Cancer drug resistance is multifactorial, driven by heritable (epi)genetic changes but also by phenotypic plasticity. In this study, we dissected the drivers of resistance by perturbing organoids derived from patients with colorectal cancer longitudinally with drugs in sequence. Combined longitudinal lineage tracking, single-cell multiomics analysis, evolutionary modeling, and machine learning revealed that different targeted drugs select for distinct subclones, supporting rationally designed drug sequences. The cellular memory of drug resistance was encoded as a heritable epigenetic configuration from which multiple transcriptional programs could run, supporting a one-to-many (epi)genotype-to-phenotype map that explains how clonal expansions and plasticity manifest together. This epigenetic landscape may ensure drug-resistant subclones can exhibit distinct phenotypes in changing environments while still preserving the cellular memory encoding for their selective advantage. Chemotherapy resistance was instead entirely driven by transient phenotypic plasticity rather than stable clonal selection. Inducing further chromosomal instability before drug application changed clonal evolution but not convergent transcriptional programs. Collectively, these data show how genetic and epigenetic alterations are selected to engender a “permissive epigenome” that enables phenotypic plasticity. Significance: Drug resistance is driven by genetic–epigenetic memory that enables cancer cells to adopt multiple phenotypic states depending on environmental conditions, supporting integration of evolutionary principles into biomarker discovery and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Early and reliable diagnosis of melanoma, a skin tumor with a poor prognosis, is extremely important. Phage display peptide libraries are a convenient screening resource for identifying bioactive peptides that interact with cancer targets. The aim of this study was to evaluate two technetium-99m tracers for angiogenesis detection in a melanoma model, using cyclic pegylated pentapeptide with RGD and NGR motifs conjugated with the bifunctional chelator mercaptoacetyltriglycine (MAG3). The conjugated peptides (10 μl of a μg/μl solution) were labeled with technetium-99m using a sodium tartrate buffer. Radiochemical evaluation was carried out by instant thin-layer chromatography and confirmed by high-performance liquid chromatography. The partition coefficient was determined and internalization assays were performed in two melanoma cell lines (B16F10 and SKMEL28). Biodistribution evaluation of the tracers was carried out in healthy animals at different time points and also in tumor-bearing mice, 120 min post injection. Blocking studies were also conducted by coinjection of cold peptides. The conjugates displayed a rather similar pharmacokinetic profile. They were radiolabeled with high radiochemical purity (>97%) and both were hydrophilic with preferential renal excretion. Yet, tumor uptake was higher for human than for murine melanoma cells, especially for [99mTc]-MAG3-PEG8-c(RGDyk) (7.85±2.34%injected dose/g 120 min post injection). The performance of [99mTc]-MAG3-PEG8-c(RGDyk) was better than the NGR tracer with regard to human melanoma uptake. In this sense, it should be considered for future radiotracer studies of tumor diagnosis.