Majority of Differentially Expressed Genes Are Down-Regulated During Malignant Transformation in a Four-Stage Model

Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model

Frida Danielssona, Marie Skogsa, Mikael Hussb, Elton Rexhepaja, Gillian O’Hurleyc, Daniel Klevebringa,d, Fredrik Ponténc, Annica K. B. Gade, Mathias Uhléna, and Emma Lundberga,1

aScience for Life Laboratory, Royal Institute of Technology (KTH), SE-17121 Solna, Sweden; bScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-17121 Solna, Sweden; cScience for Life Laboratory Uppsala, Department of Immunology, Genetics and Pathology, Uppsala University, SE-75185 Uppsala, Sweden; dDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-17111 Stockholm, Sweden; and eDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institutet, SE-17177 Stockholm, Sweden

Edited by George Klein, Karolinska Institutet, Stockholm, Sweden, and approved March 12, 2013 (received for review October 19, 2012)

The transformation of normal cells to malignant, metastatic tumor with the SV40 large-T antigen, and finally made to metastasize cells is a multistep process caused by the sequential acquirement by the introduction of oncogenic H-Ras (RASG12V) (9). We of genetic changes. To identify these changes, we compared the have used this cell-line model for a genome-wide, comprehensive transcriptomes and levels and distribution of proteins in a four- analysis of the molecular mechanisms that underlie malignant stage cell model of isogenically matched normal, immortalized, transformation and metastasis, using transcriptomics and im- fl fi transformed, and metastatic human cells, using deep transcrip- muno uorescence-based protein pro ling. tome sequencing and immunofluorescence microscopy. The data Results show that ∼6% (n = 1,357) of the human protein-coding genes are differentially expressed across the stages in the model. Interest- Morphological Changes in the Four-Stage Cell Model. The mor- ingly, the majority of these genes are down-regulated, linking phologies of the four cell lines that represent the different stages malignant transformation to dedifferentiation. The up-regulated of malignancy were studied using antibodies targeting different

A CELL BIOLOGY genes are mainly components that control cellular proliferation, subcellular structures (Fig. 1 ). As expected, the overall morphology of the primary cells is more elongated, whereas the cell whereas the down-regulated genes consist of proteins exposed fi fi morphology of the last two stages is irregular and the broblast on or secreted from the cell surface. As many of the identi ed appearance is lost. Whereas some subcellular components, such gene products control basic cellular functions that are defective as microtubules, peroxisomes, and the endoplasmic reticulum, in cancers, the data provide candidates for follow-up studies to are similar in the four cell lines, others are morphologically al- investigate their functional roles in tumor formation. When we tered. For example, the number of nucleoli is increased in the further compared the expression levels of four of the identified transformed cells, likely reflecting an increase of cellular pro- proteins in clinical cancer cohorts, similar differences were ob- liferation. In addition, the morphology of the Golgi apparatus is served between benign and cancer cells, as in the cell model. This altered, showing an aggregated perinuclear phenotype in the shows that this comprehensive demonstration of the molecular transformed cells. changes underlying malignant transformation is a relevant model To objectively evaluate and quantify differences in morphol- to study the process of tumor formation. ogy, we used automated image analysis. Based on 50 features accounting for most of the differences between the cells (Dataset S1), we show that the cells have a morphology that an automated ancer development is a multistep process where genetic fi changes are accumulated, thus progressively transforming classi er can accurately identify and distinguish between (Fig. C 1B). Nonsupervised clustering shows good agreement with BJ cells into a cancerous phenotype (1, 2). Over the last decades, = numerous investigators have studied the underlying molecular cell stage (rho 0.643). Noteworthy is that the immortalized and mechanisms for malignant transformation. This has resulted in transformed cells are more heterogeneous than the primary and metastasizing cells. many models that explain the development of a malignant phenotype of human cells, for instance the “hallmarks of cancer” by Changes in Gene Expression. The four cell lines were cultivated and Hanahan and Weinberg (3, 4). mRNA was isolated for RNA sequencing (RNA-seq). The With new technologies for deep sequencing, novel opportu- number of genes with detectable transcripts is similar in the nities to study the underlying molecular events leading to cancers different cell types (Fig. 2A), varying between 12,074 and 12,493 have emerged. A large number of investigations have analyzed using a false discovery rate (FDR) of 1%. Spearman correlations mutations occurring in tumors, such as the analysis of mRNA between duplicate samples are high (0.97–0.98, S2) and the ge- expression, microRNA expression, and DNA copy number in a netic elements introduced into the cells were confirmed (see large number of tumors in the Cancer Genome Atlas (5). Simi- legend to Fig. 2). The bioinformatics analysis identified a total of larly, the Human Protein Atlas project (6) studies human cancers 1,357 differentially expressed genes: 214 between the primary using a proteome-wide collection of antibodies, resulting in pub- licly available immunohistochemistry images covering 20 different

