A quantitative PCR method to detect blood microRNAs associated with tumorigenesis in transgenic mice
- Alice C Fan†1,
- Marianna M Goldrick†2, 4Email author,
- Jennifer Ho2,
- Yu Liang3,
- Pavan Bachireddy1 and
- Dean W Felsher1Email author
© Fan et al; licensee BioMed Central Ltd. 2008
Received: 14 April 2008
Accepted: 30 September 2008
Published: 30 September 2008
MicroRNA (miRNA) dysregulation frequently occurs in cancer. Analysis of whole blood miRNA in tumor models has not been widely reported, but could potentially lead to novel assays for early detection and monitoring of cancer. To determine whether miRNAs associated with malignancy could be detected in the peripheral blood, we used real-time reverse transcriptase-PCR to determine miRNA profiles in whole blood obtained from transgenic mice with c-MYC-induced lymphoma, hepatocellular carcinoma and osteosarcoma. The PCR-based assays used in our studies require only 10 nanograms of total RNA, allowing serial mini-profiles (20 – 30 miRNAs) to be carried out on individual animals over time. Blood miRNAs were measured from mice at different stages of MYC-induced lymphomagenesis and regression. Unsupervised hierarchical clustering of the data identified specific miRNA expression profiles that correlated with tumor type and stage. The miRNAs found to be altered in the blood of mice with tumors frequently reverted to normal levels upon tumor regression. Our results suggest that specific changes in blood miRNA can be detected during tumorigenesis and tumor regression.
Distinct miRNA profiles have been described for many cancers including hematologic and solid malignancies [1–12]. Many reports have shown that patterns of miRNA expression differ between normal and cancerous tissues [1–10, 12–20]. Gene expression profiling of traditional mRNA targets in whole blood or fractionated leukocytes has also shown correlations with many types of both neoplastic and non-neoplastic human disease, for example renal cancer and Crohn's disease [21–30]. To investigate whether miRNA patterns in blood correlated with tumorigenesis, we measured by qRT-PCR a panel of miRNAs in MYC-induced transgenic models of tumorigenesis.
First, we developed a protocol optimized for collection, storage and shipping of whole mouse blood, RNA extraction from a small volume of stored sample, and qRT-PCR assays for mouse blood miRNA profiling. To enable blood to be collected from mice at different time points and stored so that total RNA extraction and miRNA quantitation could be batch analyzed, mouse blood was mixed with an RNA stabilizing reagent (RNAlater® Tissue Collection:RNA Stabilization Solution, Ambion), transported, and stored at -20 deg C. Total RNA extraction was subsequently performed using the Mouse RiboPure™ Blood kit (Ambion).
To determine which miRNAs could be readily detected in mouse blood, a panel of 111 quantitative reverse transcriptase PCR (qRT-PCR) SYBR miRNA assays was run on initial samples of blood from normal mice (see Additional file 1). The qRT-PCR assays were normalized via several methods, including 5S rRNA, U6 RNA, and global mean. Results were validated through the detection of several miRNA using TaqMan® qRT-PCR assays. High concordance was observed in relative levels of blood miRNAs as determined by the SYBR-based and TaqMan-based assays, with a correlation value of 0.86 (Figure 1D). We conclude that qRT-PCR is a highly sensitive approach for identifying mouse blood miRNA profiles.
An important feature of our method is that its sensitivity should allow for the analysis of serial blood specimens from the same mouse. We obtained serial tail vein blood samples from mice with MYC-induced lymphoma before and after oncogene inactivation. The majority of miRNAs, including members of the let-7 family, were detectable at low levels and then increased upon tumor regression (Figure 3B) as early as 3 days after MYC inactivation even though tumors did not fully regress until after 14 days. Thus, changes in blood miRNA profiles can be determined from sequential peripheral blood samples drawn from individual mice.
An important caveat of our studies is that we cannot determine the source of the miRNA changes. A priori, we could be detecting miRNAs in rare circulating tumor cells or from host cells that are influenced by tumor growth. Regardless, the changes in miRNAs we see correlated with tumor progression, and thus may be useful as biomarkers of tumorigenesis.
