Maria J. Worsham Otolaryngology-Head & Neck Surgery

1. Network integration of epigenomic data: Leveraging the concept of master regulators in ER negative breast cancer Maria J. Worsham Otolaryngology-Head & Neck Surgery Henry Ford Health System, Detroit, MI, 48202, USA

2. No Disclosures

3. Study Rationale • As cancer therapies become increasingly more specific and begin to move past cytotoxic agents, determining the molecular features of a tumor that predict response to a given drug has become increasingly essential to match patients with optimal therapy. • Estrogen receptor (ER)-negative breast cancer (BC) is a more aggressive form of BC than ER positive with approximately double the incidence in African Americans than in Caucasian Americans. • Current strategies for defining ER negative (ER-) breast cancer (BC) are insufficient for risk stratification and accounting for health disparities between races.

4. Study Rationale • There has been relatively little advancement in changing the management of women with ER-BC mainly due to the paucity of actionable therapeutic targets. • Therefore, understanding the underlying biology of such a complex disease is key to bringing new therapeutic treatments to light

5. Study Rationale • Several molecular alterations are known to occur in genes that encode signaling proteins critical for cellular proliferation and survival.   • These genes have been defined as “driver genes” responsible for both the initiation and maintenance of malignancy. • By understanding the functions of these driver genes, it may be possible to develop specific therapies for malignancies with known driver gene alterations.   • A key question in cancer genomics is how to distinguish ‘driver’ or essential alterations, which contribute to tumorigenesis, from functionally neutral or ‘passenger’ alterations that go along for the ride.

6. Driver vs Passenger mutations • The mutations that confer a selective growth advantage to the tumor cell are called “driver” mutations. • responsible for both the initiation and maintenance of malignancy • Driver genes may be categorized as either “Mut-driver genes” or “Epi-driver genes.”

7. Driver vs Passenger mutations •  Mut-driver genes contain a sufficient number or type of driver gene mutations to unambiguously distinguish them from other genes. The focus remains primarily on genomic mutations • novel study designs (basket trials) where patients with a rare mutation, regardless of tumor histology, are then matched to investigational drugs directed at the mutation of interest. • This dominant focus on genomic mutations has overshadowed consideration of inclusion of epigenetic information.

8. Epigenetic driver genes • Epi-driver genes are expressed aberrantly in tumors but not frequently mutated; they are altered through changes in DNA methylation or chromatin modification that persist as the tumor cell divides •  Criteria for distinguishing epigenetic changes that exert a selective growth advantage “drivers” from those that do not (passenger epigenetic changes) have not yet been formulated.

9. Research Focus Application of epigenetics to cancer diagnostics and therapeutics • identify differentially methylated genes • that impact key biological functions • Bona fide drivers • ultimately are good targets i.e. biomarkers • identify master regulators for novel insights as potential prodrugs

10. Research Focus Breast Cancer (BC) • Focus: DNA methylation in ER negative breast cancer. • Illumina 450KBeadchip to interrogate the methylomes of ER negative and ER positive breast cancer • Identify ER negative specific epigenetic marks of aberrantly methylated genes • Determine their role and utility in refining classification of ER negativesubtypes • Differentiating ER negative BC in African Americans and Caucasian American women • As potential prognostic and treatment targetsfor bettermanagement ofERnegative BC.

11. Research Focus • Ongoing studies utilizing micro arrays have led to current focus in: • identifying epigenetic drivers for biomarker discovery in BC • focusing on DNA methylation.

12. Epigenetics Study of modifications to the DNA and histone proteinsthat influence chromatin structure and gene expression Conspiracy to alter transcription • DNA methylation • changes in chromatin organization

13. DNA methylation • Aberrant CpG island methylation DNA methyltransferases: DNMT1, DNMT3a, DNMT3b demethylases, DMTasa

14. changes in chromatin organization methyl-binding proteins (MeCP2) histone acetylases, HATs such as p300, pCAF, CBP histone deacetylases, HDAC1 and 2

15. Currently epigenetic therapy in the form of hypomethylating agents (e.g: decitabine) exhibit clinical efficacy in patients with AML and MDS including those patients not responding to cytotoxic therapy. Three Histone deacetylase (HDAC) inhibitors have been approved for lymphoma cancer therapy by the FDA. Vorinostat (SAHA, Zolina), Romidepsin (Istodax, FK228, FR901228, depsipeptide), and Belinostat (Beleodaq, PXD-101).

