The Missing Variability Problem in Cancer Prediction: Integrating Multilevel Factors

Abstract Efforts to predict individual cancer risk typically rely on genomic data together with environmental factors external to the organism. Yet even when these domains are considered jointly, a substantial portion of cancer risk variability remains unaccounted. This persistent predictive gap is labeled here as the missing variability problem (MVP ). Beyond further refinement of genomic and external environmental factors, existing approaches seek additional sources of variation either by downscaling to molecular processes or by upscaling to tissue-level organization. While these strategies identify relevant factors of variation, they leave open how to jointly use these factors for prediction across levels. This article proposes treating the cellular level as an integrative predictive level at which heterogeneous factors of variation can be coordinated within a single biological bearer. Given the fragmented state of multilevel cancer theory, this article advances a predictive strategy that complements the search for additional factors by asking how risk-relevant factors can be organized once they are identified. The detection of cellular dispositional properties is introduced here as an integrative predictive strategy grounded at this level: cells are characterized by relatively stable differences in how they tend to produce variability across conditions. These differences can be operationalized through measurable features, including levels of molecular noise and patterns of response to tissue-level influences. Framed in this way, cellular dispositions specify how multilevel predictors may become jointly usable.