University of Groningen
Tumor immunology in ovarian cancer
Merkus-Brunekreeft, Kim
DOI:
10.33612/diss.147014180
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Publication date:
2020
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Merkus-Brunekreeft, K. (2020). Tumor immunology in ovarian cancer. University of Groningen.
https://doi.org/10.33612/diss.147014180
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1
GENERAL INTRODUCTION, AIM,
13 12
General introduction, aim, and outline
1
GENERAL INTRODUCTION
High-grade serous ovarian cancer
Ovarian cancer is the third most common gynecological malignancy worldwide with an overall 5-year survival of ~35%. As such, ovarian cancer is the deadliest gynecological malignancy and the fifth leading cause of cancer death in women.1,2 In 2018 an
estimated 300,000 new patients were diagnosed with ovarian cancer and in the same year there were an estimated 184,000 ovarian cancer deaths worldwide.3 This poor
prognosis is largely due to diagnosis at an advanced stage, high recurrence rate and of chemo-resistance after initial treatment. Currently there are no adequate screening methods or early detectable symptoms of disease that could lead to earlier diagnosis. Factors that determine long-term survival of ovarian cancer patients are well-known clinicopathological factors, such as stage and surgical outcome, and the presence of tumor-infiltrating lymphocytes (TIL).4 Regretfully, the 5-year overall survival of ovarian
cancer patients has not improved over the past decades.
Ovarian cancer can be divided in various histological subtypes (serous, germ cell tumors, sex cord stromal tumors, mixed cell, clear cell, transitional cell, and undifferentiated) of which epithelial ovarian cancer is the most common subtype. Epithelial ovarian cancers originate either from the epithelial surface of the ovaries or fallopian tubes.5,6
Increasing evidence has shown that the majority of ovarian cancers originates in the fallopian tubes instead of the ovaries themselves.8,9 Serous ovarian cancer is the most
common subtype of epithelial ovarian cancers.10 The studies in this thesis focus on
the most common and aggressive subtype of all: high-grade serous ovarian cancer (HGSOC).
Tumor immunology
Since the beginning of twentieth century, a cytotoxic approach towards malignancies has been chosen over an immune-based strategy, but until today the cytotoxic approach fails to cure late-stage or recurring malignancies, among which is recurring ovarian cancer, notorious for its chemotherapy resistance.11-13 The first clue of immunity
in cancer stems from the late 19th century. In 1893, a surgeon named William B.
Coley developed the first immune-based treatment for cancer. In 1909 the physician-scientist Paul Ehrlich introduced the cancer immunosurveillance hypothesis.14 It took
another century before Coley’s results were connected to Ehrlich’s hypothesis. In 1959,
Ruth and John Graham published the first ever immune-modulating vaccine study in gynecologic cancer, 114 gynecologic cancer patients were treated with autologous tumor lysate, 22% of the patients showed either stable disease or remission of disease, but effects were unpredictable and the mechanism was not understood.15,16
Around the turn of the last century, Schreiber described the phenomenon of cancer immunoediting, describing the relationship between the immune system and tumor cells.17 Catalyzing progressive interest in tumor immunology, which led to the discovery
of checkpoint blockade therapy mediated through programmed cell death protein 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and the development of adoptive cell transfer (ACT) therapy.18,19 Immune checkpoint therapy attenuates
tumor microenvironment-induced suppression of T cell receptor (TCR) signaling, thereby re-activating tumor-infiltrating T cells, resulting in cancer cell elimination. The response is generally thought to involve recognition of mutation-induced amino-acid changes, so-called neo-antigens. Clinically, immune checkpoint blockade therapy has proven highly effective in treatment of a number of cancer types, including lung cancer, melanoma and uterine cancer. In ovarian cancer, responses remain limited however. Nevertheless, patients that respond show long-term disease-free survival over periods of years. No surprise, the editors of the leading scientific journal Science announced cancer immunotherapy the breakthrough of the year 2013 and the work of James P. Allison and Tasuku Honjo was honored with the Nobel prize for physiology in 2018.20
Tumor microenvironment
In 1975 Beatrice Mintz and Karl Ilmensee discovered that cancer cells only grow under certain conditions, emphasizing the importance of the tumor microenvironment (TME).21 We and others demonstrated the importance of immune cell infiltration into
the TME of ovarian tumors.22-24 In particular the number of CD8+ T cells is associated
with prolonged survival in patients treated with surgery, but not in patients treated with neoadjuvant chemotherapy.22 The underlying mechanism for this difference
remains unclear, but may be a consequence of changes in antigen-presentation (see also below). In addition, T cells were of prognostic benefit only when intraepithelially located.25 We further investigated this survival benefit in HGSOC patients and
demonstrated that the prognostic effect was not only depending on the number of infiltrating TIL and their location, but also on the treatment regimen, surgical outcome and T cell differentiation status.22 In our work, programmed cell dead ligand
tumor-1
infiltrating myeloid cells (TIM), such as tumor-associated macrophages (TAM) and myeloid-derived suppressor cells (MDSC). MDSC are associated with worse survival in ovarian cancer, at least in part by suppressing CD8+ TIL.26,27
Myeloid cells are temporarily suppressed during chemotherapy and numbers are lower than before and after the chemotherapy. Therefore, this time window could be interesting for T cell augmenting immunotherapies.
