Metastasis is the leading cause of cancer lethality, yet current predictive models can only estimate if it will occur, not when. Forecasts often deviate with an RMSE of ~15 months, limiting clinical utility. A key weakness is reliance on gene expression, which changes over time. We hypothesize that reliable prediction of metastatic timing requires modeling a driver of long-term adaptation: chromosome mis-segregations (MS). Disseminated cancer cells must adapt to foreign organ environments, a process unfolding over months or years. Somatic copy-number alterations, the primary consequence of MS, explain up to 85% of expression variance in breast cancer, underscoring their central role in adaptation. We tested this by constructing expression-informed Karyotype Fitness Landscapes (KFLs). Starting from a tumor’s karyotype and transcriptome, we simulate MS, update expression in proportion to copy number, and assign fitness using prognostic tools such as Oncotype DX. This generates landscapes linking thousands of karyotypes to predicted fitness. Using this approach we derived KFLs for breast cancer metastases in lung and brain, then simulated evolution using an agent-based model of MS we previously developed (Beck et. al, 2025). We quantified these trajectories with summary statistics capturing magnitude and dynamics of adaptation (e.g., area under the curve and slopes). Integration with MetMap phenotypic data showed that for lung metastasis, mean slope of fitness gain correlated with metastatic penetrance (r = 0.53, p = 0.061), suggesting faster adaptation predicts higher colonization likelihood. To extend these findings, we conducted long-term evolution experiments in breast and gastric cell lines under hypoxia, phosphate and glucose deprivation, with standard media as controls. Real-time growth tracking revealed progressive increases in proliferation under stress, consistent with adaptation. Karyotyping at early and late timepoints captured evolution across lineages and resource conditions. In SNU-668 cells under glucose deprivation, our neutral MS model failed to explain evolution (Fisher’s test: p ≈ 0.057). Glucose starvation imposed strong selection, with dynamics deviating sharply from neutrality (Fisher’s test: combined p ≈ 4.9 × 10⁻⁵). Both starved replicates converged on chromosome 18 loss and 21 gain as karyotype solutions. Under hypoxia, diploid SUM-159 cells remained stable, while isogenic near-tetraploid populations showed ploidy loss. In vivo results confirmed differential metastasic potential between isogenic diploid vs. tetra-ploid SUM-159 cells with ploidy loss in the latter, consistent with metabolic stress driving karyotype evolution. Together, these results establish a framework for linking MS-driven dynamics to experimental measures of metastatic behavior. By grounding predictive modeling in karyotype evolution, this approach offers a mechanistic path toward forecasting when metastasis will occur in distinct organ environments.