Time lapse videos are fun to make. So I made a video of me scoring IHC slides the old fashion way by examining slides in the microscope, and by using digital pathology scoring the slides using machine learning with QuPath. The digital workflow consists of identifying tumor and immune cells, and counting all positive and negative cells. There was no way I was going to do that manually. This is where QuPath came for my rescue.
Identifying patients that can benefit from specific drugs is essential in cancer treatment. The KEYNOTE 355 trial established a survival benefit for patients with metastatic Triple Negative Breast Cancer (TNBC) receiving the PD-L1 targeting drug pembrolizumab in patients with a PD-L1 Combined Positive Score (CPS) >= 10. CPS score is calculated by counting the number of PD-L1 positive tumor cells, the number of PD-L1 positive immune cells, divided by the total number of tumor cells. Manually counting positive and negative cells is time consuming and may be prone to variability. Using digital pathology to estimate CPS score can increase precision, accuracy and reproducibility. It may also offer the opportunity of saving time by batch processing a large number of slides. Calculating CPS score digitally requires a workflow that can detect positive and negative stained cells, as well as identifying tumor cells and immune cells, while ignoring stromal cells and artefacts. This can be achieved by training a classifier, which then can be applied for batch processing of digital slides. Here I present a workflow for how to calculate CPS score using QuPath as performed in our recent paper3. QuPath is an open-source digital pathology platform designed for the analysis of histopathological images. QuPath supports a range of image analysis tasks, making it a valuable tool in the field of pathology and biomedical research.