Colorectal cancer remains a deadly cancer due to metastatic disease. To understand the molecular mechanisms of metastasis in colon cancer, we investigated whether the copper chaperone antioxidant-1 (Atox1) protein plays a role in this process. Recent findings indicate that Atox1 protein has transcription factor activities and plays a vital role in cell proliferation in cancer cells. However, the role of Atox1 in metastasis has not been examined.Atox1 expression was determined by immunofluorescence in a tissue microarray generated from a spectrum of CRC patients. Subcellular fractionation of colon cancer cell lines SW480 and SW620 cells was used to examine the cellular location of Atox1 in the face of activin A, a cytokine that stimulates colon cancer metastasis. Atox1 expression was genetically manipulated and cellular migration measured through trans-well assay and proliferation measured by colony formation assays.Here we demonstrate that in patients with metastatic colon cancer, there is a significant increase in the expression of nuclear Atox1. Interestingly, the metastatic CRC cell line SW620 has increased nuclear localization of Atox1 compared to its related non-metastatic cell line SW480. Further, inhibition of endogenous Atox1 by siRNA in SW620 decreased colony formation and reactive oxygen species generation via decreased expression of Atox1 targets cyclin D1 and NADPH oxidase subunit p47 phox, respectively. Additionally, overexpression of nuclear-targeted but not copper binding domain-mutated Atox1 in SW480 cells increased colony formation and cell migration that was further augmented by activin A stimulation, a known enhancer of colon cancer metastasis.Our findings suggest that nuclear Atox1 might be a new therapeutic target as well as a new biomarker for metastatic colorectal cancer. Colorectal cancer (CRC) is a common and deadly cancer due to its metastatic nature . AlthougAntioxidant protein 1 (Atox1) is known to play a key role in copper homeostasis . It reguThe present study explored the role of Atox1 in CRC using metastatic and non- metastatic CRC cell lines and a patient tissue microarray (TMA). Interestingly, we found that Atox1 nuclear location was significantly correlated with the severity of metastatic CRC in a TMA of colorectal tumors from patients. Consistent with this, Atox1 is highly expressed in the nucleus in metastatic SW620 colon cancer cell line, while Atox1 is expressed more in the cytosol in non-metastatic SW480 colon cancer cell lines. Activin A treatment promotes Atox1 nuclear translocation in both colon cancer cell lines. Endogenous inhibition of Atox1 in metastatic SW620 human colon cancer cells decreased cellular migration, viability and colony formation, which was associated with decrease in cyclin D and p47 phox expression. Importantly, we found that nuclear-targeted Atox1 induces migration and colony formation in non-metastatic SW480 colon cancer cells that was further increased upon activin A stimulation. Our findings represent a functional role for Atox1 in CRC metastasis.PKKKRKVD) was derived from the SV40 large T antigen fused to the C terminus of Flag-tagged w-Atox1 (wild type) as previously described [The nuclear-targeted Atox1 plasmid (Flag-Atox1-NLS) construct containing a tripartite NLS  sequence  Institutional Review Board and tissues collected only after written informed consent was administered and signed. There are no individual case notes or any patient private health information included in this manuscript. Briefly, tumor tissue derived from colon was obtained from patients diagnosed with colorectal carcinoma at the University of Illinois Medical Center. A total of sixty colorectal resection specimens cores from the formalin fixed paraffin embedded tumor were placed in a tissue micro-array. The quality and structural integrity of the tissue blocks were appraised and evaluated before inclusion into the study as described previously . A totalFormalin-fixed and paraffin-embedded TMAs were de-paraffinized in xylene and then rehydrated in descending ethanol series. The sections were blocked in 3% BSA for 60 min to block nonspecific binding sites. Immunostaining was performed at 4°C overnight with a rabbit Atox1 antibody (1:500 dilution). FollowiThe colon cancer specimens on the TMA slides were examined and scored. A staining index for each tissue core was obtained based on published methods , 22. The2 chamber supplemented with 10% FBS and 1% Pen Strep containing media and were serum starved for 24 hours prior to treatment. To limit variability between experiments, cells were cultured to a maximum of 30 passages to minimize mutations in the cell lines. In addition, cell cultures under identical conditions of treatment were done in parallel to validate our findings. Further, all the cell lines were tested for mycoplasma prior to starting experiments. The cells were authenticated once a year and validated by 9 STR (short tandem repeat) profiling using Cell Check 9Plus and tested for mycoplasma .SW480 and SW620 cell lines were acquired from the American Type Culture Collection (ATCC). The cell lines were cultured in a 5% COCell lysates were treated with bicine chaps buffer containing 1x protease cocktail inhibitor  and prepared for western blot analysis as previously described . Blots wColon cancer cell lines were subjected to subcellular fractionation using NE-PER nuclear and cytoplasmic extraction reagents  according to manufacturer’s instruction. Fractionation efficiency was determined by western blot analysis using GAPDH as cytoplasmic and p84 as nuclear protein controls respectively.6 cells according to the manufacturer’s instruction. After 48 hours of transfection cells were left either untreated or treated with activin A  for an additional 24 hours. Protein expression of transfected cDNAs was confirmed by western blot analysis. Specific siRNA for Atox1  was transiently delivered at a final concentration of 30 nM via electroporation using AMAXA Nucleofector . Transfection efficiency was confirmed by fluorescent microscope using the pmaxGFP control vector. Forty-eight hours post transfection cell lysates were prepared for western blot analysis.Colon cancer cells were transfected with 1 ug of plasmid DNA encoding Flag tagged WT Atox1, Atox1 NLS, Atox1 CBD using lipofectamine 3000 in 12-well plates at a density of 1x104 cells/well) were plated into Transwell 12 well plates  that were coated with fibronectin (Sigma) to facilitate migration. After 6 hours, non-migrated cells at the membrane top were removed and the cells that have migrated through the membrane were fixed with 4% PFA and stained with DAPI (nuclear stain). Images were captured by fluorescent microscopy and the number of migrated cells per well was determined by counting the cells at the center of each well using image J software to digitized images. Data are expressed as number of particles counted per high powered field (hpf).Colon cancer cell lines  as previously described . The celFor colony formation assay, cells were seeded into six-well plates at a density of 100 cells per well and incubated for two weeks in DMEM containing 10% FBS at 37°C. After two weeks, colonies were fixed by cold methanol for 15 minutes and stained with 0.5% crystal violet for 1 hour. The number of colonies (>50cells/colony) was counted using a stereomicroscope and analyzed by image J software . All theAll data are expressed as means ± SD from at least three independent experiments. Statistical analyses for differences were performed via Student’s t test when two groups are analyzed and by one-way ANOVA followed by Newman-Keul’s multiple comparison analysis using Graphpad Prism 5.0 when more than two groups were analyzed. The criterion for statistical significance was p > 0.05.To characterize the role of endogenous Atox1 in CRC metastasis, immunofluorescent staining of Atox1 protein was examined in a tissue microarray (TMA) consisting of adenocarcinoma and adjacent normal epithelial mucosa from 60 CRC patients . We obseThe distinct nuclear localization of Atox1 protein in the colon cancer TMA prompted us to verify its subcellular distribution in CRC cell lines by cellular fractionation followed by immunoblotting. The nuclear localization of Atox1 protein was assessed in two cell lines derived from the same colon cancer patient. The SW480 cell line was derived from the primary colon tumor and is non-metastatic, while the SW620 colon cancer cell line was derived from the lymph node of the same patient when the cancer recurred with wide-spread metastases . EfficieTo investigate the fate of Atox1 localization after activin A stimulation, SW480 and SW620 cells were treated with or without activin A for 24 hours. Effect of Activin A was assessed by subcellular fractionation  and immuTo further investigate the mechanism by which Atox1 may regulate metastasis in CRC, the endogenous expression of Atox1 was depleted by Atox1 siRNA in the metastatic SW620 cell line . Since cRecent evidence has suggested a positive role for reactive oxygen species (ROS) in regulating EMT in cancer cells through influencing extracellular matrix remodeling, cytoskeleton remodeling, cell-cell junctions and cell mobility –29. TherTo explore whether nuclear accumulation of Atox1 has a functional consequence, we assessed cell migration of SW620 cells following Atox1 KD with and without activin A treatment. KD of Atox1 decreased SW620 cell migration when compared with cells transfected with scrambled siRNA. Interestingly, Atox1 KD attenuated activin A-induced cellular migration compared to scrambled control cells . KD of Atox1 also reduced SW620 cell number, as demonstrated by MTT assay , which wBecause our findings point to Atox1 as a positive regulator of metastasis, we over-expressed a nuclear-targeted Atox1 in the non-metastatic CRC cell line SW480 and assessed metastatic potential. To efficiently target Atox1 to the nucleus, an Atox1 nuclear localization signal (NLS) cDNA construct with a C-terminus Flag-tag was used as previously described . In addiThe wAtox and NLS Atox1 cell lines also had increased colony formation compared to control vector cells, which again were further increased by activin A treatment. Colony formation was not changed in CBD Atox1 cell lines , S3 Fig.Atox1 is a copper (Cu) chaperone protein involved in maintaining copper homeostasis. In addiIn the metastatic SW620 cells, there is more Atox1 in the nucleus compared to the non-metastatic SW480 cells. In line with the nuclear enrichment of Atox1 in the SW620 cell, there is Atox1-dependent expression of growth promoting cyclin D1 and NADPH oxidase p47 phox subunit indicative of increased ROS accumulation. Treatment with activin A stimulates translocation of Atox1 into the nucleus of SW480 cells as well as inducing a further increase in nuclear Atox1 in SW620 cells. The functional significance of nuclear Atox1 was examined first in SW620 cells where knock-down of Atox1 expression resulted in decreased cell migration and colony formation which was unaffected by activin A. Further, we demonstrate that Atox1 knockdown by siRNA induced the most prominent proliferation suppression in metastatic SW620 colon cancer cell lines. This inhibitory effect was associated with inhibition of intracellular signaling as revealed by a decrease in cyclin D1 protein expression. In contrast, transfection of wild type Atox1 or nuclear localizing Atox1 into non-metastatic SW480 cells resulted in increased cell migration and colony formation which was further enhanced by activin A. The Cu-binding domain mutated Atox 1 was unable to increase either cell migration or colon formation and interestingly, decreased the activin A stimulation of those processes. This may indicate that Cu-bound Atox1 plays a role in the activin A stimulation of cell migration and colony formation. Although the presence of Atox1 in the nucleus has been reported in different cancers –17, thisWe report that Atox1 is localized both in cytoplasm and nucleus and its nuclear localization is enriched in metastatic CRC where it influences cell migration and colony formation. Activin A, an inflammatory cytokine increased in metastatic CRC, increases the nuclear localization of Atox1. In addition, Cu-bound Atox1 appears to enhance activin A-induced cell migration. Taken together, understanding the combined actions of activin A and Atox1 may improve the current staging of colorectal cancer patients.S1 FigThe adjacent normal colon tissue on the colon cancer tissue microarray was also stained for DAPI (blue) and Atox1 (red). Shown in a representative image indicating very little staining for Atox1 in normal colon tissue.(EPS)Click here for additional data file.S2 FigImage of SW620 colon cancer cell colonies formed after treatment with either scrambled or Atox1 siRNA and an additional treatment with either 25 ng/ml of activin or control PBS (Con). The number of colonies were quantified as indicated in the methods and show in (EPS)Click here for additional data file.S3 FigImage of SW480 colon cancer cell colonies formed after transfection with the indicated vectors and an additional treatment with either 25 ng/ml of activin or control PBS (Con). The number of colonies were quantified as indicated in the methods and shown in (EPS)Click here for additional data file.S1 FileThis is the file of complete western blots used to created (PDF)Click here for additional data file.


