{"doi":"10.1158/2159-8290.cd-21-0282","title":"Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning","abstract":"ABSTRACT: Phenotypic plasticity describes the ability of cancer cells to undergo dynamic, nongenetic cell state changes that amplify cancer heterogeneity to promote metastasis and therapy evasion. Thus, cancer cells occupy a continuous spectrum of phenotypic states connected by trajectories defining dynamic transitions upon a cancer cell state landscape. With technologies proliferating to systematically record molecular mechanisms at single-cell resolution, we illuminate manifold learning techniques as emerging computational tools to effectively model cell state dynamics in a way that mimics our understanding of the cell state landscape. We anticipate that \"state-gating\" therapies targeting phenotypic plasticity will limit cancer heterogeneity, metastasis, and therapy resistance. SIGNIFICANCE: Nongenetic mechanisms underlying phenotypic plasticity have emerged as significant drivers of tumor heterogeneity, metastasis, and therapy resistance. Herein, we discuss new experimental and computational techniques to define phenotypic plasticity as a scaffold to guide accelerated progress in uncovering new vulnerabilities for therapeutic exploitation.","journal":"Cancer Discovery","year":2022,"id":236030,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":73,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9332,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":855763,"name":"Beatriz P. San Juan","orcid":"0000-0002-7868-6298","position":1,"is_corresponding":false},{"id":855764,"name":"John G. Lock","orcid":"0000-0002-3880-4106","position":2,"is_corresponding":false},{"id":254926,"name":"Smita Krishnaswamy","orcid":"0000-0001-5823-1985","position":3,"is_corresponding":false},{"id":855765,"name":"Christine L. Chaffer","orcid":"0000-0003-2620-6130","position":4,"is_corresponding":false},{"id":48974,"name":"Daniel B. Burkhardt","orcid":"0000-0001-7744-1363","position":0,"is_corresponding":true}],"reference_count":137,"raw_metadata":null,"created_at":"2026-07-19T00:21:58.728756Z","pmid":"35736000","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}