Cancers are complex diseases that operate at multiple biological scales - from atom to organism - and the purview of cancer systems biology is to integrate information between scales to derive insight into their mechanisms and therapeutic vulnerabilities. From this holistic perspective, the field has come to appreciate that the spatial context of the tumor microenvironment in intact tissues not only enables a more granular definition of disease, but also the design of more personalized and effective therapies. In spite of this promise, spatial context-preserving cytometry paradigms like multiplex tissue imaging (MTI) are beset with many challenges related to cost, computational complexity, and study design. The discovery and development of the next generation of biomarkers in cancer systems biology will require computational tools which can cope with the increasing scale and complexity of our measurements, and the work we share within serves as a step toward achieving that requirement.