human cancer types. Author contributions: E.L. designed research; F.D., M.S., G.O., and E.L. performed re- An interesting approach to studying the molecular mecha- search; M.H., E.R., D.K., and A.K.B.G. contributed new reagents/analytic tools; F.D., M.H., nisms underlying cancer is to use an isogenically matched cell E.R., D.K., and F.P. analyzed data; and F.D., M.U., and E.L. wrote the paper. model in which normal cells are progressively transformed into The authors declare no conflict of interest. malignant cells. There are several studies where genetic elements This article is a PNAS Direct Submission. such as oncogenes have been introduced to different types of Data deposition: The RNA-seq data reported in this paper have been deposited in the cells in an accumulative order as an attempt to mimic the natural National Center for Biotechnology Information Sequence Read Archive (accession no. steps of transformation (7, 8). One such cell model is the four- SRP019968). stage model based on BJ fibroblasts developed by the Weinberg 1To whom correspondence should be addressed. E-mail: [email protected]. fi group, in which primary broblast cells were immortalized with This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. telomerase reverse transcriptase (TERT), further transformed 1073/pnas.1216436110/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1216436110 PNAS Early Edition | 1of6 Downloaded by guest on September 28, 2021 Primary Immortalized Transformed Metastasizing expressed genes in relation to all detected genes. Here, extra- BJ BJ hTERT BJ hTERT Large-T BJ hTERT Large-T Ras cellular proteins and proteins in the plasma membrane are highly A enriched, whereas ribosomal and mitochondrial proteins are not. To further distinguish between differences among up- and down- regulated proteins, these groups were analyzed separately in terms

Microtubules of GO biological process. The large group of down-regulated genes (80%) relates to a diverse set of functions, such as extra- B cellular matrix production, cell adhesion, cell migration, growth factor binding, and angiogenesis. A minority (20%) of the differentially expressed genes are up-regulated and highly enriched for functions that control cellular proliferation, such as DNA Peroxisomes replication, mitotic spindle, and cell-cycle control (Dataset S3, C validated by independent method in Dataset S4).

Molecular Consequences of Cell Immortalization. Telomerase reverse transcriptase is the catalytic subunit of the enzyme telo- Mitochondria merase and is expressed in most cancers (10). The RNA-seq D analysis revealed that immortalization of the ﬁbroblasts by introduction of TERT resulted in 214 differentially expressed genes (Dataset S2). The cytosolic isozymes aldehyde dehydrogenase 1 (ALDH1A1) and erythrocyte membrane protein band 4.1-like-3 Nucleolus (EPB41L3) are detected only in primary cells, and are completely absent upon immortalization [ALDH1A1: fragments per kilobase E of exon model per million fragments mapped (FPKM) 255 to 0; EPB41L3: FPKM 14 to 0]. Immunoﬂuorescence (IF) analysis shows that whereas both the ALDH1A1 and EPB41L3 proteins

reticulum show cytoplasmic localization in the primary BJ cells, they are Endoplasmic absent in the subsequent stages of the model (Fig. 3 A and B). F Beyond alcohol metabolism, the role of ALDH1A1 is not yet understood. There are studies that report ALDH1A1 expression as a favorable prognostic factor in ovarian (11) and pancreatic cancer (12). However, contradicting data also show that positive

Golgi apparatus cells have increased invasive and metastatic capabilities (13) and that ALDH1A1 expression is correlated with poor survival in G breast cancer (14). Primary 0.15 Immortalized Regarding up-regulated genes, epiregulin (EREG), known to 0.10 Transformed be capable of stimulating proliferation of various human cells Metastasizing 0.05 through the EGF signaling pathway (15), was found to be up- 0.00

