Periodic Reporting for period 1 - PCinBC (Plasma cell heterogeneity and dynamics in patient tumors)
Berichtszeitraum: 2019-09-01 bis 2021-08-31
Breast cancer is the leading cancer among women worldwide and though there are various cancer therapies for breast cancer, many produce debilitating side effects and/or are ineffective for certain types of breast cancer. Targeting the immune system to treat tumors has revolutionized care for many cancers, but has not been as effective for breast cancer. Thus, there is an urgent need to expand breast cancer treatement options. Most current immunotherapies target T cells, but the tumor stroma is home to many immune cells, including B cell-derived antibody-producing Plasma cells, whose cancer-associated functions are less studied, but represent untapped reservoirs as therapeutic targets. Tumor-associated plasma cells infiltrate breast tumors and their presence often correlates with improved prognosis. A current bottleneck in the field is to uncover tumor-associated plasma cell dynamics within its spatial context. Single cell RNA-seq studies enable high resolution gene expression and BCR analysis; however, these studies require tissue dissociation, and thus, one cannot tell where plasma cells are in the tissue with respect to their environmental cues, which could provide important information as to their functions. Current spatial transcriptomic methods (developed by the host and Lundeberg labs) allow the study of gene expression (and by extension cells) in tissues. I therefore set out to study plasma cells within breast tumor using spatial transcriptomics and other methods. The goal with the project has been to understand several aspects of breast tumor-infiltrating plasma cell biology, including: their gene expression profiles, spatial relationships with other cells and tissue elements in the tumor (such as tumor cells), their lifespan, and other relevant information (for example their antigen receptor identities, the so-called B cell receptor, and what they bind to). Combined, this study helps to understand fundamental mechanisms of plasma cell activities within human breast tumors with the ultimate goal to provide new targets for anti-cancer therapy.
First, to better understand plasma cell phenotypes within their tissue microenvironment in breast cancer, we have collected and generated large datasets from a set of 6 individual untreated breast cancer patients, diagnosed with either Her2 positive tumors or Triple Negative breast tumors. We selected these patients since they are more likely to have high immune cell infiltration in their tumors and in the case of TNBC, lack targeted treatment options. For these patients, we have collected samples from several different tumor regions and generated datasets that include all immune cells (including plasma cells), tumor cells, spatial transcriptomics, exome sequencing, protein content, and antigen receptor information. We have found that: the amount of plasma cells (and the related B cells) varies greatly between patients, that the number of plasma cells per tumor is likely underestimated using single cell transcriptomics methods, that breast tumor tissue contains high antibody content, and that tumor-associated antibodies most commonly are of the IGG, IGA, or IGM isotype. In developing a map of plasma cell interactions with tumor cells directly, we have also set out to develop the tumor clonal evolution within the same samples. To do this, we have profiled the tumor cells in depth to understand their lineage relationships and how they relate to each other. Then we have used the spatial transcriptomics data to map these tumor cell clones within the tissue. This work will help to understand the interactions between distinct plasma cells and the tumors. This work is on-going and we are working towards generating a draft for publishing these results.
Second, in parallel, I have contributed to understanding plasma cells within breast tumors using already collected patient material from HER2 positive breast tumors (Andersson et al. Nature Communications 2021). Here, we found that: plasma cells located away from tumor cells and B cells, but instead located proximal to other immune cell types, including macrophage subsets. In this published article, we also developed a tool to predict the location of tertiary lymphoid structures in tissues using spatial transcriptomics data. These structures are important activation centers for immune cells, including B and plasma cells, within tumors (and other inflamed tissues). Together, these findings can form the basis for future functional studies into the mechanisms and roles of these interactions. These results have been published and the corresponding datasets are available to the public.
Third, to better understand plasma cell lineage relationships (i.e. using the B cell receptor as an endogenous cellular barcode to track cells) and antigen receptor information within breast tumors, we are using these generated datasets to extend the spatial transcriptomics method to enable measuring these aspects in tumor tissue sections. This method is expected to be applicable to understanding plasma cell biology beyond breast cancer, including in the context of infection, vaccine responses, and autoimmunity.
Second, in parallel, I have contributed to understanding plasma cells within breast tumors using already collected patient material from HER2 positive breast tumors (Andersson et al. Nature Communications 2021). Here, we found that: plasma cells located away from tumor cells and B cells, but instead located proximal to other immune cell types, including macrophage subsets. In this published article, we also developed a tool to predict the location of tertiary lymphoid structures in tissues using spatial transcriptomics data. These structures are important activation centers for immune cells, including B and plasma cells, within tumors (and other inflamed tissues). Together, these findings can form the basis for future functional studies into the mechanisms and roles of these interactions. These results have been published and the corresponding datasets are available to the public.
Third, to better understand plasma cell lineage relationships (i.e. using the B cell receptor as an endogenous cellular barcode to track cells) and antigen receptor information within breast tumors, we are using these generated datasets to extend the spatial transcriptomics method to enable measuring these aspects in tumor tissue sections. This method is expected to be applicable to understanding plasma cell biology beyond breast cancer, including in the context of infection, vaccine responses, and autoimmunity.
First, I expect that the breast tumor sequencing data generated for this project will be broadly used. All the datasets acquired through this project contain information about cells beyond plasma cells in parallel; for example, spatial transcriptomics generates an ‘unbiased’ coverage of all the genes expressed, and therefore can be used to ask many other questions regarding breast tumor biology. These datasets will therefore be useful for follow-up studies within the lab but also for the larger research community once they are published. Second, our work developing of new spatial transcriptomics-based methods could help facilitate research in other areas and be useful to potential industry partners. Finally, understanding basic mechanisms of disease can help to propel new treatments; it is our hope that that our findings can ultimately be useful in the clinic to help inform diagnostic or therapeutic practices practices for breast cancer, which can have broad impacts across society.