Undosed Average Dosed Average Paired T-test Pvalue Fatty Acid Summary Saturated FA 32.79% 33.29% 0.54 Monounsaturated FA 22.12% 16.70% 0.0011 n-3 PUFA 4.59% 14.49% 0.0000067 n-6 PUFA 39.78% 34.72% 0.011 n3:n6 Ratio 0.12 0.42 0.0000086 Individual Fatty Acids C14:0 0.76% 0.66% 0.51 C16:0 22.39% 22.13% 0.68 C16:1n7 2.46% 1.07% 0.25 C16:3n4 0.17% 0.10% 0.058 C18:0 9.51% 10.46% 0.077 C18:1n9 17.56% 13.87% 0.00030 C18:1n7 1.96% 1.66% 0.052 C18:2n6 28.51% 26.17% 0.22 C18:3n6 0.45% 0.27% 0.000092 C18:3n3 0.55% 0.49% 0.60 C9-T11 CLA 0.55% 0.71% 0.25 C20:0 0.14% 0.04% 0.47 C20:1n9 0.13% 0.10% 0.16 C20:2n6 0.27% 0.27% 1.00 C20:3n6 2.12% 1.28% 0.0028 C20:4n6 8.12% 6.63% 0.0047 C20:4n3 0.23% 0.19% 0.68 C20:5n3 EPA 0.92% 6.38% 0.0000058 C22:2n6 0.03% 0.03% 0.99 C22:4n6 0.28% 0.08% 0.00069 C22:5n3 0.72% 1.32% 0.0018 C22:6n3 DHA 2.17% 6.11% 0.00030

Supplemental Table 1: Change in Serum Fatty Acids

Fatty acid content is averaged before and after n-3 PUFA treatment as both type of fatty acid (top portion of the chart) and individual fatty acid (bottom portion of the chart). Changes in fatty acids were determined using a paired T-test which is reported in the last column. Fatty acids are reported as a percent of total fatty acids.

Genes Reference ADORA3, AHSG, ALOX15, ALOX5, ALOX5AP, ANXA1, AOAH, AOX1, ATRNL1, BCL6, BDKRB1, C3, C3AR1, CCL1, CCL11, CCL13, CCL2, CCL23, CCL24, CCL26, CCL5, CCL7, CCL8, CCR1, CCR2, CCR3, CCR7, CD40, CD74, CRP, CXCL1, CXCL12, CXCL2, CXCL3, CXCL5, CXCL6, DOCK2, EPHX2, F11R, FPR1, GPR68, HDAC4, HDAC9, HRH1, IL17C, IL18RAP, IL1A, IL1B, IL1R1, IL20, GDAC 2012 Analysis1 IL31RA, IL8RB, IRAK2, IRF7, KLKB1, KNG1, LTB4R, LY75, LY86, LY96, NFAM1, NFATC3, NFATC4, NFE2L1, NMI, NOS2A, NR3C1, PARP4, PF4, PLA2G7, PREX1, PRG2, PROC, PROK2, PTGS2, PTX3, S100A12, S100A8, S100A9, SARM1, SCUBE1, SELE, TICAM2, TLR1, TLR4, TLR5, TNFAIP6, TOLLIP, TPST1 Cox2, VEGF, ERK1/2, NF-Kb De Caterina 20052 CPB2, DUSP2, EBI3, SIAHBP1 Hammamieh 20073 EZH2, IGFBP3, CDH1 Dimri 20094 HMGCS2 De Rosa 20155 SCD, FADS2 Gillies 20126 HIF1A, CREB1 Tsunoda 20157 TNFa, DNMT1, DNMT3B, IL6 Cormier 20148, Li 20129 CD36, FFAR3, CD14, PDK4, FADS1 lira do Amaral 201410

CRMP1, GDNF, GFRA1, MYL9, ROBO1, ROBO3, SEMA5A, C9orf125, COL14A1, ENPP2, ERG2, PLD5, ROBO3, RUNX1T1, SEMA5A, TBX18, Stirzaker 201511 TSHZ3, ZBTB16, ZNF208, SLC6A3, C6orf174, ZNF254, DMRTA2, LHX8, WT1, WT1-AS, HOXB13, ECEL1, -OT

