<p>DNA Methylation Pattern of PRC2 Target Genes</p>

<p>Characterization of iPSCs</p>

Legends for Supplemental Figures S1-S2.

Improved classification of rare lymphoid neoplasms would be aided by a deeper understanding of their underlying molecular features and is important for diagnosis, prognosis and therapy. Tumor entities classified within the WHO category of splenic B cell lymphomas and leukemias often exhibit heterogenous, transecting features, and include hairy cell leukemia (HCL), splenic diffuse red pulp lymphoma (SDRPL), splenic marginal zone lymphoma (SMZL), and the newly described WHO entity, splenic B cell lymphoma/leukemia with prominent nucleoli (SBLPN); the latter including patients formerly classified as HCL-variant (HCL-V). Genome-wide epigenetic information provides a tumor cell fingerprint combining cell-of-origin and tumor-specific events. Here we used DNA methylation to perform an unbiased molecular subclassification and to explore novel biological aspects of these patients. Samples from patients with a pathological diagnosis of HCL, HCL-V, SDRPL and SMZL (made prior to the 5 th WHO revision and ICC classifications) were obtained from 19 institutions across 9 countries, totaling 367 patients. Cells were FACS-purified where necessary and DNA was analyzed by 450/850K Illumina DNA methylation arrays. Genetic mutations were assessed by whole-exome or targeted sequencing, IGHV-D-J sequences by Sanger sequencing, and copy number alterations (CNAs) by Illumina arrays. The 1000 most variable CpG methylation sites were used for k-means clustering. Recursive feature elimination/random forest algorithms were used to develop a classifier for DNA methylation-based subgroups with 98% accuracy. Unsupervised clustering of 197 patients diagnosed with HCL, HCL-V or SDRPL revealed 5 distinct DNA methylation (M) subgroups ( Figure 1). Subgroup assignment was stable throughout longitudinal sampling (including pre/post-treatment) and consistent between splenic, bone marrow and PBMC derived cells. A subgroup with universally clonal BRAF-V600E mutations and majority diagnosed as HCL was termed the M-HCL subgroup ( Table 1). Four other groups termed M-SBLPN1-4 contained all HCL-V and SDRPL diagnosed samples and were devoid of BRAF-V600E mutations. M-SBLPN1 comprised MAP2K1 mutations (91%) and was enriched for CREBBP, ARIDIA and TERT-promoter mutations. These patients displayed an HCL-like immunophenotype (64.3% CD25+) with 1/3 diagnosed as HCL. M-SBLPN2 exhibited the highest prevalence of TP53 mutations and concomitant genomic instability. Patients in M-SBLPN1,2 were enriched in unmutated IGHV4-34 rearrangements. M-SBLPN3,4 subgroups displayed an immunophenotype more dissimilar to HCL, mutated IGHV genes, and enrichment of IGLL5, SYK and BIRC3 mutations. M-SBLPN4 contained the most SDRPL samples, suggesting it may represent the SDRPL entity retained by the WHO. We next uncovered that 29/170 SMZL patients displayed DNA methylation patterns mapping to M-SBLPN2-4. These patients were phenotypically and molecularly similar to SBLPN (70% displaying villous morphology and depleted in IGHV1-2*04, NOTCH2, KLF2 mutations), likely representing SMZL patients suggested for reassignment to SBLPN in the updated WHO classification. To elucidate molecular pathways governing the biology of M-SBLPN subgroups, transcription factor motif enrichment analysis in hypomethylated genomic regions revealed selective activation of AP-1 in M-HCL along with ETS in M-SBLPN1,2. Both transcription factors are downstream of MAPK signaling, consistent with activating BRAF and MAP2K1 mutations in these subgroups. However, we observed strong ETS enrichment in the absence of MAP2K1 in M-SBLPN along with mutual exclusivity of MAP2K1 and TP53 mutations, suggesting TP53 mutations are driving ETS activation. Although lymphoid neoplasms rarely exhibit TERT promoter mutations, 83% of M-SBLPN1 patients showed the c.-124C>T mutation commonly observed in other cancers producing an ETS binding site and ectopic TERT activation. ETS activation and gain of an ETS site by mutation implies oncogenesis involves aberrant TERT activation in this subgroup. In summary, we have developed a DNA methylation-based classifier that resolves 4 SBLPN subgroups with distinct molecular features, and reclassifies a subset of SMZL and HCL patients, adding further information to the updated WHO/ICC entities. We reveal distinct biological pathways operating in M-SBLPN subgroups that may aid targeted therapy approaches.

Linear regression analysis of 15,798 genes with with transcript expression, DNA methylation, and DNA copy number data available.

