2021
The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group
El Bairi K, Haynes HR, Blackley E, Fineberg S, Shear J, Turner S, de Freitas JR, Sur D, Amendola LC, Gharib M, Kallala A, Arun I, Azmoudeh-Ardalan F, Fujimoto L, Sua LF, Liu SW, Lien HC, Kirtani P, Balancin M, El Attar H, Guleria P, Yang W, Shash E, Chen IC, Bautista V, Do Prado Moura JF, Rapoport BL, Castaneda C, Spengler E, Acosta-Haab G, Frahm I, Sanchez J, Castillo M, Bouchmaa N, Md Zin RR, Shui R, Onyuma T, Yang W, Husain Z, Willard-Gallo K, Coosemans A, Perez EA, Provenzano E, Ericsson PG, Richardet E, Mehrotra R, Sarancone S, Ehinger A, Rimm DL, Bartlett JMS, Viale G, Denkert C, Hida AI, Sotiriou C, Loibl S, Hewitt SM, Badve S, Symmans WF, Kim RS, Pruneri G, Goel S, Francis PA, Inurrigarro G, Yamaguchi R, Garcia-Rivello H, Horlings H, Afqir S, Salgado R, Adams S, Kok M, Dieci MV, Michiels S, Demaria S, Loi S. The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group. Npj Breast Cancer 2021, 7: 150. PMID: 34853355, PMCID: PMC8636568, DOI: 10.1038/s41523-021-00346-1.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesImmune checkpoint inhibitorsInternational Immuno-Oncology Biomarker Working GroupBiomarker Working GroupBreast cancerTriple-negative breast cancerSubgroup of womenDeath-1Middle-income countriesPD-L1TIL analysisCytotoxic treatmentCancer settingPrognostic biomarkerClinical utilityClinical validityPatient advocatesWorking GroupClinical utilizationModern oncologyPatientsLymphocytesCancerFuture approachesImmunotherapyAutomated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma
Moore MR, Friesner ID, Rizk EM, Fullerton BT, Mondal M, Trager MH, Mendelson K, Chikeka I, Kurc T, Gupta R, Rohr BR, Robinson EJ, Acs B, Chang R, Kluger H, Taback B, Geskin LJ, Horst B, Gardner K, Niedt G, Celebi JT, Gartrell-Corrado RD, Messina J, Ferringer T, Rimm DL, Saltz J, Wang J, Vanguri R, Saenger YM. Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma. Scientific Reports 2021, 11: 2809. PMID: 33531581, PMCID: PMC7854647, DOI: 10.1038/s41598-021-82305-1.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiopsyChemotherapy, AdjuvantClinical Decision-MakingDeep LearningFemaleFollow-Up StudiesHumansImage Processing, Computer-AssistedKaplan-Meier EstimateLymphocytes, Tumor-InfiltratingMaleMelanomaMiddle AgedNeoplasm StagingPatient SelectionPrognosisRetrospective StudiesRisk AssessmentROC CurveSkinSkin NeoplasmsYoung AdultConceptsTumor-infiltrating lymphocytesDisease-specific survivalEarly-stage melanomaOpen-source deep learningCutoff valueMultivariable Cox proportional hazards analysisCox proportional hazards analysisDeep learningLow-risk patientsProportional hazards analysisKaplan-Meier analysisAccurate prognostic biomarkersEosin imagesAccuracy of predictionAdjuvant therapyRisk patientsSpecific survivalPrognostic valueValidation cohortReceiver operating curvesTraining cohortTIL analysisClinical trialsPrimary melanomaPrognostic biomarker
2013
In situ techniques for protein analysis in tumor tissue
Anagnostou V, Rimm D. In situ techniques for protein analysis in tumor tissue. 2013, 76-84. DOI: 10.1017/cbo9781139046947.010.Peer-Reviewed Original ResearchSignal amplification techniqueProtein detectionAntigen of interestConventional IHCAntigen-antibody reactionLow costHuman epidermal growth factor receptor 2Epidermal growth factor receptor 2Growth factor receptor 2Selection of patientsComplementary diagnostic informationSitu techniquesFactor receptor 2Companion diagnostic testsCurrent analytical techniquesParaffin-embedded tissuesHistological diagnosisSpecific therapyDefinite diagnosisHistological subclassificationBreast cancerPrognostic biomarkerEstrogen receptorReceptor 2Morphologic evaluationConstruction and Analysis of Multiparameter Prognostic Models for Melanoma Outcome
Rothberg BE, Rimm DL. Construction and Analysis of Multiparameter Prognostic Models for Melanoma Outcome. Methods In Molecular Biology 2013, 1102: 227-258. PMID: 24258982, PMCID: PMC3912557, DOI: 10.1007/978-1-62703-727-3_13.Peer-Reviewed Original ResearchConceptsAdjuvant regimensNegative sentinel lymph node biopsyAdverse risk-benefit ratioPrognostic modelStage II melanoma patientsSentinel lymph node biopsyConventional clinicopathologic criteriaLymph node biopsyStage II melanomaMelanoma-specific survivalWide local excisionRisk-benefit ratioKi-67 assaysTumor molecular profilesComposite prognostic indicesMost patientsNode biopsyLocal excisionMelanoma patientsPrognostic indexRisk stratificationClinicopathologic criteriaMelanoma outcomesPrognostic biomarkerIndependent cohort
2008
AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma.
