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The William Harvey Research Institute - Faculty of Medicine and Dentistry

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Targeting tumour infiltrating B cells to improve treatment outcomes in ovarian cancer

Code: BC-DTP_2026_59

Title: Targeting tumour infiltrating B cells to improve treatment outcomes in ovarian cancer

Primary Supervisor: Samar Elorbany

Email: s.elorbany@qmul.ac.uk

Institute: Barts Cancer Institute

Secondary Supervisor: Fran Balkwill

Email: f.balkwill@qmul.ac.uk

Institute: Barts Cancer Institute

Lay Summary:

The most common type of ovarian cancer is called high grade serous ovarian cancer (HGSOC) and is the deadliest gynaecological cancer. Tumours are formed of cancer cells as well as other supporting cells such as blood vessels and immune cells in a building block called stroma. Treatment of HGSOC involves a combination of surgery and chemotherapy. However, these treatments have not been curative and most patients will eventually suffer from relapsed ovarian cancer.

A group of drugs called immunotherapy has recently been proven effective in treatment of cancers such as melanoma, yet they have not shown any meaningful benefit in HGSOC patients. We have previously identified novel immunotherapy targets on macrophages and T cells and shown that targeting these was effective in improving outcomes at the preclinical level in HGSOC mouse models.

In this research we will study another type of immune cell called B cells. B cells have an advantage as they are part of our innate and adaptive immune systems, so they can respond to the tumour immediately and maintain a good memory to prevent its relapse. In this project, using state‑of‑the‑art techniques, we will study how B cells change with chemotherapy and find targets to enhance their anti‑tumour response with less toxic targeted treatment.

Aims:

Objective 1: Characterize the effect of chemotherapy on TIL‑B and PC in HGSOC patients’ samples.

Objective 2: Characterize the effect of chemotherapy and other novel immunotherapies on TIL‑B in HGSOC murine samples.

Objective 3: Validate TIL‑B and PC subpopulations and their spatial distribution and neighbourhoods.

Objective 4: Study the effect of targeting TIL‑B on tumour growth and survival in an HGSOC murine model.

Reference:

  1. Elorbany, S. et al. Immunotherapy that improves response to chemotherapy in high‑grade serous ovarian cancer. Nat Commun 15, 10144 (2024). https://doi.org/10.1038/s41467-024-54295-x
  2. Milne, K. et al. Systematic analysis of immune infiltrates in high‑grade serous ovarian cancer reveals CD20, FoxP3 and TIA‑1 as positive prognostic factors. PLoS One 4, e6412 (2009). https://doi.org/10.1371/journal.pone.0006412
  3. Kroeger, D. R., Milne, K. & Nelson, B. H. Tumor‑Infiltrating Plasma Cells Are Associated with Tertiary Lymphoid Structures, Cytolytic T‑Cell Responses, and Superior Prognosis in Ovarian Cancer. Clin Cancer Res 22, 3005–3015 (2016). https://doi.org/10.1158/1078-0432.CCR-15-2762
  4. Nielsen, J. S. et al. CD20+ tumor‑infiltrating lymphocytes have an atypical CD27‑ memory phenotype and together with CD8+ T cells promote favorable prognosis in ovarian cancer. Clin Cancer Res 18, 3281–3292 (2012). https://doi.org/10.1158/1078-0432.CCR-12-0234
  5. Trub, M. & Zippelius, A. Tertiary Lymphoid Structures as a Predictive Biomarker of Response to Cancer Immunotherapies. Front Immunol 12, 674565 (2021). https://doi.org/10.3389/fimmu.2021.674565
  6. Rodriguez, A. B. et al. Immune mechanisms orchestrate tertiary lymphoid structures in tumors via cancer‑associated fibroblasts. Cell Rep 36, 109422 (2021). https://doi.org/10.1016/j.celrep.2021.109422
  7. Kasikova, L. et al. Tertiary lymphoid structures and B cells determine clinically relevant T cell phenotypes in ovarian cancer. Nat Commun 15, 2528 (2024). https://doi.org/10.1038/s41467-024-46873-w
  8. Maniati, E. et al. Mouse Ovarian Cancer Models Recapitulate the Human Tumor Microenvironment and Patient Response to Treatment. Cell Rep 30, 525–540.e7 (2020). https://doi.org/10.1016/j.celrep.2019.12.034
  9. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single‑cell RNA‑seq data using regularized negative binomial regression. Genome Biol 20, 296 (2019). https://doi.org/10.1186/s13059-019-1874-1
  10. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single‑cell transcriptomics. BMC Genomics 19, 477 (2018). https://doi.org/10.1186/s12864-018-4772-0
  11. Vazquez‑Garcia, I. et al. Ovarian cancer mutational processes drive site‑specific immune evasion. Nature (2022). https://doi.org/10.1038/s41586-022-05496-1
  12. Launonen, I. M. et al. Chemotherapy induces myeloid‑driven spatially confined T cell exhaustion in ovarian cancer. Cancer Cell 42, 2045–2063.e10 (2024). https://doi.org/10.1016/j.ccell.2024.11.005
  13. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12, 453–457 (2015). https://doi.org/10.1038/nmeth.3337
  14. Gupta, N. T. et al. Change‑O: a toolkit for analyzing large‑scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015). https://doi.org/10.1093/bioinformatics/btv359
  15. Ralph, D. K. & Matsen, F. A. Using B cell receptor lineage structures to predict affinity. PLoS Comput Biol 16, e1008391 (2020). https://doi.org/10.1371/journal.pcbi.1008391
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