Tissue microarray TMA

Transcription

1 Tissue microarray TMA Tibor Krenacs 1st Department of Pathology & Experimental Cancer Research Semmelweis University Budapest Austro-Hungarian Pathology Congress October 1-3rd 2009

2 TMA - tissue microarray Kononen et al. Nat Med 4:844, (Battifora multitumor sausage Lab Inv 55:244, 1986) In situ Gene/Chrom. copy Protein expression ADVANTAGES ISH/FISH IHC HIGH THROUGHPUT mm TMA blocks TMA slide 643 samples = Normal slide 1 sample SAVING Time Tissue Reagent Workforce Focus Well-characterised Technology standardization

3 TMA - tissue microarray RESEARCH - Biomarker validation - screening DIAGNOSTICS - Quality Assurance Amplified gene ANTIBODY probe High throughput comparative screening NORMAL TUMOR (Treated cell etc ) cdna chip OMICS - genome (Array CGH) - transcriptome (cdna chip) - proteome (MS) IHC- archived tissue TMA -in situ - individual cell, - subcellular level - by preserving tissue complexity TMA diagnostic validation Betegek túlélése %-ban Betegség túlélése a marker expresszió függvényében Target therapy marker negatív marker pozitív Diagnózistól eltelt idő Clinical validation

4 TMA Data management 100 Cases x 10 markers = x 5 parameters = pieces of information Histopathological, Clinical Data TMA 463 samples + + DATABASE information management Image and alpha-numerical data linked Digital slide based TMA system Project - from design to analyzis Budapest Mirax through Zeiss Pannoramic own brand

5 TMA - digital slide system Project-based Database linked Series of TMA blocks Series of stainings Individualized scoring Multiple assessors Image quantitation Array building Scoring Analysis Permanent archives Result validation Quality control

6 TMA Digital slide system Array design, building Data Linking Import.xls Sample data TMA Master Orientation core (liver) Cutting Staining Digitalization

7 TMA Digital slide system Analyzis software Import.xls Scoring scheme Scoring Free rearrangement Validation

8 TMA Digital slide system Scoring scheme Scoring Free rearrangement Validation

9 TMA Digital slide system Scoring scheme Scoring Free rearrangement Validation

10 TMA Digital slide system Scoring scheme Scoring Free rearrangement Validation

11 TMA Digital slide system Automated image analysis Image segmentation Color, intensity Object size (e.g. nucleus) Shape, pattern (e.g. gland) AREA/ Cell number Positive area Intensity Batch processing

12 TMA types aims Multitissue: Cohort of normal tissues - - Systemic screening for normal distribution of less known gene products Multitumor: Low series of many tumor types - Screening for target molecules (gene copies protein profile in tumors - Validation of biomarker distribution in tumors (differential diagnostics) Progression: Whole spectrum of one tissue/disease/tumor type - Tumor progression series: in situ-, early, manifest, invasive and therapy resistant - Validation of the results of genomic changes at protein level Prognostic: Samples of the same disease entitiy for clinical end-points (e.g. recovery, recurrence, death) - Screening for biomarkers of prognostic or predicitve relevance Method validation: (quality assurance) - Testing detectibility of genes of their products in previously characterised samples - e.g. testing the reliability of target biomarker detection techniques (FISH, IHC) Assesing interlaboratory and intralaboratory standards

13 TMA types aims Multitissue Cohort of normal tissues - - Screening for normal distribution of less known gene products Multitumor: Low series of many tumor types - Pilot screening for target molecules (gene copies or proteins in tumors) - Validation of biomarker distribution in tumors (differential diagnostics) Lugli et al Human Pathol 2003, 34: tumor types (5233 samples) screening for calretinin. Besides mesothelioma, calretinin was also positive in Leydig-cell tumor of testis and adrenal cortex Progression: Whole spectrum of one tissue/disease/tumor type - Tumor progression series: in situ-, early, manifest, invasive and therapy resistant - Validation of the results of genomic changes at protein level Prognostic: Samples of the same disease entitiy for clinical end-points (e.g. recovery, recurrence, death) - Screening for biomarkers of prognostic or predicitve relevance Method validation: (quality assurance) - Testing detectibility of genes of their products in previously characterised samples - e.g. testing the reliability of target biomarker detection techniques (FISH, IHC) Assesing interlaboratory and intralaboratory standards

