2022
Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment.
Choi R, Joel M, Hui M, Aneja S. Deep learning algorithm to predict pathologic complete response to neoadjuvant chemotherapy for breast cancer prior to treatment. Journal Of Clinical Oncology 2022, 40: 600-600. DOI: 10.1200/jco.2022.40.16_suppl.600.Peer-Reviewed Original ResearchPathologic complete responseNeoadjuvant chemotherapyBreast cancerComplete responseBreast MRIImproved disease-free survivalDisease-free survivalStage breast cancerPre-treatment predictionSubsets of ageNAC initiationOverall survivalPCR rateTreatment initiationUnnecessary toxicityTumor sizeSingle institutionDisease groupPatient levelPrognostic dataChemotherapyPatientsDiscordant predictionsCancerTotal test set
2016
Strategies to design clinical studies to identify predictive biomarkers in cancer research
Perez-Gracia JL, Sanmamed MF, Bosch A, Patiño-Garcia A, Schalper KA, Segura V, Bellmunt J, Tabernero J, Sweeney CJ, Choueiri TK, Martín M, Fusco JP, Rodriguez-Ruiz ME, Calvo A, Prior C, Paz-Ares L, Pio R, Gonzalez-Billalabeitia E, Hernandez A, Páez D, Piulats JM, Gurpide A, Andueza M, de Velasco G, Pazo R, Grande E, Nicolas P, Abad-Santos F, Garcia-Donas J, Castellano D, Pajares MJ, Suarez C, Colomer R, Montuenga LM, Melero I. Strategies to design clinical studies to identify predictive biomarkers in cancer research. Cancer Treatment Reviews 2016, 53: 79-97. PMID: 28088073, DOI: 10.1016/j.ctrv.2016.12.005.Peer-Reviewed Original ResearchConceptsPredictive biomarkersSpecific patient populationsPromising candidate biomarkerCancer researchUnnecessary toxicityPatient populationMultidisciplinary panelClinical studiesReliable biomarkersAnti-cancer drugsCandidate biomarkersClinical designStudy designBiomarkersPromising targetBiomarker identification studiesKey biomarkersDrugsAnticancer drugsEfficient study designStandard practiceToxicityPatientsOncologyPivotal field
2015
Discovery and Functional Validation of Novel Pediatric Specific FLT3 Activating Mutations in Acute Myeloid Leukemia: Results from the COG/NCI Target Initiative
Tarlock K, Hansen M, Hylkema T, Ries R, Farrar J, Auvil J, Gerhard D, Smith M, Davidsen T, Gesuwan P, Hermida L, Marra M, Mungall A, Mungall K, Ma Y, Zong S, Long W, Boggon T, Alonzo T, Kolb E, Gamis A, Meshinchi S. Discovery and Functional Validation of Novel Pediatric Specific FLT3 Activating Mutations in Acute Myeloid Leukemia: Results from the COG/NCI Target Initiative. Blood 2015, 126: 87. DOI: 10.1182/blood.v126.23.87.87.Peer-Reviewed Original ResearchFLT3/ITDChildren's Oncology GroupFLT3 mutationsOncology GroupPediatric cohortPediatric AMLFLT3 geneActivating mutationsTyrosine kinase domainAcute myeloid leukemiaDe novo resistanceInternal tandem duplicationJuxtamembrane domainCommon activating mutationsCo-occurring mutationsTargeted exome captureResistance conferring mutationsCOG trialsChildhood AMLUnnecessary toxicityInhibitor exposureFLT3 inhibitionNovo resistanceAdult studiesExcessive activation
2009
Evaluation of serum profiles changes after neoadjuvant chemotherapy for breast cancer using MALDI-TOF/MS procedure
Hawke D, Mazouni C, André F, Baggerly K, Baggerly K, Tsavachidis S, Buzdar A, Martin P, Kobayashi R, Pusztai L. Evaluation of serum profiles changes after neoadjuvant chemotherapy for breast cancer using MALDI-TOF/MS procedure. Journal Of Clinical Oncology 2009, 27: e22072-e22072. DOI: 10.1200/jco.2009.27.15_suppl.e22072.Peer-Reviewed Original ResearchNeoadjuvant chemotherapyBreast cancerPrimary chemotherapyHER2-positive patientsPathological complete responseBreast cancer cohortPositive patientsComplete responseUnnecessary toxicityResidual diseaseTumor responseCancer cohortBlood samplesChemotherapyResponse rateTreatment cyclesPosttreatment samplesCancerSerum samplesRD groupSignificant financial relationshipPatientsProfile changesRespondersTotal
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