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Adaptive stress response genes associated with breast cancer subtypes and survival outcomes reveal race-related differences

Adaptive stress response genes associated with breast cancer subtypes and survival outcomes... www.nature.com/npjbcancer ARTICLE OPEN Adaptive stress response genes associated with breast cancer subtypes and survival outcomes reveal race-related differences 1 2 3 1,4 1,4 1,2 Muthana Al Abo , Larisa Gearhart-Serna , Steven Van Laere , Jennifer A. Freedman , Steven R. Patierno , Eun-Sil Shelley. Hwang , 5 6 1,2 Savitri Krishnamurthy , Kevin P. Williams and Gayathri R. Devi Aggressive breast cancer variants, like triple negative and inflammatory breast cancer, contribute to disparities in survival and clinical outcomes among African American (AA) patients compared to White (W) patients. We previously identified the dominant role of anti-apoptotic protein XIAP in regulating tumor cell adaptive stress response (ASR) that promotes a hyperproliferative, drug resistant phenotype. Using The Cancer Genome Atlas (TCGA), we identified 46–88 ASR genes that are differentially expressed (2- fold-change and adjusted p-value < 0.05) depending on PAM50 breast cancer subtype. On average, 20% of all 226 ASR genes exhibited race-related differential expression. These genes were functionally relevant in cell cycle, DNA damage response, signal transduction, and regulation of cell death-related processes. Moreover, 23% of the differentially expressed ASR genes were associated with AA and/or W breast cancer patient survival. These identified genes represent potential therapeutic targets to improve breast cancer outcomes and mitigate associated health disparities. npj Breast Cancer (2022) 8:73 ; https://doi.org/10.1038/s41523-022-00431-z INTRODUCTION survival rate ranging from 29.9 to 42.5% (higher survival outcomes 9,10 in W patients) . According to the American Cancer Society (ACS) , in recent years AA patients with IBC present more frequently with higher stage (2012–2016) there has been a continuous decline in the breast and triple-negative (TN) [i.e., negative for estrogen (ER), proges- cancer death rate. Despite this, at the global level, notable terone receptor (PR), and human epidermal growth factor receptor differences in breast cancer mortality is observed among ethnic 2,3 2 (ERBB2)] or basal subtype [similar to triple negative, but with groups including younger age of onset . These race-related epidermal growth factor receptor 1 (EGFR) activation] , and disparities are likely driven by a complex interplay among exhibit shorter median survival (20 months) compared with W sociocultural differences in societal-level (e.g., racism), 12–16 patients (32 months) . Epidemiological studies suggest a neighborhood-level (e.g., pollution), and institutional-level (e.g., distinct profile of risk factors such as high body mass index access to care) determinants of health . Analysis of The Cancer (BMI), early age at first pregnancy, multiparity, and lack of Genome Atlas (TCGA) dataset adjusted for intrinsic subtype breastfeeding, factors that can lead to chronic pro-inflammatory frequency differences has reported that patients estimated to cellular stress to be associated with poor therapeutic outcomes have >50% African ancestry exhibit a worse breast cancer-free 11,17 and survival in AA IBC patients compared with W patients . interval compared to patients with >50% European ancestry . Furthermore, comparative gene expression studies from preclini- Underlying these ancestry-related disparities are likely individual- cal models and pretreatment patient samples, collected as part of level differences in physiology, genetics and genomics arising the International IBC Consortium’s effort to understand differences from forced and voluntary migrations of human populations between IBC and non-IBC and to define IBC-specific molecular around the world. Further interactions among societal-level, profiles, revealed highly activated mitogen activated protein neighborhood-level, institutional-level and individual-level deter- kinase (MAPK) and nuclear factor kappa B (NFκB) transcriptional minants of health also likely contribute to cancer disparities by profiles associated with increased pro-inflammatory and prolif- influencing allostatic load . erative signals in IBC compared with subtype and stage-matched 18–22 Emerging evidence indicates that this race-related survival gap locally advanced breast cancer . is largely due to higher incidence of aggressive subtypes of breast Preclinical studies using various breast cancer in vitro and 23–28 cancer, including basal-like, triple negative, human epidermal in vivo models from our group identified a critical role for the growth factor receptor 2 (ERBB2/HER2)-enriched subtypes, which most potent caspase inhibitor, X-linked inhibitor of apoptosis are frequently associated with early metastasis in AA compared to protein (XIAP), in linking EGFR-mediated MAPK activation and W patients . A highly representative example of an aggressive NFκB hyperactivity. In addition, higher XIAP staining was observed breast cancer designated as a cancer health disparity by NIH in invasive breast cancers compared to normal, benign ductal (NCATS/GARD) is inflammatory breast cancer (IBC). Of all clinically carcinoma in situ (DCIS), and higher XIAP also correlated with poor 29,30 distinct breast cancer subtypes, IBC is the most lethal variant with event free survival and increased lymph node involvement . high rate of metastasis, disproportionately higher incidence at Importantly, XIAP has a unique element called an internal younger ages in non-W patients, and disparity in relative 5-year ribosomal entry sequence (IRES) in its 5’ untranslated region, 1 2 Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710, USA. Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA. Center for Oncological Research (CORE), Faculty of Medicine and Health Sciences—University of Antwerp, Campus Drie Eiken‑Universiteitsplein 1, 2610 Wilrijk‑Antwerp, Belgium. 4 5 Department of Medicine, Division of Medical Oncology, Duke University School of Medicine, Durham, NC 27710, USA. Department of Pathology, MD Anderson Cancer Center, Houston, TX 77030, USA. Department of Pharmaceutical Sciences and BRITE, North Carolina Central University, Durham, NC 27707, USA. email: gayathri.devi@duke.edu Published in partnership with the Breast Cancer Research Foundation 1234567890():,; M. Al Abo et al. Fig. 1 Number of breast cancer samples in TCGA by definition, race, and PAM50 subtypes. Pie charts depicting the number of breast cancer samples in TCGA PAM50 subtypes: Basal, Her2, LumB, LumA, Normal-like for all samples (a), for samples from AA patients (b), or for samples from W patients (c). d Scores of the expression level of XIAP, OSR-, Immune-, and TGFβ-related gene signatures in breast cancer subtypes. Box plots overlaid with scatter plots depicting the calculated score for XIAP-, OSR-, immune-, and TGFβ-related ASR gene signatures in the indicated breast cancer subtypes and stratified by patient race, AA or W. The Wilcoxon signed-rank test was used to examine significance (*adjusted p-value < 0.05; **adjusted p-value < 0.001; ***adjusted p-value < 0.0001; and ns, not significant). The center line represent the medians and the bounds of box represent the confidence intervals. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation 1234567890():,; M. Al Abo et al. Table 1. The list of ASR genes and their functional pathways. Pathway ASR genes XIAP XIAP, SPANXA1, SPANXB1, SPANXC, SULT1E1, SLPI, AGFG2, POPDC3, ZIC1, CDC45, TNFSF9, SLC26A6, KYNU, H2BC11, MCM5, GINS2, CDKN2D, OBSL1, SNAI1, TP53I3, AIM2, LIG1, PIMREG, ALDOC, MCM10,ASF1B, ZNF165, CDKN1C, H2AC8, TMEM40, DBP, SLCO1B3, E2F8, LRP4, KLRC1, RALGDS, BLM, CCNE2, POLE2, ZNF26, PITPNC1, NEU1, VASH2, PLAU, GLYR1, H2BC21, NRN1, TBC1D3, COPZ2, H2BC8, KLRC2, RPL5, SEPTIN6, CRYBG3, PRR16, WDR37, TBC1D3C, FOSB, ASTE1, CLCN6, PDE4DIP, N4BP2L1, LEF1, HSPG2, POLA1, WBP1L, ZMYM5, B3GNT2, DHFR, ZNF331, ACSM3, MANSC1, ARAP2, TMEM100, SLC1A4, GPR137B, LIPG, LMAN1, LPAR6, SNX19, CYP4B1, GEM, ARHGAP29, ZFPM2, SLC14A1, PCDH7, HSD11B1, CAVIN2, ABAT, STC1, NRIP3, NAT1, PCDH9, LMAN2L, REXO5, DLEU2L, DLEU2, SYBU, MRM2, LINC-PINT, LINC00115 OSR HSPA1A, METTL7A, BLM, TYMS, KRT6B, LIG1, SCD5, POLE2, CDT1, CCNE2, SKP2, E2F8, ORC1, MCM4, CCN3, IRF4, MCM10, NAP1L3, MCM3, ASF1B, FGF2, TREM1, ABCA6, CTH, ECM2, OASL, CEBPD, TP63, RAB5A, SLC4A7, MBNL2, RBMS3, KLHL24, TXNIP, FCAR, AREG, ANXA3, PTGS2, NRG1, INHBE NFkB NFKB1, RELA, IL6, CXCL8, IRF2, FAS, IL1B, BCL2, BIRC5, SOD1, MYC MNK MKNK1, MKNK2, EIF4G1, EIF4E, SPRY2, HNRNPA1, HNRNPA2B1, NONO, KAT5, RPS6 Immune CTSA, PNP, MYCBP2, TMC6, IFI44L, NUDT1, MELK, STAT4, INHBC, ARPC2, EHD1, SART3, MBD4, ACOX1, PRKCB, NFATC3, FOLR1, MAK16, WNK1, JMJD6, IQGAP1, ABCC10, SRSF7, IGLV1-44, NUP85, SEPTIN6, ESF1, NAA15, PGS1, ANKRD11, TNPO1, PAX5, DHFR, CTBP2, BCKDK, PEX11B, CUL2, GABPB1, ATP7A, ATF2, DNAJB6, CANX, PAK2, ANXA7, TMCO1, PBXIP1, SP3, TGIF2, TOMM22, INTS12, SLC30A5, TMEM50B, TSPAN14, TBL1XR1, MRM2 TGFβ TGFB1, NDUFAF3, DAB2, RPL27A, MARCKS, CD72, HSP90B1, PPARD, ACTG1, CRK, TGFB2, AUTS2, RYK, TGIF2 JAG1-Notch JAG1, NOTCH1, DLL4, BRD4, IL6, IL1B, TNF, SNAI1, ZEB1 The ASR genes were grouped according to their function in the listed pathways. The ASR genes that are bolded represent the genes belonging to more than one of the indicated pathways. which is critical for XIAP protein translation during response to lumB, 82 Her2, 190 Basal, and 40 Normal-like (Fig. 1a; Supplementary cellular stress . Using triple-negative cell lines, novel isotype- Table 1). Metastatic samples and those lacking PAM50 subtype matched clonal isolates of tumor cells surviving exposure to acute/ information were excluded. When more than one sample belonged chronic stress stimuli, and genetically modified breast cancer cell to thesamepatient, weselectedthe onewithhighest RNA variants with differential XIAP expression, we reported that XIAP sequencing depth. upregulation allows tumor cells to survive in the presence of Racial designations in TCGA are based on patient self- 24,28 27,32 stressors like oxidative- and immune-mediated cell death identification. In the present study, race-related analysis focused stimuli, leading to clonal outgrowth of multi-drug resistant tumor on AA and W breast cancer patient datasets from TCGA (179 AA 26,33 cell populations . Analysis of XIAP-overexpressing tumors and 744 W). It is important to note that among the 113 Normal- exhibiting the characteristics of an adaptive stress response adjacent samples, there are 105 W and only 6 AA samples. (ASR) revealed a dominance of proliferative, invasive, and PAM50 subtyping within the AA and W patient tumor datasets are immunosuppressive networks of NFκB target genes, which we shown in Fig. 1b, c; Supplementary Table 1. term the “adaptive stress response (ASR) gene set”. This is highly relevant, as NFκB is recognized as a crucial mediator of ASR genes associated with oxidative stress and immune inflammatory, immune, anti-apoptotic, and antioxidant signals as response pathways score highly in basal breast cancer well as an important modulator of cancer stem cell biology, tumor subtype surveillance, and tumor rejection . The genes in the ASR gene set To build and expand on our previous analysis of IBC tumor cells have also been reported to be part of an IBC-specific 79-gene surviving under chronic stress stimuli, which identified genes in signature enriched with activated gene networks in immune the anti-apoptotic signaling, oxidative stress response, immune, pathways and the TGFβ pathway that captured nearly 25% of IBC and TGFβ-related genes, we calculated the gene signature scores 35,36 patient samples identified in the TCGA database as IBC-like . for the above pathways in the TCGA breast cancer subtypes and In the present study, we investigated the expression profile of by patient race, AA and W (Fig. 1d). The gene sets are listed in the XIAP-driven ASR gene set in breast cancer subtypes, identified Table 1 and are part of the overarching ASR gene set. We sorted race-related differentially expressed genes within this gene set, the genes in each signature into up- or downregulated genes and determined associations between ASR gene expression and according to the expression levels reported in our previous poor survival outcomes. 