  Prediction of genetic biomarkers, e.g., microsatellite instability and BRAF
in colorectal cancer is crucial for clinical decision making. In this paper, we
propose a whole slide image (WSI) based genetic biomarker prediction method via
prompting techniques. Our work aims at addressing the following challenges: (1)
extracting foreground instances related to genetic biomarkers from gigapixel
WSIs, and (2) the interaction among the fine-grained pathological components in
WSIs.Specifically, we leverage large language models to generate medical
prompts that serve as prior knowledge in extracting instances associated with
genetic biomarkers. We adopt a coarse-to-fine approach to mine biomarker
information within the tumor microenvironment. This involves extracting
instances related to genetic biomarkers using coarse medical prior knowledge,
grouping pathology instances into fine-grained pathological components and
mining their interactions. Experimental results on two colorectal cancer
datasets show the superiority of our method, achieving 91.49% in AUC for MSI
classification. The analysis further shows the clinical interpretability of our
method. Code is publicly available at
https://github.com/DeepMed-Lab-ECNU/PromptBio.