PCA3 regulated threefold (FPKM 35 to 107) upon immortalization, and −0.05 subsequently down-regulated 35-fold when the cells were trans- −0.10 0.2 0.1 formed with large-T (FPKM 107 to 3). This suggests that the el- −0.15 0 −0.20 −0.1 evated levels of EREG during the immortalization phase are not −0.2 PCA2 −0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 required in transformed and metastasizing cells, most likely due PCA1 to an alternative mechanism for sustained proliferation. Fig. 1. Morphological analysis of the cell model. (A–F) Confocal images SV40 Transformation Causes Dedifferentiation and Massive Changes of immunofluorescently stained cells, where the organelle of interest is shown in green and the nucleus is in blue. The images show staining of the in Gene Expression. The majority of all changes in this cell model following structures (targeted protein): (A) microtubules (TUBA1A), (B) occur upon large-T antigen transformation, where 856 genes are peroxisomes (ABCD3), (C) mitochondria (HSPA9), (D) nucleolus (USP36), (E) differentially expressed (Dataset S2). This broad effect is not endoplasmic reticulum (CALR), and (F) Golgi apparatus (GOLGA5). (Scale surprising, because large-T is expected to mediate its action by bars, 20 μm.) (G) Scatter plot, using the first three principal components and inhibition of the p53 and Rb family of tumor suppressors. Large-T representation of the four cell stages in the model. The principal component binding stabilizes p53, and the transformed cells should thus analysis (PCA) was run on the top 50 ranked texture and morphological contain large amounts of functionally inactive p53 (16, 17). Ac- features from images with IF staining of the nucleus, microtubules, and cordingly, our RNA-seq data show a 2.2-fold increase in p53 Golgi apparatus. mRNA levels upon large-T transformation, as also confirmed on the protein level (Fig. 3C). Upon large-T antigen binding to Rb proteins, their ability to and immortalized cells, 856 between the immortalized and regulate E2Fs is blocked (16). An effective transformation by transformed cells, and merely 31 between the transformed and large-T should therefore give rise to increased levels of E2F metastasizing cells (Fig. 2, Fig. S1, and Dataset S2). To identify transcriptional targets. In agreement with this, we observe that genes with a gradual change of expression, we compared the transformation is linked to up-regulated levels (two- to three- primary and metastasizing stages, and identified thereby an ad- fold) of transcriptional targets of E2Fs, including the cell-cycle ditional 381 differentially expressed genes. Altogether, the RNA- regulators MYBL1, CDC25A, CDC7, and CDCA7, checkpoint seq data show that the primary stage expressed the highest proteins such as BUB1B (Fig. 3D), nucleotide synthesis proteins number of protein-coding genes and that the majority (80%) of such as DHFR, DNA repair proteins such as FANCG, and DNA the differentially expressed genes are down-regulated en route replication proteins such as MCM2 (Fig. 3E) and MCM3. to malignancy. As discussed earlier, the fibroblast-like morphology of the cells Gene-set enrichment analysis was performed to identify the is lost in this step. In line with this, we observe a down-regulation types of proteins with an altered pattern of gene expression. of fibroblast markers such as α-smooth-muscle actin (ACTA2) Fig. 2B shows an enrichment analysis of subcellular localization and fibroblast activation protein (FAP), as well as many com- [gene ontology (GO) cellular component] of the differentially ponents of the extracellular matrix such as collagens, laminin,

2of6 | www.pnas.org/cgi/doi/10.1073/pnas.1216436110 Danielsson et al. Downloaded by guest on September 28, 2021 Fig. 2. Overall changes in gene AB expression across the cell model. PRIMARY IMMORTALIZED TRANSFORMED METASTASIZING Cytoplasm BJ BJ BJ BJ Nucleus (A) Schematic of the cell-line hTERT hTERT hTERT Protein complex model based on accumulative Large-T Large-T Plasma Ras Cytosol genetic changes, with summa- Mitochondrion Nucleoplasm rized RNA-seq results shown be- Cytoskeleton low the cells. The introduced Extracellular Expressed genes Endoplasmic genetic changes could be vali- 12.493 12.173 12.246 12.074 Golgi apparatus Cytoplasmic membrane- dated by the RNA-seq data as Chromosome Differentially expressed genes Nucleolus follows for TERT (FPKM), large-T Extracellular (FPKM), and RasG12V (fraction of 214 856 31 Microtubule organizing -up Endosome reads with mutation, in %) in the 38 288 1 Vacuole Nuclear envelope four cell lines, respectively: pri- -down Nuclear 176 568 30 Lysosome mary: 0, 0, 0; immortalized: 128, 1, Proteinaceous 0; transformed: 85, 2219, 0; me- Spearman correlation Ribosome 0.96 0.95 0.97 Cilium tastasizing: 103, 2685, 30. (B) Peroxisome Graph showing the over- or un- -15 % -10 % -5 0 % 5 % 10 15 derrepresentation of annotated subcellular localization for the group of differentially expressed genes compared with all detected genes. The subcellular structures are listed on the y axis and the differences in percentage points between the differentially expressed genes and all detected genes are shown on the x axis (i.e., overrepresented organelles have a positive value).

