The promise of precision oncology has been to molecularly profile, stratify, and match patients’ treatments with one or more drugs that are predicted to be effective for treating their cancers. 1–3 Numerous efforts have been made towards the development of methodologies to accomplish such a goal, including improvements in multi-omic analyses (ie, DNA, RNA, and protein) with more rapid turnaround times of test results, novel bioinformatic tools, and drug screening approaches for use in real-time. 4 Altogether, these advances in the field are allowing for personalized precision cancer care to become more of a reality than ever before.

Oncology represents a particularly challenging field for precision medicine. Indeed, high inter-tumoral heterogeneity, as well as large spatial and temporal intra-tumoral heterogeneity is often observed in tumors with similar histology. 5 Sarcomas are a class of tumors that vividly highlight this complexity, because this catchall term encompasses more than 60 different sarcoma subtypes 6 and likely even more if we further subdivide individual histologies by anatomic location, molecular driver (s), and immune infiltrates (eg, tertiary lymphoid structures). 7, 8 These factors, taken together with the extremely low incidence of each sarcoma subtype, make it extremely difficult to perform clinical trials without pooling sarcoma patients together independent of histology. Despite the fact