Shifting the paradigm in personalized cancer care through next‐generation therapeutics and computational pathology

Abstract

The incorporation of novel therapeutic agents such as antibody‐drug conjugates, radio‐conjugates, T‐cell engagers, and chimeric antigen receptor cell therapies represents a paradigm shift in oncology. Cell‐surface target quantification, quantitative assessment of receptor internalization, and changes in the tumor microenvironment (TME) are essential variables in the development of biomarkers for patient selection and therapeutic response. Assessing these parameters requires capabilities that transcend those of traditional biomarker approaches based on immunohistochemistry, in situ hybridization and/or sequencing assays. Computational pathology is emerging as a transformative solution in this new therapeutic landscape, enabling detailed assessment of not only target presence, expression levels, and intra‐tumor distribution but also of additional phenotypic features of tumor cells and their surrounding TME. Here, we delineate the pivotal role of computational pathology in enhancing the efficacy and specificity of these advanced therapeutics, underscoring the integration of novel artificial intelligence models that promise to revolutionize biomarker discovery and drug development.

Keywords

• antibody drug conjugates
• artificial intelligence
• biomarkers
• computational pathology
• deep learning
• digital pathology