2023
Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists
Harms P, Frankel T, Moutafi M, Rao A, Rimm D, Taube J, Thomas D, Chan M, Pantanowitz L. Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists. Modern Pathology 2023, 36: 100197. PMID: 37105494, DOI: 10.1016/j.modpat.2023.100197.Peer-Reviewed Original ResearchConceptsDigital spatial profilingMost clinical laboratoriesTumor microenvironmentImmunofluorescence/immunohistochemistryImmuno-oncology researchDiagnostic practiceClinical laboratoriesRoutine diagnostic useAntitumor immunityAdvanced cancerImmune populationsMultiplex stainingEosin stainingIHC stainsPractical updateClinical diagnostic practiceMultiplex immunohistochemistrySingle biomarkerImmunohistochemistryMultiplexed immunohistochemistryStandardized protocolChromogenic immunohistochemistryDiagnostic useMultiple biomarkersSerial sections
2022
Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit.
Hayes D, Herbst R, Myles J, Topalian S, Yohe S, Aronson N, Bellizzi A, Basu Roy U, Bradshaw G, Edwards R, El-Gabry E, Elvin J, Gajewski T, McShane L, Oberley M, Philip R, Rimm D, Rosenbaum J, Rubin E, Schlager L, Sherwood S, Stewart M, Taube J, Thurin M, Vasalos P, Laser J. Proceedings From the ASCO/College of American Pathologists Immune Checkpoint Inhibitor Predictive Biomarker Summit. JCO Precision Oncology 2022, 6: e2200454. PMID: 36446042, PMCID: PMC10530621, DOI: 10.1200/po.22.00454.Peer-Reviewed Original ResearchConceptsICI therapyImmune checkpoint inhibition therapyDeath ligand 1 (PD-L1) expressionMultiple predictive biomarkersTumor biomarker testsCheckpoint inhibition therapyLigand 1 expressionDeath ligand 1Field of oncologyICI benefitPredictive factorsPredictive biomarkersInhibition therapyNeoantigen expressionBiomarker testsHealth insurance organizationsUS FoodDrug AdministrationAmerican PathologistsMedicaid ServicesTherapyBiomarker developmentNational InstituteLigand 1Clinical application
2021
Targeting Pyruvate Kinase M2 Phosphorylation Reverses Aggressive Cancer Phenotypes
Apostolidi M, Vathiotis IA, Muthusamy V, Gaule P, Gassaway BM, Rimm DL, Rinehart J. Targeting Pyruvate Kinase M2 Phosphorylation Reverses Aggressive Cancer Phenotypes. Cancer Research 2021, 81: 4346-4359. PMID: 34185676, PMCID: PMC8373815, DOI: 10.1158/0008-5472.can-20-4190.Peer-Reviewed Original ResearchMeSH KeywordsActive Transport, Cell NucleusAnimalsBiomarkers, TumorCarrier ProteinsCell Line, TumorCollagenCyclic N-OxidesDrug CombinationsGenome, HumanHumansIndolizinesLamininMCF-7 CellsMembrane ProteinsMiceNeoplasm InvasivenessNeoplasm TransplantationNeoplasmsOxidation-ReductionPhenotypePhosphorylationProtein IsoformsProteoglycansProteomicsPyridazinesPyridinium CompoundsPyrrolesPyruvate KinaseThyroid HormonesTriple Negative Breast NeoplasmsConceptsTriple-negative breast cancerPyruvate kinase M2TEPP-46Breast cancerAggressive breast cancer cell phenotypesCharacteristic nuclear staining patternAggressive breast cancer subtypeAggressive breast cancer phenotypeBreast cancer cell phenotypeCDK inhibitor dinaciclibCombination of dinaciclibLack of biomarkersEffective therapeutic approachBreast cancer phenotypeBreast cancer subtypesCancer phenotypePhosphorylation of PKM2Cyclin-dependent kinase (CDK) pathwayMouse xenograft modelAggressive cancer phenotypeNuclear staining patternLower survival rateImpaired redox balancePrognostic valueCancer cell phenotypePD-L1 as a biomarker of response to immune-checkpoint inhibitors
Doroshow DB, Bhalla S, Beasley MB, Sholl LM, Kerr KM, Gnjatic S, Wistuba II, Rimm DL, Tsao MS, Hirsch FR. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nature Reviews Clinical Oncology 2021, 18: 345-362. PMID: 33580222, DOI: 10.1038/s41571-021-00473-5.Peer-Reviewed Original ResearchConceptsImmune checkpoint inhibitorsSelection of patientsPD-L1L1 antibodyImmunohistochemistry assaysPD-L1 immunohistochemistry assaysOutcomes of patientsBiomarkers of responseCompanion diagnostic assayTypes of cancerPD-1Clinical outcomesSelection biomarkerProspective comparisonClinical challengeNew therapiesFuture treatmentPatientsSolid tumorsClinical useSpecific agentsInter-assay variabilityBiomarkersCurrent roleDiagnostic assays