23,30,35,37 preclinical studies . The results from this analysis demon- strated that the score of ASR gene signatures differed among breast cancer subtypes and by patient race. We found that the RESULTS XIAP downregulated gene set exhibits differential score between Proportion of subtypes, AA, and W patient samples in the AA and W patients in LumA, LumB, and Her2 subtypes, and the TCGA breast cancer dataset XIAP upregulated gene set exhibits differential score between AA As of June 15, 2021, TCGA repository included 1222 breast cancer and W patients in Basal, Her2, LumB, and LumA subtypes. The patient samples (1109 tumor and 113 matched normal). Further- differential score of OSR signature is highest in Basal, followed by more, TCGA includes the Prosigna Prognostic Gene Signature Assay Her2, and LumB, and lowest in Normal-like subtype, compared (formerly called the PAM50 test) to categorize breast tumors into with Normal-adjacent. Comparing the OSR signature scores by five subtypes [luminal A (LumA), luminal B (LumB), Her2, Basal, and patient race indicated that scores differed in the breast cancer Normal-like] based on the expression level of genes, which have subtypes between samples from AA and W patients, especially for been found to be associated with breast cancer prognosis. In the the OSR downregulated genes. The score analysis also showed present study, we focused on primary tumor samples [1090 Primary that immune-related upregulated genes generally exhibited Solid Tumor] and normal tissue samples adjacent to the tumors [113 higher scores in tumors compared to normal. However, the Solid Tissue Normal] (referred to in the present study as Normal- immune-related downregulated genes did not exhibit a distinct adjacent). Within these, our sample set included 559 lumA, 207 pattern in different tumor subtypes. Importantly, immune-related Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 2 Identification of DE-SN and DE-SS in PAM50 subtypes. Volcano plots depicting the level of ASR genes in the indicated PAM50 subtypes compared to Normal-adjacent (a) or to each other (b). The log2 fold-change differential expression and the –log10 (adjusted p-value) are shown on the x-axis and y-axis, respectively. The points correspond to all ASR genes and the blue highlighted points represent the DE-SN (a) and DE-SS (c), which exhibit fold-change greater than 2 and adjusted p-value less than 0.05. b and d Bar plots depicting the number of DE-SN and DE-SS in a and c, respectively. The colors of the bars indicate whether the DE-SN (b) or DE-SS (d) are upregulated (green) or downregulated (blue). The numbers inside the bar correspond to the number of upregulated or downregulated DE-SN (b) or DE-SS (d). Comparisons are shown under each bar and the number of DE-SN (# of DE-SN) or DE-SS (# of DE-SS) are shown on the y-axis. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. downregulated gene expression in Her2 and Basal subtypes the number of upregulated or downregulated DE-SS varied showed significant differences (adjusted p-value < 0.0001) depending on the comparison between subtypes (Fig. 2d). For between AA and W patients. We also observed that the score of example, DE-SS in LumA vs. Normal-like were more frequently TGFβ-related downregulated genes in Her2 subtype exhibited a downregulated (26 downregulated and 4 upregulated). In marked difference between AA and W patients. contrast, DE-SS in Basal vs. LumA tended to be upregulated (39 upregulated and 20 downregulated). The proportion of DE-SN among the 226 genes was 69.4% when Identification of differential ASR gene sets in PAM50 subtypes comparing subtypes to Normal-adjacent samples and 34.8% when To identify the expression levels (combined level of all transcript comparing subtypes to each other. As a control, we identified DE-SN variants of any given gene) of the adaptive stress response (ASR) and DE-SS among 226 randomly selected genes. Unlike the ASR gene sets (Table 1) in relation to PAM50 subtypes, we performed gene set, the proportion of DE-SN was less than 3% when comparing analyses comparing gene expression in samples of each subtypes to Normal-adjacent samples and the proportion of DE-SS PAM50 subtype with Normal-adjacent samples (Fig. 2a). We was less than 3% when comparing subtypes to each other. determined differentially expressed genes between PAM50 subtypes compared with Normal-adjacent (designated DE- DE-SN and DE-SS exhibit distinct expression changes in breast SN) with > 2-fold-change in mean expression and adjusted p-value cancer molecular subtypes < 0.05. In total, we identified 88 DE-SN in Basal, 81 in Her2, 87 in luminal B, 64 in luminal A and 46 in Normal-like samples (Fig. 2band Next, we investigated whether DE-SN are shared among subtypes. Supplementary Table 2). We also determined the number of The vast majority of DE-SN are differentially expressed in more than one subtype, with only a few of the DE-SN being unique to a upregulated and downregulated DE-SN in each subtype. particular subtype (Fig. 3a). Thirty-five DE-SN were identified as Similarly, to identify differentially expressed genes within ASR gene sets between subtypes, we compared expression levels of being commonly differentially expressed across all subtypes the 226 genes in each PAM50-subtype to every other subtype compared with Normal-adjacent samples (Fig. 3b and Supple- (designated DE-SS). This analysis revealed 24 DE-SS in Basal vs. mentary Table 2). We did not find any tendency toward Her2, 36 in Basal vs. LumB, 59 Basal vs. LumA, 49 Basal vs. Normal- enrichment in upregulated or downregulated commonly differ- like, 19 Her2 vs. LumB, 35 Her2 vs. LumA, 44 Her2 vs. Normal-like, entially expressed genes across all subtypes, as 17 DE-SN were 25 LumB vs. LumA, 60 LumB vs. Normal-like, and 30 LumA vs. upregulated and 18 were downregulated among all subtypes Normal-like samples (Fig. 2c, d and Supplementary Table 3). compared with Normal-adjacent (Supplementary Fig. 1a, b). Expression levels of a number of DE-SN fluctuate dynamically Collectively, this analysis identified upregulated or downregulated DE-SS in a given subtype compared to the other subtype in each among different subtypes (Fig. 3c). For example, 4-Aminobutyrate comparison. aminotransferase (ABAT) (member of XIAP pathway/Table 1) We noted differences between DE-SN and DE-SS gene sets. In expression level was decreased 4-fold in Basal subtype compared the case of DE-SN, an equal number of genes (about half) were with Normal-adjacent but increased 3-fold in LumA compared either upregulated or downregulated compared with Normal- with Normal-adjacent. We also found other XIAP pathway genes, adjacent, regardless of the tumor subtypes (Fig. 2b). In contrast, Arylamine N-acetyltransferase (NAT1), Lipase G (LIPG), Fig. 3 Intersection among DE-SN. a Venn diagrams depicting the common DE-SN after comparing PAM50 subtypes to Normal-adjacent samples. b and c Heatmaps depicting the expression fold-change for DE-SN that are significantly changed in all PAM50 subtypes (b) or for DE- SN that are dynamically differentially expressed across PAM50 subtypes (c). Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 4 Identification of DE-SN or DE-SS in PAM50 subtypes from either AA or W patients. Bar plots depicting the number of DE-SN (a)or DE-SS (b) in breast cancer samples from AA patients. Similar to a and b, c and d depict the number of DE-SN or DE-SS in breast cancer samples from W patients, respectively. The colors of the bars, the numbers inside the bars, and the axes are as described in Fig. 2. b As in a, but the DE- SS are identified after comparison of PAM50 subtypes to each other. Intersection among DE-SN (e) or DE-SS (f) with or without stratification of samples by patient race. e and f Venn diagrams depicting the common DE-SN identified after comparison between samples of the indicated PAM50 subtype with Normal-adjacent samples or after comparison between samples of the indicated PAM50 subtypes, respectively. Light blue circles represent the DE-SN identified without stratification by patient race, the light purple circles represent the DE-SN identified among breast cancer samples from W patients only and the yellow circles represent the DE-SN identified among breast cancer samples from AA patients only. Sulfotransferase Family 1E Member 1 (SULT1E1), and Zic Family potential to identify key biological factors that contribute to breast Member 1 (ZIC1) genes and Keratin 6B (KRT6B) (member of the OSR cancer disparities. Therefore, we compared the expression of the pathway/Table 1) that were differentially expressed with varying 226 genes in PAM50 subtypes to Normal-adjacent or to other magnitudes depending on the subtype (Fig. 3c). subtypes in either samples from AA patients only or from W patients only (AA-DE-SN, AA-DE-SS, W-DE-SN, W-DE-SS, respec- tively) (Fig. 4 and Supplementary Tables 4 and 5). The number of Identification of distinct DE-SN and DE-SS in AA and W breast AA-DE-SN ranged from 36 to 71 and the number of AA-DE-SS cancer patients ranged from 12 to 52 (Fig. 4a, b). The number of W-DE-SN ranged Interrogating the differential expression of the ASR gene sets in from 35 to 87 and the number of W-DE-SS ranged from 15 to 61 breast cancer TCGA samples from AA or W patients has the npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. (Fig. 4c, d). Although the majority of the DE-SN or DE-SS identified the Normal-like subtype. The functional annotation of race-related among all samples, samples from AA patients only, or samples DE highlights their potential function in oncogenesis and are from W patients only overlapped, a number of AA- or W-DE-SN included in Table 2. and AA- or W-DE-SS were only identified after race stratification (Fig. 4e, f). Therefore, importantly, identification of a number of Association of DE-SN with breast cancer patient survival DE-SN and DE-SS are race-related. For example, in Basal subtype, To gain insight into the potential clinical relevance of race-related we identified 88 DE-SN, 72 AA-DE-SN and 81 W-DE-SN. There were DE-SN identified, we performed overall survival analysis using 61 overlapping genes among DE-SN, AA-DE-SN, and W-DE-SN, 7 TCGA clinical data. The expression levels of DE-SN and patient genes specific to samples from AA patients only, and 3 genes survival data were fitted into Cox regression models to compute specific to samples from W patients only. There were only 4 shared the hazard ratio (HR) for each DE-SN. Survival data of all patients of genes between DE-SN and AA-DE-SN compared to 19 shared each PAM50 subtype (excluding ones with metastases), were genes between DE-SN and W-DE-SN in Basal subtype (Fig. 4e). The included in the survival analysis. In all our analyses, a given DE-SN genes that are specifically differentially expressed as part of ASR was significantly associated with patient overall survival if HR > 1.5 pathways studied (Table 1) in Basal tumors of AA patients are or HR < 0.58 and p-value < 0.05. As shown in Fig. 6a–c, a number of Nucleoporin 85 (NUP85) (member of the immune pathway), Solute DE-SN were associated with the survival of all patients (regardless Carrier Family 26 Member 6 (SLC26A6) (member of the XIAP of patient race), as follows: COPI Coat Complex Subunit Zeta 2 pathway), Cathepsin A (CTSA) (member of the immune pathway), (COPZ2) in Basal; Cyclin Dependent Kinase Inhibitor 1C (CDKN1C); ATPase Copper Transporting Alpha (ATP7A) (member of the LEF1, CCAAT/enhancer-binding protein delta (CEBPD), Stearoyl-CoA immune pathway), TNF Superfamily Member 9 (TNFSF9) (member desaturase (SCD5) in Her2; and BCL2 Apoptosis Regulator (BCL2), of the XIAP pathway), Snail Family Transcriptional Repressor 1 Aldolase, Fructose-Bisphosphate C (ALDOC), Plasminogen Activator, (SNAI1) (member of the XIAP and JAG1-Notch pathways), and Urokinase (PLAU) and Caveolae Associated Protein 2 (CAVIN2)in Nuclear Receptor Interacting Protein 3 (NRIP3) (member of the XIAP Normal-like samples. The above genes listed are all members in pathway). The genes that are specifically differentially expressed in the XIAP pathway except BCL2 (member of NFkB pathway), CEBPD Basal tumors of W patients are TGFB Induced Factor