TROP2 in colorectal carcinoma: associations with histopathology, molecular phenotype, and patient prognosis

Antibody-drug conjugates (ADCs) directed to trophoblast cell surface antigen 2 (TROP2) have gained approval as a therapeutic option for advanced triple-negative breast cancer, and TROP2 expression has been linked to unfavourable outcomes in various malignancies. In colorectal carcinoma (CRC), there is still a lack of comprehensive studies on its expression frequency and its prognostic implications in relation to the main clinicopathological parameters. We examined the expression of TROP2 in a large cohort of 1,052 CRC cases and correlated our findings with histopathological and molecular parameters, tumour stage, and patient outcomes. TROP2 was heterogeneously expressed in 214/1,052 CRCs (20.3%), with only a fraction of strongly positive tumours. TROP2 expression significantly correlated with an invasive histological phenotype (e.g. increased tumour budding/aggressive histopathological subtypes), advanced tumour stage, microsatellite stable tumours, and p53 alterations. While TROP2 expression was prognostic in univariable analyses of the overall cohort (e.g. for disease-free survival, p < 0.001), it exhibited distinct variations among important clinicopathological subgroups (e.g. right- versus left-sided CRC, microsatellite stable versus unstable CRC, Union for International Cancer Control [UICC] stages) and lost its significance in multivariable analyses that included stage and CRC histopathology. In summary, TROP2 is quite frequently expressed in CRC and associated with an aggressive histopathological phenotype and microsatellite stable tumours. Future clinical trials investigating anti-TROP2 ADCs should acknowledge the observed intratumoural heterogeneity, given that only a subset of TROP2-expressing CRC show strong positivity. The prognostic implications of TROP2 are complex and show substantial variations across crucial clinicopathological subgroups, thus indicating that TROP2 is a suboptimal parameter to predict patient prognosis.