Compared to microarray-based global miRNA profiling, the real-time PCR assays used in our studies require several magnitudes less input RNA and can be performed in substantially less time. These features of our method make it tractable for the detection of miRNA expression to identify early metastasis or minimal residual neoplastic cells. Moreover, we found that specific blood miRNA profiles mirrored specific types and states of cancer. Whether our approach will also be useful for the analysis of human patients remains to be determined. Finally, our method appears to be applicable to the sequential analysis of changes in miRNA expression in other models and in clinical materials. Our approach may be useful to identify biomarkers to detect early disease states and to predict clinical response.
Materials and methods
The TRE-MYC transgenic lines generated for these experiments were described previously [31–36, 40–42]. The Eμ-tTA transgenic line for lymphoid specific expression and the Lt-tTA transgenic line for liver specific expression were both kindly provided by H. Bujard . Oncogene expression was suppressed in vivo by injecting mice intraperitoneally with 20 μg of doxycycline in PBS and adding doxycycline (200 μg/ml) to the drinking water.
Blood obtained by cardiac puncture from normal control mice of Balb/c and C57BL/6 strains was purchased from Jackson Labs (Bar Harbor MA). Blood from FVB/N and transgenic mice was collected at Stanford. To perform cardiac puncture, mice were euthanized using carbon dioxide, then the ventricle was accessed with an 18 gauge needle and 400–500 μl blood was aspirated into a 2 ml syringe. Blood was immediately discharged into a 2 ml microfuge tube preloaded with 1.3 ml RNAlater® Tissue Collection:RNA Stabilization Solution (Ambion, Austin TX), mixed by inversion, and stored at -20°C. To perform blood collection by tail vein, mice were placed under a heat lamp for 5 minutes, then a small peripheral tail incision was made. 2–10 drops of blood were collected directly into a 2 ml microfuge tube preloaded with 1.3 ml RNALater® Solution, mixed by inversion, and stored at -20°C. An average drop of blood was determined to be 24 μl. Blood/RNAlater® Solution mixtures were shipped on wet ice to a second site lab for RNA extraction and analysis.
RNA extraction and analysis
The Ambion Mouse RiboPure™-Blood RNA Isolation kit (AB cat #AM1951) was used for extraction of RNA. Briefly, samples were centrifuged and the RNAlater® Solution removed prior to disruption of the blood pellet in a guanidinium-based lysis solution, followed by organic extraction and purification of the total RNA fraction (including small RNA) by solid phase extraction onto a silica matrix. The Alternative Protocol described in the kit instruction manual for samples less than 250 ul was used for extraction of RNA from the tail vein samples. RNA yields were determined by UV absorbance using a Nanodrop instrument (ND-1000 Spectrophotometer, NanoDrop Technologies) and intactness was examined on an Agilent® 2100 bioanalyzer (Agilent Technologies). The cardiac puncture samples were diluted 1:10 before running.
MiRNA analysis was carried out using the mir Vana qRT-PCR primer sets (Ambion) and the TaqMan® MicroRNA Assays (Applied Biosystems)(26). The mir Vana qRT-PCR assays used 10 ng of input total RNA that was analyzed using target-specific primers for reverse transcription with M-MLV reverse transcriptase, followed by PCR amplification with a pair of miRNA target-specific primers and detection with SYBR® Green I nucleic acid gel stain 10,000× concentrate in DMSO (Invitrogen). Melting curve analysis was carried out for each target to assess amplification specificity; for some targets, non-specific amplification was observed in the no-template negative controls, which could not be discriminated by melt-curve analysis. The TaqMan MicroRNA Assays used 10 ng of input total RNA with miRNA-target-specific reverse transcription primers and target-specific internal hybridization probes ("TaqMan probes"), and were run in 96-well or 384-well formats. qRT-PCR assays of similar design (also purchased from Applied Biosystems) were carried out for constitutively expressed small RNAs of similar size to miRNAs (e.g. snoRNAs) and used for normalization of input RNA amount (analogous to use of constitutive mRNAs such as GAPDH for normalization of protein-coding genes).
The reverse transcription reactions were carried out for 65 min and used the AB TaqMan® microRNA Reverse Transcription Kit (AB cat #4366597) which includes M-MLV reverse transcriptase. Amplification reactions consisted of a hold of 10 min at 95°C and 40 cycles (15 sec/95°C, 60 sec/60°C) on an Applied Biosystems 7900HT Real-Time PCR System and required about 1.5 hours to complete. The assays were carried out in duplicate or triplicate and the geometric average Ct value was used to calculate relative expression for each datapoint. Unsupervised hierarchical clustering of samples was carried out with the program Cluster 3.0. Each sample was used in multiple experimental runs, and relative expression of different miRNAs was determined using identical endogenous controls in each experiment. Within each experiment, the endogenous control that had the highest Ct was set as the baseline, and the Ct between the baseline and the Ct of the small RNA control in each sample was used as a normalization factor that was added to the raw Ct for each sample. Normalized Ct values larger than 35 were reported as 35. After mean-centering the data for each miRNA and using uncentered correlation similarity metric and average linkage, the expression of miRNAs was hierarchically clustered and displayed with the TreeView 1.6.
This work is dedicated to the memory of Eiichi Koyama. We thank Juanita C. Gonzales for help in extraction and quantification of RNA and Patrick Renschler for his help formatting figures. This work was supported in part by the NCI grants 1R01 CA89305-01A1, 3RO1 CA89305-0351, 1RO1 CA105102 Lymphoma Program Project, Burroughs Welcome Fund, the Damon Runyon Foundation (DWF), and the Leukemia and Lymphoma Society (DWF, ACF).
- Barbarotto E, Schmittgen TD, Calin GA: MicroRNAs and cancer: profile, profile, profile. International journal of cancer. 2008, 122 (5): 969-977. 10.1002/ijc.23343.View ArticleGoogle Scholar
- Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K: Frequent deletions and down-regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences USA. 2002, 99 (24): 15524-15529. 10.1073/pnas.242606799.View ArticleGoogle Scholar
- Croce CM, Calin GA: miRNAs, cancer, and stem cell division. Cell. 2005, 122 (1): 6-7. 10.1016/j.cell.2005.06.036View ArticlePubMedGoogle Scholar
- , : MicroRNAs and cancer: Profile, profile, profile. International Journal of Cancer. 2008, 122 (5): 969-977. 10.1002/ijc.23343.Google Scholar
- Esquela-Kerscher A, Slack FJ: Oncomirs [mdash] microRNAs with a role in cancer. Nat Rev Cancer. 2006, 6 (4): 259-269. 10.1038/nrc1840View ArticlePubMedGoogle Scholar
- Gaur AB, Jewell DA, Liang Y, Ridzon D, Moore JH, Chen C, Ambros VA, Israel MA: Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. Cancer Res 67(6):2456-68. 2007, 67 (6): 2456-2468. 10.1158/0008-5472.CAN-06-2698.View ArticleGoogle Scholar
- He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin GA, Liu C-g, Franssila K, Suster S: The role of microRNA genes in papillary thyroid carcinoma. Proceedings of the National Academy of Sciences. 2005, 102 (52): 19075-19080. 10.1073/pnas.0509603102.View ArticleGoogle Scholar
- Jiang J, Gusev Y, Aderca I, Mettler TA, Nagorney DM, Brackett DJ, Roberts LR, Schmittgen TD: Association of MicroRNA Expression in Hepatocellular Carcinomas with Hepatitis Infection, Cirrhosis, and Patient Survival. Clin Cancer Res. 2008, 14 (2): 419-427. 10.1158/1078-0432.CCR-07-0523PubMed CentralView ArticlePubMedGoogle Scholar
- Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA: MicroRNA expression profiles classify human cancers. Nature. 2005, 435 (7043): 834-838. 10.1038/nature03702View ArticlePubMedGoogle Scholar
- Volinia S, Calin GA, Liu C-G, Ambs S, Cimmino A, Petrocca F, Visone R, Iorio M, Roldo C, Ferracin M: A microRNA expression signature of human solid tumors defines cancer gene targets. Proceedings of the National Academy of Sciences. 2006, 103 (7): 2257-2261. 10.1073/pnas.0510565103.View ArticleGoogle Scholar
- Croce CM: Oncogenes and cancer. New England Journal of Medicine, The. 2008, 358 (5): 502-511. 10.1056/NEJMra072367.View ArticleGoogle Scholar
- Chang T-C, Yu D, Lee Y-S, Wentzel EA, Arking DE, West KM, Dang CV, Thomas-Tikhonenko A, Mendell JT: Widespread microRNA repression by Myc contributes to tumorigenesis. Nature genetics. 2008, 40 (1): 43-50. 10.1038/ng.2007.30PubMed CentralView ArticlePubMedGoogle Scholar
- Chen CZ: MicroRNAs as oncogenes and tumor suppressors. New England Journal of Medicine. 2005, 353 (17): 1768-1771. 10.1056/NEJMp058190View ArticlePubMedGoogle Scholar
- Cho WCS: OncomiRs: the discovery and progress of microRNAs in cancers. Molecular cancer. 2007, 6: 60-60. 10.1186/1476-4598-6-60PubMed CentralView ArticlePubMedGoogle Scholar
- He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ: A microRNA polycistron as a potential human oncogene. Nature. 2005, 435 (7043): 828-833. 10.1038/nature03552View ArticlePubMedGoogle Scholar
- Lawrie CH: MicroRNA expression in lymphoma. Expert Opinion on Biological Therapy. 2007, 7 (9): 1363-1374. 10.1517/147125220.127.116.113View ArticlePubMedGoogle Scholar
- Lawrie DW, Gal DW, Dunlop DW, Pushkaran DW, Liggins DW, Pulford DW, Banham DW, Pezzella DW, Boultwood DW, Wainscoat DW: Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br J Haematol. 2008, 141 (5): 672-675. 10.1111/j.1365-2141.2008.07077.xView ArticlePubMedGoogle Scholar
- Rai D, Karanti S, Jung I, Dahia PLM, Aguiar RCT: Coordinated expression of microRNA-155 and predicted target genes in diffuse large B-cell lymphoma. Cancer Genetics and Cytogenetics. 2008, 181 (1): 8-15. 10.1016/j.cancergencyto.2007.10.008.PubMed CentralView ArticlePubMedGoogle Scholar
- Roldo C, Missiaglia E, Hagan JP, Falconi M, Capelli P, Bersani S, Calin GA, Volinia S, Liu CG, Scarpa A: MicroRNA Expression Abnormalities in Pancreatic Endocrine and Acinar Tumors Are Associated With Distinctive Pathologic Features and Clinical Behavior. J Clin Oncol. 2006.Google Scholar
- Sampson VB, Rong NH, Han J, Yang Q, Aris V, Soteropoulos P, Petrelli NJ, Dunn SP, Krueger LJ: MicroRNA let-7a down-regulates MYC and reverts MYC-induced growth in Burkitt lymphoma cells. Cancer research. 2007, 67 (20): 9762-9770. 10.1158/0008-5472.CAN-07-2462View ArticlePubMedGoogle Scholar
- Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV: Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proceedings of the National Academy of Sciences. 2005, 102 (31): 11023-11028. 10.1073/pnas.0504921102.View ArticleGoogle Scholar
