University of Groningen
Identification and modulation of drug targets for precision medicine in breast, lung and ovarian
cancer subtypes
Stutvoet, Thijs
DOI:
10.33612/diss.144705120
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Publication date:
2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Stutvoet, T. (2020). Identification and modulation of drug targets for precision medicine in breast, lung and
ovarian cancer subtypes. University of Groningen. https://doi.org/10.33612/diss.144705120
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Introduction
INTRODUCTION
The number of cancer patients is increasing worldwide due to aging of the general population and lifestyle-related risk factors, resulting in an estimated 18.1 million new cancer cases annually.1 Cancer has become the leading cause of death in the Western
world.2 Most cancers are treated with either surgery, radiotherapy, systemic therapy,
or a combination of these treatments. Systemic therapies, including chemotherapy, hormonal therapies, targeted agents, and immunotherapy, provide treatment options in the adjuvant and advanced-stage setting. Treatment failure is often caused by tumor resistance to therapy, resulting in disease progression and death.3
Chemotherapy preferentially kills cancer cells by targeting their increased proliferation and decreased recovery potential compared to healthy cells. Chemotherapy is applied as standard of care for patients with many advanced cancer types.4,5 Several biological
mechanisms that determine chemotherapy response have been discovered.3,4 More
recently, studies into the biology of cancer have resulted in the discovery of cancer drivers, which can now be directly targeted using antibodies and small molecule inhibitors.6–8 Targeted therapies that improve clinical outcomes for patients and have
become standard treatment, include the human epidermal growth factor receptor 2 (HER2) inhibitors trastuzumab and lapatinib in patients with HER2 overexpressing breast cancer, and the epidermal growth factor receptor (EGFR) inhibitor erlotinib in patients with EGFR mutant non-small cell lung cancer (NSCLC).9–11 However, most breast
cancers and NSCLCs do not harbor HER2 overexpression or EGFR mutations.7 Therefore,
additional tumor targets for therapy need to be uncovered to provide treatment options for a larger number of patients.
Large scale analyses, such as efforts by The Cancer Genome Atlas (TCGA) project assess mutations, and alterations in copy number, methylation and gene expression to define and characterize subgroups of cancers.12 Importantly, most of this data is accessible
through public repositories such as the Gene Expression Omnibus (GEO), enabling further research.13 These databases are steadily growing in volume and diversity,
allowing for identification of new cancer drivers and molecular cancer subtypes.14 For
example, this unveiled novel alterations in HER2, hepatocyte growth factor receptor, and guanosine triphosphate (GTP)-binding protein Ras Like Without CAAX 1 (RIT1) that are currently studied as treatment targets in NSCLC patients.15,16 Other cancer
subtype-related findings by the TCGA Research Network include the suggestion that over half the high grade serous ovarian cancers (HGSOC) harbor deficiencies in the homologous recombination pathway, greatly expanding the expected number of Poly (ADP-ribose)
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10 Chapter 1
polymerase (PARP) inhibitor sensitive HGSOC tumors.17 Subsequent clinical studies
confirmed efficacy of PARP inhibitors as maintenance therapy following a response to platinum-based therapy in patients with HGSOC, which is now standard of care.18
Although targeted therapies can induce rapid and strong responses, efficacy is often limited by recurrences with therapy-resistant disease.7,19 This indicates a need for more
treatments with long-term efficacy.
Immunotherapy is a new treatment strategy that can induce long-term tumor responses in patients. These treatments include antibodies that block the function of immune checkpoint proteins, such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), programmed cell death-1 (PD-1), and programmed death-ligand 1 (PD-L1), reinvigorating the anti-tumor immune response.20,21 In aggressive tumor types, including advanced
melanoma and NSCLC, immunotherapy can result in responses that persist after discontinuation of treatment.21,22 To improve immunotherapy response rates, patient
selection can be performed, but this is still in an early stage. For instance, tumor cell PD-L1 expression is used to select NSCLC patients for immune checkpoint inhibitor therapies. However, even from the patients with PD-L1 positive tumors 55% do not respond to these treatments, while 10% of patients with PD-L1 negative tumors respond.23–25 The
poor predictive value of tumor PD-L1 expression may be caused by transient PD-L1 expression or heterogeneity of PD-L1 expression within and between tumor lesions, none of which are taken into account when analyzing a single biopsy.26 Whole-body
imaging techniques, such as positron emission tomography (PET), may provide more insight in PD-L1 expression and improve patient selection for immunotherapy, as shown for PD-L1 targeted treatment in NSCLC, triple negative breast cancer (TNBC), and bladder cancer.27 Moreover, the non-invasive nature of PET imaging enables serial
analysis of target expression, allowing follow-up of PD-L1 expression in tumors. Currently, many trials are studying combination therapies in patients with various types of cancer to improve the efficacy of immunotherapy.28 Improved insight into immune
checkpoint biology may guide the development of new combination therapies including immunotherapy, targeted therapies, and chemotherapy.
