Pancreatic ductal adenocarcinoma (PDAC) and cholangiocarcinoma (CCA) are both deadly cancers and they share many biological features besides their close anatomical location. One of the main histological features is neurotropism, which results in frequent perineural invasion. The underlying mechanism of cancer cells favoring growth by and through the nerve fibers is not fully understood. In this review, we provide knowledge of these cancers with frequent perineural invasion. We discuss nerve fiber crosstalk with the main different components of the tumor microenvironment (TME), the immune cells, and the fibroblasts. Also, we discuss the crosstalk between the nerve fibers and the cancer. We highlight the shared signaling pathways of the mechanisms behind perineural invasion in PDAC and CCA. Hereby we have focussed on signaling neurotransmitters and neuropeptides which may be a target for future therapies. Furthermore, we have summarized retrospective results of the previous literature about nerve fibers in PDAC and CCA patients. We provide our point of view in the potential for nerve fibers to be used as powerful biomarker for prognosis, as a tool to stratify patients for therapy or as a target in a (combination) therapy. Taking the presence of nerves into account can potentially change the field of personalized care in these neurotropic cancers.

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31Chronic myeloid leukemia (CML) is driven by malignant stem cells that can persist despite 32 therapy. We have identified Metastasis suppressor 1 (Mtss1/MIM) to be downregulated in 33

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Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.