RASSF1 Yan 200612

RASSF1-AS, RARB Antil 201013

CDKN2A Bean 200714

EGFR Schley 200715 GPX3 Chen 201116, Mohamed 201417 EIB3, FOX3P Hammamieh 20073

DNMT1, CXCL2, IL-1β, NOS2, TNF-α, DNMT3a Niwa 201218, Nakano 201319

Supplemental Table 2: Candidate

Selection of the 152 candidate genes was based on either: 1) inflammation related genes from GDAC analysis of the TCGA breast cancer dataset or 2) genes involved in fatty acid metabolism, inflammation, and/or breast cancer.

A) Broad GDCA 8,586 Highly Variable Genes C) 152 Candidate Genes Samples Samples

Samples Samples

B) GDCA 8,586 Subtype Cluster Membership D) Candidate Subtype Cluster Membership

1 1 Luminal Basal Luminal Basal 0.8 0.8

0.6 0.6

0.4 0.4 of Subtype of Subtype Overall Fraction

Overall Fraction 0.2 0.2

0 0 Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster Cluster 6 5 4 3 2 1 6 5 4 3 2 1

Supplemental Figure 1: DNAm of Candidate Genes Stratifies TCGA Breast Cancer Samples similar to

GDAC Analysis

A) Non-negative matrix factorization (NMF) clustering was produced using 6 groups on the GDAC derived

8,586 most variable genes among the TCGA breast cancer cohort. B) Comparison of samples in each cluster shows that luminal and basal subtypes dominate two of the clusters identified using the 8,586 genes identified by the GDAC analysis. The y-axis shows the fraction of all TCGA samples represented in each cluster (Note: only samples identified as Luminal or Basal are reported in B) whereas A) shows all samples used in GDAC analysis). C) NMF clustering using 6 groups on the 152 candidate genes performed on the TCGA breast cancer cohort. Clustering using only the 152 candidate genes produced a similar sample grouping when compared to the GDAC analysis with 8,586 genes. D) Candidate gene clusters show that luminal and basal subtypes also dominate two clusters. CpG DNAm Difference Gene P-value Q-value Position (%) ALOX15 chr17 4639431 7.0E-04 1.6E-02 -8 ALOX5 chr10 45374653 2.0E-03 3.2E-02 -5 ALOX5 chr10 45374654 6.8E-04 1.6E-02 5 AOX1 chr2 200586226 9.6E-04 2.0E-02 -7 CDKN2A chr9 21994543 4.0E-08 5.3E-06 6 CREB1 chr2 207529992 2.6E-04 7.9E-03 10 CRMP1 chr4 5890886 1.5E-03 2.6E-02 6 CXCL12 chr10 44385636 2.8E-04 8.5E-03 -9 DMRTA2 chr1 50419460 9.4E-06 6.1E-04 10 ECEL1 chr2 232490585 3.3E-04 9.4E-03 8 EZH2 chr7 148885245 6.5E-08 8.3E-06 8 EZH2 chr7 148885258 7.6E-04 1.7E-02 6 FADS2 chr11 61827576 2.9E-04 8.6E-03 6 FASN chr17 82103451 1.2E-07 1.4E-05 7 FASN chr17 82097466 3.8E-03 4.8E-02 -5 FFAR3 chr19 35352196 4.1E-09 6.9E-07 -17 FFAR3 chr19 35352213 6.5E-09 1.1E-06 -17 FFAR3 chr19 35352218 1.7E-08 2.5E-06 -16 FFAR3 chr19 35352204 8.1E-07 7.7E-05 -14 HOXB13 chr17 48734626 2.9E-03 4.0E-02 9 IGFBP3 chr7 45921153 2.8E-04 8.3E-03 6 IL17C chr16 88637610 1.2E-03 2.2E-02 9 IRF7 chr11 615109 7.1E-04 1.6E-02 -6 LEP chr7 128241029 1.8E-04 6.0E-03 -7 LHX8 chr1 75130134 8.4E-04 1.8E-02 -11 LHX8 chr1 75125505 6.8E-04 1.6E-02 7 LHX8 chr1 75125474 3.9E-04 1.0E-02 6 MYL9 chr20 36541768 2.9E-13 1.0E-10 -18 NOS2 chr17 27808814 1.3E-04 4.9E-03 11 PREX1 chr20 48833850 6.7E-09 1.1E-06 11 RARB chr3 24829608 1.1E-03 2.1E-02 -6 RASSF1-AS1 chr3 50328541 3.9E-03 4.8E-02 13 RUNX1T1 chr8 92102072 2.1E-04 6.8E-03 8 RUNX1T1 chr8 92103027 2.8E-06 2.2E-04 8 RUNX1T1 chr8 92103035 2.7E-05 1.4E-03 6 S100A8 chr1 153394933 2.5E-05 1.3E-03 6 SLC6A3 chr5 1446597 2.0E-03 3.2E-02 5 TBX18 chr6 84773926 1.3E-06 1.1E-04 -5 TOLLIP chr11 1310191 8.4E-04 1.8E-02 12 TOLLIP chr11 1310213 3.0E-04 8.7E-03 12 TOLLIP chr11 1310233 2.6E-03 3.8E-02 10 TOLLIP chr11 1306787 4.6E-06 3.4E-04 10 TPST1 chr7 66205409 4.7E-05 2.2E-03 -6 VEGFA chr6 43771228 2.5E-03 3.7E-02 6 WT1 chr11 32430231 2.2E-03 3.3E-02 12 WT1 chr11 32435765 3.9E-03 4.8E-02 9 WT1-AS chr11 32430231 2.2E-03 3.3E-02 12 WT1-AS chr11 32435765 3.9E-03 4.8E-02 9 ZNF208 chr19 22010923 1.5E-03 2.6E-02 -6