AIM OF THIS THESIS
Providing a biological rationale for potential combination therapies and patient selection tools to enhance efficacy of immunotherapy and other targeted therapies in patients with subtypes of breast cancer, NSCLC, and ovarian cancer.
GENERAL OUTLINE OF THIS THESIS
Breast cancer subtypes harbor diverse underlying biology, enabling the use of subtype-specific therapies. In chapter 2, we review knowledge regarding immunotherapy in
breast cancer. We perform a literature search including PubMed and the ClinicalTrials. gov database and abstracts from the American Society of Clinical Oncology (ASCO) annual meetings, San Antonio Breast Cancer symposium, American Association of Cancer Research (AACR) annual meeting and the annual congresses of the European Society of Medical Oncology (ESMO). We focus on the differential immunogenicity of breast cancer subtypes and prognostic value of tumor infiltrating lymphocytes and immune checkpoints. Furthermore, we summarize the immunological effects of current therapies and describe upcoming additional immunotherapeutic treatment strategies in breast cancer.
Patients with NCSLCs without targetable driver mutations respond better to PD-1/ PD-L1 targeted immunotherapy. In chapter 3, we investigate regulation of PD-L1 in
this subtype of NSCLC in the context of EGFR and interferon gamma (IFNγ) signaling. Using data from the TCGA and validated pathway activation signatures, we search for associations between activation of downstream EGFR and IFNγ pathways and PD-L1 gene expression. Next, we validate our findings at the mRNA and protein level in lung adenocarcinoma cell lines using ligands and specific inhibitors. Additionally, we study the mechanistic basis of PD-L1 regulation in these cell lines using flow cytometry, Western blotting, MTT assays, and quantitative PCR.
For certain cancer types, PD-L1 immunohistochemistry is used to select patients for PD-1/PD-L1 targeted therapies, but performance of this biomarker is suboptimal. In
chapter 4, we evaluate performance of a human-PD-L1-specific PET tracer. This
adnectin-based, fluorine-18 labeled tracer, allows imaging on the day of administration. We characterize the tracer using human lung and ovarian cancer cell lines with different basal PD-L1 expression levels, and study binding and specificity of the tracer. Moreover, we evaluated in vivo usability of the tracer to detect basal PD-L1 expression levels and IFNγ- and the mitogen-activated protein kinase 1 and 2 (MEK1/2) inhibitor selumetinib-induced changes of PD-L1 expression levels using xenograft models of these cell lines. Despite the introduction of PARP and angiogenesis inhibitors, overall survival of patients with advanced high grade serous ovarian cancer remains poor. In chapter 5, we use
independent component analysis to extract the transcriptomic footprints of biological signals from publicly available gene expression data from 1,125 epithelial ovarian cancer
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samples, spanning all different histological subsets. We link known and unknown biology of the epithelial ovarian cancer subtypes to the uncovered transcriptomic footprints and relate these to prognosis of patients with platinum-treated serous ovarian cancer using survival tree analysis to uncover subtype-specific targets for therapy.
Patients with advanced ovarian clear cell cancer have worse survival compared to patients with other ovarian cancer subtypes due to the platinum resistant nature of their disease. In chapter 6, we look deeper into targeted therapies for ovarian clear cell
cancer. Using publicly available gene expression data from normal ovarian epithelium and clear cell ovarian cancer samples, we uncover enrichment for DNA repair signaling. Using MTT and clonogenic assays we study sensitivity of clear cell ovarian cancer cell lines to inhibition of the DNA damage response and cisplatin. To explain our findings, we look deeper into the induction and resolution of DNA damage and apoptosis after exposure to cisplatin using flow cytometry and immune fluorescence.
The findings of this thesis and the future perspectives are summarized in chapter 7. Chapter 8 provides a summary in Dutch.
REFERENCES
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. doi:10.3322/caac.21492.
2. Dagenais GR, Leong DP, Rangarajan S, et al. Variations in common diseases, hospital admissions, and deaths in middle-aged adults in 21 countries from five continents (PURE): a prospective cohort study.
Lancet. 2019;6736(19):1-10. doi:10.1016/S0140-6736(19)32007-0.
3. Kelland L. The resurgence of platinum-based cancer chemotherapy. Nat Rev Cancer. 2007;7(8):573-584. doi:10.1038/nrc2167.
4. Galmarini D, Galmarini CM, Galmarini FC. Cancer chemotherapy: A critical analysis of its 60 years of history.
Crit Rev Oncol Hematol. 2012;84(2):181-199. doi:10.1016/j.critrevonc.2012.03.002.
5. Goodin S. Oral chemotherapeutic agents: Understanding mechanisms of action and drug interactions. Am
J Heal Syst Pharm. 2007;64(9_Supplement_5):S15-S24. doi:10.2146/ajhp070034.
6. Burrell RA, McGranahan N, Bartek J, Swanton C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature. 2013;501(7467):338-345. doi:10.1038/nature12625.
