Understanding the impact of radiotherapy on metastasis formation

Primary supervisor: Luigi Ombarto, Queen Mary University of London

Secondary supervisor: Anita Grigoriadis, King’s College London

Project

Radiotherapy is standard of care for many cancer patients. Notably, while the local effects of radiotherapy at the site of radiations have been largely studied, how distant tissues respond to the systemic effect of RT has been overlooked [1]. Understanding these effects is needed to identify novel combination therapies and reduce the risk of metastasis [2].

We hypothesise that the systemic effects of local radiotherapy on the immune response will influence the survival and proliferation of disseminated tumour cells, ultimately affecting the outgrowth of metastasis. We will use a clinically relevant scenario that we have optimised to study this phenomenon in a mouse model of breast cancer metastasis.

Aims of the project

Aim 1: characterise changes in the immune cells systemically

The immune milieu will be investigated in different tissues following radiotherapy in the breast. The cells will be phenotypically and functionally characterized by flow cytometry and ex vivo assays, including a 4-component 3D co-culture (with cancer cells, fibroblasts, macrophages/monocytes and neutrophils) established in the lab to identify changes promoting cancer cell proliferation.

Aim 2: investigate the changes in the lymph nodes

Lymph nodes are normally the first site where breast cancer cells spread. We will isolate the lymph nodes and characterise how they structurally and functionally change due to systemic effects of radiotherapy. The stromal and immune cell composition of the lymph nodes will be analysed to help understanding whether these changes may support distant metastasis formation. Next, we will access large datasets of human lymph nodes from breast cancer patients [3] with Prof Grigoriadis and understand whether the same changes can be observed. Sophisticated analytical approaches available in the Grigoriadis lab will be used, and it will be explored the possibility to use deep learning and machine learning to measure the risk of metastasis.

Aim 3: understand how different radiotherapy regimens and lymph node irradiation impact on metastasis

Adjuvant radiotherapy will be administered in different regimens. Number and size of lung metastases will be evaluated by in vivo luminescence and ex vivo imaging. Both cancer and immune cells will be isolated from the metastatic lung and processed for single cell RNA sequencing. Their transcriptome will be interrogated by using state-of-art bioinformatic tools to identify changes supporting metastasis. An in vivo labelling technology we have previously developed [4,5] will identify the changes occurring in the proximity of disseminated cancer cells.

Conclusions

By using a combination of experimental setting and technologies in cancer biology, immunology and bioinformatics we will a) understand how radiotherapy influences breast cancer lung metastases; b) evaluate how the immune cells change because of radiotherapy, and identify the sub-populations supporting metastasis; c) identify the molecular changes responsible for this effect and inform on combination therapies to be given alongside radiotherapy to further reduce the risk of metastasis.

Candidate background

This project suits candidates with a background in (cancer) cell biology and/or immunology. Some knowledge in bioinformatics or willingness in learning bioinformatic skills will also be useful.

Potential Research Placements

1. Mirjana Efremova, Barts Cancer Institute, Queen Mary University of London
2. Sophie Acton, Laboratory for Molecular Cell Biology, UCL
3. Katiuscia Bianchi, Barts Cancer Institute, Queen Mary University of London

References

1. Barker HE, Paget JTE, Khan AA, Harrington KJ. The tumour microenvironment after radiotherapy: mechanisms of resistance and recurrence. Nat Rev Cancer 2015 15:409-25.
2. Ishihara D, Pop L, Takeshima T, Iyengar P, Hannan R. Rationale and evidence to combine radiation therapy and immunotherapy for cancer treatment. Cancer Immunol Immunother 2017 66(3):281-298.
3. Verghese G, Li M, Liu F, Lohan A, Kurian NC, Meena S, Gazinska P, Shah A, Oozeer A, Chan T, Opdam M, Linn S, Gillett C, Alberts E, Hardiman T, Jones S, Thavaraj S, Jones JL, Salgado R, Pinder SE, Rane S, Sethi A, Grigoriadis A. Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies. J Pathol 2023 260:376-389.
4. Ombrato L, Nolan E, Kurelac I, Mavousian A, Bridgeman V, Chakravarty P, Horswell S, Heinze I, Gonzalez-Gualda E, Matacchione G, Weston A, Kirkpatrick J, Husain E, Speirs V, Collinson L, Ori A, Lee JH, Malanchi I. Metastatic niche labelling reveals tissue parenchyma stem cell features. Nature 2019 572:603-608.
5. Ombrato L, Nolan E, Passaro D, Kurelac I, Bridgeman VL, Waclawiczek A, Duarte D, Lo Celso C, Bonnet D, Malanchi I. Generation of neighbor-labeling cells to study intercellular interactions in vivo. Nat Protocols 2021 16:872-892.