<p>HLA typing and Spectral Karyotyping (SKY) analysis</p>

<p>qRT-PCR analysis of differentiation-related genes</p>

Supplementary Table 2 XLSX file 490K, Illumina GoldenGate Methylation Standard Cancer Panel I Target CpG Annotations and differential methylation significance values for group comparisons listed in Table

Relationship between methylation and lack of SLFN11 and MGMT expression in the NCI-60.

<p>Methylation patterns of repetitive elements and PRC2 targets in teratogenic iPSCs</p>

<p>Antibodies and Primers</p>

Supplementary Table 1 XLS file 22K, Tumors and reference tissues for methylation microarray analysis

128,394 probes used for the determination of gene methylation levels.

Overexpression of ASXL3 is independent of DNA methylation status in the ASXL3 promoter

<p>Methylation patterns of repetitive elements and PRC2 targets in teratogenic iPSCs</p>

Methylation gene probe selection and correlation to expression level.

Background: DNA methylation aberrations are widespread among the malignant B lymphocytes of patients with chronic lymphocytic leukaemia (CLL), suggesting that DNA methylation might contribute to the pathogenesis of CLL. Aim: We aimed to explore the differentially methylated positions (DMPs) associated with CLL and screen the differentially methylated and expressed genes (DMEGs) by combining public databases. We aimed to observe the direction of each DMEG in CLL based on the DMPs in the promoter and the body region respectively to narrow down DMEGs. We also aimed to explore the methylation heterogeneity of CLL subgroups and the effect of B cells maturation on CLL. Methods: In this population-based case control study, we reported a genome-wide DNA methylation association study using the Infinium HumanMethylation450 BeadChip, profiling the DNA methylation of CD19+ B Cells from 48 CLL cases and 28 healthy controls. By integrating methylation data and expression data from public databases, gene sets were jointly screened, and then the relationship between methylation sites in promoter and body region and expression of each gene was explored. In addition, support vector machine (SVM) classification algorithm was used to identify subgroups of CLL cases based on methylation pattern, and the effect of B-cell differentiation related methylation sites on CLL-related sites was observed. Results: We identified 34,797 DMPs related to CLL across the genome, most of which were hypomethylated; the majority were located in gene body regions. By combining these DMPs with published DNA methylation and RNA sequencing data, we detected 26,244 replicated DMPs associated with 1,130 genes whose expression were significantly different in CLL cases. Among these DMEGs, nine low expressed DMEGs were selected with hypermethylated in promoter and hypomethylated in body region, and 83 high expressed DMEGs were selected with both hypomethylated in promoter and body region. The 48 CLL cases were divided into 3 subgroups based on methylation site by SVM algorithm. Over 92% of CpGs associated with B cell subtypes were found in CLL-related DMPs. Conclusion: The DNA methylation pattern was altered across the genome in CLL patients. The methylation of ZAP70, FMOD, and ADAMTS17 was significantly different between CLL cases and controls. Further studies are warranted to confirm our findings and identify the underlying mechanisms through which these methylation markers are associated with CLL.

Correlations between expression and DNA methylation for 17,144 transcripts.

Germline mutations within the Krebs cycle enzyme genes fumarate hydratase (FH) or succinate dehydrogenase (SDHB, SDHC, SDHD) are associated with an increased risk of aggressive and early metastasizing variants of renal cell carcinoma (RCC). These RCCs express significantly increased levels of intracellular fumarate or succinate that inhibit 2-oxoglutarate-dependent dioxygenases, such as the TET enzymes that regulate DNA methylation. This study evaluated the genome-wide methylation profiles of 34 RCCs from patients with RCC susceptibility syndromes and 11 associated normal samples using the Illumina HumanMethylation450 BeadChip. All the HLRCC (FH mutated) and SDHB-RCC (SDHB mutated) tumors demonstrated a distinct CpG island methylator phenotype (CIMP). HLRCC tumors demonstrated an extensive and relatively uniform level of hypermethylation that showed some correlation with tumor size. SDHB-RCC demonstrated a lesser and more varied pattern of hypermethylation that overlapped in part with the HLRCC hypermethylation. Combined methylation and mRNA expression analysis of the HLRCC tumors demonstrated hypermethylation and transcription downregulation of genes associated with the HIF pathway, HIF3A and CITED4, the WNT pathway, SFRP1, and epithelial-to-mesenchymal transition and MYC expression, OVOL1. These observations were confirmed in the TCGA CIMP-RCC tumors. A selected panel of probes could identify the CIMP tumors and differentiate between HLRCC and SDHB-RCC tumors. This panel accurately detected all CIMP-RCC tumors within the TCGA RCC cohort, identifying them as HLRCC -like, and could potentially be used to create a liquid biopsy-based screening tool. The CIMP signature in these aggressive tumors could provide both a useful biomarker for diagnosis and a target for novel therapies.