Homeobox 2 and SCD5 (both members of the OSR pathway). (TGIF2) (member of the immune and TGFβ pathways), SPANX Next, we investigated the association between DE-SN and Family Member B1 (SPANXB1) (member of the XIAP pathway), and overall survival within each breast cancer subtype stratified by Transmembrane Protein 40 (TMEM40) (member of the XIAP race (HR > 1.5 or HR < 0.58 and p-value < 0.05). From this analysis, pathway). we identified the following race-related survival associations: among the 88 DE-SN in Basal subtype, 7 DE-SN associated with Identification of race-related differentially expressed ASR survival among AA patients, 4 among W patients, and 1 among genes within PAM50 subtypes both AA and W patients; among the 81 DE-SN in Her2 subtype, 17 Next, we investigated if any of the ASR genes exhibited race- DE-SN associated with survival among AA patients, 2 among W related differential expression by comparing TCGA breast cancer patients, and 3 among both AA and W patients; among the 87 DE- samples of a given PAM50 subtype between AA and W patients. SN in LumB subtype, 7 DE-SN associated with survival among AA This analysis identified a number of race-related differentially patients and 5 among W patients; among the 64 DE-SN in LumA expressed ASR genes, which included 1 in Basal, 7 in Her2, 4 in subtype, 14 DE-SN associated with survival among AA patients; LumB, 3 in LumA, and 14 in Normal-like samples (Table 2). Among and among the 46 DE-SN in Normal-like subtype, 1 associated the identified race-related differentially expressed ASR genes, we with survival among AA patients (Fig. 6d and Table 3). Of particular found that Crystallin Beta-Gamma Domain Containing 3 (CRYBG3) interest in the context of breast cancer disparities are the DE-SN (member of the XIAP pathway/Table 1) is a race-related whose levels were specifically associated with AA breast cancer differentially expressed ASR gene in Her2 and LumA subtypes, patient overall survival, which included members of XIAP and OSR CXCL8 (member of the NFkB pathway/Table 1) and Stanniocalcin 1 pathway [BLM RecQ Like Helicase (BLM) and E2F Transcription Factor (STC1) (member of the XIAP pathway/Table 1) are race-related 8 (E2F8)] in Basal; the members of XIAP pathway [Cyclin Dependent differentially expressed ASR genes in Her2 and normal-like Kinase Inhibitor 1C (CDKN1C), Cell division cycle 45 (CDC45), Cyclin subtypes, and Transmembrane Protein 100 (TMEM100) (member Dependent Kinase Inhibitor 2D (CDKN2D)] and the member of the of XIAP pathway/Table 1) is a race-related differentially expressed OSR pathway [Minichromosome Maintenance Complex Component ASR gene in LumA and normal-like subtypes. 3 (MCM3)] in Her2; and the members of OSR pathways [Tumor Protein P63 (TP63) and Chromatin Licensing And DNA Replication Gene ontology analysis of AA- and W-DE-SN reveals Factor 1 (CDT1)] in LumA breast cancer, and the member of NFkB differential ontology enrichment pathway, MYC in Lum A breast cancer (Table 3). To understand the functions of the identified DE-SN in breast cancer samples from AA or W patients, we first queried for DISCUSSION associated Gene Ontology (GO) categories and then submitted for Disproportionate rates of incidence, metastatic progression and GO enrichment analysis. This GO enrichment analysis of AA- or W- poor survival outcomes are associated with aggressive subtypes of DE-SN in a given PAM50 subtype revealed GO enrichment in cell 2,3 breast cancer in self-identified African American women . Much cycle, DNA damage response, signal transduction, and regulation of this disparity in clinical outcome among African American of cell death processes (KS < 0.05) (Fig. 5a, b). patients with advanced breast cancer remains after controlling for Notably, DNA replication, metabolism, and damage response 16,38–42 medical coverage, diagnosis, and treatment access . This processes were enriched in Basal and Her2 subtypes. We suggests that additional societal-level, neighborhood-level and compared the enriched GOs for AA-DE-SN and W-DE-SN within institutional-level, and possibly individual-level, factors contribute each subtype and found differential GO enrichment, especially in to their poorer prognosis. Furthermore, multiple epidemiological Her2 and Normal-like subtypes. For example, the regulation of cell studies identify distinct non-genetic risk factors in AA women that death, ERK1 ERK2 cascade, and epithelial cell migration processes induce accumulation of inflammatory and oxidative factors were enriched among AA-DE-SN in the Her2 subtype, but not 2,7,11,13,16 among W-DE-SN in the Her2 subtype, and the DNA recombination leading to chronic stress microenvironment . Tumor cells and chromosomal organization processes were enriched among co-opt anti-apoptotic mechanisms, a hallmark of cancer ,to AA-DE-SN in the Normal-like subtype, but not among W-DE-SN in rapidly adapt to microenvironmental and therapeutic stress Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation Table 2. Differentially expressed ASR genes within a given PAM50 subtype between AA or W. Symbol Log2 FC AA - AverExp (log2) p-value Adjusted p-value Function in cancer −10 −09 TR1 Basal CEBPD 1.06 4.97 1.4E 3.9E ccaat/enhancer-binding protein δ, associates with good prognosis in breast cancer . −18 −16 TR2 Her2 CRYBG3 −1.75 3.4 1.6E 3.0E Long non-coding RNA, its depletion in tumor cells enhanced their malignant phenotypes . −09 −08 TR3 TBC1D3 1.17 −6.36 1.6E 3.5E TBC1D3 oncogene promotes the migration of breast cancer cells . −02 −02 CXCL8 −1.56 1.07 1.8E 4.6E Interleukin-8, promote breast cancer progression by increasing cell invasion, angiogenesis, and metastases and TR4 is upregulated in HER2-positive cancers . −06 −05 STC1 −1.36 5.34 2.0E 2.2E Stanniocalcin-1, the role of STC1 in breast cancer is complex, considering that some studies have shown that it TR5 exerts an oncogenic role, whereas other studies have demonstrated the opposite . −03 −03 TR6 CCNE2 −1.03 2.63 2.0E 8.2E Cyclin E2, associates with poorer prognosis in breast cancer . −04 −03 TP63 −1.7 2.66 1.5E 1.0E p53-related protein p63. ΔNp63 isoform supports a more mesenchymal phenotype associated with a higher TR7 tumorigenic and metastatic potential . −03 −03 TR8 H2AC8 1.12 1.23 2.2E 8.9E Expression of HIST1HSAE associated with disease free survival in colorectal cancer . −08 −07 TR9 LumB SPANXA1 1.02 −6.71 1.2E 2.3E Sperm Protein Associated With The Nucleus, X-Linked, Family Member A1, promote breast cancer invasion . −06 −05 SPANXC 1.41 −6.26 6.7E 6.8E Same as SPANXA1. −03 −02 TR10 NAT1 −1.29 5.35 3.1E 1.2E Arylamine N-acetyltransferase 1, a potential marker in estrogen receptor-positive tumors . −05 −04 TR11 SPANXB1 1.37 −5.18 1.3E 1.2E Sperm Protein Associated With The Nucleus, X-Linked, Family Member A1, promote breast cancer invasion . −18 −16 LumA CRYBG3 −1.49 3.4 1.6E 3.0E See above. −05 −04 SPANXB1 1.5 −5.18 1.3E 1.2E Same as SPANXA1. −04 −03 TR12 TMEM100 −1.04 0.06 3.6E 2.2E Transmembrane protein 100, Low-expression associates with poor prognosis in non-small-cell lung cancer . −07 −06 TR13 Normal-like FGF2 −1.34 2.45 7.2E 9.0E Fibroblast growth factor 2, induces breast cancer growth . −12 −11 TR14 CDT1 1.34 3.71 1.8E 7.6E Chromatin licensing and DNA replication factor 1, significantly higher in ER-negative breast cancer . −04 −03 TR15 NRG1 −1.61 1.28 5.8E 3.4E Neuregulin 1, frequently silenced by methylation in breast cancers . −04 −04 TR16 PTGS2 −1.57 0.98 1.3E 9.7E Prostaglandin-endoperoxide synthase 2, genetic variation in this gene is associated with breast cancer risk . −18 −16 CRYBG3 −1.13 3.4 1.6E 3.0E See above. −06 −05 TR17 BIRC5 1.11 5.03 9.5E 9.2E Baculoviral inhibitor of apoptosis repeat containing 5, high expression associates with poor survival . −07 −06 TR18 CDC45 1.41 3.01 3.0E 4.0E Cell division cycle 45, higher expression in cancer cells and might associate with metastasis . −03 −02 TNFSF9 1.04 −0.25 3.9E 1.4E Tumor necrosis factor superfamily member 9, an immune modulating T-cell co-stimulator with anti-tumor role TR20 −04 −03 TR21 PIMREG 1.13 2.43 5.2E 3.1E PICALM interacting mitotic regulator, overexpression promotes breast cancer aggressiveness . −05 −04 TR22 CD72 1.18 1.9 5.9E 4.7E Potential role in anti-tumor immune response . −06 −05 STC1 −1.39 5.34 2.0E 2.2E See above. −04 −03 TR23 TMEM100 −1.2 0.06 3.6E 2.2E Transmembrane protein 100, inhibits the growth and metastasis of non-small-cell lung cancer . −02 −02 CXCL8 −1.39 1.07 1.8E 4.6E See above. −08 −07 ZFPM2 −1.11 2.37 2.6E 4.5E Zinc finger protein, FOG family member 2, linked to tumor stage, metastasis, and prognosis in breast patients TR24 Race-related differentially expressed ASR genes identified after comparing breast cancer samples of a given PAM50 subtype between AA and W patients. The fold-change (FC) and the average expression (AverExp) are shown. M. Al Abo et al. compared with W patients of Her2 subtype, MBNL2 (member of the OSR pathway), TMC6 (member of the immune pathway/Table 1), PCDH7 (member of the XIAP pathway), and ACSM3 (member of the XIAP pathway) to be of significance in AA-Basal subtype patients. Interestingly, MBNL2 has been recently reported to control hypoxia response in breast cancer cells and PCDH7 was 48,49 reported to induce bone metastasis of breast cancer cells . In conjunction, race-stratified survival analysis identified the association of a set of DE-SN (such as CDKN1C, CDKN2D, TP63, STAT4, MYC, and MYCBP2) with known functions in oncogenic pathways to be distinct in AA or W patient samples, which strongly highlights the importance of stratifying tumors by patient race in survival outcomes. Notably, score analysis for the ASR pathways identified the OSR gene sets score were amplified in advanced breast cancer subtypes and in tumors from AA patients. The GO analysis of differentially expressed ASR genes between tumor and normal breast cancer samples from AAs or Ws reveal race-related molecular pathways. For example, our results suggest that dysregulation of cell death, the ERK1 ERK2 cascade, and the epithelial cell migration processes in Her2 subtype breast cancers in AA but not in W breast cancers has potentially significant implications for treatment approaches. Likewise, these findings suggest that targeting these pathways could achieve different responses when breast cancers of AAs or Ws are treated with similar drugs. These datasets provide a molecular basis for the epidemiolo- gical findings that AA patients’ breast tumors exhibit higher 50,51 oxidative stress markers compared to W patients . Therefore, understanding the underlying biology of aggressive breast cancer subtypes and variants, wherein race- and/or ancestry-related disparities exist in incidence, treatment, and survival outcomes, has the potential to aid in development of new biomarkers and treatment strategies to mitigate these disparities. Recently, Carrot-Zhang et al. have reported estimated global Fig. 5 GO enrichment analysis of DE-SN. Bar plots depicting the ancestry for 10,678 patients across 33 cancer types in TCGA . significantly, p-value < 0.05, enriched GO terms of the DE-SN in Therefore, we compared the self-identified race and the estimated PAM50 subtypes from either AA patients only (a) or from W patients only (b). The x-axis in a and b depicts the –log10 p-value yielded global ancestry for all the patients from whom we analyzed from the Kolmogorov–Smirnov test. The enriched GO terms are samples in this study. Our analysis reveals that the self-identified included next to the bars. race and estimated global ancestry of the patients to be largely concordant (Supplementary Fig. 2). Therefore, it is possible that stimuli that can lead to clonal evolution of death resistant ancestry-related individual-level differences and differences in populations . A recent retrospective analysis of a large cohort of allostatic load also contribute to the differences in ASR genes that non-metastatic, non-IBC, primary invasive breast cancer samples we have identified and found to be associated with breast cancer for the apoptotic regulator, XIAP, revealed that XIAP mRNA subtype and survival. To further understand the determinants of expression is independently associated with poor outcomes and health underlying differences in ASR genes associated with breast lower pathological complete