Tumor antigen expression and the lymph node micro-environment
For an adequate immune response antigen presentation is necessary. Major histocompatibility complex class 1 (MHC-I) is expressed on the cell surface of all nucleated cells, including immune and tumor cells, and present fragments of intracellular proteins to immune cells. MHC-I expression on myeloid and lymphoid cells is frequently higher than on cancer cells, suggesting an important role in immune regulation.22 Antigen presentation is most effectively done in the lymph nodes and is
essential for activating a T cell immune response.
Tumor draining lymph nodes (tDLN) are under direct influence of tumor-derived factors and are of great importance in establishing an effective immune response by activating naïve immune cells.28,29 For the gynecological malignancies endometrial and
cervical cancer, it was revealed that the draining lymph node immune cell composition is characterized by either an immunosuppressive or naïve signature.30,31 Currently
there is limited data on the immune cell composition of lymph nodes in ovarian cancer. Expanding our knowledge on the tDLN microenvironment in ovarian cancer patients might help us to understand the minimal responses to immunotherapy observed in ovarian cancer so far.
Carcinogenesis in ovarian cancer
In the late 19th century the idea of cancer as genetic disease arose, following several
new genetic-based hypotheses including the Knudson hypothesis, better known as the two-hit model, and the presence of tumor suppressor and oncogenes.32,33 HGSOC
is known for its high level of chromosomal instability, rapid tumor development, and the expression of the typically mutated TP53-gene.34
The lifetime risk of developing ovarian cancer is greater in women who are carriers of the BRCA1, BRCA2, or Lynch Syndrome (LS) mutation. About 1-2% of the general population will develop ovarian cancer, but in case of BRCA1, BRCA2, LS mutation, and TP53 it is respectively 45-55%, 20-30%, 10-15%, and 3%. Known carriers of these genes can intensify screening or undergo risk-reducing surgery, no other preventative
measures are known yet.35
In general, these handful of conserved mutation-induced neo-antigens in ovarian cancer is presumed to be insufficient to effectively target ovarian cancers for immune-mediated destruction. Therefore, methods are developed to identify tumor-specific targets, e.g. antigens, unique to every individual tumor. It is challenging to find tumor-specific antigens that are highly expressed by the tumor cells, but virtually absent in normal tissue, and ideally these antigens are crucial for cancer cell growth to decrease the risk of immune escape due to downregulation of these tumor-specific antigens.36 Therefore, methods are investigated and in part described in this thesis to
identify tumor-specific mutations and target these to improve the tumor-specificity of immunotherapies. These neoantigens can be analyzed for response in preclinical models prior to clinical development.
Preclinical in vivo ovarian cancer models
Existing preclinical in vivo models for ovarian cancer are of limited usage because they are either fully murine, lack a human immune system, or hindered by graft-versus-host-disease (GVHD).37,38 Therefore, a preclinical in vivo model is desired that constitutes
the human tumor and immune system but is not hampered by GVHD, however such model is hard to establish.
