwattana K, Aberdeen A, Grindstaff G, Stolz BJ, Byrne H, Harrington HA, Sousos N, Godfrey AL, Harrison CN, Psaila B, Mead AJ, Rees G, Turner GDH, Rittscher J, Royston D. Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients. Leukemia. 2022 Dec 5. doi: 10.1038/s41375-022-01773-0. Epub ahead of print. Leukemia. 2023 Jan 12;: PMID: 36470992.

2

AI-Based Morphological Fingerprinting of Megakaryocytes: a New Tool for Assessing Disease in MPN Patients. Korsuk Sirinukunwattana, Alan Aberdeen, Helen Theissen, Nikolaos Sousos, Bethan Psaila, Adam J. Mead, Gareth D.H. Turner, Gabrielle Rees, Jens Rittscher and Daniel Royston. Blood Adv. 2020 Jul 28;4(14):3284-3294. doi:  10.1182/bloodadvances.2020002230.

3

Theissen, Helen & Chakraborty, Tapabrata & Malacrino, Stefano & Royston, Daniel & Rittscher, Jens. (2022). Multi-Scale Graphical Representation of Cell Environment. 2022. 3522-3525. 10.1109/EMBC48229.2022.9871710.

4

Garcia-Ortiz A, Rodriguez-Garcia Y, Encinas J, et al. The Role of Tumor Microenvironment in Multiple Myeloma Development and Progression. Cancers (Basel). 2021;13(2).

5

Spinnato P, Filonzi G, Conficoni A, Facchini G, Ponti F, Sambri A, De Paolis M, Cavo M, Salizzoni E, Nanni C. Skeletal Survey in Multiple Myeloma: Role of Imaging. Curr Med Imaging. 2021;17(8):956-965. doi: 10.2174/1573405617666210126155129. PMID: 33573573.

6

Bernstein ZS, Kim EB, Raje N. Bone Disease in Multiple Myeloma: Biologic and Clinical Implications. Cells. 2022 Jul 27;11(15):2308. doi: 10.3390/cells11152308. PMID: 35954151; PMCID: PMC9367243.