7. Lee YT, Tan YJ, Oon CE. Molecular targeted therapy: Treating cancer with specificity. Eur J Pharmacol. 2018;834:188-196. doi:10.1016/j.ejphar.2018.07.034.
8. Joo WD, Visintin I, Mor G. Targeted cancer therapy-are the days of systemic chemotherapy numbered?
Maturitas. 2013;76(4):308-314. doi:10.1016/j.maturitas.2013.09.008.
9. Lee DH. Treatments for EGFR-mutant non-small cell lung cancer (NSCLC): The road to a success, paved with failures. Pharmacol Ther. 2017;174:1-21. doi:10.1016/j.pharmthera.2017.02.001.
10. Slamon DJ, Leyland-Jones B, Shak S, et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med. 2001;344(11):783-792. doi:10.1056/ NEJM200103153441101.
11. Harbeck N, Gnant M. Breast cancer. Lancet. 2017;389(10074):1134-1150. doi:10.1016/S0140-6736(16)31891-8.
12. Cancer Genome Atlas Research Network. The Cancer Genome Atlas pan-cancer analysis project. Nat Genet. 2013;45(10):1113-1120. doi:10.1038/ng.2764.
13. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: Archive for functional genomics data sets - Update. Nucleic
Acids Res. 2013;41(D1):991-995. doi:10.1093/nar/gks1193.
14. Benstead-Hume G, Wooller SK, Pearl FMG. “Big data” approaches for novel anti-cancer drug discovery.
Expert Opin Drug Discov. 2017;12(6):599-609. doi:10.1080/17460441.2017.1319356.
15. Collisson EA, Campbell JD, Brooks AN, et al. Comprehensive molecular profiling of lung adenocarcinoma.
Nature. 2014;511(7511):543-550. doi:10.1038/nature13385.
16. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology. Non-small cell lung cancer. November 6, 2019.
17. Bell D, Berchuck A, Birrer M, et al. Integrated genomic analyses of ovarian carcinoma. Nature.
1
14 Chapter 1
2011;474(7353):609-615. doi:10.1038/nature10166.
18. Morgan RD, Clamp AR, Evans DGR, Edmondson RJ, Jayson GC. PARP inhibitors in platinum-sensitive high-grade serous ovarian cancer. Cancer Chemother Pharmacol. 2018;81(4):647-658. doi:10.1007/s00280-018-3532-9.
19. Kuwano M, Sonoda K, Murakami Y, Watari K, Ono M. Overcoming drug resistance to receptor tyrosine kinase inhibitors: Learning from lung cancer. Pharmacol Ther. 2016;161:97-110. doi:10.1016/j. pharmthera.2016.03.002.
20. Mittal D, Gubin MM, Schreiber RD, Smyth MJ. New insights into cancer immunoediting and its three component phases--elimination, equilibrium and escape. Curr Opin Immunol. 2014;27:16-25. doi:10.1016/j. coi.2014.01.004.
21. Wei SC, Duffy CR, Allison JP. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer
Discov. 2018;8(9):1069-1086. doi:10.1158/2159-8290.CD-18-0367.
22. Nadal E, Massuti B, Dómine M, García-Campelo R, Cobo M, Felip E. Immunotherapy with checkpoint inhibitors in non-small cell lung cancer: insights from long-term survivors. Cancer Immunol Immunother. 2019;68(3):341-352. doi:10.1007/s00262-019-02310-2.
23. Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small cell lung cancer. N Engl J Med. 2015;372(21):2018-2028. doi:10.1056/NEJMoa1501824.
24. Tobin NP, Foukakis T, De Petris L, Bergh J. The importance of molecular markers for diagnosis and selection of targeted treatments in patients with cancer. J Intern Med. 2015;278(6):545-570. doi:10.1111/joim.12429. 25. Festino L, Botti G, Lorigan P, et al. Cancer treatment with anti-PD-1/PD-L1 agents: is PD-L1 expression a
biomarker for patient selection? Drugs. 2016;76(9):925-945. doi:10.1007/s40265-016-0588-x.
26. Mansfield AS, Aubry MC, Moser JC, et al. Temporal and spatial discordance of programmed cell death-ligand 1 expression and lymphocyte tumor infiltration between paired primary lesions and brain metastases in lung cancer. Ann Oncol. 2016;27(1):1953-1958. doi:10.1093/annonc/mdw289.
27. Bensch F, van der Veen EL, Lub-de Hooge MN, et al. 89Zr-atezolizumab imaging as a non-invasive approach
to assess clinical response to PD-L1 blockade in cancer. Nat Med. 2018;24(12):1852-1858. doi:10.1038/ s41591-018-0255-8.
28. Xin Yu J, Hubbard-Lucey VM, Tang J. The global pipeline of cell therapies for cancer. Nat Rev Drug Discov. May 2019. doi:10.1038/d41573-019-00090-z.