response (pCR) to anthracycline-bsed cancer subtype and survival, future studies should focus on neoadjuvant chemotherapy . Based on these clinical observa- estimating local ancestry of chromosomal regions of ASR genes tions and our previous preclinical studies identifying ASR path- and assessing association with breast cancer subtype and survival. ways linking mitogen activated ser/thr kinase (MNK), X-linked Although TCGA has a larger number of breast cancer samples inhibitor of anti-apoptotic protein (XIAP), and nuclear transcription from AA patients than for other cancers, a major challenge is the factor (NFκB)-mediated proliferative, invasive, and immunosup- limited number of breast cancer datasets available from AA 27,30,46 pressive breast tumor phenotype , the present study patients after sorting for PAM50 subtype, with just 6 samples that investigated the expression of these ASR gene sets in TCGA are designated Normal, rendering limited power for race-related breast cancer samples to identify differentially expressed genes in comparative differential gene expression analysis, and eclipsing the PAM50 subtypes and for which there are observed disparities, any potential differences in survival between breast cancer between AA and W breast cancer patients. We report herein that patients of different races or ancestries. Mechanistic studies of out of the 226 genes, which can be grouped into three ASR genes in breast cancer are ongoing along with the under- overarching biological processes or signaling axes (XIAP-MNK- standing that larger independent cohorts with samples annotated NFκB, immune, or TGFβ related), 69.4% were differentially for societal-level, neighborhood-level and institutional-level deter- expressed with fold-change > 2 and adjusted p-value < 0.05 minants of health are needed to identify and validate biomarkers. among the breast cancer subtypes compared to Normal-adjacent samples, or in comparison of subtypes to each other. METHODS Our comparative analysis of the ASR gene sets in TCGA samples stratified by AA and W race identified 29 race-related differentially Datasets and race assignment expressed ASR genes, all of which play a role in cancer biology. For The results shown here are in whole based upon data generated by the example, C-X-C Motif Chemokine Ligand 8 (CXCL8), reported to TCGA Research Network: https://www.cancer.gov/tcga, which are publicly promote breast cancer progression , in our analysis was available with prior patient’s consent and institutional review board significantly decreased (3-fold-change) among AA patients agreements in place from original authors. The TCGA RNAseq raw counts Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 6 Survival analysis of the DE-SN. Kaplan–Meier plots for the DE-SN depicting the association of DE-SN level in Basal (a), Her2 (b), and Normal-like (c) with breast cancer patient overall survival probability. The DE-SN exhibit HR > 1.5 or < 0.58 and p-value < 0.05. The survival probabilities were compared in breast cancer patients of indicated PAM50 subtypes expressing high, low, or intermediate (75th, 25th, or 25th–75th percentiles) levels of the indicated DE-SN The number of patients (pt#) of high, low, or intermediate groups are indicated. d Venn diagrams showing the number of DE-SN associated with AA or W breast cancer patient overall survival probability in the indicated PAM50 subtypes. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. Table 3. DE-SN associated with patient overall survival in AA only, W only, or both AA and W. AA W AA and W Basal ACSM3, COPZ2, MCM5, POLE2, E2F8, BLM (XIAP pathway); DLL4 COPZ2, PLAU, ABAT (XIAP pathway); TREM1 COPZ2 (XIAP pathway) (Jag1-Notch pathway); BIRC5 (NFkB pathway) (OSR pathway), CCNE2 (XIAP and OSR pathways) Her2 ACSM3, MCM10, GPR137B, CDC45, LIG1, SLC1A4, PLAU, LIG1, LEF1 (XIAP pathway); CD72 (TGFβ LIG1, LEF1 (XIAP pathway); HIST1H2BJ, CDKN2D, CDKN1C, LEF1, SLC14A1 (XIAP pathway); pathway); SCD5, CEBPD (OSR pathway) SCD5 (OSR pathway) MCM3, CCN3, PTGS2, SCD5, NRG1, NAP1L3, TXNIP (OSR pathway); NUDT1 (Immune pathway) LumB MYCBP2, STAT4 (Immune pathway); LIPG, CDKN1C, KLRC1 (XIAP PLAU, NRN1, ZFPM2 (XIAP pathway); - pathway); FGF2 ABCA6 (OSR pathway) NAP1L3 (OSR pathway); DAB2 (TGFβ pathway) LumA MCM10, LIG1, FOSB, PIMREG, GINS2, ABAT (XIAP pathway);-- METTL7A, TP63, MCM4, CDT1, ANXA3 (OSR pathway); MYC, BIRC5 (NFkB pathway); MELK (Immune pathway) Normal-like FOSB (XIAP pathway)- - DE-SN associated with breast cancer patient overall survival among AA patients only, W patients only, or both AA and W patients. The pathway, to which the ASR genes belong, are shown in parentheses and are bolded. data from TCGA repository was downloaded (June 15, 2021) using ASR gene were generated by comparing the survival probability of the TCGAbiolinks R package (version 2.16.4) . The downloaded RNAseq raw 75th or 25th percentile, with patients grouped by the expression level of count expression data were normalized for RNAseq library size and the differentially expressed ASR gene. The Kaplan–Meier plots were 54,55 63 dispersion utilizing Limma R package (version 3.44.3) . The following generated using survminer R package (version 0.4.9) . multivariate experimental design was employed: sample definition (primary solid tumor, metastatic and solid tissue normal), PAM50 subtype (Basal, Her2, Analysis of gene signature scores LumB, LumA, and Normal-like), self-identified race of the patient (African To analyze the score of each ASR gene signature (XIAP, OSR, Immune, and American (AA), White (W), Asian, Alaska native American and not reported). TGFβ pathways as shown in Table 1), we submitted the gene set of each Note that despite being described as Normal-like, the Normal-like subtype signature for score analysis using GSVA R package (version 1.36.3) . Based include tumor samples, not Normal-adjacent. on the reported change of expression, we classified the genes in each gene set into up or downregulated. After calculating the score of each gene Gene expression analysis signature in each sample, we evaluated the score by breast cancer subtype and by patient race, AA or W. To determine the statistical significance of A total of 226 genes (Table 1) in the ASR gene sets, which included 101 differences in score among different breast cancer subtypes and between XIAP-related, 11 NFκB targets, 10 MNK targets, 33 Oxidative Stress AA and W, we employed the Wilcoxon signed-rank test. Response (OSR)-related, 13 TGFβ-related, 6 JAG1-Notch targets and 52 immune-related, in addition to 14 genes that belong to more than one ASR set, were submitted for comparative analysis using the TCGA breast Data analysis invasive carcinoma expression dataset. Differential gene expression We handled the data and performed analyses using R. Rstudio was used as analysis was performed by applying the linear model of weighted or an interface for R. The following packages: tidyverse, SummarizedExperi- generalized least squares for series of arrays in limma, and the adjusted p- ment (version 1.18.2), plyr, dplyr, DT, VennDiagram, ggrepel, cowplot, and value was calculated using Benjamini-Hochberg method, as described ggplot2 were also used for data analysis and visualization. previously . Using R random function, set.seed(1991), we selected 226 random genes to perform the same analysis as a control. Reporting summary Further information on research design is available in the Nature Research Gene ontology enrichment analysis Reporting Summary linked to this article. The gene ontology (GO) terms were queried using biomaRt R package (version 2.45.8) as described . The GO terms were matched to the gene in TCGA expression datasets using ensemble identification names. Using the DATA AVAILABILITY GO terms, we performed enrichment analysis by topGO (version 2.40.0) and 58,59 The datasets used in this manuscript can be downloaded from TCGA repository. The org.Hs.eg.db (version 3.11.4) R packages . For this analysis, we selected retrieved data from TCGA repository is available from the corresponding author on ASR genes that demonstrated differential expression, with a differential fold- reasonable request. change >2 as well as adjusted p-value < 0.05, between the compared samples. The selected node size in topGo analysis was 10, the algorithm was classic, which tests the over-representation of GO terms within the group of CODE AVAILABILITY differentially expressed genes, and the statistic test was Kolmogorov–Smirnov . We determined KS < 0.05 as a cutoff for statistical The code for downloading the datasets, normalizing, and performing the differential significance. The GO terms were further reduced using rrvgo (version 1.0.2) . expression analysis can be found on GitHub (https://github.com/muthalpy/ ASRgenes_TCGA). Survival analysis Received: 29 June 2021; Accepted: 5 April 2022; The clinical data associated with TCGA expression data were used to perform survival analyses. The vital status and time until death for all patients (including AA, W, Asian and Alaska native American and not reported) belonging to the indicated PAM50 subtype were appropriately annotated and fitted into Cox Proportional-Hazards Model. The survival R package (version 3.2.11) was used to compute the hazard ratio per unit REFERENCES and p-value of a given differentially expressed ASR gene using Breslow 1. DeSantis, C. E. et al. Breast cancer statistics, 2019. CA: Cancer J. Clin. 69, 438–451 method for the maximum likelihood estimator for the cumulative baseline (2019). hazard function. 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Reprints and permission information is available at http://www.nature.com/ 63. Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. survminer: drawing survival reprints curves using’ggplot2’. R package version 0.3 (2017). 64. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). in published maps and institutional affiliations. ACKNOWLEDGEMENTS This work was supported in part by Duke School of Medicine Bridge Award (GRD); NCI- P20 NCCU-Duke Cancer Disparities Translational Research Partnership (S.P.; G.R.D., K.P. Open Access This article is licensed under a Creative Commons W., J.F.); NCI-P20 Project 2 (G.R.D., K.P.W.); NCI-P20 Predoctoral Diversity Supplement Attribution 4.0 International License, which permits use, sharing, 3P20CA202925-04S2 (L.G.S.), Department of Defense Breast Cancer Breakthrough level adaptation, distribution and reproduction in any medium or format, as long as you give 2 Award W81XWH-17-1-0297 (G.R.D.), NCI of NIH Award R01CA264529 (G.R.D.), The IBC appropriate credit to the original author(s) and the source, provide a link to the Creative Network Foundation Gift (G.R.D.), NIH Basic Research in Cancer Health Disparities R01 Commons license, and indicate if changes were made. The images or other third party Award R01CA220314 (S.P., J.F.), Prostate Cancer Foundation Movember Challenge material in this article are included in the article’s Creative Commons license, unless Award #18CHAL04 (S.P., J.F., M.A.). The authors would like to thank Dr. Michael Morse indicated otherwise in a credit line to the material. If material is not included in the and Dr. Susan Dent for helpful discussions and Alexandra Bennion at Duke article’s Creative Commons license and your intended use is not permitted by statutory Undergraduate Trinity School of Arts and Science for editorial assistance. regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. AUTHOR CONTRIBUTIONS M.A.: conceptualization, methodology, formal analysis, visualization, writing—original © The Author(s) 2022 draft preparation, writing—reviewing and editing. L.G.: methodology, visualization, Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png npj Breast Cancer Springer Journals

Adaptive stress response genes associated with breast cancer subtypes and survival outcomes reveal race-related differences