2020
Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images
Noorbakhsh J, Farahmand S, Foroughi pour A, Namburi S, Caruana D, Rimm D, Soltanieh-ha M, Zarringhalam K, Chuang JH. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nature Communications 2020, 11: 6367. PMID: 33311458, PMCID: PMC7733499, DOI: 10.1038/s41467-020-20030-5.Peer-Reviewed Original ResearchConceptsConvolutional neural networkWhole slide imagesPower of CNNsNormal convolutional neural networkImage data miningColon cancer imagesData miningCNN accuracyCancer imagesNeural networkHistopathological imagesManual inspectionSlide imagesData typesClassifier comparisonSignificant accuracyHistological imagesImage analysisSpatial similarityImagesClassifier pairsClassificationMutation classificationAccuracyMiningHow current assay approval policies are leading to unintended imprecision medicine
Salgado R, Bellizzi AM, Rimm D, Bartlett JMS, Nielsen T, Holger M, Laenkholm AV, Quinn C, Cserni G, Cunha IW, Alvarado-Cabrero I, Cree I. How current assay approval policies are leading to unintended imprecision medicine. The Lancet Oncology 2020, 21: 1399-1401. PMID: 33098760, DOI: 10.1016/s1470-2045(20)30592-1.Peer-Reviewed Original Research
2019
Suppressing miR-21 activity in tumor-associated macrophages promotes an antitumor immune response
Sahraei M, Chaube B, Liu Y, Sun J, Kaplan A, Price NL, Ding W, Oyaghire S, García-Milian R, Mehta S, Reshetnyak YK, Bahal R, Fiorina P, Glazer PM, Rimm DL, Fernández-Hernando C, Suárez Y. Suppressing miR-21 activity in tumor-associated macrophages promotes an antitumor immune response. Journal Of Clinical Investigation 2019, 129: 5518-5536. PMID: 31710308, PMCID: PMC6877327, DOI: 10.1172/jci127125.Peer-Reviewed Original ResearchConceptsTumor-associated macrophagesMiR-21 expressionTumor growthMiR-21Immune responseCytotoxic T cell responsesC motif chemokine 10Antitumor immune responseT cell responsesAntitumoral immune responseTumor immune infiltratesInduction of cytokinesPotential therapeutic implicationsMiR-21 inhibitionStages of carcinogenesisAngiostatic phenotypeTumor cell deathIL-12Immune infiltratesTherapeutic implicationsSolid tumorsTumor neovascularizationTumor progressionTumor microenvironmentTumor pathogenesisArtificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 2019, 16: 703-715. PMID: 31399699, PMCID: PMC6880861, DOI: 10.1038/s41571-019-0252-y.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceHumansMedical OncologyNeoplasmsPathology, ClinicalPrecision MedicineConceptsArtificial intelligenceMachine learning toolsDigital pathologyUse of AIDeep neural networksLearning toolsStained tissue specimensWhole slide imagesFeature-based methodologyNeural networkIntelligencePotential future opportunitiesMorphometric phenotypesNetworkValidation datasetComputational approachToolMiningEnormous divergenceDatasetImagesPrecision oncologyFrameworkComplex processFuture opportunitiesSiglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy
Wang J, Sun J, Liu LN, Flies DB, Nie X, Toki M, Zhang J, Song C, Zarr M, Zhou X, Han X, Archer KA, O’Neill T, Herbst RS, Boto AN, Sanmamed MF, Langermann S, Rimm DL, Chen L. Siglec-15 as an immune suppressor and potential target for normalization cancer immunotherapy. Nature Medicine 2019, 25: 656-666. PMID: 30833750, PMCID: PMC7175920, DOI: 10.1038/s41591-019-0374-x.Peer-Reviewed Original ResearchConceptsNormalization cancer immunotherapyTumor microenvironmentSiglec-15Antibody blockadeCancer immunotherapyImmune suppressorMyeloid cellsAntigen-specific T cell responsesB7-H1/PDTumor-infiltrating myeloid cellsB7-H1 moleculesAnti-tumor immunityT cell responsesPotential targetImmune evasion mechanismsInhibits tumor growthMacrophage colony-stimulating factorColony-stimulating factorB7-H1Evasion mechanismsMouse modelHuman cancer cellsTumor growthCell responsesGenetic ablation
