The significant sex differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. We propose that one barrier to translation is that sex differences in cancer phenotypes resemble sex differences in height: highly overlapping, but distinct, male and female population distributions that vary continuously between female- and male- skewed extremes. A consequence of this variance is that sex-specific treatments are rendered unrealistic, and our translational goal should be adaptation of treatment to the variable sex-effect on targetable pathways. To develop a tool that could advance this goal, we applied a Bayesian Nearest Neighbor (BNN) analysis to 8370 cancer transcriptomes from 26 different adult and 4 different pediatric cancer types to establish patient-specific Transcriptomic Indices (TI). TI precisely positions a patient's whole transcriptome on axes of mechanistic phenotypes like cell cycle signaling and immunity that exhibit skewing in the cancer population relative to sex-identified extremes (poles). Importantly, the TI approach reveals that even when TI values are identical, underlying mechanisms in male and female individuals can differ in identifiable ways. Thus, cancer type, patient sex, and TI value provides a novel and patient- specific mechanistic identifier that can be used for precision cancer treatment planning.