One of the many investigated models is the patient-derived xenograft (PDX) model. Ovarian cancer (OC) PDX models are valuable since they largely preserve the complexity of the OC tumor microenvironment.39 OC PDX models can be established in various
ways; however, all of these models lack a functional human immune system. Various methods are investigated to introduce components of the human immune system into PDX models: (1) the engraftment of donor-unrelated CD34 human hematopoietic stem cells and (2) the injection of human immune cells.40-44 Although the first method
introduces both the innate and adaptive immune system in the murine model and is not hampered by the development of GVHD, it is of limited value because of the lack of thymic education of the T cells. The second model reconstitutes (partial) the immune system in the PDX models but is limited by the early onset of GVHD, ethical restrictions and only partial reconstitution of the human immune system in mice. A solution for this problem could be the use of donor-matched immune cells, preferably tumor-specific immune cells.45
Another challenge is the limited access to and availability of patient material. This poses several other challenges for the establishment of a sustainable preclinical OC PDX model since large quantities (>1x108 cells) of immune cells are needed to obtain
17 16
General introduction, aim, and outline
1
clinical results and numerous immune-humanized mice are necessary for statistically significant results.46 Therefore, methods are sought to generate more tumor tissue,
cells and ways to identify and select tumor-specific T cells, and (clonally) expand these cells for therapy. In 2018 it was shown that it is possible to identify neo-antigen specific T cells, even in low mutational tumors like ovarian cancer, paving the way for mutation-based personal therapy. However, they were unable to establish a preclinical in vivo model to validate their findings.47 In 2019 it was demonstrated
that neoantigens found in a primary ovarian tumor were retained in a patient-derived ovarian cancer model and the possible activation of autologous T cells due to these neoantigens.48
Until a proper preclinical in vivo model is established an alternative could be using ovarian cancer (OC) organoids. Köpper et al. (2019) managed to establish 56 OC organoids of 32 patients that can be used for drug-screening assays and maintain the histological and genomic characteristics of the original tumor.49 Even more importantly,
the organoids can be xenografted in murine models enabling preclinical in vivo testing of treatments demonstrating their potential usage in the field of personalized medicine.
AIM OF THIS THESIS
Increasing the understanding of the role of the immune system in ovarian cancer in relation to the current standard-of-care treatments and exploring possible new treatment strategies for patients with ovarian cancer.
1
OUTLINE OF THIS THESIS
Chapter 2 demonstrates that immune contexture is defined by tissue of origin and
independent of chemotherapy. It is shown that draining lymph nodes in ovarian cancer patients are characterized by a quiescent microenvironment and that T cell differentiation is heterogeneous across tumor tissue, lymph nodes and in the peripheral blood.
Chapter 3 shows that CD103+ TIL in HGSOC are phenotypically diverse TCR TCRαβ+
CD8αβ+ T cells expressing the therapeutic targets PD-1 and CD27, indicating that
CD103+ TIL are formed as a result of an active antitumor response that possibly can
be reactivated by checkpoint inhibition.
Chapter 4 shows that minimal major histocompatibility complex class 1 (MHC-I)
expression after neoadjuvant chemotherapy in ovarian cancer is negatively associated with T-cell-dependent outcome. This may explain why T cell infiltration in tumors of chemotherapy-naïve patients is associated with a favorable prognosis and not in tissue of patients that received chemotherapy prior to surgery.
Chapter 5 discusses the construction and in vitro validation of fusion-proteins
specifically delivering CD40 ligand (CD40L) to various types of malignant cells, including ovarian cancer, showing immune cell activation, proliferation and tumor cell apoptosis. This strategy acknowledges the importance of proper antigen presentation to surveying T cells.
Chapter 6 describes the establishment of an ovarian cancer patient-derived xenograft
model and our view how to introduce human immune system components to the model. Furthermore, it discusses the identification of tumor-specific neoantigens and the process of selecting neoantigen specific T cells and expanding them.
In chapter 7, the significance of the abovementioned studies is summarized. Followed by a general discussion, conclusion and future perspectives.
In chapter 8 a Dutch summary for layman is found. In the appendices, a list of abbreviations; list of contributing authors; list of publications; acknowledgements and curriculum vitae of the author of this PhD-thesis can be found.
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