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www.nature.com/npjbcancer ARTICLE OPEN Adaptive stress response genes associated with breast cancer subtypes and survival outcomes reveal race-related differences 1 2 3 1,4 1,4 1,2 Muthana Al Abo , Larisa Gearhart-Serna , Steven Van Laere , Jennifer A. Freedman , Steven R. Patierno , Eun-Sil Shelley. Hwang , 5 6 1,2 Savitri Krishnamurthy , Kevin P. Williams and Gayathri R. Devi Aggressive breast cancer variants, like triple negative and inflammatory breast cancer, contribute to disparities in survival and clinical outcomes among African American (AA) patients compared to White (W) patients. We previously identified the dominant role of anti-apoptotic protein XIAP in regulating tumor cell adaptive stress response (ASR) that promotes a hyperproliferative, drug resistant phenotype. Using The Cancer Genome Atlas (TCGA), we identified 46–88 ASR genes that are differentially expressed (2- fold-change and adjusted p-value < 0.05) depending on PAM50 breast cancer subtype. On average, 20% of all 226 ASR genes exhibited race-related differential expression. These genes were functionally relevant in cell cycle, DNA damage response, signal transduction, and regulation of cell death-related processes. Moreover, 23% of the differentially expressed ASR genes were associated with AA and/or W breast cancer patient survival. These identified genes represent potential therapeutic targets to improve breast cancer outcomes and mitigate associated health disparities. npj Breast Cancer (2022) 8:73 ; https://doi.org/10.1038/s41523-022-00431-z INTRODUCTION survival rate ranging from 29.9 to 42.5% (higher survival outcomes 9,10 in W patients) . According to the American Cancer Society (ACS) , in recent years AA patients with IBC present more frequently with higher stage (2012–2016) there has been a continuous decline in the breast and triple-negative (TN) [i.e., negative for estrogen (ER), proges- cancer death rate. Despite this, at the global level, notable terone receptor (PR), and human epidermal growth factor receptor differences in breast cancer mortality is observed among ethnic 2,3 2 (ERBB2)] or basal subtype [similar to triple negative, but with groups including younger age of onset . These race-related epidermal growth factor receptor 1 (EGFR) activation] , and disparities are likely driven by a complex interplay among exhibit shorter median survival (20 months) compared with W sociocultural differences in societal-level (e.g., racism), 12–16 patients (32 months) . Epidemiological studies suggest a neighborhood-level (e.g., pollution), and institutional-level (e.g., distinct profile of risk factors such as high body mass index access to care) determinants of health . Analysis of The Cancer (BMI), early age at first pregnancy, multiparity, and lack of Genome Atlas (TCGA) dataset adjusted for intrinsic subtype breastfeeding, factors that can lead to chronic pro-inflammatory frequency differences has reported that patients estimated to cellular stress to be associated with poor therapeutic outcomes have >50% African ancestry exhibit a worse breast cancer-free 11,17 and survival in AA IBC patients compared with W patients . interval compared to patients with >50% European ancestry . Furthermore, comparative gene expression studies from preclini- Underlying these ancestry-related disparities are likely individual- cal models and pretreatment patient samples, collected as part of level differences in physiology, genetics and genomics arising the International IBC Consortium’s effort to understand differences from forced and voluntary migrations of human populations between IBC and non-IBC and to define IBC-specific molecular around the world. Further interactions among societal-level, profiles, revealed highly activated mitogen activated protein neighborhood-level, institutional-level and individual-level deter- kinase (MAPK) and nuclear factor kappa B (NFκB) transcriptional minants of health also likely contribute to cancer disparities by profiles associated with increased pro-inflammatory and prolif- influencing allostatic load . erative signals in IBC compared with subtype and stage-matched 18–22 Emerging evidence indicates that this race-related survival gap locally advanced breast cancer . is largely due to higher incidence of aggressive subtypes of breast Preclinical studies using various breast cancer in vitro and 23–28 cancer, including basal-like, triple negative, human epidermal in vivo models from our group identified a critical role for the growth factor receptor 2 (ERBB2/HER2)-enriched subtypes, which most potent caspase inhibitor, X-linked inhibitor of apoptosis are frequently associated with early metastasis in AA compared to protein (XIAP), in linking EGFR-mediated MAPK activation and W patients . A highly representative example of an aggressive NFκB hyperactivity. In addition, higher XIAP staining was observed breast cancer designated as a cancer health disparity by NIH in invasive breast cancers compared to normal, benign ductal (NCATS/GARD) is inflammatory breast cancer (IBC). Of all clinically carcinoma in situ (DCIS), and higher XIAP also correlated with poor 29,30 distinct breast cancer subtypes, IBC is the most lethal variant with event free survival and increased lymph node involvement . high rate of metastasis, disproportionately higher incidence at Importantly, XIAP has a unique element called an internal younger ages in non-W patients, and disparity in relative 5-year ribosomal entry sequence (IRES) in its 5’ untranslated region, 1 2 Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710, USA. Department of Surgery, Duke University School of Medicine, Durham, NC 27710, USA. Center for Oncological Research (CORE), Faculty of Medicine and Health Sciences—University of Antwerp, Campus Drie Eiken‑Universiteitsplein 1, 2610 Wilrijk‑Antwerp, Belgium. 4 5 Department of Medicine, Division of Medical Oncology, Duke University School of Medicine, Durham, NC 27710, USA. Department of Pathology, MD Anderson Cancer Center, Houston, TX 77030, USA. Department of Pharmaceutical Sciences and BRITE, North Carolina Central University, Durham, NC 27707, USA. email: gayathri.devi@duke.edu Published in partnership with the Breast Cancer Research Foundation 1234567890():,; M. Al Abo et al. Fig. 1 Number of breast cancer samples in TCGA by definition, race, and PAM50 subtypes. Pie charts depicting the number of breast cancer samples in TCGA PAM50 subtypes: Basal, Her2, LumB, LumA, Normal-like for all samples (a), for samples from AA patients (b), or for samples from W patients (c). d Scores of the expression level of XIAP, OSR-, Immune-, and TGFβ-related gene signatures in breast cancer subtypes. Box plots overlaid with scatter plots depicting the calculated score for XIAP-, OSR-, immune-, and TGFβ-related ASR gene signatures in the indicated breast cancer subtypes and stratified by patient race, AA or W. The Wilcoxon signed-rank test was used to examine significance (*adjusted p-value < 0.05; **adjusted p-value < 0.001; ***adjusted p-value < 0.0001; and ns, not significant). The center line represent the medians and the bounds of box represent the confidence intervals. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation 1234567890():,; M. Al Abo et al. Table 1. The list of ASR genes and their functional pathways. Pathway ASR genes XIAP XIAP, SPANXA1, SPANXB1, SPANXC, SULT1E1, SLPI, AGFG2, POPDC3, ZIC1, CDC45, TNFSF9, SLC26A6, KYNU, H2BC11, MCM5, GINS2, CDKN2D, OBSL1, SNAI1, TP53I3, AIM2, LIG1, PIMREG, ALDOC, MCM10,ASF1B, ZNF165, CDKN1C, H2AC8, TMEM40, DBP, SLCO1B3, E2F8, LRP4, KLRC1, RALGDS, BLM, CCNE2, POLE2, ZNF26, PITPNC1, NEU1, VASH2, PLAU, GLYR1, H2BC21, NRN1, TBC1D3, COPZ2, H2BC8, KLRC2, RPL5, SEPTIN6, CRYBG3, PRR16, WDR37, TBC1D3C, FOSB, ASTE1, CLCN6, PDE4DIP, N4BP2L1, LEF1, HSPG2, POLA1, WBP1L, ZMYM5, B3GNT2, DHFR, ZNF331, ACSM3, MANSC1, ARAP2, TMEM100, SLC1A4, GPR137B, LIPG, LMAN1, LPAR6, SNX19, CYP4B1, GEM, ARHGAP29, ZFPM2, SLC14A1, PCDH7, HSD11B1, CAVIN2, ABAT, STC1, NRIP3, NAT1, PCDH9, LMAN2L, REXO5, DLEU2L, DLEU2, SYBU, MRM2, LINC-PINT, LINC00115 OSR HSPA1A, METTL7A, BLM, TYMS, KRT6B, LIG1, SCD5, POLE2, CDT1, CCNE2, SKP2, E2F8, ORC1, MCM4, CCN3, IRF4, MCM10, NAP1L3, MCM3, ASF1B, FGF2, TREM1, ABCA6, CTH, ECM2, OASL, CEBPD, TP63, RAB5A, SLC4A7, MBNL2, RBMS3, KLHL24, TXNIP, FCAR, AREG, ANXA3, PTGS2, NRG1, INHBE NFkB NFKB1, RELA, IL6, CXCL8, IRF2, FAS, IL1B, BCL2, BIRC5, SOD1, MYC MNK MKNK1, MKNK2, EIF4G1, EIF4E, SPRY2, HNRNPA1, HNRNPA2B1, NONO, KAT5, RPS6 Immune CTSA, PNP, MYCBP2, TMC6, IFI44L, NUDT1, MELK, STAT4, INHBC, ARPC2, EHD1, SART3, MBD4, ACOX1, PRKCB, NFATC3, FOLR1, MAK16, WNK1, JMJD6, IQGAP1, ABCC10, SRSF7, IGLV1-44, NUP85, SEPTIN6, ESF1, NAA15, PGS1, ANKRD11, TNPO1, PAX5, DHFR, CTBP2, BCKDK, PEX11B, CUL2, GABPB1, ATP7A, ATF2, DNAJB6, CANX, PAK2, ANXA7, TMCO1, PBXIP1, SP3, TGIF2, TOMM22, INTS12, SLC30A5, TMEM50B, TSPAN14, TBL1XR1, MRM2 TGFβ TGFB1, NDUFAF3, DAB2, RPL27A, MARCKS, CD72, HSP90B1, PPARD, ACTG1, CRK, TGFB2, AUTS2, RYK, TGIF2 JAG1-Notch JAG1, NOTCH1, DLL4, BRD4, IL6, IL1B, TNF, SNAI1, ZEB1 The ASR genes were grouped according to their function in the listed pathways. The ASR genes that are bolded represent the genes belonging to more than one of the indicated pathways. which is critical for XIAP protein translation during response to lumB, 82 Her2, 190 Basal, and 40 Normal-like (Fig. 1a; Supplementary cellular stress . Using triple-negative cell lines, novel isotype- Table 1). Metastatic samples and those lacking PAM50 subtype matched clonal isolates of tumor cells surviving exposure to acute/ information were excluded. When more than one sample belonged chronic stress stimuli, and genetically modified breast cancer cell to thesamepatient, weselectedthe onewithhighest RNA variants with differential XIAP expression, we reported that XIAP sequencing depth. upregulation allows tumor cells to survive in the presence of Racial designations in TCGA are based on patient self- 24,28 27,32 stressors like oxidative- and immune-mediated cell death identification. In the present study, race-related analysis focused stimuli, leading to clonal outgrowth of multi-drug resistant tumor on AA and W breast cancer patient datasets from TCGA (179 AA 26,33 cell populations . Analysis of XIAP-overexpressing tumors and 744 W). It is important to note that among the 113 Normal- exhibiting the characteristics of an adaptive stress response adjacent samples, there are 105 W and only 6 AA samples. (ASR) revealed a dominance of proliferative, invasive, and PAM50 subtyping within the AA and W patient tumor datasets are immunosuppressive networks of NFκB target genes, which we shown in Fig. 1b, c; Supplementary Table 1. term the “adaptive stress response (ASR) gene set”. This is highly relevant, as NFκB is recognized as a crucial mediator of ASR genes associated with oxidative stress and immune inflammatory, immune, anti-apoptotic, and antioxidant signals as response pathways score highly in basal breast cancer well as an important modulator of cancer stem cell biology, tumor subtype surveillance, and tumor rejection . The genes in the ASR gene set To build and expand on our previous analysis of IBC tumor cells have also been reported to be part of an IBC-specific 79-gene surviving under chronic stress stimuli, which identified genes in signature enriched with activated gene networks in immune the anti-apoptotic signaling, oxidative stress response, immune, pathways and the TGFβ pathway that captured nearly 25% of IBC and TGFβ-related genes, we calculated the gene signature scores 35,36 patient samples identified in the TCGA database as IBC-like . for the above pathways in the TCGA breast cancer subtypes and In the present study, we investigated the expression profile of by patient race, AA and W (Fig. 1d). The gene sets are listed in the XIAP-driven ASR gene set in breast cancer subtypes, identified Table 1 and are part of the overarching ASR gene set. We sorted race-related differentially expressed genes within this gene set, the genes in each signature into up- or downregulated genes and determined associations between ASR gene expression and according to the expression levels reported in our previous poor survival outcomes. 