Outline. Up un1l the 1970s. Adrenal Cor1cal Adenoma (ACA) 2/5/16

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2 Genomic Appraisal of Adrenocor1cal Carcinoma: What Prac1cing Pathologists Need to Know Thomas Giordano Department of Pathology Outline Introductory comments about ACC and its pathology Molecular profiling work Expression efforts Genomic efforts ENSAT TCGA Up un1l the 1970s Adrenal Cor1cal Adenoma (ACA) Pathology of adrenocorjcal tumors was a bit murky and mysjcal Adenoma v. carcinoma challenging SystemaJc studies that defined the features associated with malignant behavior Small, solitary tumors up to 5 cm and 50 grams 1

3 Adrenal Cor1cal Carcinoma (ACC) Adrenal Cor1cal Carcinoma (ACC) Very large - 20 cm tumor Histopathology Systems for Evalua1on of Adrenocor1cal Tumors Weiss system Un-weighted numerical system 3 or more of the following correlates with clinical malignancy High nuclear grade MitoJc rate: > 5 / 50 HPF Atypical mitoses < 25% clear cells Diffuse architecture Venous invasion Sinusoidal invasion Capsular invasion 2

4 Resec1on - Only Cura1ve Treatment Range of Morphologies Range of Morphologies Intratumoral Heterogeneity Ki67 Ki67 3

5 Capsular Invasion Vascular Invasion Pathology Challenges DiagnosJcally difficult intermediate cases Overall prognosis assessment PredicJon and risk assessment Risk of local recurrence Risk of metastajc disease Response to therapy Diagnos1cally Difficult Case that Illustrates the Weiss Criteria 45 year old female with 6 cm le` adrenal tumor 85 grams, circumscribed yellow tumor Adequately excised with negajve margins 4

6 Nested Architecture Lipid Rich Areas Encapsulated Rare Mito1c Figures 5

7 Lung Metastasis 5 yrs s/p Resec1on: S1ll Well-Differen1ated Treatment algorithm Hereditary 2002: Syndromes What role can genomic / molecular profiling studies plan in the assessment of adrenal tumors? Can genomic data provide informajon above and beyond that provided by standard clinicopathologic assessment? 6

8 B9 ACC IGF2 Prior work with DNA microarrays Differen1ally expressed genes Insulin-like growth factor 2 (IGF2) Global Clustering (3D-PCA) IGF2 Carcinoma TOP2A H19 Adenoma Normal 7

9 Focus on ACCs Kaplan-Meier Survival Analysis, Cluster 1 vs 2 Hierarchical clustering using data from all probe sets divides the ACCs into 2 groups Cluster 1 and 2 Ask: Is it clinically significant? What is driving this separajon? 23 cases Cluster imperfectly reflects mito1c grading Profiling results in agreement with mito1c grading Cluster 1 Poor outcome 14/16 high grade 2/16 low grade Cluster 2 Beeer outcome 6/17 high grade 11/17 low grade 8

10 Parallel ENSAT Studies in agreement ENSAT Genomic Study 9

11 TCGA Adrenocor1cal Cancer Study Gary Hammer and I pejjoned TCGA leadership to start a rare cancer project on ACC Needed a global cohort to find enough cases Got the green light from TCGA Assembled a cohort from North America, South America, Europe and Australia New cases from ENSAT TCGA Mission The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the applicajon of genome analysis technologies, including largescale genome sequencing. Background and Goals Joint NCI and NHGRI effort Launched in 2006 as a pilot Expanded in 2009 with goals: Establish infrastructure for effecjve team science Develop a scalable pipeline Demonstrate the feasibility of a large-scale, high throughput approach to idenjfying the molecular parts-list Make the data publicly and broadly available to the cancer community while protecjng pajent privacy q 2005 NCAB Report Pilot GBM Report Ovarian Report TCGA Timeline ARRA Funding Project Expansion Rare Projects Initiated 9 tumor types closed 10,000 cases complete Pilot Projects: GBM and Ovarian carcinoma (~500 cases ea.) Establish infrastructure for effective team science Develop a scalable pipeline Demonstrate the feasibility of a large-scale, high throughput approach to identifying the molecular parts-list Make the data publicly and broadly available to the cancer community while protecting patient privacy q Expansion 2010 to 2014: Add tumor types Enhancement of sample acquisijon & program staff Add Genome Data Analysis Centers Publish Benchmark Marker Papers Established FFPE protocols Completely characterize 10,000 th case q Analysis Comple1on : Finish marker papers on rare & challenging-to-accrue tumors Complete Pan-Cancer Analysis Broader sharing of tools, analyjcal methods Analysis Completion

12 Pipeline for Comprehensive Characterization TCGA: No Platform Left Behind Tissue Sample GDAC 3 months 2 years Pathology QC DNA & RNA Isola1on, QC ~90d Sequencing Data Storage at DCC & Expression, CGHub CNA & LOH, Epigene1cs SNP 6.0 ~45d Methylation ~60d mirnaseq ~105d mrnaseq ~120d DNAseq Exome ~180d Integra1ve Analysis Comprehensive Characteriza1on of a Cancer Genome ~12-24 months 25* forms of cancer Glioblastoma multiforme (brain) Squamous cell carcinoma (lung) Serous adenocarcinoma (ovary) Etc. Etc. Etc. Biospecimen Core Resource with more than 150 Tissue Source Sites 6 Cancer Genomic Characterization Centers 3 Genome Sequencing Centers 7 Genome Data Analysis Centers Data Coordinating Center Multiple data types Clinical diagnosis Treatment history Histologic diagnosis Pathologic report/images Tissue anatomic site Surgical history Gene expression/rna sequence Chromosomal copy number Loss of heterozygosity Methylation patterns mirna expression DNA sequence RPPA (protein) Subset for Mass Spec Substantial Network Endocrine Neoplasia was almost leg out! Breast CNS Endocrine Papillary thyroid (n=496) Adrenocor1cal (n=91) Pheo / paraganglioma (n=177) GI GU Thoracic Melanoma Hematologic Sarcoma Head and Neck SCC Uveal Melanoma 11