16. Genome-wide methylation Illumina platforms • GoldenGate; 1505 CpG sites: 807 genes • 27K: 27,578 CpG sites: 14,000 genes • 450k: 485,577 CpGs; 99% Ref genes: 28,000 •  Illumina MethylationEPIC: over 850,000 CpGs

17. Coverage of gene transcripts from UCSC database

18. Breast Cancer A drilldown approach to identifying an ER negative-specific gene signature: Komen: KG110218 • The starting point was a discovery step using the Illumina Infinium HumanMethylation450 BeadChip • profiled whole genomic DNA from 40 primary ER negative, 40 ER positive, and 40 normal breast tissue

19. A drilldown approach to identifying an ER negative-specific gene signature • Degree of methylation was calculated as a β-value (ranging from 0 to 1) and M-values [log (β/ (1- β)] were used for significance tests. • With 30 sets of paired tumor/normal tissue, generalized estimating equation (GEE) was performed to account for their correlation. • A 3-tiered approach: to call out genes in which methylation changed dramatically between ER positive and ER negative subtypes

20. A drilldown approach to identifying an ER negative-specific gene signature • Tier 1: computed adaptive false discovery rates (FDR) values for all CpGs/ (or their averages for each gene) to be 0.05 or lower.  • Tier 2: the CpGs/genes to include genes with a 2-fold change (ratio >= 2.0 or ratio <= 0.5). • Tier 3: CpGs/genes to include genes with an absolute difference between the mean β of =>0.2.

21. ER-Negative (n=40) vs ER-Positive (n=40): Differentially methylated CpGs and genes • *aFDR< 0.05

22. . A drilldown approach to identifying an ER negative-specific gene signature • Key Findings:Overall, ER- BC tumors were more hypermethylated than hypomethylated when compared to normal breast tissue and ER+ BC

23. A drilldown approach to identifying an ER negative-specific gene signature: Key Findings . • Identified the top 70 highly ranked differentially methylated genes • 56 were hypermethylated and 14 hypomethylated. • Expression verification using the TaqMan low-density array expression assays • Positive correlation with gene expression for the majority of genes. Scatter plot for the z and t-statistics for the 70 genes

24. A drilldown approach: Key Findings Associated Network Functions . • Significant rankings in canonical pathways and bionetworks (Ingenuity Pathway Analysis resulted in further downsizing to 48 genes.

25. A drilldown approach: Key Findings • Network: Connective Tissue Development and Function, Embryonic Development, Organ Development. Differentially methylated genes between ER negative and ER positive in this pathway are highlighted . • Significant rankings in canonical pathways and bionetworks (Ingenuity Pathway Analysis resulted in further downsizing to 48 genes.

26. A drilldown approach to identifying an ER negative-specific gene signature: Key Findings • Top Canonical pathways . • Significant rankings in canonical pathways and bionetworks (Ingenuity Pathway Analysis resulted in further downsizing to 48 genes.

27. A drilldown approach to identifying an ER negative-specific gene signature: Key Findings . • Methylation status as either hypermethylated or hypomethylated targeting the 450K differentially methylated CpG environ was confirmed for 41 genes using targeted sequencing (Zymo Research).

28. A drilldown approach to identifying an ER negative-specific gene signature: Key Findings . • Further filtering of the 41 genes with 450K TCGA data sets (UCSC Brower 2015) for final downsizing and additional refinement to a 16 gene candidate panel to include the top ranked hypomethylated and hypermethylated genes. BC cases n=837:Clinical heatmap: Orange: ER positive; Blue: ER negative. Genomic Heatmap: Blue < 0; Red >0, Grey: No data2222

29. Causal Network Analysis (CNA) software from Ingenuity Pathway Analysis • Illuminate possible causes and mechanisms underlying the biological activities of a 16 ER-negative specific gene methylation signature to further establish their potential as ‘drivers’ of ER negative specific BC. • The database found in IPA, which makes CNA possible, is based on the Ingenuity Knowledge Base, which is a collection of 5 million observations made from biomedical literature. • CNA provides a conceptual snapshot to hypothesize relationships based on published data with experimental data. • Sheds insights into causal connections between diseases, genes and networks of upstream regulators

30. A master regulator is a gene or drug positioned as the central or master hub that has the ability to command or influence downstream events. Causal Network Analysis (CNA) software from Ingenuity Pathway Analysis