23,30,35,37 preclinical studies . The results from this analysis demon- strated that the score of ASR gene signatures differed among breast cancer subtypes and by patient race. We found that the RESULTS XIAP downregulated gene set exhibits differential score between Proportion of subtypes, AA, and W patient samples in the AA and W patients in LumA, LumB, and Her2 subtypes, and the TCGA breast cancer dataset XIAP upregulated gene set exhibits differential score between AA As of June 15, 2021, TCGA repository included 1222 breast cancer and W patients in Basal, Her2, LumB, and LumA subtypes. The patient samples (1109 tumor and 113 matched normal). Further- differential score of OSR signature is highest in Basal, followed by more, TCGA includes the Prosigna Prognostic Gene Signature Assay Her2, and LumB, and lowest in Normal-like subtype, compared (formerly called the PAM50 test) to categorize breast tumors into with Normal-adjacent. Comparing the OSR signature scores by five subtypes [luminal A (LumA), luminal B (LumB), Her2, Basal, and patient race indicated that scores differed in the breast cancer Normal-like] based on the expression level of genes, which have subtypes between samples from AA and W patients, especially for been found to be associated with breast cancer prognosis. In the the OSR downregulated genes. The score analysis also showed present study, we focused on primary tumor samples [1090 Primary that immune-related upregulated genes generally exhibited Solid Tumor] and normal tissue samples adjacent to the tumors [113 higher scores in tumors compared to normal. However, the Solid Tissue Normal] (referred to in the present study as Normal- immune-related downregulated genes did not exhibit a distinct adjacent). Within these, our sample set included 559 lumA, 207 pattern in different tumor subtypes. Importantly, immune-related Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 2 Identification of DE-SN and DE-SS in PAM50 subtypes. Volcano plots depicting the level of ASR genes in the indicated PAM50 subtypes compared to Normal-adjacent (a) or to each other (b). The log2 fold-change differential expression and the –log10 (adjusted p-value) are shown on the x-axis and y-axis, respectively. The points correspond to all ASR genes and the blue highlighted points represent the DE-SN (a) and DE-SS (c), which exhibit fold-change greater than 2 and adjusted p-value less than 0.05. b and d Bar plots depicting the number of DE-SN and DE-SS in a and c, respectively. The colors of the bars indicate whether the DE-SN (b) or DE-SS (d) are upregulated (green) or downregulated (blue). The numbers inside the bar correspond to the number of upregulated or downregulated DE-SN (b) or DE-SS (d). Comparisons are shown under each bar and the number of DE-SN (# of DE-SN) or DE-SS (# of DE-SS) are shown on the y-axis. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. downregulated gene expression in Her2 and Basal subtypes the number of upregulated or downregulated DE-SS varied showed significant differences (adjusted p-value < 0.0001) depending on the comparison between subtypes (Fig. 2d). For between AA and W patients. We also observed that the score of example, DE-SS in LumA vs. Normal-like were more frequently TGFβ-related downregulated genes in Her2 subtype exhibited a downregulated (26 downregulated and 4 upregulated). In marked difference between AA and W patients. contrast, DE-SS in Basal vs. LumA tended to be upregulated (39 upregulated and 20 downregulated). The proportion of DE-SN among the 226 genes was 69.4% when Identification of differential ASR gene sets in PAM50 subtypes comparing subtypes to Normal-adjacent samples and 34.8% when To identify the expression levels (combined level of all transcript comparing subtypes to each other. As a control, we identified DE-SN variants of any given gene) of the adaptive stress response (ASR) and DE-SS among 226 randomly selected genes. Unlike the ASR gene sets (Table 1) in relation to PAM50 subtypes, we performed gene set, the proportion of DE-SN was less than 3% when comparing analyses comparing gene expression in samples of each subtypes to Normal-adjacent samples and the proportion of DE-SS PAM50 subtype with Normal-adjacent samples (Fig. 2a). We was less than 3% when comparing subtypes to each other. determined differentially expressed genes between PAM50 subtypes compared with Normal-adjacent (designated DE- DE-SN and DE-SS exhibit distinct expression changes in breast SN) with > 2-fold-change in mean expression and adjusted p-value cancer molecular subtypes < 0.05. In total, we identified 88 DE-SN in Basal, 81 in Her2, 87 in luminal B, 64 in luminal A and 46 in Normal-like samples (Fig. 2band Next, we investigated whether DE-SN are shared among subtypes. Supplementary Table 2). We also determined the number of The vast majority of DE-SN are differentially expressed in more than one subtype, with only a few of the DE-SN being unique to a upregulated and downregulated DE-SN in each subtype. particular subtype (Fig. 3a). Thirty-five DE-SN were identified as Similarly, to identify differentially expressed genes within ASR gene sets between subtypes, we compared expression levels of being commonly differentially expressed across all subtypes the 226 genes in each PAM50-subtype to every other subtype compared with Normal-adjacent samples (Fig. 3b and Supple- (designated DE-SS). This analysis revealed 24 DE-SS in Basal vs. mentary Table 2). We did not find any tendency toward Her2, 36 in Basal vs. LumB, 59 Basal vs. LumA, 49 Basal vs. Normal- enrichment in upregulated or downregulated commonly differ- like, 19 Her2 vs. LumB, 35 Her2 vs. LumA, 44 Her2 vs. Normal-like, entially expressed genes across all subtypes, as 17 DE-SN were 25 LumB vs. LumA, 60 LumB vs. Normal-like, and 30 LumA vs. upregulated and 18 were downregulated among all subtypes Normal-like samples (Fig. 2c, d and Supplementary Table 3). compared with Normal-adjacent (Supplementary Fig. 1a, b). Expression levels of a number of DE-SN fluctuate dynamically Collectively, this analysis identified upregulated or downregulated DE-SS in a given subtype compared to the other subtype in each among different subtypes (Fig. 3c). For example, 4-Aminobutyrate comparison. aminotransferase (ABAT) (member of XIAP pathway/Table 1) We noted differences between DE-SN and DE-SS gene sets. In expression level was decreased 4-fold in Basal subtype compared the case of DE-SN, an equal number of genes (about half) were with Normal-adjacent but increased 3-fold in LumA compared either upregulated or downregulated compared with Normal- with Normal-adjacent. We also found other XIAP pathway genes, adjacent, regardless of the tumor subtypes (Fig. 2b). In contrast, Arylamine N-acetyltransferase (NAT1), Lipase G (LIPG), Fig. 3 Intersection among DE-SN. a Venn diagrams depicting the common DE-SN after comparing PAM50 subtypes to Normal-adjacent samples. b and c Heatmaps depicting the expression fold-change for DE-SN that are significantly changed in all PAM50 subtypes (b) or for DE- SN that are dynamically differentially expressed across PAM50 subtypes (c). Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 4 Identification of DE-SN or DE-SS in PAM50 subtypes from either AA or W patients. Bar plots depicting the number of DE-SN (a)or DE-SS (b) in breast cancer samples from AA patients. Similar to a and b, c and d depict the number of DE-SN or DE-SS in breast cancer samples from W patients, respectively. The colors of the bars, the numbers inside the bars, and the axes are as described in Fig. 2. b As in a, but the DE- SS are identified after comparison of PAM50 subtypes to each other. Intersection among DE-SN (e) or DE-SS (f) with or without stratification of samples by patient race. e and f Venn diagrams depicting the common DE-SN identified after comparison between samples of the indicated PAM50 subtype with Normal-adjacent samples or after comparison between samples of the indicated PAM50 subtypes, respectively. Light blue circles represent the DE-SN identified without stratification by patient race, the light purple circles represent the DE-SN identified among breast cancer samples from W patients only and the yellow circles represent the DE-SN identified among breast cancer samples from AA patients only. Sulfotransferase Family 1E Member 1 (SULT1E1), and Zic Family potential to identify key biological factors that contribute to breast Member 1 (ZIC1) genes and Keratin 6B (KRT6B) (member of the OSR cancer disparities. Therefore, we compared the expression of the pathway/Table 1) that were differentially expressed with varying 226 genes in PAM50 subtypes to Normal-adjacent or to other magnitudes depending on the subtype (Fig. 3c). subtypes in either samples from AA patients only or from W patients only (AA-DE-SN, AA-DE-SS, W-DE-SN, W-DE-SS, respec- tively) (Fig. 4 and Supplementary Tables 4 and 5). The number of Identification of distinct DE-SN and DE-SS in AA and W breast AA-DE-SN ranged from 36 to 71 and the number of AA-DE-SS cancer patients ranged from 12 to 52 (Fig. 4a, b). The number of W-DE-SN ranged Interrogating the differential expression of the ASR gene sets in from 35 to 87 and the number of W-DE-SS ranged from 15 to 61 breast cancer TCGA samples from AA or W patients has the npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. (Fig. 4c, d). Although the majority of the DE-SN or DE-SS identified the Normal-like subtype. The functional annotation of race-related among all samples, samples from AA patients only, or samples DE highlights their potential function in oncogenesis and are from W patients only overlapped, a number of AA- or W-DE-SN included in Table 2. and AA- or W-DE-SS were only identified after race stratification (Fig. 4e, f). Therefore, importantly, identification of a number of Association of DE-SN with breast cancer patient survival DE-SN and DE-SS are race-related. For example, in Basal subtype, To gain insight into the potential clinical relevance of race-related we identified 88 DE-SN, 72 AA-DE-SN and 81 W-DE-SN. There were DE-SN identified, we performed overall survival analysis using 61 overlapping genes among DE-SN, AA-DE-SN, and W-DE-SN, 7 TCGA clinical data. The expression levels of DE-SN and patient genes specific to samples from AA patients only, and 3 genes survival data were fitted into Cox regression models to compute specific to samples from W patients only. There were only 4 shared the hazard ratio (HR) for each DE-SN. Survival data of all patients of genes between DE-SN and AA-DE-SN compared to 19 shared each PAM50 subtype (excluding ones with metastases), were genes between DE-SN and W-DE-SN in Basal subtype (Fig. 4e). The included in the survival analysis. In all our analyses, a given DE-SN genes that are specifically differentially expressed as part of ASR was significantly associated with patient overall survival if HR > 1.5 pathways studied (Table 1) in Basal tumors of AA patients are or HR < 0.58 and p-value < 0.05. As shown in Fig. 6a–c, a number of Nucleoporin 85 (NUP85) (member of the immune pathway), Solute DE-SN were associated with the survival of all patients (regardless Carrier Family 26 Member 6 (SLC26A6) (member of the XIAP of patient race), as follows: COPI Coat Complex Subunit Zeta 2 pathway), Cathepsin A (CTSA) (member of the immune pathway), (COPZ2) in Basal; Cyclin Dependent Kinase Inhibitor 1C (CDKN1C); ATPase Copper Transporting Alpha (ATP7A) (member of the LEF1, CCAAT/enhancer-binding protein delta (CEBPD), Stearoyl-CoA immune pathway), TNF Superfamily Member 9 (TNFSF9) (member desaturase (SCD5) in Her2; and BCL2 Apoptosis Regulator (BCL2), of the XIAP pathway), Snail Family Transcriptional Repressor 1 Aldolase, Fructose-Bisphosphate C (ALDOC), Plasminogen Activator, (SNAI1) (member of the XIAP and JAG1-Notch pathways), and Urokinase (PLAU) and Caveolae Associated Protein 2 (CAVIN2)in Nuclear Receptor Interacting Protein 3 (NRIP3) (member of the XIAP Normal-like samples. The above genes listed are all members in pathway). The genes that are specifically differentially expressed in the XIAP pathway except BCL2 (member of NFkB pathway), CEBPD Basal tumors of W patients are TGFB Induced Factor Homeobox 2 and SCD5 (both members of the OSR pathway). (TGIF2) (member of the immune and TGFβ pathways), SPANX Next, we investigated the association between DE-SN and Family Member B1 (SPANXB1) (member of the XIAP pathway), and overall survival within each breast cancer subtype stratified by Transmembrane Protein 40 (TMEM40) (member of the XIAP race (HR > 1.5 or HR < 0.58 and p-value < 0.05). From this analysis, pathway). we identified the following race-related survival associations: among the 88 DE-SN in Basal subtype, 7 DE-SN associated with Identification of race-related differentially expressed ASR survival among