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BackgroundThe presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed.ResultsWe released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available.ConclusionsA unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Therapies targeting immune checkpoint molecules CTLA-4 and PD-1/PD-L1 have advanced the field of cancer immunotherapy. New mAbs targeting different immune checkpoint molecules, such as TIM3, CD27, and OX40, are being developed and tested in clinical trials. To make educated decisions and design new combination treatment strategies, it is vital to learn more about coexpression of both inhibitory and stimulatory immune checkpoints on individual cells within the tumor microenvironment. Recent advances in multiple immunolabeling and multispectral imaging have enabled simultaneous analysis of more than three markers within a single formalin-fixed paraffin-embedded tissue section, with accurate cell discrimination and spatial information. However, multiplex immunohistochemistry with a maximized number of markers presents multiple difficulties. These include the primary Ab concentrations and order within the multiplex panel, which are of major importance for the staining result. In this article, we report on the development, optimization, and application of an eight-color multiplex immunohistochemistry panel, consisting of PD-1, PD-L1, OX40, CD27, TIM3, CD3, a tumor marker, and DAPI. This multiplex panel allows for simultaneous quantification of five different immune checkpoint molecules on individual cells within different tumor types. This analysis revealed major differences in the immune checkpoint expression patterns across tumor types and individual tumor samples. This method could ultimately, by characterizing the tumor microenvironment of patients who have been treated with different immune checkpoint modulators, form the rationale for the design of immune checkpoint-based immunotherapy in the future.