Gustavson MD, Molinaro AM, Tedeschi G, Camp RL, Rimm DL. AQUA analysis of thymidylate synthase reveals localization to be a key prognostic biomarker in 2 large cohorts of colorectal carcinoma. Archives Of Pathology & Laboratory Medicine 2008, 132: 1746-52. PMID: 18976010, DOI: 10.5858/132.11.1746.Peer-Reviewed Original ResearchConceptsThymidylate synthase expressionSynthase expressionColorectal carcinomaSignificant associationDisease-specific survivalColon cancer outcomesColon cancer survivalKey prognostic biomarkersRetrospective cohortNodal statusPrognostic valueColorectal cancerCancer outcomesCancer survivalPathologic classificationPrognostic biomarkerLarge cohortSecond cohortAQUA scoreCytoplasmic expressionNuclear expressionCohortHigh nuclearCytoplasmic ratioFirst cohortEstrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome
Harigopal M, Heymann J, Ghosh S, Anagnostou V, Camp RL, Rimm DL. Estrogen receptor co-activator (AIB1) protein expression by automated quantitative analysis (AQUA) in a breast cancer tissue microarray and association with patient outcome. Breast Cancer Research And Treatment 2008, 115: 77-85. PMID: 18521745, DOI: 10.1007/s10549-008-0063-9.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAutomationBiomarkers, TumorBreast NeoplasmsFemaleGene Expression Regulation, NeoplasticHumansMultivariate AnalysisNuclear Receptor Coactivator 3Oligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsReceptors, EstrogenReceptors, ProgesteroneRegression AnalysisTranscription FactorsTreatment OutcomeConceptsHigh AIB1 expressionTranscription intermediary factor 2Poor patient outcomesAIB1 expressionTissue microarrayPatient outcomesHER2/neu statusBreast cancer tissue microarrayFluorescent immunohistochemical stainingWorse overall survivalUnivariate survival analysisBreast cancer specimensCancer tissue microarrayHER2/neuCoregulatory proteinsCox univariate survival analysesBreast tissue microarraysOverall survivalER statusPR statusPrognostic significanceIndependent associationBreast cancerPrognostic biomarkerImmunohistochemical staining
2006
Evaluation of the Prognostic Value of Cellular Inhibitor of Apoptosis Protein in Epithelial Ovarian Cancer Using Automated Quantitative Protein Analysis
Psyrri A, Yu Z, Bamias A, Weinberger PM, Markakis S, Kowalski D, Camp RL, Rimm DL, Dimopoulos MA. Evaluation of the Prognostic Value of Cellular Inhibitor of Apoptosis Protein in Epithelial Ovarian Cancer Using Automated Quantitative Protein Analysis. Cancer Epidemiology Biomarkers & Prevention 2006, 15: 1179-1183. PMID: 16775178, DOI: 10.1158/1055-9965.epi-06-0120.Peer-Reviewed Original ResearchConceptsEpithelial ovarian cancerOvarian cancerPrognostic valuePaclitaxel-based combination chemotherapyOnly significant prognostic factorAdvanced stage ovarian cancerSignificant prognostic factorsOvarian cancer patientsProtein levelsImportant prognostic biomarkerMean followSurgical debulkingCombination chemotherapyOverall survivalPrognostic factorsClinical outcomesMultivariable analysisEntire cohortCancer patientsPrognostic biomarkerPrognostic variablesMembranous expressionApoptosis proteinSurvival rateCellular inhibitor
2005
Subcellular Localization and Protein Levels of Cyclin-Dependent Kinase Inhibitor p27 Independently Predict for Survival in Epithelial Ovarian Cancer
Psyrri A, Bamias A, Yu Z, Weinberger PM, Kassar M, Markakis S, Kowalski D, Efstathiou E, Camp RL, Rimm DL, Dimopoulos MA. Subcellular Localization and Protein Levels of Cyclin-Dependent Kinase Inhibitor p27 Independently Predict for Survival in Epithelial Ovarian Cancer. Clinical Cancer Research 2005, 11: 8384-8390. PMID: 16322299, DOI: 10.1158/1078-0432.ccr-05-1270.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBiomarkers, TumorCell DifferentiationCohort StudiesCombined Modality TherapyCyclin-Dependent Kinase Inhibitor p27Cystadenocarcinoma, SerousFemaleHumansMiddle AgedNeoplasm StagingNeoplasms, Glandular and EpithelialOvarian NeoplasmsPrognosisSubcellular FractionsSurvival RateTissue Array AnalysisConceptsNuclear p27 expressionOvarian cancerP27 expression levelsOverall survivalP27 expressionPlatinum-paclitaxel combination chemotherapyAdvanced stage ovarian cancerDisease-free survivalSignificant prognostic factorsStage ovarian cancerEpithelial ovarian cancerValuable prognostic biomarkerExpression levelsP27 protein expressionCyclin-dependent kinase inhibitor p27Mean followSurgical debulkingCombination chemotherapyPrognostic factorsMultivariable analysisPrognostic valueImmunohistochemical assessmentPrognostic biomarkerPrognostic variablesImmunofluorescence-based method