WHO 2016 CNS What have we learnt for the future?

Transcription

1 WHO 2016 CNS What have we learnt for the future? H.K. Ng Free ppt at Louis DN & Ellison DW et al., 2016 H K, how can we cope with the new molecular requirements in the WHO? Chitra Sarkar, India WHO Classification of CNS Tumors 2016 WHO 2016 has let the scientific world down Richard Gilbertson, ISPNO June 2016 We are ten years ahead of WHO Martin van den Bent, EANO Oct 2016

2 A dinosaur? A beginning not an end Inter-observer variability in histology Histology review of a major EORTC trial on anaplastic oligodendrolgial tumour (Kros JNEN 2007) 114 tumours reviewed by 9 international experts Five out of 9 pathologists agree with the consensus diagnosis (6 out of 9) less than 60% of times Aldape K et al., 2000 Kros JM et al., 2007

3 Hartmann C et al. Acta Neuropathologica 2010 I-A ATRX+IDH1 I-CF : ATRX/1p19q/CIC/FUBP1 (either Jiao et al WHO 2016 Molecular pathology is required in addition to histology In China and Hong Kong, have we met WHO criteria? Have we met criteria for standard of care? Martin van den Bent Leading EORTC neuro-oncologist All the hardware and techniques for meeting WHO criteria are in the regional hospitals One pathologist a diagnosis Two pathologists an argument Three pathologists chaos Problems with pathology departments Understanding needs of brain tumor patients We have to work hard to make them understand

4 Aims of providing molecular information Classification/Diagnosis Prognostication Prediction to treatment Further use of molecular biomarkers Development of further biomarkers Identification of treatment targets Genomics of drug sensitivity Tumor heterogeneity Liquid biopsies Tumor aetiology Clonal selection WHO classification 2016 Louis DN & Ellison DW et al., 2016

5 FISH procedure DNA + Fluorophore-conjugated nucleotides Labeling Protease digestion Fluorophore-tagged probes Denaturation Hybridization Tissue section Double-stranded DNA Single-stranded DNA Hybrids Post-hybridization wash Mounting with counterstain Signal scoring Imaging According to Michael Weller, 2016 Green/red = Target and reference probes Blue = DAPI-stained nucleus/chromosomes Fluorescence microscope 1p/19q codeleted group 1p/19q codeleted group 1p/19q noncodeleted group 1p/19q noncodeleted group Li YX, Shi ZF, Ng HK. Oncotarget2016 Cairncross et al van den Bent et al. 2013

6 Isocitrate dehydrogenase (IDH)1 Median survival : 31 months for mutated GBM (IDH1 or 2), 15 months for wild-type 65 months for anaplastic astrocytomas mutated, 20 months for wild-type Also Parsons et al. Science 2008 Procedure of IDH1 Sequencing with FFPA 1)Tissue from tumour area 2)Proteinase K Prepare cell lysate PCR amplification DNA Polymerase DNTP MgCl2 Cell lysate F& RPrimer Denaturation: 95 c,20sec Annealing: 60 c,20sec Extension: 72 c,30sec DNA Sequencing Perform gel electrophoresis Sequencing reaction Yan et al. NEJM 2009 Gupta et al IDH in lower grade gliomas?? Promoter 1,295,250 1,295, C250T -124 C228T -58 TSS 1 ATG TERT -CCCCTCCCGGGTCCCCGGCCCAGCCCCCTCCGGG- C250T C228T -CCCCTTCCGGG- -CCCTTCCGGG- - ETS (E-twenty-six) transcription factors binding site C228T C250T Mutated TERT promoter Chan AK, Ng HK Oncotarget, 2015 Wild type TERT promoter C228 C250

7 Prognostic significance of TERT in LGG IDH wild type lower grade gliomas (n=74) IDH wild type astrocytomas (n=65) Survival (%) TERT wt (n=58) p=0.001 Survival (%) TERT wt (n=58) p=0.001 Survival (%) p=0.007 TERT wt (n=49) Survival (%) TERT wt (n=49) p=0.008 Prediction of response To RT-Chemo, II and III (retrospective) TERT mut (n=16) TERT mut (n=16) TERT mut (n=16) TERT mut (n=16) PFS (months) OS (months) PFS (months) OS (months) Yao, Ng Oncotarget 2015 Chan and Ng, Modern Pathology 2015 Also, Killela P, Yan H, Bigner DD. Oncotarget, 2015 Among Low Grade Gliomas which are IDH-, 1p19q non-deleted, TERT-(n=80) Chan, Mao, Ng. New England Journal of Medicine, in press

