Many types of cancer exhibit high heterogeneity between individual cells, a key factor that promotes the overall ability of the tumor to both proliferate and resist treatment. By identifying what drives this heterogeneity, these cancers can be better understood, modeled, and treated. Single-cell RNA sequencing elucidates transcriptomic features that define subpopulations within heterogeneous samples that are not easily detected through conventional means. The transcriptome is distinct between different forms of cancer and changes in response to treatment as subpopulations die off and survivors exhibit changed dynamics. By looking at only data sequenced from untreated cells, but across several distinct types of cancer, and in each case identifying the features that most distinctly separate the subpopulations, certain commonalities are found. These commonly differentially expressed features, including both overarching gene sets such as ribosomal proteins and individual oncogenes such as PTTG1, are implicated as key elements of heterogeneity in cancer in a general sense.