  The cells and their spatial patterns in the tumor microenvironment (TME) play
a key role in tumor evolution, and yet the latter remains an understudied topic
in computational pathology. This study, to the best of our knowledge, is among
the first to hybridize local and global graph methods to profile orchestration
and interaction of cellular components. To address the challenge in
hematolymphoid cancers, where the cell classes in TME may be unclear, we first
implemented cell-level unsupervised learning and identified two new cell
subtypes. Local cell graphs or supercells were built for each image by
considering the individual cell's geospatial location and classes. Then, we
applied supercell level clustering and identified two new cell communities. In
the end, we built global graphs to abstract spatial interaction patterns and
extract features for disease diagnosis. We evaluate the proposed algorithm on
H&E slides of 60 hematolymphoid neoplasms and further compared it with three
cell level graph-based algorithms, including the global cell graph, cluster
cell graph, and FLocK. The proposed algorithm achieved a mean diagnosis
accuracy of 0.703 with the repeated 5-fold cross-validation scheme. In
conclusion, our algorithm shows superior performance over the existing methods
and can be potentially applied to other cancer types.