2018
Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning
Klauschen F, Müller K, Binder A, Bockmayr M, Hägele M, Seegerer P, Wienert S, Pruneri G, de Maria S, Badve S, Michiels S, Nielsen TO, Adams S, Savas P, Symmans F, Willis S, Gruosso T, Park M, Haibe-Kains B, Gallas B, Thompson AM, Cree I, Sotiriou C, Solinas C, Preusser M, Hewitt SM, Rimm D, Viale G, Loi S, Loibl S, Salgado R, Denkert C, Group O. Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning. Seminars In Cancer Biology 2018, 52: 151-157. PMID: 29990622, DOI: 10.1016/j.semcancer.2018.07.001.Peer-Reviewed Original ResearchConceptsClassical image segmentationLearning-based approachImage analysis approachImage segmentationTraining dataConventional machineExplainable machineVisual approachPlausibility checksML resultsSegmentationMachineSuch approachesLimited precisionShape propertiesDecision-making processLarge amountScoring approachComplex propertiesAnalysis approachHeatmapsTIL quantificationObjectsBiomedical researchEstimationComparison of Laboratory-Developed Tests and FDA-Approved Assays for BRAF, EGFR, and KRAS Testing
Kim AS, Bartley AN, Bridge JA, Kamel-Reid S, Lazar AJ, Lindeman NI, Long TA, Merker JD, Rai AJ, Rimm DL, Rothberg PG, Vasalos P, Moncur JT. Comparison of Laboratory-Developed Tests and FDA-Approved Assays for BRAF, EGFR, and KRAS Testing. JAMA Oncology 2018, 4: 838-841. PMID: 29242895, PMCID: PMC6145687, DOI: 10.1001/jamaoncol.2017.4021.Peer-Reviewed Original ResearchConceptsLaboratory-developed testsPT responseCompanion diagnosticsClinical laboratory testingKRAS testingOncology CommitteeMAIN OUTCOMEUS FoodDrug AdministrationPractice characteristicsDiagnostic testingTumor contentProficiency testingVariant-specific differencesEGFRBRAFClinical diagnostic testingMajority of laboratoriesKRASAssaysLaboratory testingPerformance of laboratoriesKit manufacturersResponseParticipants
2017
Implications of the tumor immune microenvironment for staging and therapeutics
Taube JM, Galon J, Sholl LM, Rodig SJ, Cottrell TR, Giraldo NA, Baras AS, Patel SS, Anders RA, Rimm DL, Cimino-Mathews A. Implications of the tumor immune microenvironment for staging and therapeutics. Modern Pathology 2017, 31: 214-234. PMID: 29192647, PMCID: PMC6132263, DOI: 10.1038/modpathol.2017.156.Peer-Reviewed Original ResearchConceptsTumor immune microenvironmentImmune microenvironmentTumor typesTumor microenvironmentAnti-PD-1/PD-L1Therapeutic targetPD-1/PD-L1 axisFirst line treatment algorithmHost antitumor immune responseEarly stage colorectal carcinomaLocal immune contextureImmune checkpoint inhibitorsPD-L1 axisAntitumor immune responseImmune-based therapiesPD-L1 antibodiesAbundance of CD8Th1 helper cellsNovel therapeutic targetPotential therapeutic targetPrimary organ siteNew candidate biomarkersNumerous tumor typesSpecific tumor typesCurrent TNM
2016
Early and multiple origins of metastatic lineages within primary tumors
Zhao ZM, Zhao B, Bai Y, Iamarino A, Gaffney SG, Schlessinger J, Lifton RP, Rimm DL, Townsend JP. Early and multiple origins of metastatic lineages within primary tumors. Proceedings Of The National Academy Of Sciences Of The United States Of America 2016, 113: 2140-2145. PMID: 26858460, PMCID: PMC4776530, DOI: 10.1073/pnas.1525677113.Peer-Reviewed Original ResearchConceptsMetastatic lineagesGenetic changesEarly genetic divergenceMolecular evolutionary modelsSingle genetic changeDivergent lineagesTumor phylogeneticsDivergence timesAncestral stateGenetic divergenceCancer lineagesPhylogenetic analysisEvolutionary processesLineagesCancer evolutionMultiple originsDriver genesCancer biologyCancer progressionSomatic mutationsTumor developmentEvolutionary modelsDriver mutationsChronogramMutations