AA patients, 4 among W patients, and 1 among genes within PAM50 subtypes both AA and W patients; among the 81 DE-SN in Her2 subtype, 17 Next, we investigated if any of the ASR genes exhibited race- DE-SN associated with survival among AA patients, 2 among W related differential expression by comparing TCGA breast cancer patients, and 3 among both AA and W patients; among the 87 DE- samples of a given PAM50 subtype between AA and W patients. SN in LumB subtype, 7 DE-SN associated with survival among AA This analysis identified a number of race-related differentially patients and 5 among W patients; among the 64 DE-SN in LumA expressed ASR genes, which included 1 in Basal, 7 in Her2, 4 in subtype, 14 DE-SN associated with survival among AA patients; LumB, 3 in LumA, and 14 in Normal-like samples (Table 2). Among and among the 46 DE-SN in Normal-like subtype, 1 associated the identified race-related differentially expressed ASR genes, we with survival among AA patients (Fig. 6d and Table 3). Of particular found that Crystallin Beta-Gamma Domain Containing 3 (CRYBG3) interest in the context of breast cancer disparities are the DE-SN (member of the XIAP pathway/Table 1) is a race-related whose levels were specifically associated with AA breast cancer differentially expressed ASR gene in Her2 and LumA subtypes, patient overall survival, which included members of XIAP and OSR CXCL8 (member of the NFkB pathway/Table 1) and Stanniocalcin 1 pathway [BLM RecQ Like Helicase (BLM) and E2F Transcription Factor (STC1) (member of the XIAP pathway/Table 1) are race-related 8 (E2F8)] in Basal; the members of XIAP pathway [Cyclin Dependent differentially expressed ASR genes in Her2 and normal-like Kinase Inhibitor 1C (CDKN1C), Cell division cycle 45 (CDC45), Cyclin subtypes, and Transmembrane Protein 100 (TMEM100) (member Dependent Kinase Inhibitor 2D (CDKN2D)] and the member of the of XIAP pathway/Table 1) is a race-related differentially expressed OSR pathway [Minichromosome Maintenance Complex Component ASR gene in LumA and normal-like subtypes. 3 (MCM3)] in Her2; and the members of OSR pathways [Tumor Protein P63 (TP63) and Chromatin Licensing And DNA Replication Gene ontology analysis of AA- and W-DE-SN reveals Factor 1 (CDT1)] in LumA breast cancer, and the member of NFkB differential ontology enrichment pathway, MYC in Lum A breast cancer (Table 3). To understand the functions of the identified DE-SN in breast cancer samples from AA or W patients, we first queried for DISCUSSION associated Gene Ontology (GO) categories and then submitted for Disproportionate rates of incidence, metastatic progression and GO enrichment analysis. This GO enrichment analysis of AA- or W- poor survival outcomes are associated with aggressive subtypes of DE-SN in a given PAM50 subtype revealed GO enrichment in cell 2,3 breast cancer in self-identified African American women . Much cycle, DNA damage response, signal transduction, and regulation of this disparity in clinical outcome among African American of cell death processes (KS < 0.05) (Fig. 5a, b). patients with advanced breast cancer remains after controlling for Notably, DNA replication, metabolism, and damage response 16,38–42 medical coverage, diagnosis, and treatment access . This processes were enriched in Basal and Her2 subtypes. We suggests that additional societal-level, neighborhood-level and compared the enriched GOs for AA-DE-SN and W-DE-SN within institutional-level, and possibly individual-level, factors contribute each subtype and found differential GO enrichment, especially in to their poorer prognosis. Furthermore, multiple epidemiological Her2 and Normal-like subtypes. For example, the regulation of cell studies identify distinct non-genetic risk factors in AA women that death, ERK1 ERK2 cascade, and epithelial cell migration processes induce accumulation of inflammatory and oxidative factors were enriched among AA-DE-SN in the Her2 subtype, but not 2,7,11,13,16 among W-DE-SN in the Her2 subtype, and the DNA recombination leading to chronic stress microenvironment . Tumor cells and chromosomal organization processes were enriched among co-opt anti-apoptotic mechanisms, a hallmark of cancer ,to AA-DE-SN in the Normal-like subtype, but not among W-DE-SN in rapidly adapt to microenvironmental and therapeutic stress Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation Table 2. Differentially expressed ASR genes within a given PAM50 subtype between AA or W. Symbol Log2 FC AA - AverExp (log2) p-value Adjusted p-value Function in cancer −10 −09 TR1 Basal CEBPD 1.06 4.97 1.4E 3.9E ccaat/enhancer-binding protein δ, associates with good prognosis in breast cancer . −18 −16 TR2 Her2 CRYBG3 −1.75 3.4 1.6E 3.0E Long non-coding RNA, its depletion in tumor cells enhanced their malignant phenotypes . −09 −08 TR3 TBC1D3 1.17 −6.36 1.6E 3.5E TBC1D3 oncogene promotes the migration of breast cancer cells . −02 −02 CXCL8 −1.56 1.07 1.8E 4.6E Interleukin-8, promote breast cancer progression by increasing cell invasion, angiogenesis, and metastases and TR4 is upregulated in HER2-positive cancers . −06 −05 STC1 −1.36 5.34 2.0E 2.2E Stanniocalcin-1, the role of STC1 in breast cancer is complex, considering that some studies have shown that it TR5 exerts an oncogenic role, whereas other studies have demonstrated the opposite . −03 −03 TR6 CCNE2 −1.03 2.63 2.0E 8.2E Cyclin E2, associates with poorer prognosis in breast cancer . −04 −03 TP63 −1.7 2.66 1.5E 1.0E p53-related protein p63. ΔNp63 isoform supports a more mesenchymal phenotype associated with a higher TR7 tumorigenic and metastatic potential . −03 −03 TR8 H2AC8 1.12 1.23 2.2E 8.9E Expression of HIST1HSAE associated with disease free survival in colorectal cancer . −08 −07 TR9 LumB SPANXA1 1.02 −6.71 1.2E 2.3E Sperm Protein Associated With The Nucleus, X-Linked, Family Member A1, promote breast cancer invasion . −06 −05 SPANXC 1.41 −6.26 6.7E 6.8E Same as SPANXA1. −03 −02 TR10 NAT1 −1.29 5.35 3.1E 1.2E Arylamine N-acetyltransferase 1, a potential marker in estrogen receptor-positive tumors . −05 −04 TR11 SPANXB1 1.37 −5.18 1.3E 1.2E Sperm Protein Associated With The Nucleus, X-Linked, Family Member A1, promote breast cancer invasion . −18 −16 LumA CRYBG3 −1.49 3.4 1.6E 3.0E See above. −05 −04 SPANXB1 1.5 −5.18 1.3E 1.2E Same as SPANXA1. −04 −03 TR12 TMEM100 −1.04 0.06 3.6E 2.2E Transmembrane protein 100, Low-expression associates with poor prognosis in non-small-cell lung cancer . −07 −06 TR13 Normal-like FGF2 −1.34 2.45 7.2E 9.0E Fibroblast growth factor 2, induces breast cancer growth . −12 −11 TR14 CDT1 1.34 3.71 1.8E 7.6E Chromatin licensing and DNA replication factor 1, significantly higher in ER-negative breast cancer . −04 −03 TR15 NRG1 −1.61 1.28 5.8E 3.4E Neuregulin 1, frequently silenced by methylation in breast cancers . −04 −04 TR16 PTGS2 −1.57 0.98 1.3E 9.7E Prostaglandin-endoperoxide synthase 2, genetic variation in this gene is associated with breast cancer risk . −18 −16 CRYBG3 −1.13 3.4 1.6E 3.0E See above. −06 −05 TR17 BIRC5 1.11 5.03 9.5E 9.2E Baculoviral inhibitor of apoptosis repeat containing 5, high expression associates with poor survival . −07 −06 TR18 CDC45 1.41 3.01 3.0E 4.0E Cell division cycle 45, higher expression in cancer cells and might associate with metastasis . −03 −02 TNFSF9 1.04 −0.25 3.9E 1.4E Tumor necrosis factor superfamily member 9, an immune modulating T-cell co-stimulator with anti-tumor role TR20 −04 −03 TR21 PIMREG 1.13 2.43 5.2E 3.1E PICALM interacting mitotic regulator, overexpression promotes breast cancer aggressiveness . −05 −04 TR22 CD72 1.18 1.9 5.9E 4.7E Potential role in anti-tumor immune response . −06 −05 STC1 −1.39 5.34 2.0E 2.2E See above. −04 −03 TR23 TMEM100 −1.2 0.06 3.6E 2.2E Transmembrane protein 100, inhibits the growth and metastasis of non-small-cell lung cancer . −02 −02 CXCL8 −1.39 1.07 1.8E 4.6E See above. −08 −07 ZFPM2 −1.11 2.37 2.6E 4.5E Zinc finger protein, FOG family member 2, linked to tumor stage, metastasis, and prognosis in breast patients TR24 Race-related differentially expressed ASR genes identified after comparing breast cancer samples of a given PAM50 subtype between AA and W patients. The fold-change (FC) and the average expression (AverExp) are shown. M. Al Abo et al. compared with W patients of Her2 subtype, MBNL2 (member of the OSR pathway), TMC6 (member of the immune pathway/Table 1), PCDH7 (member of the XIAP pathway), and ACSM3 (member of the XIAP pathway) to be of significance in AA-Basal subtype patients. Interestingly, MBNL2 has been recently reported to control hypoxia response in breast cancer cells and PCDH7 was 48,49 reported to induce bone metastasis of breast cancer cells . In conjunction, race-stratified survival analysis identified the association of a set of DE-SN (such as CDKN1C, CDKN2D, TP63, STAT4, MYC, and MYCBP2) with known functions in oncogenic pathways to be distinct in AA or W patient samples, which strongly highlights the importance of stratifying tumors by patient race in survival outcomes. Notably, score analysis for the ASR pathways identified the OSR gene sets score were amplified in advanced breast cancer subtypes and in tumors from AA patients. The GO analysis of differentially expressed ASR genes between tumor and normal breast cancer samples from AAs or Ws reveal race-related molecular pathways. For example, our results suggest that dysregulation of cell death, the ERK1 ERK2 cascade, and the epithelial cell migration processes in Her2 subtype breast cancers in AA but not in W breast cancers has potentially significant implications for treatment approaches. Likewise, these findings suggest that targeting these pathways could achieve different responses when breast cancers of AAs or Ws are treated with similar drugs. These datasets provide a molecular basis for the epidemiolo- gical findings that AA patients’ breast tumors exhibit higher 50,51 oxidative stress markers compared to W patients . Therefore, understanding the underlying biology of aggressive breast cancer subtypes and variants, wherein race- and/or ancestry-related disparities exist in incidence, treatment, and survival outcomes, has the potential to aid in development of new biomarkers and treatment strategies to mitigate these disparities. Recently, Carrot-Zhang et al. have reported estimated global Fig. 5 GO enrichment analysis of DE-SN. Bar plots depicting the ancestry for 10,678 patients across 33 cancer types in TCGA . significantly, p-value < 0.05, enriched GO terms of the DE-SN in Therefore, we compared the self-identified race and the estimated PAM50 subtypes from either AA patients only (a) or from W patients only (b). The x-axis in a and b depicts the –log10 p-value yielded global ancestry for all the patients from whom we analyzed from the Kolmogorov–Smirnov test. The enriched GO terms are samples in this study. Our analysis reveals that the self-identified included next to the bars. race and estimated global ancestry of the patients to be largely concordant (Supplementary Fig. 2). Therefore, it is possible that stimuli that can lead to clonal evolution of death resistant ancestry-related individual-level differences and differences in populations . A recent retrospective analysis of a large cohort of allostatic load also contribute to the differences in ASR genes that non-metastatic, non-IBC, primary invasive breast cancer samples we have identified and found to be associated with breast cancer for the apoptotic regulator, XIAP, revealed that XIAP mRNA subtype and survival. To further understand the determinants of expression is independently associated with poor outcomes and health underlying differences in ASR genes associated with breast lower pathological complete response (pCR) to anthracycline-bsed cancer subtype and survival, future studies should focus on neoadjuvant chemotherapy . Based on these clinical observa- estimating local ancestry of chromosomal regions of ASR genes tions and our previous preclinical studies identifying ASR path- and assessing association with breast cancer subtype and survival. ways linking mitogen activated ser/thr kinase (MNK), X-linked Although TCGA has a larger number of breast cancer samples inhibitor of anti-apoptotic protein (XIAP), and nuclear transcription from AA patients than for other cancers, a major challenge is the factor (NFκB)-mediated