Prognostic value and functional role of germinal centers in lymph nodes of immunotherapy treated breast cancers
Primary Supervisor: Dr Anita Grigoriadis, Cancer and Pharmaceutical Sciences, King’s College London
Secondary Supervisor: Dr Dinis Calado Immunity and Cancer Laboratory, The Francis Crick Institute
The prognostic value of lymph node involvement and of tumour-infiltrating lymphocytes is well established in high-risk breast cancers. However, it is less studied whether morphological pattern alterations in cancer-free lymph nodes provide valuable information for the prediction of breast cancer disease progression. We found that the formation of germinal centers (GC) in cancer-free lymph nodes is a prognostic factor of better outcome in high-risk breast cancer patients. GCs are dynamics structures, formed during the course of a T follicular helper cell (TFH) dependent immune response to antigen in which B-cells differentiate into memory B-cells and plasma cells. Despite the clear prognostic value, a functional role for GCs in breast cancer has not been investigated. Combinations of chemotherapy and immune checkpoint blockade immunotherapy (anti-PD-1/PD-L1) has recently been FDA approved for high-risk breast cancer patients. Further research is, however, critical to identify why patients are responsive/resistant to immunotherapy. In lymph nodes, PD1 is expressed by TFH and PD-L1 by germinal center B-cells, and whereas some studies suggest that inhibition of PD-1 signalling supresses the GC response others support enhancement . Currently, it is unclear how (anti-PD-1/PD-L1) immunotherapies in breast cancer impact the GC response and whether that in turn alters the therapeutic response and breast cancer disease progression. Grounded on the expertise in breast cancer molecular pathology and bioinformatics (Grigoriadis) and GC B-cell formation and differentiation (Calado), this project aims to investigate the prognostic value of GC formation and to test GC function in mouse models of immunotherapy treated breast cancers. The information gathered using the mouse models will be translated by studying the disease in human.
Our preliminary data using an orthotopic mouse model of breast cancer recapitulated the formation of GCs in the lymph node. We will test whether this phenotype occurs across a series of genetically engineered breast cancer models derived cell lines. GC formation is dependent on antigenic stimulation and we will investigate experimentally the role of neo-antigen in tumours with high mutational burden in driving GC formation and the prominent anti-tumour immunity at the primary tumour site. These studies will be performed in the presence or absence of combinations of chemotherapy and immune checkpoint blockade immunotherapy (anti-PD-1/PD-L1). The GCs in these conditions will be characterised in-depth, including population dynamics and B-cell differentiation by flow-cytometry, multiplex immune cell CyTOF, and 3D multifluorescent H-REM.
These studies will be complemented by others in which genetic or chemical ablation of GCs is performed to investigate their function in breast cancer disease progression and response to treatment. Building on our machine learning methods to capture the histological alterations in H&E slides, the student will annotate digitised whole slide images of well characterised human breast cancers and patient matched cancer-free and metastatic lymph nodes, including samples from patients treated with immune checkpoint blockade immunotherapy (anti-PDL1). This provides an opportunity for interspecies (human/mouse) comparisons to identify alterations that function as biomarkers of therapy success and/or that allow to risk-stratify patients to the appropriate treatment.
The successful candidate would have a degree in biology/ immunology/ biochemistry and have strong quantitative and analytical ability with some background in computer programming (R, Python or similar).
In addition to meeting the standard academic eligibility criteria applicants to this position will be expected to hold either a 4-year integrated MSci degree with 1st class honours, or a Bachelor degree (in a Natural Sciences or other related subject) at 2:1 or better plus a Masters with Distinction.
Potential research placements
1. Training in 3D image analysis to study the morphological changes in evolving premetastatic lymph nodes of breast cancer models, using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). Centre for Advanced Biomedical Imaging, Prof Simon Walker-Samuel, UCL.
2. CyTOF (Cytometry Time-Of-Flight) training to decipher the different cell populations in the premetastatic lymph node of breast cancer patients and mouse models, Prof Tony Ng, KCL.
3. Curation of lymph node data from clinical trials of hormone-receptor negative breast cancer patients treated with chemotherapy and immunotherapy. Prof Peter Schmid & Prof Louise Jones, BCI, QMUL.
The funding for this studentship covers students with home tuition fee status only. For more information on home tuition fee status please visit the UKCISA website. Please note that we will only be able to offer studentships to candidates that have home tuition fee status or provide evidence that they can fund the international portion of the tuition fee from external sources (i.e. not self-funded).
1. Grigoriadis et al., Histological scoring of immune and stromal features in breast and axillary lymph nodes is prognostic for distant metastasis in lymph node-positive breast cancers J. Pathol. Clin. Res. Jan 8;4(1):39-54. doi: 10.1002/cjp2.87. (2018)
2. Calado DP et al., The cell-cycle regulator c-Myc is essential for the formation and maintenance of germinal centers. Nat. Immunology Nov;13(11):1092-100. doi: 10.1038/ni.2418. (2012)
3. Schmid P et al., Atezolizumab and Nab-Paclitaxel in Advanced Triple-Negative Breast Cancer. 2018 N. Engl. J. Med. Nov 29;379(22):2108-2121. doi: 10.1056/NEJMoa1809615
4. Nutt SL et al., Give and take in the germinal center. Nat. Immunology Jun;11(6):464-6. doi: 10.1038/ni0610-464 (2010)
5. Rosenthal et al., Neoantigen-directed immune escape in lung cancer evolution Nature Mar;567(7749):479-485. doi: 10.1038/s41586-019-1032-7 (2019)