2014
Quantitative measurement of cancer tissue biomarkers in the lab and in the clinic
Carvajal-Hausdorf D, Schalper KA, Neumeister V, Rimm DL. Quantitative measurement of cancer tissue biomarkers in the lab and in the clinic. Laboratory Investigation 2014, 95: 385-396. PMID: 25502176, PMCID: PMC4383674, DOI: 10.1038/labinvest.2014.157.Peer-Reviewed Original Research
2013
A Prospective, Multi-Institutional Diagnostic Trial to Determine Pathologist Accuracy in Estimation of Percentage of Malignant Cells
Viray H, Li K, Long TA, Vasalos P, Bridge JA, Jennings LJ, Halling KC, Hameed M, Rimm DL. A Prospective, Multi-Institutional Diagnostic Trial to Determine Pathologist Accuracy in Estimation of Percentage of Malignant Cells. Archives Of Pathology & Laboratory Medicine 2013, 137: 1545-9. PMID: 24168492, DOI: 10.5858/arpa.2012-0561-cp.Peer-Reviewed Original ResearchConceptsFalse-negative test resultsMalignant cellsMulti-institutional studyColon tissue specimensCriterion standardPatient careTissue specimensTumor tissueDiagnostic trialPathologists' accuracyGenetic alterationsNuclear countsPathologist estimationEstimation of percentageVisual estimationCurrent studyCellsTesting failures
2010
Quantitative evaluation of protein expression as a function of tissue microarray core diameter: is a large (1.5 mm) core better than a small (0.6 mm) core?
Anagnostou VK, Lowery FJ, Syrigos KN, Cagle PT, Rimm DL. Quantitative evaluation of protein expression as a function of tissue microarray core diameter: is a large (1.5 mm) core better than a small (0.6 mm) core? Archives Of Pathology & Laboratory Medicine 2010, 134: 613-9. PMID: 20367312, DOI: 10.5858/134.4.613.Peer-Reviewed Original Research
2009
GOLPH3 modulates mTOR signalling and rapamycin sensitivity in cancer
Scott KL, Kabbarah O, Liang MC, Ivanova E, Anagnostou V, Wu J, Dhakal S, Wu M, Chen S, Feinberg T, Huang J, Saci A, Widlund HR, Fisher DE, Xiao Y, Rimm DL, Protopopov A, Wong KK, Chin L. GOLPH3 modulates mTOR signalling and rapamycin sensitivity in cancer. Nature 2009, 459: 1085-1090. PMID: 19553991, PMCID: PMC2753613, DOI: 10.1038/nature08109.Peer-Reviewed Original ResearchConceptsTarget of rapamycinTrans-Golgi networkHuman cancersGenome-wide copy number analysisCopy number analysisRetromer complexGolgi proteinsHuman cancer cellsRapamycin sensitivityNew oncogeneGOLPH3Integrative analysisPotent oncogeneGenomic profilesBiochemical dataCancer cellsFunction studiesNumber analysisYeastSolid tumor typesCell sizeOncogeneMTORRapamycinMTOR inhibitorsChapter 1 The Function, Proteolytic Processing, and Histopathology of Met in Cancer
Hanna JA, Bordeaux J, Rimm DL, Agarwal S. Chapter 1 The Function, Proteolytic Processing, and Histopathology of Met in Cancer. Advances In Cancer Research 2009, 103: 1-23. PMID: 19854350, DOI: 10.1016/s0065-230x(09)03001-2.Peer-Reviewed Original ResearchConceptsHepatocyte growth factorExpression of METLocalization of MetClinicopathological characteristicsMET receptor tyrosine kinaseTherapeutic targetCancer typesReceptor tyrosine kinasesCancer treatmentGrowth factorCancer cellsCell proliferationMetSProteolytic processingHistopathologyCancerTyrosine kinaseRecent studiesImproper regulationNuclear localizationAntibodies
2008
A Decade of Tissue Microarrays: Progress in the Discovery and Validation of Cancer Biomarkers
Camp RL, Neumeister V, Rimm DL. A Decade of Tissue Microarrays: Progress in the Discovery and Validation of Cancer Biomarkers. Journal Of Clinical Oncology 2008, 26: 5630-5637. PMID: 18936473, DOI: 10.1200/jco.2008.17.3567.Peer-Reviewed Original Research
2006
What's in a name?
Rimm DL. What's in a name? Archives Of Pathology & Laboratory Medicine 2006, 130: 934-5. PMID: 16831045, DOI: 10.5858/2006-130-934-wian.Peer-Reviewed Original Research