Although metabolism is profoundly altered in human liver cancer, the extent to which experimental models, e.g. cell lines, mimic those alterations is unresolved. Here, we aimed to determine the resemblance of hepatocellular carcinoma (HCC) cell lines to human liver tumours, specifically in the expression of deregulated metabolic targets in clinical tissue samples.P < 0.05) were used for pathway enrichment and gene ontology (GO) analyses. Further, we compared the topmost gene expression, pathways, and GO from poorly differentiated cell lines to the pattern from four human HCC datasets (623 tumour tissues). In well- versus poorly differentiated cell lines, and in representative models HLE and HUH7 cells, we specifically assessed the expression pattern of 634 consistently deregulated metabolic genes in human HCC. These data were complemented by quantitative PCR, proteomics, metabolomics and assessment of response to thirteen metabolism-targeting compounds in HLE versus HUH7 cells.We compared the overall gene expression profile of poorly-differentiated  to well-differentiated  HCC cell lines in three publicly available microarray datasets. Three thousand and eighty-five differentially expressed genes in ≥2 datasets , and amino acid metabolism . In contrast, HUH7 cells showed a higher expression of 98 metabolic targets upregulated in tumours . Metabolomics revealed that the genomic portrait of HLE cells co-exist with profound reliance on glutamine to fuel tricarboxylic acid cycle, whereas HUH7 cells use both glucose and glutamine. Targeting glutamine pathway selectively suppressed the proliferation of HLE cells.We found that poorly-differentiated HCC cells display upregulated MAPK/RAS/NFkB signaling, focal adhesion, and downregulated complement/coagulation cascade, PPAR-signaling, among pathway alterations seen in clinical tumour datasets. In HLE cells, 148 downregulated metabolic genes in liver tumours also showed low gene/protein expression – notably in fatty acid β-oxidation , urea cycle , molecule transport  contains supplementary material, which is available to authorized users. TP53, TERT promoter and CTNNB1 mutations are well known [Hepatocellular carcinoma (HCC) is the most common type of liver cancer, which also include cholangiocarcinoma as well as secondary liver cancer that metastasized from distant organ-sites, e.g. colon and pancreas. HCC is a highly heterogeneous and poorly understood tumour entity. While a lot of its molecular alterations, e.g. ll known , the cliSimilar to human HCC, the corresponding cell lines used for in vitro studies are heterogeneous in their molecular and phenotypic portraits. For example, HCC cell lines show differential proliferative response to Src/Abl inhibitor dasatinib  as well We recently identified 284 metabolic genes upregulated in at least 6 of 8 human HCC microarray datasets, and also 350 downregulated metabolic genes under the same criteria. Two hundred and one of these genes were highlighted as predictive of overall survival in a cohort of HCC patients, underscoring the potential clinical significance of the genes . Here, wP < 0.05) were separated into two categories – i.e. the upregulated and downregulated genes in poorly differentiated cell lines. Using Venn diagram tool (http://bioinfogp.cnb.csic.es/tools/venny/), genes exclusively upregulated or downregulated in ≥2 datasets (unless otherwise indicated) were selected for comparison with human HCC tissues genomic profile. Comparison with human HCC profile was in three parts. First, examining how the topmost 100 significantly upregulated or downregulated genes in all three cell line datasets are expressed in four patients datasets . Second, examining the cell lines' expression of previously published list of consistent metabolic genes (n = 284 upregulated and 350 downregulated HMGs) that were derived from ≥6 human HCC microarrays, including GSE14520 (used in this study) [P < 0.05) and in ≥2 cell line datasets (P < 0.05). The second and third parts are discussed next. For the first part, the z-score of expression fold was calculated for each human HCC dataset. Then, the topmost genes identified from cell line data were extracted from each human dataset, and subsequently ranked by their average z-score across the four human datasets. With the + or – z-score values determined for each gene in all four human datasets, a gene significantly upregulated/downregulated in poorly differentiated HCC cell lines and upregulated/downregulated in the human datasets were considered to be concordantly expressed in both settings. In contrast, all genes significantly upregulated in poorly differentiated cell lines, but downregulated in tumours, and vice versa, were considered to be discordant .Microarray datasets GSE36133 (from CCLE project) , GSE3581https://www.ebi.ac.uk/arrayexpress/) for GSE57083, and cBioPortal platform for the CCLE project. Differences in measured mRNA (i.e. HLE - HUH7) were used to determine the relative expression of HMGs by the respective cell lines in each dataset. Subsequently, HLE cells were considered to mimic the up- or downregulated HMGs it concordantly express at a higher or lower level in both datasets. HMGs that showed discordant expression pattern in both datasets are more mimicked by HUH7 cells.To uncover the HMGs mimicked by the HCC cell lines, all genes significantly expressed in ≥2 of the earlier mentioned cell line datasets were overlapped with the 284 upregulated and 350 downregulated HMGs. This was followed by the selection of specific metabolic genes concordantly upregulated/downregulated in poorly differentiated cell lines as those more mimicked by the more ‘cancer-like’ cell lines. On the other hand, HMGs with discordant or opposite expression pattern were considered to be more portrayed by well-differentiated cell lines. In addition, HMGs expression was analyzed, focusing on HUH7 and HLE (two cell lines we mainly used for subsequent phenotypic assays). Specifically, gene expression data for these two cell lines were obtained via ArrayExpress database (https://david.ncifcrf.gov/). For human HCC microarrays, we have previously shown pathway enrichment [n = 2017) or downregulated (n = 1547) genes in all four human HCC datasets were separately analysed to determine their associated molecular function (MF), cellular component (CC) and biological process (BP). For the cell lines, pathway enrichment and GO analysis were performed using genes upregulated or downregulated in poorly differentiated HCC cell lines (at least in two datasets). Based on P < 0.0001 and false discovery rate (FDR) < 25%, the topmost 10 enriched pathways as well as top five MF, CC, and BP were selected.Pathway enrichment and gene ontology (GO) analysis were performed using the bioinformatics platform DAVID 6.8  supplemented with 2 mM glutamine (Gln), 10% heat inactivated fetal bovine serum, penicillin (100 U ml− 1), and streptomycin (100 μg ml− 1). For glucose or glutamine deprivation experiments, the cells were first seeded into culture plates and incubated overnight. Thereafter, the cells were washed 1× with Hank’s Balanced Salt Solution followed by further incubation with culture media lacking the respective metabolites. Compounds used to interfere with metabolism in the cell lines are listed in the Additional file Human HCC cell lines HUH7, HEPG2, HLF and HLE were obtained from the Japanese Cancer Research Resources Bank or American Type Culture Collection. The cells were cultured at 37 °C in a humidified incubator with 5% CO5 in 6 well plates (7 replicate wells for HUH7 and 8 for HLE). The cells were cultured for 48 h. Thereafter, culture media was aspirated off and the cells were washed by gentle swirling with 3 ml phosphate buffered saline per well. This step was repeated twice, after which cells were detached by scraping, and transferred into 1.5 ml vials. The cell suspension was then centrifuged at 800 rpm for 5 min at 4 °C and the supernatant discarded. The remaining cell pellets were stored at − 80 °C until analysis. Proteomic analysis was conducted by mass spectrometry as previously described [P < 0.05 after analysis of variance were considered significant in our analyses, which includes pathway enrichment, GO, and overlap with HMGs.For proteomic comparison, HUH7 and HLE cells were seeded at a density of 5 × 10escribed . ProteinP < 0.05, unless otherwise indicated. Where applicable, results were expressed as mean ± standard deviation (SD). Data were analysed with GraphPad Prism v6 . Genecards database (http://www.genecards.org) was used for crosschecking gene names and functions. The differentially expressed genes in human HCC microarrays GSE14520, GSE25097, GSE1898, and GSE55092 were determined by using NCBI GEO2R, comparing normal liver/adjacent non-tumours versus liver tumours. Information about metabolomics and other experiments performed with the cell lines are included in the Additional file Statistical significance was accepted as n = 1584 upregulated and 1501 downregulated, P < 0.05) in at least two of the three datasets  or lowly expressed  in poorly differentiated cell lines  were downregulated in most human HCC microarrays  , TTR, EPCAM, apolipoproteins , MTTP, complement 3 and 5 (C3/C5), among other, reflecting ~ 85% concordance with patients’ tumour samples. The notable exemptions were villin 1 (VIL1), VSNL1, MBNL3, KLB, AFP and glypican 3 (GPC3), which are all consistently upregulated in liver tumour datasets. It is noteworthy that while many of these downregulated genes are novel candidates in HCC . Intriguingly, where significantly altered, more than half of the top upregulated genes in the cell lines  Fig. . In the KP) Fig. , includiHCC Fig. , AFP andn = 82, Additional file P < 0.05 at least in one dataset) in poorly differentiated cell lines relative to those well-differentiated. Examples are mitogen-activated protein kinase (MAPK)/RAS family members (HRAS and NRAS), hemochromatosis (HFE), BRD7, ATM, vimentin (VIM), which are upregulated in poorly differentiated cell lines and in human HCC datasets , FAH, PNPLA3, GSTT1, ATP7B, SERPINA1, NFE2L2, CUL3 and MDM2 , the other ‘HCC-associated’ genes such as FRK, HNF1A, ARID1B, KEAP1, β-catenin (CTNNB1), FGF19, LGR5 and glutamine synthetase (GLUL) were lowly expressed in poorly differentiated cell lines (i.e. more expressed in well-differentiated cells) and consistently upregulated in human liver tumours   Fig. . The enc01) Fig. , and shohttps://david.ncifcrf.gov). Pathway enrichment of the upregulated genes indicated an activation of cancer pathways, MAPK/RAS pathway, focal adhesion, proteoglycans in cancer, nuclear factor kappa B signaling and tumour necrosis factor signaling , as well as those consistently downregulated, e.g. liver-specific glutaminase (GLS2), complement 7 and 9 (C7/9), CXCL12, CYP1A2, CYP2E1, GYS2, HAMP, IGF1, LCAT, SLC22A1, etc, n = 1547) . MF protein binding and cell adhesion-related activities, CC nucleoplasm and cytoplasm, as well as virus-related BP were within the top five of the respective GO terms for the human and cell line datasets  was cancer-oriented albeit heterogeneous  were strongly involved in metabolic processes as observed with genomic data. Thus, at proteomic level, the poorly differentiated cell lines also strongly mimic the downregulation of metabolic components as seen in tumours. Carbon metabolism surprisingly appeared in the pathway enrichment of proteins more expressed as well as those lowly expressed in HLE cells  are concordantly expressed by the surrogate cell lines has not yet been reported. We identified HMGs whose expression pattern are more mimicked by poorly differentiated HCC cell lines. These include  92 of 350 downregulated