Translational Research

Identifying biomarkers for treatment response prediction

Who will benefit from immunotherapy?

Currently, immunotherapy treatment decision-making is based on the quantification of Program Death Ligand 1 (PD-L1) or in clinical trials based on the percentage of tumour-infiltrating lymphocytes (TILs) in pathology samples. Pathologists apply thresholds to assessments visually, which are image-dependent and operator-dependent. As a result, the critical decision of administering immunotherapy is made by applying very sensitive thresholds to possibly inaccurate and subjective quantification of a largely variable stain. Therefore, there is a need for reliable and more accurate biomarkers that can aid in the selection of cancer patients eligible for immunotherapy.

Who can get targeted treatment?

Microsatellite instability (MSI) determines whether patients with solid tumors respond exceptionally well to immunotherapy. However, in clinical practice, not every patient is tested for MSI, because this requires additional genetic or immunohistochemical tests. Therefore, a prediction model for MSI or other mutational processes, such as defects in DNA repair mechanisms, directly from WSI has the potential to provide better immuno- and targeted therapy patient stratification.

Who can be spared chemotherapy?

Previous research has suggested that for breast and other solid tumors, the interaction between the tumor and its microenvironment is connected to the outcome and response to treatment. For example, in breast cancer, the stromal compartment of the tumor contains more prognostic information than the epithelial component. Thus, an automated assessment of the tumor geometry, in combination with clinical, pathological and molecular factors, can be used to optimize the risk assessment of disease recurrence and treatment response in patients with early-stage breast cancer.