and emilin. Altogether, these results indicate that SV40T ex- more pronounced decrease of the protein (Fig. 3H), suggesting pression results in a major dedifferentiation of the cells. additional posttranslational regulation. Interestingly, a strong staining of ANXA1 is observed in the plasma membrane and Few Genes Show Altered Expression upon Mutant Ras Transformation. nucleus of the primary cells, whereas only nuclear staining is Although transformed and metastasizing cells show similar observed in the immortalized cells. Loss of plasma membranous growth properties in vitro, there is one essential difference: The ANXA1 has been observed in premalignant lesions of the oral RasG12V-expressing cell type is capable of forming tumors in cavity (24), and nuclear localization has been correlated with

mice (9). Despite this, only 30 down-regulated genes and 1 up- shorter overall patient survival (25). Thus, we observe a corre- CELL BIOLOGY regulated gene are identified as a consequence of the Ras mu- lation of expression levels and altered subcellular localization tation (Dataset S2). Interestingly, many of the down-regulated of ANXA1 during the early events of cancer development, genes are components of the extracellular matrix, and may well which highlights that the role of ANXA1 in cancer should be be of importance for metastasis and invasion. For instance, further evaluated. laminin α4 chain is down-regulated gradually across the model, Fig. 3I shows staining of nestin (NES) in a typical intermediate almost completely disappearing in metastasizing cells. In addi- filament pattern in primary and immortalized cells, whereas tion, carboxypeptidase A4 shows a pronounced down-regulation the expression is almost completely abolished in the transformed (FPKM 67 to 1). The cytokine bone morphogenetic protein and metastasizing cells. The mechanisms that regulate the ex- 4 (BMP4), known to suppress the proliferation of cancer cells pression of NES in solid tumors remain unclear. However, our and to stimulate cell migration and invasion (18, 19), is also results do not support earlier suggestions that NES levels cor- down-regulated. These proteins are interesting targets for relate with malignant grades and undifferentiated states of further studies aimed at identifying potential markers for tumors (26). metastatic capabilities. Altogether, a high correlation is observed between differential The only up-regulated gene in this step is cytokine granulocyte gene expression on the RNA and protein levels, and information colony stimulation factor 3 (CSF3). This protein acts in hema- about the spatial distribution of the protein on the single-cell topoiesis by controlling the proliferation, differentiation, and level provides additional information as for EPB41L3, MCM2, function of granulocytes. Previous studies have observed that this ANLN, and ANXA1. protein is up-regulated during cell migration in wound-healing models (20) and that H-Ras–driven increase of CSF3 promotes Network Analysis of Proteins Related to Cell-Cycle Progression, human breast cell invasion via metalloproteinase 2 (MMP2) (21). Apoptosis, and Cell Migration and Adhesion. To get a better insight into the molecular mechanisms underlying different hall- Validation of Differentially Expressed Genes on the Protein Level. marks of cancer in this model, we constructed functional To investigate how well the changes in gene expression on the interaction networks for the differentially expressed genes re- RNA level are translated to the protein level, we performed IF lated to the following cellular processes: cell-cycle progression, analysis of a selection of proteins. A number of up-regulated apoptosis, and cell adhesion and migration (Fig. 4 and Datasets genes were studied, here exemplified by TP53, BUB1B, MCM2, S5 and S6). anillin (ANLN), and EEF1A2. These proteins show increased IF Fig. 4A shows the network for genes involved in cell-cycle staining across the model (Fig. 3 C–G). In many cases, it is not progression. The cluster of up-regulated genes represents the the expression level in each cell that is increased but rather the core machinery needed for mitosis, such as Aurora kinases A and fraction of cells in the population that express the protein. For B. Other up-regulated centromeric proteins are CENPK and instance, MCM2 is only expressed in ∼10% of primary cells, but CENP1, members of the kinesin-like protein family (KIF18, this fraction gradually increases until all cells in the population KIF23, KIF2C) needed for movement of chromosomes during express this protein in the metastasizing stage. The same phe- cell division, and subunits of the condensing complex (NCAPH, nomenon is observed for ANLN (Fig. 3F), which has been shown NCAPG). Other up-regulated genes are minichromosome main- to be overexpressed in many tumor types (22). It is tempting tenance proteins (MCM2 and MCM10) that constitute key com- to speculate that this group of up-regulated genes reflects one ponents of the prereplication complex. Furthermore, regulators single hallmark of cancer—increased proliferative signaling. of cell-cycle progression are up-regulated, such as BUB1B in- As such, these genes may be potential markers for cancer cell volved in the spindle checkpoint, c15orf42 involved in the G2/M proliferation and poor patient outcome, as already reported for checkpoint, and CDC25A needed for progression from G1 to S. ANLN (23). This is indeed interesting, as defective chromosomal segrega- We observed a gradual down-regulation of annexin A1 tion can cause genetic instability, a condition highly associated (ANXA1) across the model. The IF analysis reveals an even with tumorigenesis (27). The cluster of down-regulated genes is

Danielsson et al. PNAS Early Edition | 3of6 Downloaded by guest on September 28, 2021 Primary Immortalized Transformed Metastasizing caspase 10, and several annexins, such as ANXA1 and ANXA4. BJ BJ TERT BJ hTERT Large-T BJ hTERT Large-T Ras The annexins both have phospholipase A2 inhibitory activity and A are considered to be antiapoptotic. Earlier studies have shown conﬂicting results about ANXA expression in tumors. A study has shown that early loss of ANXA1 in breast carcinomas is maintained in both invasive and metastatic tumors (32), whereas ALDHA1A1 another study showed that ANXA1 inhibits the epithelial-to- B mesenchymal transition and abolishes metastasis (33). The results reported here suggest a possible role for down-regulation of ANXA1 in the early events of malignant transformation, already at the stage of immortalization. EPB41L3 Fig. 4C shows the network for genes involved in cell adhesion and migration. Here also, the majority of genes are down-regu- C lated with increased transformation. This group of gene products contains many extracellular matrix components, such as collagens (COL1A1, COL3A1, COL5A1, COL5A3, COL6A3, CO- TP53 L7A1, COL8A2, COL13A1, COL15A1, COL16A1, COL18A1) and the major noncollagenous constituents of basement mem- D branes, laminin (LAMA2, LAMB1, LAMC1) and emilin (EMI- LIN1, EMILIN2). Another example of a component of the extracellular matrix is versican (VCAN). Interestingly, we observe an initial down-regulation of VCAN upon immortalization BUB1B (FPKM 3 to 0.3) and a subsequent up-regulation in the metastasizing cells (FPKM 1.4 to 5.4). It has previously been shown E that VCAN can promote metastatic processes (34), and our results indicate that VCAN may have a dual role, both early and late in tumorigenesis. MCM2 Role of ANXA1, 3-Hydroxybutyrate Dehydrogenase, Type 1, Alanyl Aminopeptidase, and ANLN in Prostate and Colon Tumorigenesis. F Many genes identiﬁed in this study are obvious candidates for follow-up studies of their potential role in cancer development.

ANLN To determine whether the differentially expressed genes identi- ﬁed in the model are of clinical relevance, we used immunohistochemistry (IHC) to evaluate the expression of a subset of G proteins in a clinical prostate cancer cohort including normal prostate, primary prostate cancer of different Gleason grades, and prostate cancer metastasis. Antibodies targeting 10 proteins

EEF1A2 were tested and 3 of these, 3-hydroxybutyrate dehydrogenase, type 1 (BDH1), ANXA1, and alanyl aminopeptidase (ANPEP), showed differential protein expression when comparing benign, H primary cancer, and metastatic cancer (Fig. 5). A fourth target, ANLN, showed only a few positive cell nuclei in the tested prostate tissues and was further tested in a similar colorectal ANXA1 cancer cohort including benign colonic mucosa, primary cancer, and metastases. I The expression of ANLN was observed in the nuclei of normal glandular cells mainly located at the base of colonic crypts. We could observe an increase in the fraction of ANLN-positive nu- NES clei of tumor cell samples compared with control. For this rea- son, ANLN expression was assessed according to the percentage of positive nuclei, using 10% as the cutoff. With this cutoff, 90% Fig. 3. Confocal images of immunoﬂuorescently stained proteins with dif- of cores with benign colorectal nuclei were negative, whereas ferential expression. The protein of interest is shown in green and the nu- 70% and 60% of cores with primary and metastatic colorectal cleus is in blue. The images show staining of the following proteins cancer, respectively, were positive. (corresponding FPKM values): (A) ALDH1A1 (256, 0, 0, 0), (B) EPB41L3 (13.8, The mitochondrial dehydrogenase BDH1 showed a granular 0, 0, 0), (C) TP53 (16.6, 33.8, 73.1, 65.2), (D) BUB1B (11.3, 19.1, 41.7, 41.8), (E) cytoplasmic localization in prostatic epithelium. Whereas only MCM2 (34.8, 41.1, 103, 108), (F) ANLN (99.2, 142, 250, 237), (G) EEF1A2 (18.8, 6% of the cores representing normal prostate displayed positive 3.74, 154. 29.7), (H) ANXA1 (526, 440, 253, 201), and (I) NES (105, 93.9, 1.85, BDH1 protein expression, 79% and 73% of cores with primary 4.27). For images D–F, the DAPI channel is available in Fig. S2. (Scale bars, 20 μm.) prostate cancer and metastasis were BDH1 positive, respectively. ANXA1 displayed cytoplasmic and membranous expression in the prostatic epithelium, and expression was observed in 90% of functionally more diverse and contains cell-signaling molecules cores with benign prostatic glands compared with 49% of pri- such as interleukins (IL7, IL1B, IL8), growth factors (FGF7, mary prostate cancer cores and only 13% of cores representing FGF2, PDGFC, PDGFD), and many insulin-like growth factor- metastasis. ANPEP displayed a similar cytoplasmic protein ex- binding proteins (IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7). pression pattern in prostatic epithelium. ANPEP expression was The involvement of IGFBPs in cancer is not fully understood, but observed in 89% of cores with benign prostatic glands, whereas there are reports suggesting that down-regulation of these pro- only 36% and 33% of cores with primary and metastatic prostate teins correlates with cancer progression (28–31). cancer were positive, respectively. Automated quantitative IHC Fig. 4B shows the network for genes involved in apoptosis. image analysis performed on prostate cohorts and manual anal- Here, most genes are down-regulated, including Fas, caspase 1, ysis showed a high concordance with Spearman’s rho correlation