8 MGMT gene silencing and benefit from RT and temozolomide in GBM not part of the classification Roger Stupp Brain conference Jan 6-7 M. Hegi et al, NEngJMed 352: , % vs 14% respond o TMZ/RT Diffuse mid-line H3K27M glioma (Diffuse infantile pontine glioma) H3K27M glioma WHO years old female Thalamic GBM K27M-H3.3 mutations (AAG ATG, lysine methionine) Fontebasso & Jabado Brain Pathology 2013

9 Single gene diagnostics Immunohistochemistry IDH1-R132H BRAF-V600E H3K27M ATRX p53 INA CIC FUBP1 EGFR, EGFRvIII FFPE FISH 1p/19q BRAF fusion EGFR amplification Direct sequencing MS-PCR MGMT IDH1/2 TERT H3F3A, HIS1H3B BRAF WHO classification diffuse gliomas (Mayo, UCSF, TCGA) >600 grades II and III 289 grades II and III diffuse gliomas Methylation Expression acgh mirna Weller, on behalf of EANO Submitted Eckel-Passow JE & Jenkins RB et al., 2015 TCGA, 2015

10 Bandopadhayay P Pediatric Blood & Cancer 2014 All of Hong Kong, unpublished data V600E V600 BRAF sequencing with FFPE around 10% of pilocytic astrocytoma

11 WHO book 2016 ependymoma

12 Protocol for ependymoma IHC for NELL2 and LAMA2 12/M, recurrent supratentorial ependymoma L1CAM Or P-NF p65 Witt & Pfister Cancer Cell 2011 Multiple genes in brain tumor diagnostics Panel sequencing - mutation Nanostring expression and RNA seq Taylor, Acta Neuropathologica 2012; WHO 2016

13 Target sequencing for cancer From a private lab in Hong Kong Procedures of NanoString ncounter Technologies Step 1: RNA is extracted from FFPE tissue. Step 2: Capture and reporter probes are hybridized to target RNA in solution. -gene-specific reporter probes are labeled with different combination of fluorochromes. Example of raw data generated by the nanostring ncounter Technologies -Contains positive & negative controls, 22 subgroupspecific genes, and 3 housekeeping genes. Detection of large number gene signature is achieved through the use of different combination of fluorochromes (barcode). Step 3: Hybrids are immobilized on cartridge. Positive hybridization controls Negative hybridization controls cartridge Step 4: Probes are aligned and read by imaging system. Barcode Count Genes 3 DKK2 4 EMK2 1 KCNA1 22 subgroup-specific genes 3 housekeeping genes

14 Example of output for subgroup prediction and normalized data created by R script Genomic technology often used in the higher centresfor diagnosis but not everybody has, Samples G2314 G2319 G2471 G2587 G2616 G2644 WNT Confidence Subgroup WNT Group4 Group4 Group4 Group4 SHH DKK EMX GAD Generally, the confidence needs to be >0.9 to be trust. and not always for FFPE* TNC WIF SHH Group 3 Group 4 ATOH EYA HHIP PDLIM SFRP EGFL GABRA IMPG MAB21L NPR NRL EOMES KCNA KHDRBS OAS Panel sequencing Methylome array Expression array Array CGH SNP array RBM UNC5D Not available in neuro-oncology clinical practice yet in Hong Kong But many international centres available Andreas Von Deimling Nobel Laureate - Dylan The times they are a-changin As the present now Will later be past You better start swimming or You ll sink like a stone The times they are a-changin David Capper

15 Molecular brain tumor diagnosis CUHK Hua Shan Hospital Acknowledgement Chinese University of Hong Kong Neurosurgery Wai S. Poon Danny Chan Funding : HMRF, SK Yee, Children Cancer Foundation Hua Shan Hosital Neurosurgery Liangfu Zhou Ying Mao Jinsong Wu Yu Yao