31. Causal Network Analysis (CNA) software from Ingenuity Pathway Analysis: ER negative BC • CNA software identified 4 hierarchical networks and their corresponding master regulatory molecules (significant z score of absolute 2) • diethylstilbestrol • transcription regulator SP1 • MSH2 • 15-ketoprostaglandin E2

32. Causal Network Analysis (CNA) software from Ingenuity Pathway Analysis: ER negative BC •  Diethylstilbestrol and SP1 had direct regulatory influence (depth level 1) to the candidate moleculesALPL, CCND1, EGFR, ESR1 and CCND1, CIRBP, EGFR, ESR1, respectively.

33. CNA raised the profile of ALPL, CCND1, CIRBP, EGFR, ESR1 (5/16 candidate genes) for further consideration as potential epigenetic drivers for ER negative BC. Master Regulator 15-ketoprostaglandin E2: Chemical - endogenous mammalian Master Regulator MSH2: Enzyme

34. SP1 is predicted to lead to inhibition consistent with expression of the molecule in the dataset(blue edges).

35. Summary CNA raised the profile of 5/16 candidate genes: • Currently epigenetic therapy in the form of hypomethylating agents (e.g., decitabine) exhibit clinical efficacy in patients with AML and MDS including those patients not responding to cytotoxic therapy. • In this study, CNA raised the profile of ALPL, CCND1, CIRBP, EGFR, ESR1 (5/16 candidate genes) for further consideration as potential epigenetic drivers for eventual development of targeted therapy for ER negative BC.

36. A new classification system of cancer subtypes using master regulators. • Efforts underway to identify the master regulators of every tumor represented in TCGA, on a sample-by-sample basis • develop a new classification system of cancer subtypes using Master Regulators to Reclassify Cancer Subtypes

37. A new classification system of cancer subtypes using master regulators. • Perform integrative analysis of genomic data from the Cancer Genome Atlas (TCGA) and proteomic data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) • recategorize tumors collected in TCGA based on the master regulator genes that determine their state.

38. A new classification system of cancer subtypes using master regulators. • Once the master regulators for all of the tumors in the database have been identified, tumor samples in the top 20 cancer types represented in TCGA will be reclassified, using a pan-cancer approach. • Expect to reveal a limited repertoire of master regulators that ultimately drive a large faction of the tumors, many of which should be independent of traditional organ based tumor classification. • Using the DIGGIT algorithm, they will also look upstream of master regulators, within regulatory networks, to identify the genomic and epigenomic alterations that determine their aberrant activity

39. Integrated data set for the comparison and contrast of multiple tumor types The Pan-Cancer project: Six platforms of omics characterizations were performed creating a “data stack” (upper right panel) in which data elements across the platforms are linked by the fact that tissue material from the same samples were assayed, thus maximizing the potential of integrative analysis. Use of the data enables the identification of general trends including common pathways (lower panel) revealing master regulatory hubs activated (red) or deactivated (blue) across different tissue types. Nat Genet. 2013 Oct; 45(10): 1113–120.

40. TCGA's Pan-Cancer Atlas • The Cancer Genome Atlas (TCGA) has been a landmark effort to generate comprehensive, multidimensional maps of genomic changes on over 11,000 cancer cases from 33 different cancer types. • provides a uniquely comprehensive, in-depth, and interconnected understanding of how, where, and why tumors arise in humans. • April 2018: TCGA's Pan-Cancer Atlas Papers • clustering of tumors • how key oncogenic processes contribute to tumor development, how certain signaling pathways are altered in cancer, and more. • A collection of 29 papers first week of April 2018

41. TCGA's Pan-Cancer Atlas • PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. • Identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. • Using the iCluster algorithm and data on aneuploidy, DNA hypermethylation, mRNA, microRNA, and proteins from the tumor samples • identified 28 molecular subtypes, more than they uncovered in their initial pan-cancer analysis in 2014. • As a singular and unified point of reference, the Pan-Cancer Atlas is an essential resource for the development of new treatments in the pursuit of precision medicine.

42. ENT/Head & Neck Clinic Clinicians Oncologists Surgeons Residents Nurses Support Staff Laboratory Kang Mei Chen MD Josena K. Stephen MD Public Health Sciences George Divine Ph.D. Indrani Datta MS Pathology Dhananjay Chitale MD Cancer Genetics ResearchPartnership