University of Groningen PET imaging and in silico analyses to support personalized treatment in oncology Moek, Kirsten DOI: 10.33612/diss.112978295 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2020 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Moek, K. (2020). PET imaging and in silico analyses to support personalized treatment in oncology. Rijksuniversiteit Groningen. https://doi.org/10.33612/diss.112978295 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. Download date: 30-09-2021 06 The antibody-drug conjugate target landscape across a broad range of tumor types Kirsten L. Moek1, Derk Jan A. de Groot1, Elisabeth G.E. de Vries1, Rudolf S.N. Fehrmann1 1 Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands Ann Oncol. 2017;28:3083-3091 Chapter 06 Abstract Background Antibody-drug conjugates (ADCs), consisting of an antibody designed against a specific target at the cell membrane linked with a cytotoxic agent, are an emerging class of therapeutics. Since ADC tumor cell targets do not have to be drivers of tumor growth, ADCs are potentially relevant for a wide range of tumors currently lacking clear oncogenic drivers. Therefore, we aimed to define the landscape of ADC targets in a broad range of tumors. Materials and methods PubMed and ClinicalTrials.gov were searched for ADCs that are or were evaluated in clinical trials. Gene expression profiles of 18,055 patient derived tumor samples representing 60 tumor (sub)types and 3,520 healthy tissue samples were collected from the public domain. Next, we applied Functional Genomic mRNA-profiling to predict per tumor type the overexpression rate at the protein level of ADC targets with healthy tissue samples as a reference. Results We identified 87 ADCs directed against 59 unique targets. A predicted overexpression rate of ≥ 10% of samples for multiple ADC targets was observed for high incidence tumor types like breast cancer (n = 31 with n = 23 in triple negative breast cancer), colorectal cancer (n = 18), lung adenocarcinoma (n = 18), squamous cell lung cancer (n = 16) and prostate cancer (n = 5). In rare tumor types we observed, amongst others, a predicted overexpression rate of 55% of samples for CD22 and 55% for ENPP3 in adrenocortical carcinomas, 81% for CD74 and 81% for FGFR3 in osteosarcomas, and 95% for c-MET in uveal melanomas. Conclusion This study provides a data driven prioritization of clinically available ADCs directed against 59 unique targets across 60 tumor (sub)types. This comprehensive ADC target landscape can guide clinicians and drug developers which ADC is of potential interest for further evaluation in which tumor (sub)type. 134 The antibody-drug conjugate target landscape across a broad range of tumor types Introduction Despite progress in anticancer drug treatment including molecularly targeted agents that inhibit specific oncogenic “driver” pathways, most patients still die of metastatic disease. Therefore, there remains an unmet need to develop new systemic treatment options to improve survival of cancer patients. Numerous patients fail to benefit from molecularly targeted agents because their tumors lack oncogenic drivers to target. In this context, an interesting emerging class of therapeutics are antibodies bound to a cytotoxic agent, known as antibody- drug conjugates (ADCs). ADC targets do not have to be drivers of tumor growth to be meaningful because they serve as an entry point for the cytotoxic agent. This makes ADCs potentially relevant for a wide range of tumors. After an ADC is bound to its tumor specific molecular target, the cytotoxin is internalized and activated. This allows the selective cellular tumor delivery of a high concentration of the cytotoxin that would cause severe dose-limiting toxicities if administered systemically. To prevent unintended biodistribution the total body target expression should favor the tumor instead of healthy tissues.1 An established example of an ADC is trastuzumab emtansine, which is currently part of standard of care in patients with human epidermal growth factor receptor 2 (HER2) overexpressing 2 metastatic breast cancer. 06 Immunohistochemical (IHC) analyses allows to investigate the protein expression of ADC targets in different tumor (sub)types and healthy tissues. However, large scale IHC analysis for a target is time consuming and demands extensive resources. Therefore, we currently lack data about the expression of ADC targets for numerous tumor types, which impedes potential effective treatment with available ADCs in a significant subset of cancer patients. To this end, we used the recently developed method of functional genomic mRNA profiling (FGmRNA profiling) to predict overexpression rates of ADC targets at the protein level.3 FGmRNA profiling can correct a gene expression profile of an individual tumor for physiological and experimental factors, which are considered not to be relevant for the observed tumor phenotype. In this manuscript, we applied FGmRNA profiling to a large database containing a broad spectrum of different tumor and healthy tissue (sub)types. Subsequently, we used the resulting FGmRNA profiles to prioritize potential ADC targets per tumor (sub) 135 Chapter 06 type. In addition, we present an overview of ADCs which are currently marketed or in clinical development for anti-cancer treatment. Materials and methods Search strategy To identify targets for clinically available ADCs PubMed was searched at the latest April 2017. The following search terms were used: “antibody-drug conjugate”, “cancer”, “tumor” and “oncology” in various combinations, spelling variants and synonyms. The search was limited to manuscripts published in English and involving clinical trials. Reviews were excluded. In addition, ClinicalTrials.gov was searched in April 2017 for on-going studies with ADCs with the search terms [antibody-drug conjugate] AND [cancer]. Finally, abstracts and posters from the ASCO 2015/2016 and ECCO-ESMO 2015 and ESMO 2016 meetings were selected using “antibody-drug conjugate” as search term. Moreover, information on ADCs, ADC targets, linked cytotoxins, tumor type and status of clinical development (phase 1-3) was collected. If we could not find that information in the previously described sources we searched Embase to collect additional information using the name of the identified ADC as term. In case an ADC is in different phases of clinical development for a specific indication, we chose to systematically report the highest phase. Data acquisition Publicly available microarray expression data was extracted from the Gene Expression Omnibus (GEO).4 The analysis was confined to the Affymetrix HG-U133 Plus 2.0 platform (GEO accession identifier: GPL570). Samples were included for analysis if they represented healthy tissue or cancer tissue obtained from patients or healthy individuals and raw data was available. Only tumor (sub)types with ≥ 5 samples were included for analysis. Pre-processing and quality control was performed as previously described.3,5 For the breast cancer cohort receptor status was collected or inferred as described before.5,6 Predicting overexpression rates of ADC targets at the protein level First, we applied FGmRNA profiling to each individual sample, both cancer and healthy tissue. For a detailed description of FGmRNA profiling we refer to Fehrmann et al.3 136 The antibody-drug conjugate target landscape across a broad range of tumor types In short, we analyzed 77,840 expression profiles of publicly available samples with principal component analysis and found that a limited number of “Transcriptional Components” (TCs) capture the major regulators of the mRNA transcriptome. Subsequently, we identified a subset of TCs that described non-genetic regulatory factors. We used these non-genetic TCs as covariates to correct microarray expression data and observed that the residual expression signal (i.e. FGmRNA profile) captures the downstream consequences of genomic alterations on gene expression levels. Subsequently, for each individual gene that is targeted by ADC(s) we determined the percentage of samples per tumor (sub)type with a significant increased FGmRNA signal, which is considered a proxy for protein overexpression. The threshold was defined in the set of FGmRNA profiles of healthy tissues by calculating the 97.5th percentile for the FGmRNA signal