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. Because ADC tumour cell targets do not have to be drivers of tumour growth, ADCs are potentially relevant for a wide range of tumours currently lacking clear oncogenic drivers. Therefore, we aimed to define the landscape of ADC targets in a broad range of tumours. 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 tumour samples representing 60 tumour (sub)types and 3520 healthy tissue samples were collected from the public domain. Next, we applied Functional Genomic mRNA-profiling to predict per tumour 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 tumour 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 tumour 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 tumour (sub)types. This comprehensive ADC target landscape can guide clinicians and drug developers which ADC is of potential interest for further evaluation in which tumour (sub)type.
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. Because ADC tumour cell targets do not have to be drivers of tumour growth, ADCs are potentially relevant for a wide range of tumours currently lacking clear oncogenic drivers. Therefore, we aimed to define the landscape of ADC targets in a broad range of tumours. 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 tumour samples representing 60 tumour (sub)types and 3520 healthy tissue samples were collected from the public domain. Next, we applied Functional Genomic mRNA-profiling to predict per tumour 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 tumour 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 tumour 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 tumour (sub)types. This comprehensive ADC target landscape can guide clinicians and drug developers which ADC is of potential interest for further evaluation in which tumour (sub)type.
Authors: Stuart W Hicks; Chiara Tarantelli; Alan Wilhem; Eugenio Gaudio; Min Li; Alberto J Arribas; Filippo Spriano; Roberta Bordone; Luciano Cascione; Katharine C Lai; Qifeng Qiu; Monica Taborelli; Davide Rossi; Georg Stussi; Emanuele Zucca; Anastasios Stathis; Callum M Sloss; Francesco Bertoni Journal: Haematologica Date: 2019-02-07 Impact factor: 9.941
Authors: Sara Ponziani; Giulia Di Vittorio; Giuseppina Pitari; Anna Maria Cimini; Matteo Ardini; Roberta Gentile; Stefano Iacobelli; Gianluca Sala; Emily Capone; David J Flavell; Rodolfo Ippoliti; Francesco Giansanti Journal: Int J Mol Sci Date: 2020-07-31 Impact factor: 5.923