14 TMA types aims Multitissue Cohort of normal tissues - - Screening for normal distribution of less known gene products Multitumor: Low series of many tumor types - Screening for target molecules (gene copies protein profile in tumors - Validation of biomarker distribution in tumors (differential diagnostics) Progression: Whole spectrum of one tissue/disease/tumor type - Tumor progression series: in situ-, early, manifest, invasive and therapy resistant - Validation of the results of genomic changes at protein level Prognostic: Samples of the same disease entitiy for clinical end-points (e.g. recovery, recurrence, death) - Screening for biomarkers of prognostic or predicitve relevance Method validation: (quality assurance) - Testing detectibility of genes of their products in previously characterised samples - e.g. testing the reliability of target biomarker detection techniques (FISH, IHC) Assesing interlaboratory and intralaboratory standards

15 TMA types aims Multitissue Cohort of normal tissues - - Screening for normal distribution of less known gene products Multitumor: Low series of many tumor types - Screening for target molecules (gene copies protein profile in tumors - Validation of biomarker distribution in tumors (differential diagnostics) Progression: Whole spectrum of one tissue/disease/tumor type - Tumor progression series: in situ-, early, manifest, invasive and therapy resistant - Validation of the results of genomic changes at protein level Prognostic: Samples of the same disease entitiy for clinical end-points (e.g. recovery, recurrence, death) - Screening for biomarkers of prognostic (predicitve relevance) Method validation: (quality assurance) - Testing detectibility of genes of their products in previously characterised samples - e.g. testing the reliability of target biomarker detection techniques (FISH, IHC) Assesing interlaboratory and intralaboratory standards

16 TMA types aims Multitissue Cohort of normal tissues - - Screening for normal distribution of less known gene products Multitumor: Low series of many tumor types - Screening for target molecules (gene copies protein profile in tumors - Validation of biomarker distribution in tumors (differential diagnostics) Progression: Whole spectrum of one tissue/disease/tumor type - Tumor progression series: in situ-, early, manifest, invasive and therapy resistant - Validation of the results of genomic changes at protein level Prognostic: Samples of the same disease entitiy for clinical end-points (e.g. recovery, recurrence, death) - Screening for biomarkers of prognostic or predicitve relevance Method validation: (quality assurance) - Testing detectability of genes or their products in samples of known status - e.g. testing the reliability of target biomarker detection techniques (FISH, IHC) Assessing interlaboratory and intralaboratory standards

17 TMA - Lung cancer progression profiling p p+m LC 33 cases I 26 cases LCMet m II III 116 samples BrMet NCLSC Tissue microarray -TMA Biomarkers growth proliferation adhesion apoptosis 29 markers Immunohistochemistry Differentially expressed proteins β-catenin β-catenin CyclinD1 A B C CyclinD3 Collagen XVII CD44v6 D E F

18 TMA - Lung cancer progression profiling NSCLC progression Hierarchical Cluster analysis UP-regulated DOWN-regulated p I β-catenin CAS Collagen XVII, CD44v6 Cyclin D1, Caspase-9 Collagen XVII, CD44v6 Cyclin D1,-D3, Caspase-3 β-catenin p-m II p16 Topoisomerase II-α m III Percent survival β-catenin+ β-catenin- Percent survival In-Cluster Out-Cluster Survival after 1st diagnosis (months) Survival after 1st diagnosis (months)

19 GCTB EGFR in osteoclastogenesis Counting TRAP + Osteoclasts Automated image analysis Cases: 3x70=210 Batch processing 1st recurrence stong EGFR Primary negative EGFR 1st recurrence dense TRAP+ OC Primary rare TRAP+ OC

20 TMA - Tumor signaling (in situ) Activity RTK and downstream signaling Grade 3 breast cc. (Giant cell tumor of bone) RTK signaling Growth Proliferation Survival Invasion RAS EGFR (HER2) RTK pakt Target therapy Resistance Other RTK Down-stream praf Phospho-proteins NFkB perk1-2 (PDGFR, c-met, c-kit, IGFR) pstat1

21 TMA - Test validation and EQA HER2 predictive testing in breast cancer 3+ IHC vs FISH IHC vs other IHC Cases: 3x70 breast cancer TMA cores Herceptest CB11 SP Kappa correlations: Herceptest vs CB11-0,89 Herceptest vs SP3-0,94 CB11 vs SP3-0,92

22 Tissue microarray - TMA Research and Quality Assurance High throughput biomarker validation and screening Molecular morphology correlations in pathological processes Comparative studies of protein expression (gene copy number) at cellular/subcellular level with preserving information of tissue complexity Validation/Screening for potential biomarkers involved in: tumor development, progression, prognosis (targets of therapy) Complex protein expression profiling of tumors Validation of existing predicitive test reproducibility (HER2, ER) Database management and using digital slide based systems are essential

23 Acknowledgements Prof. Laszlo Kopper Prof Andras Matolcsy Edit Parsch STAFF- Students STAFF