From:  Targeting tumor transition windows

 Temporal data framework for tracking candidate tumor transition windows.

Sampling pointBiological questionData typesWhat it can revealMain limitation
Baseline before therapyWhat tumor states exist before treatment?Tumor biopsy, genomic profiling, single-cell profiling, ctDNA, imagingPre-existing heterogeneity, dominant clones, baseline pathway activityCannot distinguish pre-existing states from therapy-induced adaptation
Early on-treatmentHow does the tumor initially respond to therapeutic pressure?Serial biopsy, single-cell ribonucleic acid sequencing (scRNA-seq), single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), ctDNA kinetics, cfDNA methylation, imagingEarly adaptive states, persister-like programs, stress-response activationShort-lived states may be missed if sampling is too sparse
Response phaseWhich adaptive states persist during tumor regression or stabilization?ctDNA dynamics, CTC analysis, imaging, transcriptomic or epigenomic profilingResidual disease, persister enrichment, metabolic or signaling compensationLow tumor burden may reduce liquid biopsy sensitivity
Molecular progressionWhat resistance signals appear before clinical relapse?ctDNA mutation tracking, cfDNA methylation, fragmentomics, serial imagingEmerging resistant clones, epigenetic-state shifts, molecular relapseBiomarker thresholds are not fully standardized
Clinical progressionWhich resistant state has become stabilized?Tumor biopsy, single-cell or spatial profiling, ctDNA, imagingFixed resistance mechanisms, clonal expansion, lineage switchingLater sampling may miss the earlier transition window
Post-progression reassessmentHow should subsequent therapy be selected?Integrated genomic, transcriptomic, epigenomic, proteomic, metabolic, and imaging dataNew therapeutic dependencies and resistance architectureRequires complex multi-omics integration and clinical validation

CTCs: circulating tumor cells; ctDNA: circulating tumor deoxyribonucleic acid.