POTENTIAL OF BIOBANK INFORMATION SYSTEM WITHIN COMPLEX RESEARCH PROCESSES

Transcription

1 POTENTIAL OF BIOBANK INFORMATION SYSTEM WITHIN COMPLEX RESEARCH PROCESSES Peter Hufnagl, Charité Universitätsmedizin Berlin HiMSS Potsdam, Nov , 2017

2 CONTENT 1. Biobank concept 2. Offered services 3. Sample storage 4. Typical BIH research project 5. Virtual microscopy 6. Summary

3 BIH Biobank Concept One biobank - 2 locations MDC Campus Berlin Buch (CBB) mainly for long-term storage of liquid biomaterials of large clinical and epidemiological studies Charité-BIH Biobank mainly for storage of liquid and tissue biomaterials collected during patient care and clinical/research studies present at all 3 Charité campuses (main building at the CVK) Foto: Promo/Andrew Alberts in Tagesspiegel online

4 BIH Biobank concept At both sites are similar infrastructures available Automated Biobanking Storage Systems (Liconic ) Biomaterial Information System (CentraXX ) Both sites are accessible to all BIH researchers All materials are collected, stored and documented following strict data protection rules and ethical regulations 5

5 Services provided by the ZeBanC Acquisition, processing and analysis of all types of biomaterials Storage of biomaterials and their derivatives at various temperatures (down to 196 C) Documentation of sample-specific processes Linkage to patient clinical information Support on project planning and execution of prospective collections/studies Adoption and integration of existing retrospective biomaterial collections Usage of the biobank database structure (client-specific) 6

6 Sample storage 7

7 The automated biorepository store Properties - Liconic - 80 C up to 1 million samples Tube picking Tube and plate tracking Temperature logging Backup cooling system Source: Liconic-Kiwi brochure 8

8 Extra Services Production of tissue microarray of FFPE tissue samples Gene expression analysis using NanoString Amplicon-based high-density Next Generation Sequencing (NGS) High-resolution scanning of histological sections Image analysis, detection of lesions, quantification, mouse models, 9

9 number of samples/aliquots Sample types: Prospective collection 25,000 20,000 15,000 DNA whole blood cellular blood components serum plasma frozen tissue FFPE other (e.g. H&E sections, urine) 10,000 5, * 10

10 number of project requests Number of project requests Pathology (tissue bank) AHF Biobank KN-HI Biobank 180 BIH TCR-CRG project CSB BAPTISe Adipositas AG Damm (incl. Conko) DHZB Clinical studies (until Nov) 11

11 Web-based Project Request Portal PAP (PAP Projektanfrageportal) REQUEST PROCESS AVAILABLE SAMPLES REQUEST MANAGEMENT Choose project type CentraXX Management of project requests Enter master files BIOBANKS HOMEPAGE Management of approval procedure Specifiy samples COORDINATOR Documentation Choose required services Specify required sample data RESEARCHER Project Request Portal SCIENTIFIC REVIEW BOARD Transfer of material / data PROJECT DATA AND DOCUMENTATION 12

12 Typical project within BIH Pipeline from target discovery through systems medicine to mutation-specific TCR gene therapy SP2, SP3, SP6 Identify recurrent somatic mutations SP1, SP2 Identify immunogenic epitopes SP2, SP3, SP4 Identify rejection epitopes SP6, SP7 Identify suitable patients SP7 and help by all SP Start clinical trial Identify peptides with high pmhc affinity SP1, SP2, SP3 Isolate TCRs SP2, SP3, SP4 Establish clinical gene transfer protocols SP4, SP5 Establish GMP in the ECRC SP7 SP1 Kloetzel (Charité, CCM) SP2 Blankenstein (MDC and Charité) SP3 Schreiber (EVF and University of Chicago) SP4 Uckert (MDC and Humboldt University) SP5 Izsvák (MDC) SP6 Hummel (Charité, CBF) SP7 Pezzutto (Charité, CBF and MDC) 13

13 Virtual Microscopy Applications Heatmap of prostate biopsy specimens with 30% cancer Red indicates a high likelihood of cancer, whereas transparent/green indicates a low likelihood. H&E Heatmap 14

14 Value of a sample Sample Clinical Data Whole Slide Images V A L U E Quantification of Morphology Next Generation Sequencing Data Data generated during experiments... 15

15 VM Integration into CentraXX 16

16 VM Integration Automated image processing including slide identification (Barcode/OCR), WSI PACS-based archiving, technical quality inspection using image analysis and web-based access to whole slide images via intranet. Scanmaster: Quality assessment 17

17 Automatic Quality Assessment Green: quality sufficiant Red: not sharp, possible artefacts 18

18 Navigation after Registration Navigation under virtual micoscopes based on DL controlled registration algorithms. Pseudo 3D Navigation 5 Sections on 1 Slide Biopsy Specimen Intra-Slide Navigation 4 Sections on 1 Slide Biopsy Specimen Inter-Slide Navigation 4 Marker on 4 Slides Surcical Specimen 19

19 Detection of Growth Pattern METHODS PipeLine Definition of ROIs on the first serial section Registration of WSI sections Transformation of ROIs Registration and Transformation of ROI on neighboring sections Generation of ROI tiles with subsequent generation of stack videos. Segmentation of histological structures deep learning 3D Rendering to create 3D Modell 20

20 Detection of Growth Pattern - Visualization Creating Virtual Stacks in 3D to reveal growth-patterns of early colorectal adenomas and abbarant crypt foci Virtual Stack 75 Sections Interactive 3D Model 50 Sections 21

21 Registration of Real and Atlas Data Allen Brain Atlas Pre-Registered atlas layer (a) (d) Average Serial Two Photon (STP) Tomographie (a) (b) (c) Nissl Stains (b) Annotationen (c) (c) Brain Explorer GUI (d) 22

22 Annotation Transformation between Stains Surgical sections of the stomach or esophago-gastric junction: HER2 WSI annotated by ten pathologists: HER2+ tumor and HER2- tumor Annotation transformations Global rigid transformation & & Local affine transformation 23

23 Breast Cancer: Detection of Lymph Node Metastasis

24 Breast Cancer: Lymph Node Biopsy

25 Breast Cancer: Lymph Node Histology

26 Breast Cancer: Training of a DNN

27 Breast Cancer: Application of DNN

28 Huge Work Load for Tissue Annotation Possibility to generate additional training data based on models 29

29 Conclusions 1. We are ready to collect and store data and samples 2. All types of samples (Liquid, tissue, cell lines, etc.) 3. Virtual slides are a tremendous additional value 4. Quality of clinical data and samples/ sample data is a key issue 5. Automatic determination of quality data is important 6. Image analysis on thousands of slides needs DL technology 7. Sample value and quality of data are connected directly 30

30 THANK YOU!

31 CONTACT Prof. Dr. P. Hufnagl Head IT, Biobank Berlin Institute of Health (BIH) Charité Campus Mitte (CCM) Phone: