Abstract
MIF and other cytokines are frequently detected at elevated levels of abundance in solid tumours. Their involvement in tumour biology has been studied for many years, and, with the advent of postgenomic tools such as next-generation DNA and RNA sequencing, and mass spectrometry-driven protein profiling, the underlying mechanisms can be studied in a systematic and quantitative way. This chapter discusses recent studies by our group that have shown that MIF and CD74 are mechanistically involved in breast cancer progression. Analysis of recently released data from the Cancer Genome Atlas (TCGA) as well as our proteomics data is presented and discussed. TCGA data show that MIF and CD74 are rarely mutated in cancer but are consistently overexpressed at the level of mRNA. Furthermore, using high-resolution mass spectrometry to analyse tumour protein abundance, we have identified MIF and CD74 among the proteins that are overexpressed in metastatic triple-negative breast tumours. A cell-based model showed that when CD74 is overexpressed, it interferes with the function of a known tumour suppressor, Scribble, leading to enhanced invasion, possibly because the functions of Scribble in maintaining cell polarity are compromised. The underlying mechanism, yet to be fully elucidated, involves deregulation of Scribble phosphorylation on specific sites in its C-terminal proline-rich domain.
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References
Fornier M, Risio M, Van Poznak C, Seidman A (2002) HER2 testing and correlation with efficacy of trastuzumab therapy. Oncology (Williston Park, NY) 16:1340–1348, 1351–1342; discussion 1352, 1355–1348
Bauer KR, Brown M, Cress RD, Parise CA, Caggiano V (2007) Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype: a population-based study from the California cancer registry. Cancer 109:1721–1728
Cleator S, Heller W, Coombes RC (2007) Triple-negative breast cancer: therapeutic options. Lancet Oncol 8:235–244
Dent R, Hanna WM, Trudeau M, Rawlinson E, Sun P, Narod SA (2009) Pattern of metastatic spread in triple-negative breast cancer. Breast Cancer Res Treat 115(2):423–428
Dent R, Trudeau M, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA (2007) Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 13:4429–4434
Leng L, Metz C, Fang Y, Xu J, Donnelly S, Baugh J, Delonery T, Chen Y, Mitchell RA, Bucala R (2003) MIF Signal Transduction Initiated by Binding to CD74. J Exp Med. 197:1467–1476
Gutman H, Kott I, Rabinerson D, Reiss R (1986) Macrophage migration inhibition factor test in breast tumors. Isr J Med Sci 22:812–816
Katoh M, Katoh M (2004) Human FOX gene family (review). Int J Oncol 25:1495–1500
Kops GJ, Medema RH, Glassford J, Essers MA, Dijkers PF, Coffer PJ, Lam EW, Burgering BM (2002) Control of cell cycle exit and entry by protein kinase B-regulated forkhead transcription factors. Mol Cell Biol 22:2025–2036
Alldridge L, Metodieva G, Greenwood C, Al-Janabi K, Thwaites L, Sauven P, Metodiev M (2008) Proteome profiling of breast tumors by gel electrophoresis and nanoscale electrospray ionization mass spectrometry. J Proteome Res 7:1458–1469
Greenwood C, Metodieva G, Al-Janabi K, Lausen B, Alldridge L, Leng L, Bucala R, Fernandez N, Metodiev MV (2012) Stat1 and CD74 overexpression is co-dependent and linked to increased invasion and lymph node metastasis in triple-negative breast cancer. J Proteomics75(10):3031–3040
Metodieva G, Greenwood C, Alldridge L, Sauven P, Metodiev M (2009) A peptide-centric approach to breast cancer biomarker discovery utilizing label-free multiple reaction monitoring mass spectrometry. Proteomics Clin Appl 3:78–82
Metodieva G, Nogueira-de-Souza NC, Greenwood C, Al-Janabi K, Leng L, Bucala R, Metodiev MV (2013) CD74-dependent deregulation of the tumor suppressor scribble in human epithelial and breast cancer cells. Neoplasia 15:660–668
Leth-Larsen R, Lund R, Hansen HV, Laenkholm AV, Tarin D, Jensen ON, Ditzel HJ (2009) Metastasis-related plasma membrane proteins of human breast cancer cells identified by comparative quantitative mass spectrometry. Mol Cell Proteomics 8:1436–1449
Burton JD, Ely S, Reddy PK, Stein R, Gold DV, Cardillo TM, Goldenberg DM (2004) CD74 is expressed by multiple myeloma and is a promising target for therapy. Clin Cancer Res 10:6606–6611
Sapra P, Stein R, Pickett J, Qu Z, Govindan SV, Cardillo TM, Hansen HJ, Horak ID, Griffiths GL, Goldenberg DM (2005) Anti-CD74 antibody-doxorubicin conjugate, IMMU-110, in a human multiple myeloma xenograft and in monkeys. Clin Cancer Res 11:5257–5264
Stein R, Qu Z, Cardillo TM, Chen S, Rosario A, Horak ID, Hansen HJ, Goldenberg DM (2004) Antiproliferative activity of a humanized anti-CD74 monoclonal antibody, hLL1, on B-cell malignancies. Blood 104:3705–3711
Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372
Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127:635–648
Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386
Daub H, Olsen JV, Bairlein M, Gnad F, Oppermann FS, Korner R, Greff Z, Keri G, Stemmann O, Mann M (2008) Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol Cell 31:438–448
Bilder D, Li M, Perrimon N (2000) Cooperative regulation of cell polarity and growth by drosophila tumor suppressors. Science 289:113–116
Bilder D, Schober M, Perrimon N (2003) Integrated activity of PDZ protein complexes regulates epithelial polarity. Nat Cell Biol 5:53–58
Nakajima Y, Meyer EJ, Kroesen A, McKinney SA, Gibson MC (2013) Epithelial junctions maintain tissue architecture by directing planar spindle orientation. Nature 500:359–362
Feigin ME, Akshinthala SD, Araki K, Rosenberg AZ, Muthuswamy LB, Martin B, Lehmann BD, Berman HK, Pietenpol JA, Cardiff RD, Muthuswamy SK (2014) Mislocalization of the cell polarity protein scribble promotes mammary tumorigenesis and is associated with basal breast cancer. Cancer Res 74:3180–3194
Zhan L, Rosenberg A, Bergami KC, Yu M, Xuan Z, Jaffe AB, Allred C, Muthuswamy SK (2008) Deregulation of scribble promotes mammary tumorigenesis and reveals a role for cell polarity in carcinoma. Cell 135:865–878
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Appendix
Appendix
R code to merge individual RNA-Seq data files, cluster by CD74 and MIF expression, perform survival analysis and find genes which are co-regulated with MIF and CD74 in breast tumours. RNA-Seq data files are downloaded along with clinical information from TCGA (https://tcga-data.nci.nih.gov). The individual files are then merged and transposed to create a data frame containing patient data in rows and genes in columns. This is merged with the clinical information table using patients’ barcodes. Survival analysis is then performed using the R package “Survival”. Co-regulated genes are identified by calculating the Spearman rank-based correlation coefficient.
#RNA-seq import and merging #Download data files from TCGA site. Unpack into working directory temp = list.files(pattern="*.genes.normalized_results") myfiles = lapply(temp, read.delim) data<- NULL data<- myfiles[1] for (i in 2:length(myfiles)){ data<-merge(data, myfiles[i], by="gene_id") } names(data)<- c("gene_id", temp)) write.csv(data, "RNAseq_breast.csv") rownames(data)<-data$gene_id data$gene_id<-NULL data.breast<-t(data) #Download clinical data and import into R. Merge with the RNA-seq data using patients barcodes to produce a #dataframe called “data.tumors.only #Then get MIF and CD74 expression levels: cd74<- data.tumors.only[,3483] #CD74 is column 3483 in the table mif<- data.tumors.only[,11053] #MIF is column 11053 in the table #Cluster patients by expression of CD74 and MIF dataCD74MIF<-data.frame(cd74,mif) hc<- hclust(dist(scale(log(dataCD74MIF))), "ward.D2") plot(hc) #Look at the clustering and save as graphics file cl<- cutree(hc, 2) #Use clustering to do survival analysis library(survival) survival<- as.numeric(ifelse(data.tumors.only$days_to_last_followup!="[Not Available]", data.tumors.only$days_to_last_followup,data.tumors.only$days_to_death)) vital<- ifelse(data.tumors.only$vital_status!="Dead", 0,1) surv<-Surv(survival,vital) survdiff(surv~cl)#calculate p-value fit<- survfit(surv~cl) plot(fit, col=c("grey", "black"), xlim=c(0,5000), ylim=c(0.2,1), cex=0.5, xlab="Days to event", ylab="Survival") #Analysis of co-expressed proteins: calculate Spearman rho for all proteins against CD74 and MIF corMIF<- apply(data.tumors.only[,-c(1:110)], 2, function(x) cor(x, mif, method="spearman")) corCD74<- apply(data.tumors.only[,-c(1:110)], 2, function(x) cor(x, cd74, method="spearman")) write.csv(cbind(names, corMIF), "corMIF.csv", row.names=F) write.csv(cbind(names, corCD74), "corCD74.csv", row.names=F)
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Metodiev, M.V. (2017). CD74, MIF and Breast Tumorigenesis: Insights from Recent Large-Scale Tumour Genomics and Proteomics Studies. In: Bucala, R., Bernhagen, J. (eds) MIF Family Cytokines in Innate Immunity and Homeostasis. Progress in Inflammation Research. Springer, Cham. https://doi.org/10.1007/978-3-319-52354-5_3
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DOI: https://doi.org/10.1007/978-3-319-52354-5_3
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