HMGs Fig. . Of noteous Fig. . In contlls Fig. . HoweverIn line with the notion that the metabolic gene profile of the cell lines is reflected at the protein level, the expression pattern of the top deregulated ‘metabolic proteins’ in HLE relative to HUH7 cells (fold difference > 2) overlapped with that of the encoding HMGs Fig. . For exaIt is noteworthy that some metabolic genes , though differentially expressed between well- and poorly differentiated cell lines, were at protein level not distinctly expressed in HLE compared to HUH7 cells. Examples are RRM1, RRM2, PAICS, ITPA, PPAT and GART (in nucleotide biosynthesis) and PGLS, TALDO1 and DERA in the pentose phosphate pathways (PPP). Along with these were over 20 candidates in ribosomal protein synthesis , hexokinase (HK1), PFKP, PGK1, ENO1, and lactate dehydrogenases (LDHA/B) are more expressed in HLE cells, but glucose transporter 6 (SLC2A6), hexokinase muscle isoform (HK2), ALDOA, BPGM, pyruvate kinase (PKM), DLAT, and MPC2 are upregulated HMGs more expressed in HUH7 cells. Similarly, asparagine synthetase (ASNS), glutamine transporters , FASN in fatty acid synthesis (lipogenesis), and PSPH (in serine pathway) are all expressed higher in HUH7 cells. Furthermore, yet unknown but consistently upregulated HMGs such as PLCB1, GNPAT, PLA2G7 and CDS1 in membrane lipids, as well as ALG3, B3GALNT1, CHPF2, DOLK and LYZ in glycan metabolism are more expressed in HUH7 cells. Other upregulated metabolic genes that showed higher expression in HUH7 cells include NADPH oxidase 4 (NOX4), phosphogluconate dehydrogenase (PGD in PPP) and AKR1B10 (among the topmost upregulated metabolic gene in human HCC datasets), as well as key players in cholesterol metabolism  and TCA cycle  , are low in HLE cells as in human HCC datasets  emerged as lowly expressed in HLE cells. This cell line also mostly mimicked downregulated small molecule transporters seen in tumours, e.g. NPC1L1, SLC25A15/20, SLC2A2, SLC1A1 and SLC7A2 , PHGDH, PSAT1, GLUL, glutamate dehydrogenase 1 (GLUD1), among others. Indeed, the corresponding proteins encoded by many downregulated HMGs showed low expression in HLE cells, thus reinforcing the gene-level data  in serine pathway, and transamination mediators (e.g. GOT1 and GPT1) as in patients’ datasets. Further, although HLE cells express a higher level of citrate synthase (CS), they lowly express MPC1 as noted earlier and also several TCA cycle enzymes as seen in patients datasets  are all lowly expressed in HLE cells. Taken together, our findings on the metabolic profiles shared between tumours and the poorly differentiated cell lines support the notion that these cells could be important surrogates for clinically-relevant HCC metabolism studies.Our observation is that the cell lines representative of well (e.g. HUH7) and poorly differentiated (e.g. HLE) HCC subclasses, both, mimic the expression pattern of human HCC-derived metabolic genes (HMGs)  are more expressed in HUH7 cells. It is therefore difficult to exclude that the well-differentiated cell lines also display tumour molecular signatures. Rather, given that these cell lines are low migrating, low proliferating and are known to be more hepatocyte-like [While poorly differentiated cell lines more closely mimic human HCC profiles, the well-differentiated cells, exemplified by HUH7 cells, also showed striking resemblance to human tumours. For example, considering gene or protein expression differences, we identified almost 100 upregulated HMGs more expressed in HUH7 than in HLE cells. These upregulated HMGs are mostly involved in lipogenesis, small molecule transport, glycan-, membrane lipid- and cholesterol metabolism. Furthermore, with the exemption of GLS (more expressed in HLE cells), few amino acid-related genes upregulated in HCC tissues  is a known metabolic feature of cancer . HoweverCDH1, ALB and EPCAM are highly expressed in HUH7 cells. This cell line also express a high level of many metabolic targets consistently upregulated in HCC patients microarrays  [HRAS, NRAS, APC, HFE, MMP9, and a high enrichment of MAPK/RAS, NFKB, and TNF signaling pathways are all evident in HLE cells as often seen in cancer. Prospective hypotheses could interrogate the potential contribution of such enriched pathways in modulating HCC metabolism and progression.Beyond identifying resemblance to tumour metabolic profile, our study uncovered other altered targets in the cell lines that could be relevant signatures in HCC. For example, BASP1, LDHB, CD44 (high in HLE) and APOA1, ASGR1, ITIH2, AGT, AFP (high in HUH7 cells) emerged as the most consistently top targets . The impact of these candidates on HCC metabolic fate is currently  unclear. Of note, besides AFP and GPC3 which are highly studied biomarkers for early HCC detection , CDH1, A, PLCB1) , thus ra. Taken together with the metabolite level changes that coexist with these gene alterations, our findings provide useful insights that could lead to future identification of HCC subclasses with unique nutrient requirements. This will facilitate the prospects of targeting metabolism for HCC therapy, and pave way for delineating metabolic enzymes that are biomarkers or also indispensable drivers of HCC. Importantly, our work will guide the selection of suitable experimental models for the translational studies of specific metabolic pathway alterations seen in human liver cancer.In light of the growing relevance of genomics in refining clinical HCC subclasses and therapeutic prospects , 2, 41, Additional file 1:Figure S1. Phenotypic and molecular characteristics of HCC cell lines. Figure S2. Overlap of genes deregulated in human HCC datasets, and the top ranked ontology of the highly expressed genes (tumours and poorly differentiated cell lines). Figure S3. Overlap of the gene and protein expression pattern in the cell lines compared with human HCC-tissue derived metabolic genes. Figure S4. Metabolic pathways targeted by the drugs used in this study and the effect of glycolysis inhibition or glutamine withdrawal on the proliferation of HCC cell lines. (PDF 1024 kb)Methods and Figures. Additional file 2:Table S1. Genes more or lowly expressed in poorly differentiated relative to the well-differentiated cell lines. Includes all differentially expressed genes . Datasets used: GSE57083, GSE36133 (CCLE) and GSE35818. (DOCX 166 kb)Additional file 3:Table S2. HCC-associated genes. Contains a set of genes often associated with HCC, including those related to epithelial–mesenchymal transition. (DOCX 19 kb)Additional file 4:Table S3. Top pathway enrichment and the specific genes involved. Enrichment analysis was performed with the genes more (3a) or (3b) lowly expressed in the poorly differentiated relative to well-differentiated HCC cell lines. (DOCX 18 kb)Additional file 5:Table S4. Consistent genes in all four human HCC datasets analysed in this study. These are the genes differentially upregulated  or downregulated  in HCC relative to non-tumour liver tissues in each of the four datasets. (XLSX 56 kb)Additional file 6:Table S5. Complete list of downregulated HMGs concordantly low in the p.d. cells . Also included are 475 proteins indistinctly expressed under the same conditions. (XLSX 260 kb)Additional file 8:Table S7. Human HCC tissue-derived metabolic genes (HMGs) mimicked by HLE or HUH7 cells upon comparison. Contains HMGs whose expression pattern in tumours (high or low expression) is reflected either in HLE  or HUH7 cells , when the cells are compared. The included HMGs showed consistent expression pattern in the respective cell lines both in GSE57083 and GSE36133 (CCLE) datasets. Those that also showed similar pattern at the protein level are indicated. (DOCX 25 kb)


SPOP is one of the important subunits for CUL3/SPOP/RBX1 complex tightly connected with tumorigenesis. However, its exact roles in different cancers remain debatable. Here, we identify CYCLIN E1, as a novel substrate for SPOP. SPOP directly interacts with CYCLIN E1 and specific regulates its stability in prostate cancer cell lines. SPOP/CUL3/RBX1 complex regulates CYCLIN E1 stability through poly-ubiquitination. CDK2 competes with SPOP for CYCLIN E1 interaction, suggesting that SPOP probably regulates the stability of CDK2-free CYCLIN E1. CYCLIN E1 expression rescued proliferation, migration, and tumor formation of prostate cancer cell suppressed by SPOP. Furthermore, we found SPOP selectively regulates the substrates’ stability and signaling pathways in prostate cancer and CCRC cell lines, suggesting that complicated mechanisms exist for SPOP to regulate substrate specificity. Altogether, we have revealed a novel mechanism for SPOP in suppressing prostate cancer and provided evidence to show SPOP has dual functions in prostate cancer and CCRC. Drosophila [SPOP was initially identified as a nuclear protein that exhibits a speckled localization pattern . Nuclearosophila –19. MostExtensive genomic studies identified SPOP as a tumor-suppressor in prostate cancer (PCa) tissues , 21. MulSPOP was also identified as an oncogene in clear cell renal carcinoma (CCRC) . SPOP isThus, a debate is currently existing in the field about SPOP’s function in tumorigenesis , 22. WheIn the present study, we have revealed a new mechanism for SPOP in repressing PCa and identified CYCLIN E1, an oncogene and important cyclin involved in G1/S transition, as a new SPOP substrate. We show here SPOP interacts with CYCLIN E1, selectively regulates its stability in prostate and bladder cancer cell lines, and regulates PCa tumorigenesis through CYCLIN E1 degradation. Furthermore, we show here SPOP has substrate specificity and regulates distinguished transcription programs in PCa and CCRC cell lines, suggesting complicated mechanisms exit for SPOP substrate selectivity in different cancers.When exploring the underlying mechanisms for SPOP-regulated tumorigenesis, we found that the exogenous expressed SPOP and CYCLIN E1 interact with each other in HEK293 cells Fig. . We furt4Cl, chloroquine (CQ) and 3-MA, inhibitors for lysosome and autophagy, respectively. Only MG132 inhibited the effect of SPOP on CYCLIN E1 stability, further suggesting the process is dependent on proteasome , one colon cancer cell line (HCT116), two liver cell lines (HepG2 and HL7702), one bone osteosarcoma cell line (U2OS), one cervical cancer cell line (HeLa), one bladder cancer cell lines (UM-UC-3) and one prostate cancer cell line (22RV1)  Fig. . QuantitIn DU145, we successfully verified the interaction between endogenous CYCLIN E1 and SPOP with co-immunoprecipitation Fig. . OverexpTo test whether CYCLIN E1 is one of the substrates for SPOP, in vivo and in vitro ubiquitination assays were performed. SPOP expression greatly increased the poly-ubiquitination of CYCLIN E1 in HEK293 cells Fig. . Then SPTo examine the effect of SPOP mutants in prostate cancer on CYCLIN E1, four constructs containing most frequent SPOP mutations were generated. When CYCLIN E1 was co-expressed with these mutants, it was much more stable than that with wild-type SPOP Fig. . Co-immuPreviously, two ubiquitin E3 ligases for CYCLIN E1 were reported, FBXW7 and RhoBTB3 –28. To c173-177, which both abolish the interaction with CDK2 [To explore the underlying mechanism how CDK2 inhibits SPOP-dependent CYCLIN E1 ubiquitination, we used a CDK2 kinase dead mutant, T160A, and found it can still inhibit CYCLIN E1 degradation by SPOP as wild-type Fig. . Meanwhiith CDK2 , 31, werSPOP stable knockdown cell line, but not that for CCNE1, probably due to the cell cycle defect without CYCLIN E1. The flow cytometry results indicated SPOP deficiency increased S phase cells after release  after nes Fig. , which fnes Fig. . The mRNnes Fig. . These gSPOP is one of the most frequent mutated genes in PCa. In the current study, we identified CYCLIN E1 as its substrate. SPOP directly interacts with CYCLIN E1, poly-ubiquitinates CYCLIN E1 in vivo and in vitro, and represses cancer-related phenotypes induced by CYCLIN E1 expression. Thus, we reveal a new mechanism for SPOP in repressing PCa tumorigenesis.An early structural study revealed a consensus sequence in the substrates for SPOP recognition . In our CYCLIN E1 is one of the important proteins for cell cycle progression and it is well-known that CYCLIN E1 couples with CDK2 and regulates G1/S transition and DNA replication , 34. MorOur data showed that SPOP regulates CYCLIN E1 stability specific in some prostate and bladder cancer cell lines, but not in the other tested cell lines. SPOP overexpression could successfully decrease CYCLIN E1 in nearly all the tested cells. In some cell lines, CYCLIN E1 even decreased after SPOP knockdown, suggesting some unknown factors involves in regulating CYCLIN E1 stability with SPOP in a cell type dependent manner. We have further showed that SPOP selectively regulates its substrates in PCa and CCRC cell lines. Importantly, the xenograft experiments showed that SPOP represses tumor growth rising from a PCa cell line and enhances tumor formation of a CCRC cell line. A recent study indicated that SPOP mutants has cancer specific preference in endometrial cancer to degrade BET family proteins . Our datHEK293, HeLa, HCT116, HepG2, DU145, UMUC3, and HL7702 cell lines were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% penicillin and streptomycin. ACHN, 786-O, 769-P, PC3, SV-HUC-1, T24, U2OS, and 22RV1 cell lines were cultured in RPMI 1640 supplemented with 10% FBS, 1% penicillin and streptomycin. Caki-1 cell lines were cultured in McCOY’s 5A supplemented with 10% FBS, 1% penicillin and streptomycin. All the cell lines were purchased from Cell Bank of Chinese Academy of Science. The tests for mycoplasma contamination were performed by the vendor.Antibodies against Flag (Sigma F3165), HA (Origene TA100012), Myc , EGFP (Abmart 264076), β-Actin , CUL3 (Epitomics #2489-1), RBX1 (Epitomics #5296-1), CYCLIN E1 , ERG , DEK (BD 610948), DAXX (CST #4533), PTEN , FBXW7 , RHOBTB3 (LSBIO LS-C385850) were purchased from indicated commercial sources. Rabbit anti-SETD2 was raised and now commercial available at Abclonal. Mouse anti-SPOP was raised at the Wuhan Institute of Virology, CAS.Cell proliferation and migration were analyzed with RTCA assay as described before . Cells whttp://david.abcc.ncifcrf.gov).Two biological replicates were sequenced for each sample. mRNA-seq library was performed by using Illumina Tru-Seq library construction kit. A 5 μg of total RNA was used as initiation and then prepared according to the manufacturer’s instruction. mRNA-seq libraries were sequenced using HiSeq2000 for 100-bp paired-end sequencing. Quality control of mRNA-seq data was performed using Fatsqc, and then low quality bases were trimmed. After quality control, data were mapped to hg19 genome reference by Tophat2 and allow maximum 2 mismatch. Cufflinks were used to find out differential expression genes. Gene ontology analysis was performed using DAVID (http://gepia.cancer-pku.cn/) [The disease free survival  analysis based on gene expression via GEPIA performs (pku.cn/) . GEPIA uhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100743.The original RNA-Seq data were submitted to the GEO database at the following link, Supplemental Materials


Recent genomic analysis has shown that many of the most commonly used ovarian cancer cell lines have been mischaracterised, leading to erroneous conclusions and a gap in the translation of laboratory research into novel treatments for patients. Here, we use 10 epithelial ovarian cancer cell lines to investigate 2D migration, cell cycle parameters and 3D invasion behaviour into different substrates and find significant differences between the behaviours of cell lines from different origins. Cell lines derived from non-serous carcinomas migrated more quickly and were more likely to invade into Matrigel and collagen I substrates than cell lines derived from high-grade serous carcinomas. However not all cell lines derived from non-serous carcinomas exhibited similar invasive behaviour. These findings may reflect differences in the behaviour of the primary tumour types from which the cell lines were derived, given that high-grade serous carcinomas typically expand and spread over peritoneal surfaces. These results provide the basis of an  Despite the improvement in prognosis for most solid tumours, epithelial ovarian cancer prognosis remains relatively unchanged2, with only one major new treatment introduced in the last 30 years3. The term ovarian cancer describes several histotypes which show distinct cellular origin, molecular aberrations and disease progression in patients4; however, this heterogeneity is not reflected in the treatments currently available. Furthermore, the term ovarian cancer may be misleading as many ovarian cancers arise from non-ovarian tissue. The single consolidating feature of ovarian cancers is the localised dissemination of tumour cells to the ovary and pelvic organs2. Ovarian cancer is difficult to identify clinically since patients often present with non-specific symptoms such as bloating and fatigue5. Indeed, 80% of women are diagnosed at an advanced stage where 5-year survival is only 5% compared to 92% if the cancer is detected early5. For women diagnosed at a late stage, the treatment options remain limited and, even if the cancer is detected early, the chance of relapse is 70% within 18 months5.Ovarian cancer is the most lethal gynaecological disease in women and, in 2018, there will be approximately 300,000 new diagnoses of ovarian cancer worldwide6; it encompasses all cancers that arise from epithelium and involve the ovary7. The most common histological types are high-grade serous, low-grade serous, endometrioid, clear cell and mucinous carcinomas. High-grade serous ovarian carcinomas (HGS) commonly arise in the epithelium of the fallopian tube fimbria and subsequently present as apparently ovarian tumours after implantation in the ovary  and have strong links to endometriosis, a disease that affects 10% of the female population9. Ovarian endometriosis is thought to arise from retrograde menstruation and results in endometrial tissue growing outside the uterine body, most commonly on the ovaries . We show clear behavioural differences between high-grade serous and non-serous ovarian cancer cell lines and propose the use of these cell lines as an in vitro model to investigate distinct ovarian cancer tumour types.In 2013 Domcke et al.11 and Beaufort et al.12. On this basis, OVCAR3, COV318, PE01, PE04 and FUOV1 were considered high -grade serous (HGS) and A2780, OVCAR8, OAW42, SKOV3 and TOV21G non-serous (NS). The keratinocyte-derived HACAT cell line was included as a non-ovarian control.The panel of cell lines was categorised based on the genomic profiles outlined by Domcke 2D migration behaviour was assessed by relative wound density using the IncuCyte ZOOM® wound healing assay , the 2 least migratory cell lines (FUOV1 and PEO1) and the 2 cell lines that behaved differently from the other cell lines in the HGS and NS groups in the migration assay (A2780 and COV318). In the collagen invasion assay, only 2 cell lines invaded into the collagen matrix, both NS cell lines (TOV21G and A2780). None of the HGS cell lines proliferated on top of or invaded into the collagen matrix Fig. . SKOV3 s17 commonly associated with epithelial-mesenchymal transition (EMT)18. A2780 cells formed a thick proliferative cell layer on top of the collagen and invaded in small clumps of cells. This cluster invasion phenotype is characteristic of epithelial tumour cells16.The collagen invasion assay is more qualitatively informative than the Matrigel invasion assay as it is possible to visualise the morphology of the invading cells by taking a cross-section of the collagen matrix. TOV21G cells formed a thin layer of cells on top of the collagen, degraded the collagen or proliferated in a cavity and invaded as single cells with mesenchymal morphology. The single cells invaded outwards from the proliferative cell layer. This spindle-like morphology is characteristic of sarcomatous tumour cell invasionPrevious studies investigating ovarian cancer cell line behaviour commonly use cell lines such as OVCAR3 and SKOV3 and focus on investigating how genetic manipulation or drug treatments can increase or decrease invasive and migratory behaviour. However, to our knowledge the endogenous behaviour of these cell lines stratified by histotype has not been carried out previously. We show that high-grade serous carcinoma-derived (HGS) cell lines behave differently from cell lines derived from non-serous carcinomas (NS) in four different behavioural assays. Our findings highlight the need for careful cell line selection in order that the data derived from these cell lines can be interpreted appropriately.in vitro is very difficult and many of the genetic mutations required to immortalise cell lines are found only in late-stage tumours, consistent with the observation that most cell lines have the potential to grow as metastases19. In keeping with this argument, TOV21G was derived from stage 3 clear cell carcinoma20; and SKOV3 and OAW42 were both derived from ascitic fluid (from post-chemotherapy patients)22, which is associated with higher stage disease. Given that NS tumours are normally diagnosed and treated prior to metastasis, the behaviour differences of these cell lines cannot therefore be used to replicate and understand the behaviour of primary NS tumours; however, they are useful for understanding the rare but more aggressive advanced stage NS disease.In general, the NS cell lines migrated more rapidly than HGS cell lines, which seems counter-intuitive as HGS tumours are typically more clinically aggressive, spread throughout the peritoneum and have a worse patient prognosis compared to NS tumours. Moreover, NS ovarian tumours commonly present at an early stage before metastasis has occurred. However, establishing cell lines from primary tumours in vitro do not necessarily segregate the same way. Most importantly the cell lines do not all behave as expected based on their putative identity.It is important to highlight that although the HGS and NS cell lines showed different migration behaviour when analysed as distinct groups, the behaviours of individual cell lines did not segregate as clearly. A2780 is an NS cell line but behaved similarly to PEO1, PEO4 and FUOV1 which are HGS cell lines. Similarly, COV318 is classified genomically as a HGS cell line but behaves similarly to TOV21G and OVCAR8 which are NS cell lines. These findings highlight that, although these cell lines can be divided into different categories by their mutation profiles, their behaviours 0/G1 is well documented24. Once a cell enters S phase it is committed to DNA replication, which is a very energy intensive process, making it unlikely that these cells can be involved in other cellular processes, such as invasion. The cell cycle findings provide evidence that the migration behaviour differences are not a result of proliferation. Furthermore, we found no systematic relationship between doubling time, estimated by cell count, and migration behaviour for the cell lines used in this study. For example, A2780, HACAT and TOV21G cells all had similar doubling times  but their migration behaviour was markedly different; A2780 migrated much more slowly than TOV21G and HACAT, failing to close the wound area after 60 hr. In contrast HACAT cells migrated quickly, closing the wound area in 33 hr but TOV21G cells took 60 hr despite a similar proliferation rate. These results are also in keeping with data derived from live imaging of invasion showing that cells in G1/G0 are more likely to invade and, if the same invasive cells cycle into S phase, they cease invasion until the cell cycle is complete24.The link between cancer cell invasion and cell cycle arrest in G8. Even very large tumours only invade the superficial layers of the bowel and omentum and not the deeper layers8. It is rare to find HGS metastases to the bone or brain, which are common with other epithelial tumours such as breast and colon cancers which undergo several stages of intra-/extravasation before they seed metastases in other organs8. The behaviour of the NS cell lines is similarly counter-intuitive given that the disease is primarily non-metastatic. However, TOV21G was derived from the tumour of a patient diagnosed with stage 3 clear cell carcinoma20 and, anecdotally, patients with advanced stage NS tumours have a poor prognosis, similar to or even worse than those with HGS tumours, in keeping with the hypothesis that the behaviour of TOV21G replicates the rare advanced stage of the disease. Information on the origins of SKOV3 and OVCAR8 is limited25 but SKOV3 was derived from ascitic fluid rather than primary tumour27. Given that the formation of ascites is associated with advanced disease the invasive behaviour exhibited by SKOV3 also represents the behaviour of advanced stage disease28. HGS cells are characterised by a TP53 mutation but OVCAR8, which is categorised as non-serous and behaves similarly to other NS cell lines, also has a TP53 mutation . It is therefore unlikely that, in these cell lines, the differences in the migration and invasion behaviours of HGS compared to NS cells are driven by TP53 mutation as previously suggested by Ahn et al.29. Indeed, the most motile cell lines (including OVCAR8) have mutations in signalling pathways such as MAPK, PI3K and WNT11, which generally lead to overactivity in those pathways, and could explain the NS phenotype we observe. HGS cell lines characterised by only a TP53 mutation would not have this phenotype and would be susceptible to genomic instability. In parallel, clinical evidence supports the idea that HGS tumour cells do not have to migrate quickly to seed metastasis as the disease develops undetected over several years.Similarly to migration, the finding that HGS cell lines are not invasive may seem unexpected. However, HGS tumours do not have to physically invade through tissue to seed metastatic tumours throughout the peritoneal cavityin vivo tumourigenicity experiments. Hernandez et al.30 compared the ability of a different but overlapping panel of ovarian cancer cell lines to form tumours in nude mice over 6 weeks. They found that OVCAR8, SKOV3 and A2780 had high tumourigenicity when injected intraperitoneally, whereas OVCAR3, PEO1 and PEO4 had low tumourigenicity. Although the cell lines were not stratified by histotype in the in vivo study, the tumourigenic cell lines OVCAR8, SKOV3 and A2780 are derived from NS tumours and the non-tumourigenic cell lines OVCAR3, PEO1 and PEO4 are derived from HGS tumours.The differences in invasive behaviour between HGS and NS cell lines used in this study are supported by in vitro is challenging. The transwell invasion assay uses Matrigel, an extract of Engelbreth-Holm-Swarm (EHS) tumour which contains basement membrane components. Matrigel is commonly used in invasion assays but its suitability as an in vitro basement membrane substitute has been challenged31. Furthermore, the epithelial ovarian cancer microenvironment is composed of various different proteins and cell types. Collagen I is the most abundant structural protein within the ovarian extracellular matrix (ECM)32; therefore we used the collagen/fibroblast organotypic invasion assay to determine whether ovarian cancer cell lines behave differently when they encounter an ECM component more replicative of the in vivo environment.Replicating the tumour microenvironment 35. However, this heterogeneity cannot be explained by differences in tumour origin or molecular profile as both are associated with endometriosis and they have a similar genomic landscape. The differences in behaviours of NS cell lines in Matrigel and collagen highlights the importance of the substrate and may be related to the ECM context that the original cell line was derived from. Although the details in the original paper are sparse, A2780 was derived from untreated primary tumour material34. TOV21G was also derived from tumour material whereas SKOV3 was derived from ascites21. It is therefore interesting that TOV21G invades aggressively into both Matrigel and collagen, whereas SKOV3 shows invasion into Matrigel but not collagen; conversely A2780 invades aggressively into collagen but not Matrigel. Moreover, all the HGS cell lines apart from FUOV1 are derived from ascites39. Thus, overall, 2 of the 3 cell lines derived from primary tumours (TOV21G and A2780) attached and invaded into collagen but none of the cell lines derived from ascites exhibited this behaviour. This suggests that cell lines derived from the primary tumour have the ability to attach and invade into collagen whereas cell lines derived from ascites may not. This is consistent with the fact that tumour cells floating in ascitic fluid are able to survive and grow in suspension, without attachment to, or invasion into, a solid matrix.The heterogeneity of the NS cell lines compared to the HGS cell lines may in part be explained by the fact that the NS category encompasses cell lines which were derived from clear cell and endometrioid carcinomasin vitro experiments using these cell lines.Cell lines are vital as a first step to understanding molecular and behavioural changes that result in tumour development and progression. However, the current approach of using cell lines to investigate ovarian cancer, without stratifying by histotypes, is hindering the advancement of the field. Our data provide evidence that ovarian cancer cell line behaviour correlates to some extent with tumour histotype and hence origin. However, the variability between cell lines indicates that behavioural characterisation should be taken into account, in addition to tumour histotype, origin and molecular profile, when interpreting the results of in vitro. Representative in vitro models will help overcome the current gap in the translation of laboratory research to novel therapies for ovarian cancer patients. Future research should focus on using appropriately selected cell lines for the development of in vitro models to understand the molecular and behavioural differences between ovarian tumour types.Taken together these findings provide a basis for the selection of relevant ovarian cancer cell lines to investigate the differences between HGS and NS tumours All reagents were purchased from Thermo Fisher Scientific  unless otherwise stated.2. OVCAR3, SKOV3, OVCAR8, PEO1, PEO4, COV318, FUOV1, A2780, TOV21G and OAW42 were cultured in RPMI Medium 1640 (1 × ) supplemented with 10% fetal calf serum (FCS), 1X non-essential amino acids , 100 µL/mL (v/v) penicillin-streptomycin, 1 mM sodium pyruvate (Sigma Aldrich) and 1 µL/mL bovine insulin (Sigma Aldrich). All ovarian cancer cell lines were cultured in the same medium to control for biases due to varied levels of growth factors or different serum levels, in keeping with the approach of Beaufort et al.12. All of the lines had been acclimatised to this medium for at least 3 months prior to experimentation. HACAT was cultured in DMEM (1×) + GlutaMAX supplemented with 10% FCS, 100 µL/mL (v/v) penicillin-streptomycin. Fibroblasts were cultured in DMEM (1×) +GlutaMAX supplemented with 10% FCS. Cell lines were routinely tested for mycoplasma contamination and all cell experiments were carried out in sterile conditions. Cell lines were passaged using 0.05% trypsin-EDTA (1×). All experiments were carried out with cell lines no more than 10 passages apart to ensure biological repeats and limit the mutational effect of passaging. Cell lines were not synchronised prior to experimentation.Human epithelial ovarian adenocarcinoma cell line SKOV3 was acquired from ATCC. Human epithelial ovarian adenocarcinoma cell lines NIH: OVCAR-3 (OVCAR3), OVCAR8, PEO1, PEO4, COV318 and FUOV1 were kindly supplied by David Melton from the University of Edinburgh. Human epithelial ovarian adenocarcinoma cell lines A2780, TOV21G and OAW42 were kindly supplied by Charlie Gourley from the University of Edinburgh. Human keratinocyte cell line HACAT was kindly supplied by Lana Woolford from the University of Edinburgh. All cell lines were authenticated using STR profiling . Red fluorescent protein telomerase reverse transcriptase (TERT) immortalised fibroblasts (RTIF) generated from skin punch biopsies were a kind gift of Andrew Campbell . All cell lines were maintained in a humidified incubator at 37 °C with 5% CO6 cells/mL cell suspension (5 × 105 cells) were seeded into every well of an ImageLock™ 96 well plate . The plate was then incubated at 37 °C, 5% CO2 for 4 hours (hr). After the incubation period, the 96 well WoundMaker™ (Essen Bioscience) was used to simultaneously create wounds in all wells. The plate was then washed twice with culture media  to remove any cell debris. 100 µL of normal cell culture media was then added to each well and the plate was placed inside the IncuCyte ZOOM® incubator system (Essen Bioscience). Plates were imaged every 3 hr for 60 hr. The percentage area covered by cells inside the wound area relative to the percentage area covered by cells outside of the wound area (relative wound density) at every time point was analysed. 3 technical repeats were carried out for each cell line and this procedure was repeated 3 times to ensure 3 biological repeats.For each cell line, 100 µL of a 5 × 105 cells were transferred to a fluorescence-activated cell sorting (FACS) tube and the content centrifuged for 5 minutes (min) at 400xg. To fix the cells, the supernatant was discarded and the pellet resuspended in 300 μL PBS supplemented with 50% FCS. 900 μL of ice-cold 70% ethanol was then slowly added whilst gently tube vortexing. The samples were stored at 4 °C. On the day of analysis, 3 mL PBS was added to the samples and the content centrifuged for 5 min at 400xg. The supernatant was discarded and the pellet re-suspended in 300 μL PBS supplemented with 0.1 μL/mL DAPI, 0.1% Triton x100. The samples were then stored for 1 hr in the dark and analysed using flow cytometry (BD LSRFortessa X-20). This procedure was carried out with 3 biological repeats per cell line.For each cell line, 5 × 10et al.40. Matrigel  was diluted in a 1:1 ratio with 1% serum culture media. Then 100 μL of the dilute Matrigel was added into the centre of each Transwell (Corning) and the plate was incubated for approximately 1 hr at 37 °C, 5% CO2. During this time, a cell suspension of 5 × 105 cells/mL was prepared in normal cell culture media for each cell line. Once the Matrigel had set, the transwells were inverted and placed onto the lid of the 24-well plate. Next 100 μL of the cell suspension (5 × 104 cells) was pipetted onto the underside of the filter. The transwells were then carefully covered with the base of the plate and placed (inverted) in an incubator at 37 °C, 5% CO2 for 3 hr to allow the cells to attach to the transwell. After the incubation period, the plate was turned the right way up and each transwell dipped into 1 mL 1% serum media to wash; this was repeated 3 times. The transwell was left in the final wash media and 100 μL normal cell culture media was added to the top of the transwell (on top of the Matrigel). The plate was then incubated at 37 °C, 5% CO2 for 5 days. On the 5th day SYTO™9 green fluorescent nucleic acid stain was diluted 1/2000 into 1% serum media in a falcon tube and vortexed. Next 0.5 mL of this solution was added to the wells of a 24-well plate. The plate was removed from the incubator and a transwell was added to each of the wells with SYTO™9 media. Then 0.5 mL of the SYTO™9/media solution was pipetted onto the top of each transwell to submerge the gel in staining solution. The plate was then incubated for at least 1 hour at 37oC with 5% CO2 and imaged immediately afterwards.This method is a modified version of Hennigan 13. Three separate assays were carried out per cell line giving 3 biological repeats with four Z-series taken for each sample per assay.Transwells were placed on a thin coverslip and images were acquired on a Nikon Confocal A1R confocal microscope using a 20X NA 0.75 objective. The microscope is equipped with a 405 nm diode, Argon laser and 561 and 648 nm laser lines and four photomultiplier tubes. Data were acquired using NIS Elements AR software . Images were scanned at 1x zoom, 0.25 speed and 1x line average. Optical sections were scanned at 15 μm intervals (Z-steps) moving up from the underside of the filter into the Matrigel. The percentage area covered by cells in each Z-step image was quantified in Image J using the thresholding feature. For quantification purposes, only cells in the Z-step 45μm or above were considered invasive as described previouslyet al.41. A confluent T75 flask of fibroblasts was passaged and a cell suspension of 1 × 106 cells prepared. The fibroblasts were then centrifuged at 400xg for 5 min. During this time, the collagen mix was prepared and all components kept on ice to ensure the collagen did not set; 3 mL MEM (10 × ) was added to 25 mL rat tail collagen I (approximately 2.6 mg/mL). Then 0.25 M NaOH (in-house) was added dropwise whilst gently mixing until the collagen mix turned orange but not pink (approximately 2.5 mL NaOH). The fibroblasts were resuspended in 3 mL FCS, added to the collagen solution and gently mixed. 2.5 mL of the collagen/fibroblast mixture was then quickly added to each well of two 6-well plates avoiding any bubbles. The plates were incubated at 37 °C, 5% CO2 for 10 min to allow the mixture to set. The collagen/fibroblast matrix was then detached from the sides of the wells and 1 mL of fibroblast culture media carefully added on top of the matrix. The plates were incubated at 37 °C, 5% CO2 for approximately 8 days to allow the collagen/fibroblast matrix to contract to 1.5 cm diameter.Collagen I was prepared from rat tails as described by Timpson 4/mL. 1 mL of the cell suspension was then seeded on top of the matrix. The 24-well plate was then incubated at 37 °C with 5% CO2 for 5 days to allow cells to grow confluent.Once the collagen/fibroblast matrices had contracted, blunt forceps were used to gently move them to a 24-well plate. All cell lines were then split and resuspended in normal growth media at approximately 8 × 102 for 10 days to allow the cells to invade. After 10 days the matrix was removed from the grid and cut in half using a scalpel. The collagen matrix was then added to a falcon tube with 5 mL 4% (w/v) paraformaldehyde to fix overnight.Tripods were made out of stainless steel grids and autoclaved prior to use. The sterile grids were placed into a 6 cm dish and normal cell culture media added to a level just above the grid. Using blunt forceps the collagen/fibroblast matrix was transferred to the grid and the media gently aspirated so that the bottom of the matrix is in contact with media but not submerged. This creates an air/liquid interface to facilitate invasion. Dishes were incubated at 37 °C with 5% COThe fixed collagen/fibroblast matrix was embedded in wax, sectioned and stained with haematoxylin and eosin (H&E) by the CRUK Edinburgh Centre Pathology & Phenomics Lab. Stained sections were imaged on the Nanozoomer XR model, data captured using NDP scan v 3.1. 40x magnification.All statistical analysis was carried out in SPSS and graphs created in GraphPad Prism unless otherwise stated. The Kolmogorov-Smirnov (KS) test was used to determine the normality of the data. For normally distributed data (KS: p > 0.05), parametric tests such as the independent samples t-test were used. For non-normally distributed data (KS: p < 0.05), the non-parametric Mann-Whitney U test was used. Significance was set at p < 0.05 for all statistical analyses. Wound healing data were analysed using two-way repeated measures ANOVA (GraphPad Prism); the normality of the data was assumed and limitations of this are acknowledged.


Neopterin has been extensively used as a clinical marker of immune activation during inflammation in a wide range of conditions and stresses. However, the analysis of neopterin alone neglects the cellular reactions that generate it in response to interferon-γ. Neopterin is the oxidation product of 7,8-dihydroneopterin, which is a potent antioxidant generated by interferon-γ-activated macrophages. 7,8-Dihydroneopterin can protect macrophage cells from a range of oxidants through a scavenging reaction that generates either neopterin or dihydroxanthopterin, depending on the oxidant. Therefore, plasma and urinary neopterin levels are dependent on both macrophage activation to generate 7,8-dihydroneopterin and subsequent oxidation to neopterin. This relationship is clearly shown in studies of exercise and impact-induced injury during intense contact sport. Here, we argue that neopterin and total neopterin, which is the combined value of 7,8-dihydroneopterin and neopterin, could provide a more comprehensive analysis of clinical inflammation than neopterin alone. The level of clinical inflammation occurring as a result of physical trauma, cardiovascular disease, cancer, bacterial, parasitic infections, and viral infections is often assessed by measuring the concentration of plasma and urinary neopterin ,2,3,4,5.The convenience of measuring neopterin ignores the basic biology that macrophages do not enzymatically synthesize neopterin. Neopterin is an oxidized form of 7,8-dihydroneopterin, a product of γ-interferon-mediated upregulation of GTP cyclohydrolase I (GTPCH1) . TherefoInterferons α and γ, lipopolysaccharide, and possibly phorbol ester all upregulate the activity of monocyte-derived macrophage GTPCH1 . GTPCH1 In turn, 7,8-dihydroneopterin and neopterin are readily transported in either direction across the cell membrane. In cell culture, the addition of 7,8-dihydroneopterin to an incubation media results in the relatively rapid accumulation of 7,8-dihydroneopterin within the cells .7,8-Dihydroneopterin is a potent radical scavenging and chain-breaking antioxidant, which can out compete α-tocopherol for the lipid peroxyl radical during low-density lipoprotein (LDL) oxidation, even though it is water soluble . Peroxyl7,8-Dihydroneopterin, but not neopterin or xanthopterin, downregulates the level of CD36, which is the primary scavenger receptor responsible for the uptake of oxidized LDL and foam cell formation in atherosclerosis ,39. OxLDNeopterin has been reported to activate inducible nitric oxide synthase (iNOS) in rat smooth muscle cells , but supThe evidence suggests that measuring either neopterin or 7,8-dihydroneopterin alone cannot provide a complete picture of oxidative stress or immune activation. Rather, the combination of both biomarkers elucidates a more robust mechanism of the inflammatory process, with an analysis of neopterin being a measure of the oxidative status within the cells, and 7,8-dihydroneopterin being a measure of interferon-mediated cellular activation. Moreover, measuring the ratio between 7,8-dihydroneopterin and neopterin may elucidate differences in overall oxidative stress versus immune activation in different disease models and physical trauma.Neopterin was first isolated from human urine in 1967  and usedAlthough ELISA offers high throughput analysis within hospitals, small-scale clinical and research laboratory analysis have used HPLC. Since neopterin is highly fluorescent, nanomolar concentrations can be reliably detected after separation from other compounds. C18 has been popular, but we have found isocratic analysis on strong cation exchange (SCX) columns at pH 2.5 that were excellent for urine analysis ,35. HoweAlthough an abundance of detection methodologies is promising, sample preparation still remains an important aspect. Since urine contains very low levels of proteins, and HPLC columns have become relatively inexpensive compared to sample clean-up costs, an HPLC analysis of urine usually involves the direct injection of diluted urine into the HPLC . In compIn spite of 7,8-dihydroneopterin’s biosynthetic heritage providing potential for an excellent marker of immune activation, it has been widely neglected in clinical or experimental research. 7,8-Dihydroneopterin’s low fluorescence and nM concentration in plasma make it relatively difficult to detect. 7,8-Dihydroneopterin can be easily detected at μM concentration by its absorbance at 254 nm, but in clinical samples, it is usually at the nM level. There is also no ELISA kit that is commercially available for 7,8-dihydroneopterin, kits are only available for neopterin. 7,8-Dihydroneopterin is also both heat and UV light-sensitive in comparison to neopterin .The usual approach to measure 7,8-dihydroneopterin is oxidation to neopterin using an acidic iodine solution ,13,58 orAnother reason that neopterin has been favored over total neopterin analysis is because 7,8-dihydroneopterin can be labile. In air-saturated solution at 25 °C, 7% of the 7,8-dihydroneopterin was lost over 4 h . This raThe two-step process to measure 7,8-dihydroneopterin can be avoided using HPLC coupled with mass spectrometry (LC-MS or LC-tandem mass spectrometry (LC-MS/MS)) ,59. The While neopterin has been measured repeatedly as an assessment of macrophage activation and infiltration for several decades ,61,62,63Recently, neopterin and 7,8-dihydroneopterin have been used as indicators of immune system activation in sport and exercise medicine, and are gaining momentum over other traditional inflammatory markers . Their bNeopterin has been routinely measured in several high-intensity exercise studies. Neopterin has been shown to rise in response to high-intensity cycling , ultra-eTypically, exercise studies have only measured neopterin ,7. WhilsRecent studies examining impact-induced trauma during exercise have begun to elucidate the importance of measuring neopterin in addition to 7,8-dihydroneopterin. The combined analysis provides a comprehensive overview of exercise-induced changes in oxidative status and immune system activation ,71,72. FTo complicate the exercise-related research on neopterin, recent evidence ,71,72 haCollectively, sport and exercise research that have measured neopterin and 7,8-dihydroneopterin have provided evidence that the change in one does not necessarily result in a change in the other. To critically and correctly evaluate a clinical outcome, treatment efficacy, or the oxidative status/immune system activation of an individual, neopterin and 7,8-dihydroneopterin should be simultaneously measured. There is a clear need to bring the analysis of neopterin and 7,8-dihydroneopterin from sport and exercise medicine back into the classical clinical environment. The measurement of neopterin and total neopterin would provide a clearer and more sensitive measure of patient oxidative stress and inflammation during clinical events.


