In the microenvironment of a malignancy, tumor cells do not exist in isolation, but rather in a diverse
ecosystem consisting not only of heterogeneous tumor-cell clones, but also normal cell types such as fibroblasts,
vasculature, and an extensive pool of immune cells at numerous possible stages of activation and differentiation.
This results in a complex interplay of diverse cellular signaling systems, where the immune cell component is
now established to influence cancer progression and therapeutic response. It is experimentally difficult and laborious
to comprehensively and systematically profile these distinct cell types from heterogeneous tumor samples in
order to capitalize on potential therapeutic and biomarker discoveries. One emerging solution to address this
challenge is to computationally extract cell-type specific information directly from bulk tumors. Such in silico
approaches are advantageous because they can capture both the cell-type specific profiles and the tissue systems
level of cell-cell interactions. Accurately and comprehensively predicting these patterns in tumors is an important
challenge to overcome, not least given the success of immunotherapeutic drug treatment of several human cancers.
This is especially challenging for subsets of closely related immune cell phenotypes with relatively small
gene expression differences, which have critical functional distinctions. Here, we outline the existing and emerging
novel bioinformatics strategies that can be used to profile the tumor immune landscape.