Precision oncology is a targeted approach to diagnosing and treating cancer based on an individual’s specific profile. In personalised cancer therapy, biomarkers (measurable and quantifiable biological molecules that indicate a specific biological state or condition) are used to predict the course of the disease (prognostic biomarkers) and which patients will have the highest probability (predictive biomarkers) of responding or having adverse side effects to particular therapies.
Our team focuses on developing Computational Pathology approaches to find and validate biomarkers to be implemented in daily clinical practice to empower precision oncology.
Combining clinical, pathology and genomics data with image analysis of the tumour, we aim to find the balance between over- and under-treatment of women's ovarian and breast cancer. We develop models to recognise the tumour and its microenvironment using pathology samples from the clinic. We analyse different regions within tumour samples from the time of diagnosis, relapse and metastasis and combine it with data describing RNA, DNA and protein sequences of single cells to study the impact of intra-tumour heterogeneity upon cancer immunity and progression. These image-based quantitative results, together with genomic analysis of the cancer-immune interactions, can be complementary to identity biomarkers for prediction of treatment response to empower precision oncology.
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.
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.
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.
Ultimately, we want to help the right person, to get the right treatment, at the right time!
For more details, please visit cpath.nl