Featured Publications
A clonal expression biomarker associates with lung cancer mortality
Biswas D, Birkbak N, Rosenthal R, Hiley C, Lim E, Papp K, Boeing S, Krzystanek M, Djureinovic D, La Fleur L, Greco M, Döme B, Fillinger J, Brunnström H, Wu Y, Moore D, Skrzypski M, Abbosh C, Litchfield K, Al Bakir M, Watkins T, Veeriah S, Wilson G, Jamal-Hanjani M, Moldvay J, Botling J, Chinnaiyan A, Micke P, Hackshaw A, Bartek J, Csabai I, Szallasi Z, Herrero J, McGranahan N, Swanton C. A clonal expression biomarker associates with lung cancer mortality. Nature Medicine 2019, 25: 1540-1548. PMID: 31591602, PMCID: PMC6984959, DOI: 10.1038/s41591-019-0595-z.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerClinicopathological risk factorsCell lung cancerLung cancer mortalityPrognostic gene expression signaturesCancer cell proliferationGene expression signaturesCancer mortalityLung cancerRisk factorsExpression-based biomarkersCopy number gainsDisease subtypesClinical descriptorsTranscriptomic biomarkersIndividual tumorsCancer typesDiagnostic precisionMolecular biomarkersExpression signaturesCell proliferationDNA copy number gainsBiomarkersPatientsIntratumor heterogeneityProspective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma
Biswas D, Liu Y, Herrero J, Wu Y, Moore D, Karasaki T, Grigoriadis K, Lu W, Veeriah S, Naceur-Lombardelli C, Magno N, Ward S, Frankell A, Hill M, Colliver E, de Carné Trécesson S, East P, Malhi A, Snell D, O’Neill O, Leonce D, Mattsson J, Lindberg A, Micke P, Moldvay J, Megyesfalvi Z, Dome B, Fillinger J, Nicod J, Downward J, Szallasi Z, Hackshaw A, Jamal-Hanjani M, Kanu N, Birkbak N, Swanton C. Prospective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma. Nature Cancer 2025, 6: 86-101. PMID: 39789179, PMCID: PMC11779643, DOI: 10.1038/s43018-024-00883-1.Peer-Reviewed Original ResearchConceptsLung adenocarcinomaStage I diseaseClinicopathological risk factorsSurvival of patientsResponse to treatmentRNA sequencing dataI diseaseSequence dataMetastatic clonesNeedle biopsyIndividual tumorsLung expressionTranscription signalsPrognostic informationWhole exomeExpressed genesChemotherapy sensitivityProspective validationSurvival associationsTranscriptomic heterogeneityHuman tumorsEvolutionary measuresChromosomal instabilityRisk factorsNatural history
2024
Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction
Wu J, Biswas D, Ryan M, Bernstein B, Rizvi M, Fairhurst N, Kaye G, Baral R, Searle T, Melikian N, Sado D, Lüscher T, Grocott‐Mason R, Carr‐White G, Teo J, Dobson R, Bromage D, McDonagh T, Shah A, O'Gallagher K. Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction. European Journal Of Heart Failure 2024, 26: 302-310. PMID: 38152863, DOI: 10.1002/ejhf.3115.Peer-Reviewed Original ResearchConceptsLeft ventricular ejection fractionDiagnosis of HFpEFEuropean Society of CardiologyHeart failureNatural language processingElectronic health recordsEuropean Society of Cardiology criteriaClinical diagnosis of HFEuropean Society of Cardiology diagnostic criteriaDiagnostic criteriaVentricular ejection fractionRetrospective cohort studyDiagnosis of HFSociety of CardiologyClinician-assigned diagnosisConsecutive patientsHFpEF patientsEjection fractionElectronic health record dataAcute cardiovascular eventsExpert clinical reviewNatural language processing methodsNatural language processing pipelineHFpEFCardiovascular events
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply