The analysis of thousands of cancer and normal tissue gene expression profiles can guide clinical development of antibody-drug coniugates in the right patient population

A pan-cancer analysis of the expression of antibody-drug conjugate (ADC) targets by San Raffaele Hospital and Michelangelo Foundation with the support of AIRC Foundation, recently published in the European Journal of Cancer, can be useful in finding the right ADC for the right cancer type; moreover, the web platform developed by researchers for this analysis can assist other researchers in prioritizing new ADC targets for clinical development.

To assess the expression of ADC targets and potential down-stream determining factors of activity across pan-cancer and normal tissue, researchers identified ADC and the corresponding 54 targets from several clinical trials and compared their relative expression in each cancer type compared to a pool of normal tissues. Results showed that the target distribution identified appealing cancer types for the development of each ADC; moreover, the co-expression of multiple targets suggests opportunities for ADC combinations. Researchers also found differences in metastatic and primary tissues, with the first over-expressing some ADCs targets. Additional information was gathered from the expression levels of genes potentially implicated in ADC response downstream of the target and from the expression of potentially competing ADC targets, with resistance/sensitivity markers highlighting high inter-patient heterogeneity. The authors thus propose a ‘targetgram’ that displays the relative expression levels of the multiple ADC targets that a single cancer might express and the combined level of resistance and sensitivity genes, respectively, in the same tissue; this ‘targetgram’ might help prioritize treatment selection for individual patients when multiple ADCs may be clinically available. Authors also developed a web tool and interface that could be used by investigators to prioritize potential targets for ADC development based on expression patterns across cancers and in normal tissues. As authors conclude, «The analysis is the most comprehensive assessment of the therapeutic potential of ADCs across a broad range of cancer types using currently available data. Through investigation of large publicly available datasets, we identify novel therapeutic opportunities for ADCs that can be tested in the clinic. Our web-interactive tools can assist in prioritizing novel emerging ADC targets for clinical development».