Supplemental Table 3: Candidate Gene Promoter DMCs

Differentially methylated CpGs (DMCs) of candidate gene promoters were identified using MethylKit v1.2.0 which tested for DNAm change between the untreated and treated samples for all pass filter CpGs (≥10 reads coverage in ≥ 16 samples). A CpG was considered a DMC if the multiple test corrected P-value (Q-value) was less than 0.05 and the average DNAm difference between the untreated and the treated samples was at least

5%. Here, the candidate gene promoter DMCs were identified by intersecting the candidate genes with all promoter DMCs.

Adjusted Term Overlap P-value Genes P-value Photodynamic therapy-induced HIF-1 survival signaling 3/37 0.00003 0.00379 NOS2; IGFBP3; VEGFA Effects of Nitric Oxide 2/8 0.00007 0.00469 NOS2; AOX1 Vitamin D Pathway 4/182 0.00020 0.00886 CDKN2A; ALOX5; IGFBP3; S100A8 Circadian rhythm related genes 4/201 0.00029 0.00971 CREB1; NOS2; LEP; EZH2 regulation in adipogenesis 2/22 0.00056 0.01415 CREB1; LEP Senescence and Autophagy in Cancer 3/105 0.00062 0.01415 CDKN2A; IGFBP3; IRF7 Tumor suppressor activity of SMARCB1 2/31 0.00112 0.01905 CDKN2A; EZH2 Toll-like Receptor Signaling 2/31 0.00112 0.01905 TOLLIP; IRF7 Bladder Cancer 2/40 0.00186 0.02814 CDKN2A; VEGFA Structural Pathway of Interleukin 1 (IL-1) 2/49 0.00278 0.03154 TOLLIP; IRF7 Hepatitis C and Hepatocellular Carcinoma 2/49 0.00278 0.03154 NOS2; VEGFA NO/cGMP/PKG mediated Neuroprotection 2/47 0.00256 0.03154 CREB1; NOS2 G1 to S cell cycle control 2/64 0.00470 0.03895 CREB1; CDKN2A Oncostatin M Signaling Pathway 2/65 0.00484 0.03895 CREB1; VEGFA MECP2 and Associated Rett Syndrome 2/62 0.00442 0.03895 CREB1; EZH2 RIG-I-like Receptor Signaling 2/60 0.00414 0.03895 CXCL12; IRF7 Non-small cell lung cancer 2/66 0.00499 0.03895 CDKN2A; RARB Sterol Regulatory Element-Binding (SREBP) signaling 2/69 0.00544 0.03895 CREB1; FASN AMP-activated Kinase (AMPK) Signaling 2/69 0.00544 0.03895 LEP; FASN Leptin signaling pathway 2/76 0.00657 0.04465 CREB1; LEP SCFA and skeletal muscle substrate metabolism 1/6 0.00956 0.04645 FFAR3 Robo4 and VEGF Signaling Pathways Crosstalk 1/6 0.00956 0.04645 VEGFA Nicotine Metabolism 1/6 0.00956 0.04645 AOX1 Pancreatic adenocarcinoma pathway 2/89 0.00891 0.04645 CDKN2A; VEGFA Allograft Rejection 2/89 0.00891 0.04645 CXCL12; VEGFA Pathways in clear cell renal cell carcinoma 2/85 0.00815 0.04645 FASN; VEGFA Apoptosis Modulation and Signaling 2/91 0.00929 0.04645 CDKN2A; TOLLIP Apoptosis 2/84 0.00797 0.04645 CDKN2A; IRF7 MicroRNA for Targeting Cancer Growth and Vascularization in Glioblastoma 1/7 