13 TCGA is Big Data Status Oct 2014 TCGA ACC Data Whole exome sequencing, n = 91 mrna sequencing, n=78 mirna sequencing, n=79 DNA copy number, n=89 DNA methylajon, n=79 RPPA, n=45 Clinical data Pathology data Muta1on density Muta1on density correlates MutaJon density correlated with many clinicopathologic and molecular features, even in organ-confined disease 12

14 Whole exome sequencing: Significantly mutated genes using MutSigCV Hun1ng for gene fusions Excited about the prospect of finding recurrent gene fusions Rebuilt our ACC Jssue arrays in anjcipajon of doing FISH validajon Used two methods to detect fusions Found 156 fusion events in 48 of 78 tumors Copy number data indicated a breakpoint in 65% of cases No recurrent fusions found Fusion landscape Some interes1ng private gene fusions involving known cancer genes 13

15 Copy Number Altera1ons in 2000: Adrenocor1cal carcinoma is characterized by a high frequency of chromosomal gains and high-level amplifica1ons Not unexpected that copy number altera1ons became a big part of the TCGA story Roel Verhaak, Siyuan Zheng, Andrew Cherniack, Bradley Murray with input from ENSAT team Tour de force computajonal analysis of a complicated issue Genes, Chromosomes and Cancer Volume 28, Issue 2, pages , 22 MAY 2000 DOI: /(SICI) (200006)28:2<145::AID-GCC3>3.0.CO; 2-7 hep://onlinelibrary.wiley.com/doi/ /(sici) (200006)28:2<145::aid-gcc3>3.0.co;2-7/full#fig1 Focal copy number altera1ons TERF2/TRF2 Telomeric Repeat Binding Protein 2 Novel TERF2 amplifica1on Maybe unique within TCGA Nature 447,

16 NegaJve regulator of Wnt signaling pathway Leads to degradajon of Wnt receptor complex proteins Hypothesis: DeleJon of ZNRF3 represents an alterajve way to acjvate Wnt pathway in ACC About 20% of ACCs Mutually exclusive with CTNNB1 mutajons ZNRF3 dele1on Copy number defines different classes of ACC With different survival Transcriptome 2 large classes Noisy tumors most aggressive Looks a lot like the low and high mito1c grades 4 granular classes profound differences between classes 15

17 Cluster of Cluster Landscape view Most CTNNB1 muta1ons in Groups I and II Mutated Pathways Summary Adrenal Differen1a1on Score 16

18 HotNet2 4 significant subnetworks Cell cycle arrest and apoptosis Ben Raapael, Brown RB signaling pathway Wnt / B-catenin signaling pathway How best to translate to pa1ent care? Pathology Will be looking for ways to deliver the 3 class solujon to roujne cases Maybe a simple as Ki67 IHC stain Probably more complicated Therapy Supports the view that combined inhibijon of IGF2 and Wnt pathways for the largest subset f cases Other pathways in smaller subsets Depends on specific alterajons, e.g. mtor fusion 17

19 Highlights People Created a genomic resource for adrenal cancer research Discovered novel somajc alterajons, e.g. TERF2 Copy number / whole genome doubling Expanded role of WNT pathway alterajons Integrated analysis Overall 3 classes of tumors Pan-cancer analysis EdiJng and planning to submit soon Roel Verhaak Siyuan Zheng More People Siyuan Zheng, Andrew D. Cherniack, Ninad Dewal, Richard A. Moffie, Luda Danilova, Bradley A. Murray, Antonio M. Lerario, Tobi Else, Theo A. Knijnenburg, Giovanni Ciriello, Seungchan Kim, Guillaume Assie, Olena Morozova, Rehan Akbani, Juliann Shih, Katherine A. Hoadley, Toni K. Choueiri, Jens Waldmann, Ozgur Mete, A. Gordon Robertson, Maehew Meyerson, Michael J. Demeure, Felix Beuschlein, Anthony J. Gill, Ana C. Latronico, Maria C. Fragosa, Leslie. Cope, Electron Kebebew, Mouhammed A. Habra, Timothy G. Whitsee, Kim Bussey, William E. Rainey, Sylvia L. Asa, Jérôme Bertherat, MarJn Fassnacht, David A.Wheeler, Cancer Genome Atlas Research Network, Gary D. Hammer*, Thomas J. Giordano*, Roel G. W. Verhaak Jerome Conn ENDOCRINE ONCOLOGY PROGRAM Ron Koenig Adrenal Thyroid James Sisson Ed Schteingart Norm Thompson Plus many more (TSSs, etc.) from TCGA network TCGA program office Kenna Shaw for listening and suppor1ng the project Frederick Coller William Beierwaltes 7 DECADES Max Wicha Cancer Endocrine Peter Arvan 18

20 Ques1ons ENDOCRINE ONCOLOGY PROGRAM 2015 U Michigan Big House 19