Vitamin A is an essential micronutrient that participates in a wide rangeof biological processes. Retinoic acid (RA) is an active metabolite of vitamin Athat functions as an immune regulator. Graft-versus-host disease (GVHD) is amajor complication of allogeneic hematopoietic stem cell transplantation (HSCT).It is characterized by extensive inflammation arising from an alloimmuneresponse involving various host and donor immune cells. Since vitamin A affectsdifferent immune cell lineages and regulates an array of immune responses,vitamin A, and more specifically retinoic acid, is likely to influence theincidence and/or severity of GVHD. Indeed, recent preclinical and clinical datasupport this concept. In this review, we briefly summarize recent advances inour understanding of the potential role of vitamin A in modulating GVHD riskafter allogeneic HSCT. Vitamin A is a fat-soluble vitamin that plays an essential role in a widerange of biological processes. Retinoic acid (RA), the major metabolite ofvitamin A, is vital for embryonic development, cell differentiation,proliferation, and apoptosis. RA is also important for visual function andimmune homeostasis. Vitamin A is obtained from a diet containing carotenoids(vitamin A precursors found in plants) or retinyl esters  . Retinyl9-cis-RA binds RXRspreferentially, whereas both all-trans-RA and9-cis-RA are ligands of RARs [Vitamin A has long been recognized as an important factor for maintainingimmune homeostasis. It has been well documented that vitamin A deficiencyimpairs anti-pathogen immunity and is associated with increased susceptibilityto infections . Most im of RARs ,7. Thee+ T cell polarization. RA inhibits the generation of Th17cells in vitro. In the presence of TGF-β, RA facilitates the conversionof CD4+Foxp3- T cells intoCD4+Foxp3+ induced regulatory T cells (iTregs) [RA can act on many innate and adaptive immune cell lineages to regulatevarious immune responses –10. One (iTregs) . RA also(iTregs) . In addi(iTregs) . Therefo(iTregs) ,16. On t(iTregs) .Both st(iTregs) . Thus, RAll-trans RA is used clinically fortreating patients with acute promyelocytic leukemia due to its ability to inducethe terminal differentiation of leukemic cells. Retinoids have been reported toregulate the survival and maturation of human DCs, the most potent antigenpresenting cells of the immune system [in vitro andin vivo [RA is also well known for its effects on myeloid cell differentiation,survival, and function. e system . Recent e system ,22. RA cin vivo ,24. Furtin vivo ,26.+T cells as well as CD8+ cytotoxic T cells cause target tissue damagecharacteristic of GVHD. Among these three phases, T cell activation,differentiation, and migration take the central stage of GVHD pathogenesis.Therefore, any factors that can influence these T cell properties have thepotential to affect GVHD risk after allogeneic HSCT.Allogeneic hematopoietic stem cell transplantation (HSCT) is an effectivetreatment for many hematological malignancies and nonmalignant disorders. Eachyear, many patients diagnosed with leukemia, lymphoma, and multiple myeloma willreceive an allogeneic stem cell transplant in an effort to cure theirhematologic malignancy. Although the conditioning regimen consisting ofradiation and chemotherapy may directly kill tumor cells, the curative potentialof allogeneic HSCT relies on a donor T cell mediated graft-versus-leukemiaeffect . UnfortuEmerging evidence suggests that vitamin A can affect GVHD development andseverity. Indeed, recent pre-clinical and clinical data support the concept thatvitamin A and/or retinoic acid can modulate GVHD risk after allogeneic HSCT.+CD4+/Foxp3+CD4+inVAD recipients compared to control mice. These studies demonstrated that dietaryvitamin A levels can modulate GVHD mortality by influencing donor T cellmigration to GVHD target organs after experimental allogeneic HSCT.The GI tract is one of major target organs of acute GVHD . To causSubsequently, two independent groups reported that genetic inhibition ofRAR-α signaling in donor T cells significantly reduced theiralloreactivity and ability to cause lethal GVHD ,37. TheyThus, we further examined the effects of donor vitamin A deficiency andpharmacological inhibition of donor T cell retinoic acid pathway on GVHDseverity . We founEmerging clinical studies have also supported the concept that vitamin Ais a potential factor that modulates GVHD risk, although some of the resultsobtained so far are inconsistent with animal studies.+ effector T memory cells in patients with vitamin A levelsbelow the median.In a recent study , LounderIn another interesting clinical study, Tong and colleagues investigatedthe association between serum vitamin A levels and the development of ocularGVHD . They meall-trans RA isnot the only active metabolite of vitamin A, whether vitamin A can act throughan RA-independent manner to modulate GVHD warrants further study.Furthermore, Tong et al. found that treatment with vitamin A ointmentimproved ocular GVHD in vitamin A deficient patients, providing further evidencethat vitamin A metabolism may be involved in the pathogenesis of ocular GVHD. Itwill be interesting to see if vitamin A supplementation can reduce intestinalGVHD in pediatric patients after allogeneic BMT . ContinuIt is interesting that a common micronutrient such as vitamin A can modulatethe severity of a systemic disease such as GVHD. Preclinical studies demonstratedthat exogenous RA intensifies GVHD and vitamin A-deficient recipients show reducedintestinal GVHD after allogeneic HSCT –38. Clin


Cell lines are widely-used models to study metastatic cancer although the extent to which they recapitulate the disease in patients remains unknown. The recent accumulation of genomic data provides an unprecedented opportunity to evaluate the utility of them for metastatic cancer research. Here, we reveal substantial genomic differences between breast cancer cell lines and metastatic breast cancer patient samples. We also identify cell lines that more closely resemble the different subtypes of metastatic breast cancer seen in the clinic and show that surprisingly, MDA-MB-231 cells bear little genomic similarities to basal-like metastatic breast cancer patient samples. Further comparison suggests that organoids more closely resemble the transcriptome of metastatic breast cancer samples compared to cell lines. Our work provides a guide for cell line selection in the context of breast cancer metastasis and highlights the potential of organoids in these studies. While cell lines and organoids are extensively used to study cancer, how closely they resemble the disease in patients remains unclear. Here, Liu et al. shed light on this issue by comparing the genomic and transcriptomic profiles of different breast cancer cell lines and organoids to data from patient-derived breast cancer metastases. However, the fact that many drugs with promising preclinical evidence fail in the clinic urges the reinvestigation of cell lines as tumor models2. The differences between cell lines and tumors have raised the critical question to what extent cell lines recapitulate the biology of tumor samples4.Cancer cell lines were initially derived from tumors and cultured in a two-dimensional environment. Due to the merit of cell culture, they have been widely used as models to study cancer biology and test drug candidates5. The Cancer Cell Line Encyclopedia (CCLE) characterized both genomic and transcriptomic profiles for more than 1000 cell lines6. Domcke et al.7 performed a comprehensive comparison of molecular profiles between 47 ovarian cancer cell lines and ovarian tumor samples and showed that several rarely used cell lines more closely resembled high-grade serous ovarian tumor samples than popular cell lines. We examined the transcriptome similarity between hepatocellular carcinoma (HCC) cell lines and HCC tumor samples and demonstrated that nearly half of the HCC cell lines did not resemble HCC tumor samples8. Jiang et al.9 conducted a comprehensive comparison of molecular portraits between breast cancer cell lines and primary breast cancer samples, and uncovered both similar and dissimilar molecular features.The emergence of large-scale genomic data provides an unprecedented opportunity to quantify the biological differences between cancer cell lines and human tumors. The Cancer Genome Atlas (TCGA) project characterized both genomic and transcriptomic profiles for more than 10,000 human tumor samples across over 32 tumor types11. Previous cell line evaluation analysis was mainly performed in reference to primary tumors. It remains unknown whether cell lines closely resemble metastatic cancer and thus are appropriately used in translational research. Robinson et al.12 performed whole-exome and transcriptome sequencing on 500 adult patients with metastatic solid tumors and recently released their dataset (MET500). This large-scale genomic profiling combined with existing genomic data allows the evaluation of the utility of cell lines as models for metastatic cancer. Using breast cancer as a case study, we comprehensively compare multiple types of molecular features between breast cancer cell lines and metastatic breast cancer samples , PIK3CA (0.35), TTN (0.29), OBSCN (0.19), and ESR1 (0.14). We identified 19 differentially mutated genes between MET500 and TCGA samples (FDR < 0.001) and the five most significant genes were ESR1, TNK2, OBSCN, CAMKK2, and CLK1  were mutated in at least 50% of cell lines, suggesting that majority of these gene mutations could be recapitulated by only a few cell lines. Surprisingly, 9 of the 75 genes  were not mutated in any cell line. Notably, ESR1 has been identified as a driver gene of cancer metastasis and associated mutations can cause endocrine resistance of metastatic breast cancer cells14, yet none of the cell lines could be used to appropriately model it.Consistent with previous research, we identified a long-tailed mutation spectrum of the 1630 genes in MET500 breast cancer samples and 69 of them were highly mutated  in MET500 breast cancer samples. Further analysis of somatic mutation profiles of the 25 genes in TCGA breast cancer samples confirmed their hypermutations were specific to breast cancer cell lines  profiles between MET500 breast cancer samples and CCLE breast cancer cell lines. We observed a high correlation of median CNV values across the 1630 commonly genotyped genes . The top 20 cell lines were all breast cancer cell lines, suggesting metastatic breast cancer cells retain the transcriptomic signature from the tissue they originated in and cell lines have the potential to resemble the transcriptome of them  is proven to be an effective approach to evaluate cell lines for research purposehem Fig. . MDA-MB-We next assessed whether cell lines resembling the transcriptome of samples from different metastatic sites were identical. We were only able to consider liver and lymph node (the two sites which have at least nine samples) due to the lack of enough samples from other sites in the MET500 dataset. For each of them, we performed metastatic-site-specific TC analysis  and found the results were highly correlated Fig.  with MDAGiven the genomic heterogeneity of breast cancer, we further asked whether cell lines resembling the transcriptome of metastatic breast cancer of different subtypes were identical. To address this question, we first determined the PAM50 subtype of MET500 breast cancer samples with R package genefu and then applied t-SNE to visualize them Fig. . We founTo confirm the robustness of our TC analysis results derived from the comparison between CCLE and MET500 RNA-Seq data, we downloaded CCLE gene expression data profiled by microarray and then searched the GEO database and assembled a microarray dataset containing the expression values of another 103 independent metastatic breast cancer samples. We repeated the TC analysis with microarray data and found results obtained from the two platforms were highly consistent with each other. First, there was a large overlap of the top-ranked cell lines. Out of the 10 cell lines having highest transcriptome similarity with the 103 metastatic breast cancer samples, 6 of them were within the 10 cell lines having highest transcriptome similarity with MET500 breast cancer samples. Second, both metastatic-site-specific and subtype-specific TC analysis results showed high correlations , BT483 , EFM192A , and HCC70 , respectively. Using criteria of FDR ≤ 0.01, we identified 20, 28, and 24 significant cell lines for LuminalA, LuminalB, and Her2-enriched subtypes, respectively. Notably, most of these significant cell lines were derived from metastatic sites and 18 were shared by the three subtypes. Surprisingly, no cell line passed the criterion for Basal-like subtype. We further examined whether this was due to the limited number of Basal-like MET500 breast cancer samples, but found that the number of LuminalA samples was even less than that of Basal-like samples. After we used a more loosened FDR cutoff of 0.05, we obtained 22 significant cell lines for Basal-like subtype. All statistical testing results are listed in Supplementary Data We attempted to identify cell lines which closely resemble the transcriptome of different subtypes of metastatic breast cancer based on the results of subtype-specific TC analysis. Given a subtype, we noticed that for a random CCLE cell line, its transcriptome similarity with MET500 breast cancer samples of that subtype approximately followed a normal distribution ; however, such significant KRT14 differential expression