proliferative, invasive, and immunosup- limited number of breast cancer datasets available from AA 27,30,46 pressive breast tumor phenotype , the present study patients after sorting for PAM50 subtype, with just 6 samples that investigated the expression of these ASR gene sets in TCGA are designated Normal, rendering limited power for race-related breast cancer samples to identify differentially expressed genes in comparative differential gene expression analysis, and eclipsing the PAM50 subtypes and for which there are observed disparities, any potential differences in survival between breast cancer between AA and W breast cancer patients. We report herein that patients of different races or ancestries. Mechanistic studies of out of the 226 genes, which can be grouped into three ASR genes in breast cancer are ongoing along with the under- overarching biological processes or signaling axes (XIAP-MNK- standing that larger independent cohorts with samples annotated NFκB, immune, or TGFβ related), 69.4% were differentially for societal-level, neighborhood-level and institutional-level deter- expressed with fold-change > 2 and adjusted p-value < 0.05 minants of health are needed to identify and validate biomarkers. among the breast cancer subtypes compared to Normal-adjacent samples, or in comparison of subtypes to each other. METHODS Our comparative analysis of the ASR gene sets in TCGA samples stratified by AA and W race identified 29 race-related differentially Datasets and race assignment expressed ASR genes, all of which play a role in cancer biology. For The results shown here are in whole based upon data generated by the example, C-X-C Motif Chemokine Ligand 8 (CXCL8), reported to TCGA Research Network: https://www.cancer.gov/tcga, which are publicly promote breast cancer progression , in our analysis was available with prior patient’s consent and institutional review board significantly decreased (3-fold-change) among AA patients agreements in place from original authors. The TCGA RNAseq raw counts Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73 M. Al Abo et al. Fig. 6 Survival analysis of the DE-SN. Kaplan–Meier plots for the DE-SN depicting the association of DE-SN level in Basal (a), Her2 (b), and Normal-like (c) with breast cancer patient overall survival probability. The DE-SN exhibit HR > 1.5 or < 0.58 and p-value < 0.05. The survival probabilities were compared in breast cancer patients of indicated PAM50 subtypes expressing high, low, or intermediate (75th, 25th, or 25th–75th percentiles) levels of the indicated DE-SN The number of patients (pt#) of high, low, or intermediate groups are indicated. d Venn diagrams showing the number of DE-SN associated with AA or W breast cancer patient overall survival probability in the indicated PAM50 subtypes. npj Breast Cancer (2022) 73 Published in partnership with the Breast Cancer Research Foundation M. Al Abo et al. Table 3. DE-SN associated with patient overall survival in AA only, W only, or both AA and W. AA W AA and W Basal ACSM3, COPZ2, MCM5, POLE2, E2F8, BLM (XIAP pathway); DLL4 COPZ2, PLAU, ABAT (XIAP pathway); TREM1 COPZ2 (XIAP pathway) (Jag1-Notch pathway); BIRC5 (NFkB pathway) (OSR pathway), CCNE2 (XIAP and OSR pathways) Her2 ACSM3, MCM10, GPR137B, CDC45, LIG1, SLC1A4, PLAU, LIG1, LEF1 (XIAP pathway); CD72 (TGFβ LIG1, LEF1 (XIAP pathway); HIST1H2BJ, CDKN2D, CDKN1C, LEF1, SLC14A1 (XIAP pathway); pathway); SCD5, CEBPD (OSR pathway) SCD5 (OSR pathway) MCM3, CCN3, PTGS2, SCD5, NRG1, NAP1L3, TXNIP (OSR pathway); NUDT1 (Immune pathway) LumB MYCBP2, STAT4 (Immune pathway); LIPG, CDKN1C, KLRC1 (XIAP PLAU, NRN1, ZFPM2 (XIAP pathway); - pathway); FGF2 ABCA6 (OSR pathway) NAP1L3 (OSR pathway); DAB2 (TGFβ pathway) LumA MCM10, LIG1, FOSB, PIMREG, GINS2, ABAT (XIAP pathway);-- METTL7A, TP63, MCM4, CDT1, ANXA3 (OSR pathway); MYC, BIRC5 (NFkB pathway); MELK (Immune pathway) Normal-like FOSB (XIAP pathway)- - DE-SN associated with breast cancer patient overall survival among AA patients only, W patients only, or both AA and W patients. The pathway, to which the ASR genes belong, are shown in parentheses and are bolded. data from TCGA repository was downloaded (June 15, 2021) using ASR gene were generated by comparing the survival probability of the TCGAbiolinks R package (version 2.16.4) . The downloaded RNAseq raw 75th or 25th percentile, with patients grouped by the expression level of count expression data were normalized for RNAseq library size and the differentially expressed ASR gene. The Kaplan–Meier plots were 54,55 63 dispersion utilizing Limma R package (version 3.44.3) . The following generated using survminer R package (version 0.4.9) . multivariate experimental design was employed: sample definition (primary solid tumor, metastatic and solid tissue normal), PAM50 subtype (Basal, Her2, Analysis of gene signature scores LumB, LumA, and Normal-like), self-identified race of the patient (African To analyze the score of each ASR gene signature (XIAP, OSR, Immune, and American (AA), White (W), Asian, Alaska native American and not reported). TGFβ pathways as shown in Table 1), we submitted the gene set of each Note that despite being described as Normal-like, the Normal-like subtype signature for score analysis using GSVA R package (version 1.36.3) . Based include tumor samples, not Normal-adjacent. on the reported change of expression, we classified the genes in each gene set into up or downregulated. After calculating the score of each gene Gene expression analysis signature in each sample, we evaluated the score by breast cancer subtype and by patient race, AA or W. To determine the statistical significance of A total of 226 genes (Table 1) in the ASR gene sets, which included 101 differences in score among different breast cancer subtypes and between XIAP-related, 11 NFκB targets, 10 MNK targets, 33 Oxidative Stress AA and W, we employed the Wilcoxon signed-rank test. Response (OSR)-related, 13 TGFβ-related, 6 JAG1-Notch targets and 52 immune-related, in addition to 14 genes that belong to more than one ASR set, were submitted for comparative analysis using the TCGA breast Data analysis invasive carcinoma expression dataset. Differential gene expression We handled the data and performed analyses using R. Rstudio was used as analysis was performed by applying the linear model of weighted or an interface for R. The following packages: tidyverse, SummarizedExperi- generalized least squares for series of arrays in limma, and the adjusted p- ment (version 1.18.2), plyr, dplyr, DT, VennDiagram, ggrepel, cowplot, and value was calculated using Benjamini-Hochberg method, as described ggplot2 were also used for data analysis and visualization. previously . Using R random function, set.seed(1991), we selected 226 random genes to perform the same analysis as a control. Reporting summary Further information on research design is available in the Nature Research Gene ontology enrichment analysis Reporting Summary linked to this article. The gene ontology (GO) terms were queried using biomaRt R package (version 2.45.8) as described . The GO terms were matched to the gene in TCGA expression datasets using ensemble identification names. Using the DATA AVAILABILITY GO terms, we performed enrichment analysis by topGO (version 2.40.0) and 58,59 The datasets used in this manuscript can be downloaded from TCGA repository. The org.Hs.eg.db (version 3.11.4) R packages . For this analysis, we selected retrieved data from TCGA repository is available from the corresponding author on ASR genes that demonstrated differential expression, with a differential fold- reasonable request. change >2 as well as adjusted p-value < 0.05, between the compared samples. The selected node size in topGo analysis was 10, the algorithm was classic, which tests the over-representation of GO terms within the group of CODE AVAILABILITY differentially expressed genes, and the statistic test was Kolmogorov–Smirnov . We determined KS < 0.05 as a cutoff for statistical The code for downloading the datasets, normalizing, and performing the differential significance. The GO terms were further reduced using rrvgo (version 1.0.2) . expression analysis can be found on GitHub (https://github.com/muthalpy/ ASRgenes_TCGA). Survival analysis Received: 29 June 2021; Accepted: 5 April 2022; The clinical data associated with TCGA expression data were used to perform survival analyses. The vital status and time until death for all patients (including AA, W, Asian and Alaska native American and not reported) belonging to the indicated PAM50 subtype were appropriately annotated and fitted into Cox Proportional-Hazards Model. The survival R package (version 3.2.11) was used to compute the hazard ratio per unit REFERENCES and p-value of a given differentially expressed ASR gene using Breslow 1. DeSantis, C. E. et al. Breast cancer statistics, 2019. CA: Cancer J. Clin. 69, 438–451 method for the maximum likelihood estimator for the cumulative baseline (2019). hazard function. 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Alexa, A. & Rahnenfuhrer, J. topGO: enrichment analysis for gene ontology. R COMPETING INTERESTS package version 2.48.0 (2010). The authors declare no competing interests. 59. Carlson, M., Falcon, S., Pages, H. & Li, N. org. Hs. eg. db: Genome wide annotation for human. R package version 3.8.2 (2013). 60. Warner, E. T. et al. Racial and ethnic differences in breast cancer survival: med- ADDITIONAL INFORMATION iating effect of tumor characteristics and sociodemographic and treatment fac- Supplementary information The online version contains supplementary material tors. J. Clin. Oncol. 33, 2254–2261 (2015). available at https://doi.org/10.1038/s41523-022-00431-z. 61. Sayols, S. rrvgo: a Bioconductor package to reduce and visualize Gene Ontology terms. https://ssayols.github.io/rrvgo (2020). Correspondence and requests for materials should be addressed to Gayathri R. Devi. 62. Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model (Springer, 2000). Reprints and permission information is available at http://www.nature.com/ 63. Kassambara, A., Kosinski, M., Biecek, P. & Fabian, S. survminer: drawing survival reprints curves using’ggplot2’. R package version 0.3 (2017). 64. Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013). in published maps and institutional affiliations. ACKNOWLEDGEMENTS This work was supported in part by Duke School of Medicine Bridge Award (GRD); NCI- P20 NCCU-Duke Cancer Disparities Translational Research Partnership (S.P.; G.R.D., K.P. Open Access This article is licensed under a Creative Commons W., J.F.); NCI-P20 Project 2 (G.R.D., K.P.W.); NCI-P20 Predoctoral Diversity Supplement Attribution 4.0 International License, which permits use, sharing, 3P20CA202925-04S2 (L.G.S.), Department of Defense Breast Cancer Breakthrough level adaptation, distribution and reproduction in any medium or format, as long as you give 2 Award W81XWH-17-1-0297 (G.R.D.), NCI of NIH Award R01CA264529 (G.R.D.), The IBC appropriate credit to the original author(s) and the source, provide a link to the Creative Network Foundation Gift (G.R.D.), NIH Basic Research in Cancer Health Disparities R01 Commons license, and indicate if changes were made. The images or other third party Award R01CA220314 (S.P., J.F.), Prostate Cancer Foundation Movember Challenge material in this article are included in the article’s Creative Commons license, unless Award #18CHAL04 (S.P., J.F., M.A.). The authors would like to thank Dr. Michael Morse indicated otherwise in a credit line to the material. If material is not included in the and Dr. Susan Dent for helpful discussions and Alexandra Bennion at Duke article’s Creative Commons license and your intended use is not permitted by statutory Undergraduate Trinity School of Arts and Science for editorial assistance. regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. AUTHOR CONTRIBUTIONS M.A.: conceptualization, methodology, formal analysis, visualization, writing—original © The Author(s) 2022 draft preparation, writing—reviewing and editing. L.G.: methodology, visualization, Published in partnership with the Breast Cancer Research Foundation npj Breast Cancer (2022) 73

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Published: Jun 13, 2022

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