2021
Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations
Patwardhan GA, Marczyk M, Wali VB, Stern DF, Pusztai L, Hatzis C. Treatment scheduling effects on the evolution of drug resistance in heterogeneous cancer cell populations. Npj Breast Cancer 2021, 7: 60. PMID: 34040000, PMCID: PMC8154902, DOI: 10.1038/s41523-021-00270-4.Peer-Reviewed Original ResearchHeterogeneous cancer cell populationsCancer cell populationsTriple-negative breast cancerSingle-cell RNA sequencingCell populationsFitness advantageRNA sequencingMDA-MB-231 TNBC cellsDrug resistanceMechanisms of resistanceVitro screening assaysClonal dynamicsTNBC cellsScreening assaysResistant clonesPatterns of resistanceConcomitant treatmentTherapy combinationsBreast cancerClinical studiesTreatment doseTreatment scheduleBarcodesSequencingTreatment
2019
The paracrine induction of prostate cancer progression by caveolin-1
Lin C, Yun E, Lo U, Tai Y, Deng S, Hernandez E, Dang A, Chen Y, Saha D, Mu P, Lin H, Li T, Shen T, Lai C, Hsieh J. The paracrine induction of prostate cancer progression by caveolin-1. Cell Death & Disease 2019, 10: 834. PMID: 31685812, PMCID: PMC6828728, DOI: 10.1038/s41419-019-2066-3.Peer-Reviewed Original ResearchConceptsCastration-resistant prostate cancerCancer stem cellsTumor-derived exosomesProstate cancerCav-1Cancer progressionSubpopulation of cancer stem cellsAssociated with stem cell phenotypeCancer immune evasionProstate cancer progressionStem cell capabilitiesStem cell phenotypePromote cancer developmentPresence of Cav-1Heterogeneous cancer cell populationsCancer cell populationsNeuroendocrine differentiationNeuroendocrine transdifferentiationEpithelial-mesenchymal transitionNFkB signaling pathwayTherapeutic resistanceTumor cellsImmune evasionChemotherapeutic resistanceParacrine inductionMolecular Biology and Evolution of Cancer: From Discovery to Action
Somarelli JA, Gardner H, Cannataro VL, Gunady EF, Boddy AM, Johnson NA, Fisk J, Gaffney SG, Chuang JH, Li S, Ciccarelli FD, Panchenko AR, Megquier K, Kumar S, Dornburg A, DeGregori J, Townsend JP. Molecular Biology and Evolution of Cancer: From Discovery to Action. Molecular Biology And Evolution 2019, 37: 320-326. PMID: 31642480, PMCID: PMC6993850, DOI: 10.1093/molbev/msz242.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsEvolutionary processesMolecular evolutionary processesEvolution of cancerCancer cell populationsEcological nichesNew therapeutic modesCancer evolutionEcological theoryMolecular biologyCancer biologyCancer progressionSuite of conceptsCell populationsBiologyNicheEvolutionCirculatory systemDeeper understandingDiscoveryCancer
2013
TLR2 enhances ovarian cancer stem cell self-renewal and promotes tumor repair and recurrence
Chefetz I, Alvero A, Holmberg J, Lebowitz N, Craveiro V, Yang-Hartwich Y, Yin G, Squillace L, Soteras M, Aldo P, Mor G. TLR2 enhances ovarian cancer stem cell self-renewal and promotes tumor repair and recurrence. Cell Cycle 2013, 12: 511-521. PMID: 23324344, PMCID: PMC3587452, DOI: 10.4161/cc.23406.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCarcinoma, Ovarian EpithelialDrug Resistance, NeoplasmFemaleHomeodomain ProteinsHumansHyaluronan ReceptorsInflammationMiceMice, NudeMyeloid Differentiation Factor 88Nanog Homeobox ProteinNeoplasm Recurrence, LocalNeoplasms, Glandular and EpithelialNeoplastic Stem CellsNF-kappa BOctamer Transcription Factor-3Ovarian NeoplasmsSOXB1 Transcription FactorsToll-Like Receptor 2Tumor Cells, CulturedTumor MicroenvironmentConceptsOvarian cancer stem cellsCancer stem cellsTumor repairEOC stem cellsTLR2-MyD88NFκB pathwaySpecific pro-inflammatory pathwaysStem cellsMajority of patientsEpithelial ovarian cancer stem cellsPrimary ovarian cancerPro-inflammatory pathwaysPro-inflammatory microenvironmentCell populationsStemness-associated genesChemoresistant recurrent diseaseRecurrent diseaseEOC patientsRecent compelling evidenceOvarian cancerTumor injuryRecurrenceCancer cell populationsTumor initiationCancer cellsEvaluation of chemosensitivity prediction using quantitative dose–response curve classification for highly advanced/relapsed gastric cancer
Matsuo T, Nishizuka SS, Ishida K, Endo F, Katagiri H, Kume K, Ikeda M, Koeda K, Wakabayashi G. Evaluation of chemosensitivity prediction using quantitative dose–response curve classification for highly advanced/relapsed gastric cancer. World Journal Of Surgical Oncology 2013, 11: 11. PMID: 23339659, PMCID: PMC3562164, DOI: 10.1186/1477-7819-11-11.Peer-Reviewed Original ResearchConceptsDose-response curveChemosensitivity testGastric cancerResistant cancer cell populationsStandard chemotherapy regimensPeak plasma concentrationDose-response patternDrug dose-response curvesPrimary chemotherapyChemotherapy regimensRecurrent diseaseStandard chemotherapyResultsA totalChemosensitivity evaluationPlasma concentrationsChemosensitivity patternsChemoresistant tumorsResistant patternChemotherapyDrug resistanceDrug sensitivityCancer cell populationsConclusionsThese resultsCisplatinCell populations
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