Nuclear Irregularity as a Universal Diagnostic Tool in Solid Tumors

As tumors develop, cancer cells accumulate diverse genomic and phenotypic alterations to meet heightened demands for energy production and biosynthesis. Loss of lamina function and perturbations in energy production are associated with pronounced aberrations in cellular morphology, particularly within nuclear architecture and the plasma membrane. Systematic analysis of nuclear morphology can reveal conserved structures across diverse cancer types, enabling disease state stratification, biomarker discovery, and potential avenues for personalizing therapy to minimize recurrence risk. To this end, this study analyzes an imaging mass cytometry (IMC) breast cancer dataset, differentiating cancerous and non-cancerous nuclei with a p-value of 1.02e-06. In addition, this study achieves an accuracy of 78% and an F1 score of 72% using a computational and machine learning-based pipeline for analyzing the morphological heterogeneity of nuclei and protein expression, enabling the characterization of patient-specific tumor phenotypes. Unlike traditional morphology analysis pipelines limited to specific imaging platforms, this workflow enables cross-cohort and cross-cancer comparison, capturing tumor-specific phenotypic deviations at a single-cell resolution. The resulting phenotypic profiles could inform prognosis, treatment, and monitoring of therapeutic response. Summary As cancers become more aggressive and require more energy, typically uniform and organized cells begin to develop abnormal features to support their heightened needs. Studies have found that the prevalence of abnormal features is directly associated with the speed at which the tumor grows, but also the body’s ability to fight back. This study aims to streamline the analysis of these irregular features across all cancer types, providing a clearer picture of how nuclei distinguish stages of cancer and aid in rapidly clinically assessing at-risk or affected patients. Using the nuclear abnormality score developed, this study was able to identify subpopulations of highly irregular cancer cells and successfully separate them with a p-value of 1.222e-21. By comparing the expression of cancer proteins with these irregularities, we can begin to develop insights that can be used across all imaging techniques to understand the cancer’s inner workings and learn to predict relapse before it even occurs.