0.01115 0.04891 VEGFA EV release from cardiac cells and their functional effects 1/7 0.01115 0.04891 CXCL12 Molybdenum cofactor (Moco) biosynthesis 1/7 0.01115 0.04891 AOX1 Supplemental Table 4: Significantly Enriched Pathways using Candidate Gene Promoter DMCs Candidate genes that contained a DMC in their promoter region were used to determine which pathways were significantly enriched for DNAm changes after n-3 PUFA treatment. Overlap provides the number of candidate gene promoter DMCs that were found in the pathway out of the total number of genes in the pathway.

DNAm Color Scale

Percent DNAm Change after Treatment

Supplemental Figure 2: Focal Adhesion-PI3K-Akt-mTOR-signaling pathway

A) The Focal Adhesion-PI3K-Akt-mTOR-signaling pathway was found to be significantly enriched for both candidate gene promoter DMCs and for hypermethylation of genome-wide promoter DMCs (hypergeometric

FDR p-value = 0.023). Only CpGs where the magnitude of the individual’s DNAm change was greater than 5% were counted so that the variability was not overrepresented by small changes. Hypermethylated DMCs are localized to the cytokine and chemokine receptors and the AKT portion of the pathway. Pathway was constructed using WikiPathways and Cytoscape.

Upstream Hypermethylation

DNAm Color Scale

Percent DNAm Change after Treatment

Supplemental Figure 3: Toll-Like Receptor Signaling Pathway

A) The Toll-like Receptor Signaling Pathway was found to be significantly enriched for both candidate gene promoter DMCs and for hypermethylation of genome-wide promoter DMCs (hypergeometric FDR p-value =

0.029). Only CpGs where the magnitude of the individual’s DNAm change was greater than 5% were counted so that the variability was not overrepresented by small changes. Hypermethylation occurs downstream of

TLR1, 2, 4, 5, and 6 and upstream of the pro-inflammatory cytokines. Pathway was constructed using

WikiPathways and Cytoscape.

Variation as a Function of DNAm in PBMCs

Supplemental Figure 4: Variation as a Function of DNAm in PBMCs

The variation in DNAm between samples was calculated in order to determine the power of detecting DNAm changes. The variation of DNAm for all participants was plotted as function of DNAm and fit to a parabola (grey dashed line). The variation of DNAm in our PBMC samples as assayed by RRBS was lower ( of fit SD =

20%) than the variation determined using publicly available human PBMC data20 (GSE57107) that was assayed using the Infinium 450k Array (max of fit = 31%; data not shown).

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