4of6 | www.pnas.org/cgi/doi/10.1073/pnas.1216436110 Danielsson et al. Downloaded by guest on September 28, 2021 REEP1

BNC1 cells, and cells capable of forming metastasis. It is important to A RBM38 B

L1CAM HSPB3 point out that a cell model has limitations mimicking the complex C15orf42 KIF18A CENPI HELLS CENPK CSE1L NCAPH POLD4 SOX11 SCIN HSPB8 ERCC6L BUB1B TRPV4 in vivo situation of cancer, including the impact of the tumor CHTF18 AURKB TTK

DMC1 KIF2C CDCA7 GAL POLD1 KIF23 NCAPG MCM2 COMP GINS1 SEMA3A FANCG MCM10 AURKA GULP1 microenvironment. However, this cell model allowed us to sep- ACTC1 TBX5 CRYAB RFC4 UBE2C CRYAB TP53I11 TGM2 LPAR1 CLSPN PKD2 ANXA1 HOXA13 CDC7 CCNB1 DHFR RBL2 FRZB STEAP3 SNCA KLF5 CCNG2TRAIP ARHGEF4 PTPRK ZEB1 LZTS1 CXCL12 SRGN SMO arate the molecular mechanisms related to the steps of immor- RARG ANGPT1 BMP2 HTRA3 ID3 TP53INP1 CITED2 DRAM1 SMAD6 FOXO1 CDC25A COL18A1 GBX2 INHBB UNC5B BTG2 NKX3-1 TOB1 TCF7 TGFB2 PMP22 PBX1 TBX2 CAV2 TEK SEMA4D PTK2B FSCN1 HIPK2 CRLF1 MEF2C HTRA1 RBL1 SULF1 THBS1 ID3 GAS1 ID2 DIXDC1 GJA1WNT5A GAS1 JAG1 C13orf15 talization, transformation, and invasion and metastasis. SPRY2 TGFB1 CLU RNF144B CLU RARB SESN1OSGIN1 SATB1 APPL2 OSR2 CDKN2BHES1 CDKN2A NFIB ACE BMP4 TBX5 KRT18 CDKN1A TNFRSF11B THBS1 TUBA4A INHBAFGF2 MDK FGF5 HGF KITLG ID1 EYA2 NR4A3 TIMP1 PDGFDWNT5A AR GJA1 NFATC4 TIMP1 ALX4 IGFBP5 PHLDA3 SMO WNT2 ADAMTS1 BMP4 BMP2 GAS7 TGFBR2 BCL2 PTGS2 DAB2FLT1 FGF7 G0S2 CDKN1A JAK3 The network analysis of the differentially expressed genes MARCKSL1 IGF1R RUNX3 TRAIP TNFRSF1B IL1B GSN TNFRSF19 GNG4 PDGFRA JUNB FRZB STEAP3 IGF1R BTG2 PTN CDH13 THBS3 TGFB2FGF2 INHBA IL7 CITED2 GLI1 HGF KITLG WFDC1 DSP CDKN2A IGFBP3CD9 GLUL COL18A1 IGFBP7 IGFBP4 TCF7 RUNX3 FOXO1 TGFB1 CTGF RASSF2 SPRY2 PDGFC NLRP2 MITF CASP1 PDGFRB TGFBI IL8PTGS2 DUSP2 NUPR1 NES TP53INP1 IGFBP6 NOV FSD1 TNFRSF10C BCL2 provided several insights of general interest. The up-regulated CDA MITF FAS AR ADAMTSL4 SERPINF1 TIMP2ACVRL1 CSF3 SMPD1 PLAUCSF1 PRKCZ NFATC1 STAT6 IGFBP2 TLR4 SOCS2 PTK2B IGF2 IGFBP3 ENPP1 SLIT3 LDOC1 IL1B IL7 RGS2 PYCARD DUSP6 SOCS2 CD274 FAS RARB A2MR CARD9 SERPINB2 PTGER2 CASP10 SERPINE2 KRT18 RIPK3 EREG TERT genes are often directly involved in proliferation and cell-cycle TACSTD2 ADORA2B KAZALD1 ADRA2A NPPB NDN TIAM1 CXCL12 PERP MAPK13 TNFRSF11A CSE1L BMF PTGS1AGTR1 CARD11 SERPINB9 LMNB1 HIPK2 BST2 RARG ANXA1 TSPYL5 OLR1 IFITM1 F2R PTGES LIPG DPP4 control. However, the majority of the differentially expressed CAPN1 BDKRB2 TNFSF4 PPIF ESPL1 S1PR3 PAX8 CADM1 ADRA1D ANXA4 GPNMB BDKRB1 CARD10 SSTR1 CLIP3 genes (80%) are down-regulated. This suggests that the major GAL

DAPK2 BUB1B BEX2 route toward malignancy involves turning off of genes rather than

DIDO1 switching on the expression of novel genes. Interestingly, the down-regulated genes are highly enriched for proteins present on C CNTN3 the outside of the cell, either as exposed on the surface of the plasma membrane or secreted. The massive down-regulation PODN ﬁ APLP1 of genes, including many typical broblast proteins, indicates a gradual dedifferentiation of cells on route to malignancy. In FBLIM1 CITED2 PCDH8 AEBP1 TNS1

APBA1 LGALS3BP FERMT1 LPXN Benign tissue Primary tumor Metastasis TEK HAPLN1 A SSPN

EFS HBEGF COL13A1 PODXL SRGAP1 LAMB1 PTK2B EPDR1 COL18A1 COL5A3 COL11A1 NID2 VCAN BDH1 ROBO2 CXCL12 LAMA2 ITGA7ITGA1 CD9 COL7A1 ITGA10 COL16A1 COL8A2 DPP4 F2RL1 ITGA11L1CAM LAMC1 MCAM COL5A1COMP F2R FLT1 FAT3 CD36 ITGB5 RND3 THBS1 ITGA2 F10 CD4 F11R KITLG COL3A1 COL6A3ITGA8 JAM2 BDKRB1 ADAM12 THBS3SDC3 THY1 COL1A1 OLR1 SIRPA PLXNC1 TGFB2 COL15A1 CSF1 PDGFRB ANXA1 NRP2 IGFBP5 CTGF CELL BIOLOGY CELSR1 ADRA2A BCL2 TGFBI ACVRL1 WNT5A

FSCN1 POSTN GPNMB PDGFRA SMAD7 FAT4 DSP IGFBP7 BMP2 TPBG IGF1RCDH11 OLFM4 SEMA5A CDH6

FOXF1 ANPEP EMB JUP EMILIN2 CHRD CLDN11 FOXE1 TBX5 CDH13 EMILIN1 CDH4 NLGN1 PTPRK NEO1 HES1 SNAI1 CADM1

NRXN3 ANLN PCDH9 EDIL3

CADM3 FEZ1

B BDH1 Histology Prostate Benign Primary Metastasis Total SPON2 WWC1 CXADR tissue tumor Protein expression Negative 19 (76%) 11(21%) 4 (27%) 34 Positive Fig. 4. Functional interaction networks for differentially expressed genes 6 (24%) 42 (79%) 11 (73%) 59 > 10 nuclei 53 assigned to the following categories: (A) cell-cycle process, (B) apoptosis, and Total 25 15 93 (C) cell adhesion and migration. Nodes are colored based on the gene ex- Fisher exact test (p-value), Chi Square (p-value): < 0.0001, < 0.0001 pression pattern: down-regulation (green), up-regulation (red), and mixed pattern (blue). Edges are colored based on the source of information: ANXA1 Histology Prostate Benign Primary Metastasis Total coexpression (red), co-occurrence (light blue), experimental (black), fusion tissue tumor (purple), homology (dark blue), green (knowledge), and gray (text mining). Protein expression Negative 2 (7%) 28 (51%) 13 (87%) 43 Positive 25 (93%) 27 (49%) 2 (13%) 54 > 10 nuclei fi Total 27 55 15 97 coef cients of 0.618, 0.693, and 0.782 for BDH1, ANXA1, and Fisher exact test (p-value), Chi Square (p-value): < 0.0001, < 0.0001 ANPEP, respectively (Fig. S3). ANPEP Histology In summary, ANLN and BDH1 showed low expression in nor- Prostate Benign Primary Metastasis Total mal epithelium compared with the corresponding cancer cells, tissue tumor Protein expression Negative 3 (11%) 35 (64%) 10 (67%) 48 whereas ANXA1 and ANPEP showed high expression compared Positive 25 (89%) 20 (36%) 5 (33%) 50 with corresponding cancer cells. These results are in accordance with > 10 nuclei the up- and down-regulation observed for these proteins in the BJ Total 28 55 15 98 cell model. Both Fisher’s exact and χ2 tests resulted in highly signif- Fisher exact test (p-value), Chi Square (p-value): < 0.0001, < 0.0001 icant P values (<0.0001) for ANLN, BDH1, ANXA1, and ANPEP ANLN Histology Colon Benign Primary Metastasis Total when comparing benign epithelium and cancer cells (Fig. 5B). tissue tumor Protein expression Negative 18 (90%) 17 (30%) 7 (40%) 42 Positive Discussion 2 (10%) 40 (70%) 11 (60%) 53 > 10 nuclei Here we describe a comprehensive study of a cell model for Total 20 57 18 95 cancer malignancy using a combination of deep RNA sequencing Fisher exact test (p-value), Chi Square (p-value): < 0.0001, < 0.0001 and analysis of the corresponding proteins of differentially Fig. 5. (A) Examples of immunohistochemically stained sections from be- expressed genes. We demonstrate how the molecular mecha- nign glands, primary tumor, and metastasis of colon (ANLN) or prostate nisms underlying the different steps of malignancy as well as the (BDH1, ANXA1, and ANPEP). The specific protein staining is shown in brown. hallmarks of cancer can be scrutinized by a genome-wide ap- Arrows mark concomitant normal prostatic glands adjacent to growth of proach. The cell model based on human fibroblasts covers normal primary prostate cancer. (B) Summarized results from manual assessment of cells with limited life span to immortalized cells, transformed IHC stained prostate tissue biopsies.

Danielsson et al. PNAS Early Edition | 5of6 Downloaded by guest on September 28, 2021 this study, we have performed sequencing of the cellular mRNA between benign epithelium and cancer cells for ANLN in colon and performed a gene-centric data evaluation. A global gene and BDH1, ANPEP, and ANXA1 in prostate. This compre- expression encompassing the full transcriptome, including hensive demonstration of the molecular changes that underlie microRNAs and alternatively spliced transcripts, may provide an malignant transformation and metastasis may serve as a model even more complete view of tumorigenesis. In addition, an ex- for studies to understand the complexity of both human cells and tended analysis of protein posttranslational modiﬁcations could tumor development and progression. provide additional insight, especially into the ﬁnal step of the model, where few genes were differentially expressed. Materials and Methods A classical proteomics strategy based on 2D PAGE analysis and Cell Cultivation and RNA Sequencing. Cells were cultivated at 37 °C in a 5% (vol/vol)

isobaric tag for relative and absolute quantitation (iTRAQ) has CO2 environment. RNA was extracted and samples were prepared according to earlier been used to study this fibroblast cell-line model and allowed standardized protocols before being sequenced on an Illumina HiSeq 2000. the discovery of 201 differentially expressed proteins (35). Most of the proteins identified by Pütz et al. could be confirmed by our Immunofluorescence Microscopy and Immunohistochemistry. Immunofluores- study, although we were, thanks to the higher sensitivity of RNA- cent staining of cells and image acquisition were essentially performed as seq, able to identify as many as 1,357 differentially expressed described in Fagerberg et al. (36). Tissue microarrays were created as pre- genes, corresponding to ∼6% of all human genes. Many of the viously described with duplicate 1-mm formalin-fixed, paraffin-embedded genes identified in this study are suitable for more in-depth studies tissue cores from benign cases as well as cases from primary tumors and to investigate their role in tumorigenesis, for instance ALDH1A1, metastases for both prostate and colon tissue. Tissue was collected and BMP4, CSF3, VCAN, ANXA1, BDH1, ANPEP, and NES. Some stored with consent from patients at the Department of Clinical Pathology, examples of genes possibly involved in the transformation from Uppsala University Hospital, Uppsala, Sweden. Anonymized tissue samples benign cells to transformed phenotype are listed in Table S1 with were used for immunohistochemistry in accordance with Swedish laws and regulations, and with approval from the Regional Ethical Board in Uppsala. examples from each of the proposed hallmarks of cancer. Immunohistochemistry and digital slide scanning were performed as pre- To conclude, we demonstrate how a combined transcriptomics viously described (37). and protein analysis approach can be used to scrutinize the fi Detailed descriptions of material and methods are available in SI Materials hallmarks of cancer and de ne the molecular changes that ac- and Methods. company the immortalization, transformation, and metastatic fi capabilities of human cells. The identi ed proteins may serve as ACKNOWLEDGMENTS. We thank Dr. William C. Hahn (Harvard Medical promising candidates for markers for the increased cellular School) for the BJ cell line and its derivatives; Science for Life Laboratory proliferation, malignant transformation, invasive growth, or Stockholm for help with massively parallel sequencing and bioinformatics metastatic capacity of tumor cells. We furthermore demonstrate analysis; and the entire staff of the Human Protein Atlas project and Robert that the altered expression pattern of four proteins identified Murphy (Carnegie Mellon University) for providing feature extraction scripts. This work was supported by grants from The Knut and Alice Wallen- using the BJ cell model shows similar patterns of deregulation in berg Foundation, the strategic grant to the Science for Life Laboratory, and human tissue samples representing different stages of cancer. the Seventh Framework Programme Marie Curie Industry-Academia Partnership Hence, a significant difference in expression was observed and Pathways Program FAST-PATH.

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