OPTIMARK PROJECT: NEW QUALITY MARKERS FOR FFPE NON-TUMORAL TISSUE Cristina Villena Pulmonary Biobank Consortium CIBERES. Scientific coordinator Spanish National Biobank Network – ISCIII. Coordinator
Spanish National Biobank Network 0001 CIBERES- H. U. SON ESPASES 0002 IDIS- C.H.U SANTIAGO 0004 CNIO 0006 FJD 0007 NAVARRABIOMED 0008 MD ANDERSON 0010 HOSPITAL RAMON Y CAJAL 0011 IMIM 0013 H12O 0014 F-CIEN 0016 IDIBAPS 0017 FISABIO 0018 CLINICA UNIVERSIDAD DE NAVARRA 0019 IDIVAL 0020 HOSPITAL UNIV. PUERTA DE HIERRO 0021 BIOBANCO VASCO 0039 INSTITUTO ARAGONES CIENCIAS SALUD 0023 ISPA 0040 INSTITUTO INV. SANIT. H. CLINICO SAN 0024 IDIBELL CARLOS 0025 IDIPAZ 0041 BBSPA 0027 IRBLleida 0042 INSTITUTO INV. SANITARIA GREGORIO 0029 INSTITUTO INV. S. PERE VIRGILI MARAÑON (IiSGM) 0032 INSTITUTO INV. BIOMED. LA CORUÑA 0043 H LA FE (INIBIC) 0044 U Salamanca 0033 INSTITUTO INV. BIOMED. GIRONA DR.JOSEP 0045 HUGTiP TRUETA 0047 VHIR 0034 INSTITUTO INV. SANITARIA GALICIA SUR 0049 INCLIVA 0036 HOSPITAL SANT JOAN DE DEU 0050 HOSPITAL DE LA PRINCESA 0037 H. U. FUNDACION ALCORCON 0051 FIVO 0038 IMIB XXXX ISCIII -BioNER
Biospecimens and Personalized Medicine Personalized medicine (PM) using biomarkers in screening, diagnosis, treatment, and monitoring of cancer will depend on accurate, reproducible data derived from patient samples in the clinical setting. PM is limited to pre-analytical factors , compromising sample stability, molecular integrity, quality and … BIOMARKER DISCOVERY. Biobanks need: • To know the effect of pre-analytical variables on biospecimens (quality and integrity). • To determine the potential applications into research of biospecimens stored in biobanks and Pathology Services.
OPTIMARK project OBJECTIVES 1. Identify the pre-analytical variables more relevant in tissues, which could affect the clinical biomarkers development. 2. Develop and validate a common procedure to clarify the potential use of the samples in research, especially those without pre-analytical control. 3. To allow collections to be available to the scientific and biotechnological community, with a very good control of their pre-analytical variables, so that they can evaluate the sensitivity of their developing biomarkers to these factors.
Methods • Pre-analytical factor: storage time • Healthy tissue (non-tumor) • 374 patients (30-65 y.o.; in brain up to 75 y.o. without vascular pathology) Tissue Brain Breast Colon Endomet. Lung Stomach n 47 67 75 26 46 17 • Checking non other relevant pathologies in the organ selected. C : A : B : Control: extraction D : extraction extraction extraction time extraction time time time <1 between time >20 between 1 between 5 year 10 to 20 years to 5 years to 10 years years If we are able to find a sensitivity biomarker with the sample age, probably we could use it to evaluate the impact of other pre-analytical factors.
Experimental design Brain 1st Antigenicity igenicity Statistical 2nd Antig igeni enicity ity analysi ysis analysis and analysi ysis Breast (Aperio Versa 200) new (Aperio Versa 200) biomarker Ki67 TTF-1 testing Colon SDHB Vimentin Validation B-catenin CD31 of results Endomet BCL2 Brain cFOS Lung Lung Stomach
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases Antigenicity Colon Ki67 (nucleus) Intensity: +++ red, ++ orange, + yellow, 0 blue CONTROL A D B C
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases Antigenicity Colon Ki67 (nucleus)
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases Antigenicity Endometrium CD31 (membrane) Intensity: +++ red, ++ orange, + yellow, 0 blue CONTROL A B C
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases Analysis Antigenicity CD31 (membrane) CD31 Average Positive Intensity 180 160 140 120 100 80 Control 60 A B 40 C 20 D 0 Brain Lung Colon Breast Endometrium Stomach
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases RNA Integrity Number (RIN)
CIBER Enfermedades Respiratorias Hospital Universitari Son Espases
Recommend
More recommend