was not detected between MDA-MB-231 and MET500 breast cancer samples of non-Basal-like subtypes ; in addition, we also re-processed the RNA-Seq data of another seven MDA-MB-231 cell line samples collected from SRA database. We re-performed TC analysis between the breast cancer cell lines (with re-processed data) and Basal-like MET500 breast cancer samples and drew similar conclusion as our previous analysis Fig. . Recentlpes Fig. . Our ana21. However, their utility to model metastatic cancer has not been comprehensively evaluated with large-scale genomic data. To fill this gap, we performed additional TC analysis on 26 patient-derived breast cancer organoids using RNA-Seq data. The aforementioned subtype-specific TC analysis showed that the Basal-like subtype had relatively lower correlation with other subtypes and we also observed similar trend in organoids  culture technology, more and more tumor patient-derived organoids have been established and widely used in translational researchids Fig. . We nextids Fig. .Fig. 5Co22 to infer the values of hidden factors (k = 1) and re-performed DE analysis mentioned above and identified 749 subtype-and-model-independent DE genes, all of which were among the previously identified 1017 subtype-and-model-independent DE genes.Our TC analysis has shown that in vitro models such as cell lines and organoids could resemble the transcriptome of metastatic breast cancer to some extent. However, they are still different in many aspects. To characterize such differences, we performed differential gene expression analysis among MET500 breast cancer samples, CCLE breast cancer cell lines, and organoids .Category 2: Only significant in MET500-vs-CCLE comparison .Category 3: Significant in both MET500-vs-organoids and MET500-vs-CCLE comparisons .Category 4: Not significant in either comparison .We further performed gene set differential activity (DA) analysis on the 50 MSigDB hallmark gene sets to characterize differences regarding to specific biological processes Fig. . For nonInterestingly, 27 gene sets could be consensually classified into one specific category, regardless of the subtype. Figure In cancer research, cell lines have been traditionally used to test drug candidates and study disease mechanism. The genomic profile comparison showed that breast cancer cell lines poorly recaptured somatic mutation patterns of metastatic breast cancer samples, while their CNV profiles were more consistent. Moreover, it is worth noting that cell lines carried many specific genomic alternations, possibly due to culture effects. Examples included the 25 genes presenting cell-line-specific hypermutation. Such large genomic differences and variations revealed by the comparison indicates the importance of selecting cell lines to represent heterogeneous metastatic cancer samples. This study investigated two important factors  that need to be considered during cell line selection.Metastatic sites have their distinct microenvironment that has a large impact in shaping the genomic profiles of metastatic cancer cells. However, the metastatic-site-specific TC analysis did not identify cell lines with metastatic-site-specific utility, which seems not to reflect the impact of microenvironment. Further differential gene expression analysis revealed higher expression of immune-related genes in metastatic cancer samples (compared to cell lines), suggesting that the media used to culture cancer cell lines did not model tumor microenvironment appropriately. Therefore, we conclude cell lines evaluated in this study do not carry indicative genomic signatures that are shaped by the microenvironment of individual metastatic sites and that might be the reason why we did not find metastatic-site-specific cell lines.Breast cancer is quite heterogeneous and we showed that PAM50 subtypes were maintained in metastatic breast cancer cells. Considering the large genomic difference between Basal-like and other subtypes, it is not surprising that in subtype-specific TC analysis Basal-like subtype showed lower correlation with others. Our analysis reveals the importance and necessity of subtype-specific cell line selection. In the future as data continue to accumulate, more factors can be considered for appropriate cell line selection and we can start building an ad-hoc mapping algorithm: inputs would be the characteristics of metastatic cancer samples  as well as the specific scientific question of interest and the output would be a list of appropriate cell lines.19 showed that breast epithelial cell populations corresponded to breast cancer subtypes. Previous researchers have associated MDA-MB-231 with Claudin-low subtype and our analysis suggested that the cell type of origin of MDA-MB-231 cell line may not be Basal cells23. In the future, as more single-cell RNA-Seq data become available, it would be valuable to accurately determine the cell type of origin of MDA-MB-231 and optimize its usage in cancer metastasis-related studies.Surprisingly, we found MDA-MB-231, the widely used triple-negative cell line in metastatic breast cancer research, was dramatically different from Basal-like metastatic breast cancer samples. According to our analysis, HCC70 seems to be a better model, but this does not mean it can be directly employed to study cancer metastasis as many other criteria are needed for the assessment. Triple-negative breast cancer is itself highly heterogeneous. In a recent study, Nguyen et al.24. Together with our comparative studies, we conclude that although the value of organoids in translational research has not been fully recognized, while their high genomic similarity with patient samples warrants further investigation.Organoids are recently established using 3D culture methods. Our analysis suggested that compared to cell lines, they resemble the transcriptome of patient samples more closely, which is a critical characteristic in drug testing. It is also important to note that cell lines evaluated in our study were established much earlier than organoids. During the culturing process, they could have accumulated additional genomic alternations, which may partially explain why organoids are more tightly correlated with patient samples. Recent studies have shown that organoids preserve the histological architecture, gene expression, and genomic landscape of the original tumor9. It has been shown that gene expression is one of the most informative features to predict drug response and weighting cell lines based on their transcriptome similarity with patient samples increases predictive power in gene expression-based drug discovery27. Therefore, in this paper we ranked cell lines according to TC analysis results. Note that different expression features could be employed to measure transcriptome similarity depending on the question. For example, for researchers who specifically focused on the molecular mechanisms of cancer metastasis, it may not be necessary to require the cell line model (such as MDA-MB-231) to resemble the whole transcriptome of patient samples as long as it could mimic the key biological processes in cancer metastasis. In such scenario, ranking cell lines according to their invasiveness might be more appropriate.Prior to this study, researchers have proposed different computational methods to measure the similarity between cell lines and patient samplesIn summary, by leveraging publicly available genomic data, we comprehensively evaluated the utility of breast cancer cell lines as models for metastatic breast cancer. Our study introduces a simple framework for cell line selection which can be easily extended to other cancer types. Although there are concerns about data quality and discrepancies between different studies/platforms, our large-scale analysis and cross-platform validation hopefully addresses these concerns and demonstrates the power of leveraging open data to gain biological insights of cancer metastasis. We hope that the recommendations in this study may facilitate improved precision in selecting relevant cell lines for modeling in metastatic breast cancer research, which may accelerate the translational research.29. FPKM values were used as gene expression measure. To keep consistent with other RNA-Seq datasets, only the RNA-Seq samples profiled with PolyA protocol were considered. The somatic mutation and CNV data of MET500 samples were downloaded from MET500 web portal (https://met500.path.med.umich.edu/downloadMet500DataSets).The raw RNA-Seq data of MET500 breast cancer samples  were downloaded from dbGap (under accession number phs000673.v2.p1) and further processed using RSEMhttps://portals.broadinstitute.org/ccle). The raw RNA-Seq data of 55 CCLE breast cancer cell lines were downloaded from GDC Legacy Archive  and further processed by RSEM. For each CCLE breast cancer cell line, we computed spearman rank correlation between gene expression values quantified by CCLE pipeline and RSEM. Supplementary Fig. 30. Therefore, in our analysis we used the filtered version of CCLE somatic mutation MAF file  in which common polymorphism variants have been excluded.All CCLE pre-processed data  were downloaded from the CCLE data portal (32 on 12 April 2018 (using R package cgdsr) and RSEM-processed gene expression data were downloaded from UCSC Xena data portal (https://xena.ucsc.edu/)33.For TCGA Breast Invasive Ductal Carcinoma samples, somatic mutation calling results were downloaded from cBioPortal34.The RNA-Seq data of patient-derived organoids were from BC Organoids Biobank38. The GEO accession numbers used were GSE11078, GSE14017, GSE14018, and GSE54323.We searched GEO and manually assembled a microarray dataset containing gene expression values of 103 metastatic breast cancer samplesThe gene expression data of lung-metastasis-derived MDA-MB-231 were downloaded from GEO under accession number GSE2603. The RNA-Seq data of non-CCLE MDA-MB-231 were downloaded from SRA and the accession numbers are ERR1982279, ERR1982280, ERR2022825, SRR2532366, SRR4822549, SRR6451704, and SRR6451705.Detailed statistics of the above datasets are listed in Supplementary Data https://data.broadinstitute.org/ccle_legacy_data/hybrid_capture_sequencing/CCLE_hybrid_capture1650_HGNC_info_2012.02.20.txt). Then, we applied the following steps to get the final 1630 highly confident genes.Ten gene symbols which do not have associated HGNC records were removed.Nine gene symbols which do not have associated RefSeq records  were removed.One Y-chromosome located gene PRKY was removed.We downloaded the list of 1650 genes covered by CCLE hybrid capture sequencing from CCLE data portal  to test whether a gene has significantly lower mutation frequency in metastatic breast cancer samples. To control FDR, we applied the Benjamini–Hochberg procedure on left-tailed and right-tailed p-values, respectively39. We noticed that somatic mutations of MET500 cohorts were called by Varscan2 (ref. 40) while TCGA somatic mutation data hosted on cBioPortal were called by MuTect30. To exclude the possibility that the differences inferred between MET500 and TCGA primary tumors were due to pipeline batch effects, we downloaded Varscan2 processed TCGA somatic mutation data from the GDC portal  and found gene mutation frequency was highly consistent between the two databases  were also highly correlated . Given RNA-Seq profiles of a cell line (or an organoid) and several patient samples, we compute spearman rank correlation (across the 1000 marker genes) between the cell line (organoid) and each sample and the median value of computed spearman rank correlation values was defined as the transcriptome similarity of the cell line (organoid) with the patient samples. For microarray data, a similar procedure was applied and the 1000 most-varied probe sets were used to compute correlation values.We also extended the above method to compute CNV similarity. Instead of selecting “marker genes”, all of the 1630 commonly genotyped genes were used.42. To visualize tumor samples with t-SNE, we first computed the pair-wise distance between every two samples as 1 minus the spearman rank correlation across PAM50 genes and then applied the function Rtsne to perform 2D reduction43.The genefu package was used to determine breast cancer subtypehttps://www.ncbi.nlm.nih.gov/pubmed/). For each cell line, we searched with a keyword”[cell line name] metastasis”. We repeated this step for the terms “metastatic”, “breast cancer”, and “metastatic breast”. These searches returned highly correlated results, so we used the search terms which returned the most results: “[cell line name] metastasis”.The number of PubMed abstracts or full texts mentioning a CCLE breast cancer cell line was determined using the PubMed Search feature on 10 May 2018  > 1) and DAVID bioinformatics sever was used to perform Gene Ontology enrichment analysishttp://software.broadinstitute.org/gsea/msigdb/)49. Then, for each gene set the two-sided Wilcoxon rank-sum test was used to assign the p-value in the comparison of ssGSEA scores between MET500 samples and cell lines (or organoids).In DA analysis, we first used the R package GSVA to compute ssGSEA scores for the 50 MSigDB hallmark gene sets (51. Tumor purity was estimated using ESTIMATE16. CNTools was used to map the segmented CNV data to genes52. If not specified, the two-sided Wilcoxon rank-sum test was used to compute the p-value in hypothesis testing.All of the analysis was conducted in R. The ggplot2 and ComplexHeatmap packages were used for data visualizationFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting SummarySource Data