- Burczynski ME, Dorner AJ: Transcriptional profiling of peripheral blood cells in clinical pharmacogenomic studies. Pharmacogenomics. 2006, 7 (2): 187-202. 10.2217/14622418.104.22.168View ArticlePubMedGoogle Scholar
- Burczynski ME, Peterson RL, Twine NC, Zuberek KA, Brodeur BJ, Casciotti L, Maganti V, Reddy PS, Strahs A, Immermann F: Molecular Classification of Crohn's Disease and Ulcerative Colitis Patients Using Transcriptional Profiles in Peripheral Blood Mononuclear Cells. J Mol Diagn. 2006, 8 (1): 51-61. 10.2353/jmoldx.2006.050079PubMed CentralView ArticlePubMedGoogle Scholar
- Burczynski ME, Twine NC, Dukart G, Marshall B, Hidalgo M, Stadler WM, Logan T, Dutcher J, Hudes G, Trepicchio WL: Transcriptional Profiles in Peripheral Blood Mononuclear Cells Prognostic of Clinical Outcomes in Patients with Advanced Renal Cell Carcinoma. Clin Cancer Res. 2005, 11 (3): 1181-1189.PubMedGoogle Scholar
- Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P: Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005, 309 (5740): 1577-1581. 10.1126/science.1113329View ArticlePubMedGoogle Scholar
- Sharp FR, Xu H, Lit L, Walker W, Apperson M, Gilbert DL, Glauser TA, Wong B, Hershey A, Liu D-Z: The Future of Genomic Profiling of Neurological Diseases Using Blood. Arch Neurol. 2006, 63 (11): 1529-1536. 10.1001/archneur.63.11.1529View ArticlePubMedGoogle Scholar
- Tang Y, Gilbert DL, Glauser TA, Hershey AD, Sharp FR: Blood Gene Expression Profiling of Neurologic Diseases: A Pilot Microarray Study. Arch Neurol. 2005, 62 (2): 210-215. 10.1001/archneur.62.2.210View ArticlePubMedGoogle Scholar
- Tang Y, Lu A, Ran R, Aronow BJ, Schorry EK, Hopkin RJ, Gilbert DL, Glauser TA, Hershey AD, Richtand NW: Human blood genomics: distinct profiles for gender, age and neurofibromatosis type 1. Molecular Brain Research. 2004, 132 (2): 155-167. 10.1016/j.molbrainres.2003.10.014View ArticlePubMedGoogle Scholar
- Tang Y, Nee AC, Lu A, Ran R, Sharp FR: Blood genomic expression profile for neuronal injury. J Cereb Blood Flow Metab. 2003, 23 (3): 310-319. 10.1097/00004647-200303000-00006View ArticlePubMedGoogle Scholar
- Tang Y, Schapiro MB, Franz DN, Patterson BJ, Hickey FJ, Schorry EK, Hopkin RJ, Wylie M, Narayan T, Glauser TA: Blood expression profiles for tuberous sclerosis complex 2, neurofibromatosis type 1, and Down's syndrome. Annals of neurology. 2004, 56 (6): 808-814. 10.1002/ana.20291View ArticlePubMedGoogle Scholar
- Felsher DW, Bishop JM: Reversible tumorigenesis by MYC in hematopoietic lineages. Molecular cell. 1999, 4 (2): 199-207. 10.1016/S1097-2765(00)80367-6View ArticlePubMedGoogle Scholar
- Jain M, Arvanitis C, Chu K, Dewey W, Leonhardt E, Trinh M, Sundberg CD, Bishop JM, Felsher DW: Sustained loss of a neoplastic phenotype by brief inactivation of MYC. Science. 2002, 297 (5578): 102-104. 10.1126/science.1071489View ArticlePubMedGoogle Scholar
- Beer S, Zetterberg A, Ihrie RA, McTaggart RA, Yang Q, Bradon N, Arvanitis C, Attardi LD, Feng S, Ruebner B: Developmental context determines latency of MYC-induced tumorigenesis. PLoS Biol. 2004, 2 (11): e332- 10.1371/journal.pbio.0020332PubMed CentralView ArticlePubMedGoogle Scholar
- Shachaf CM, Kopelman AM, Arvanitis C, Karlsson A, Beer S, Mandl S, Bachmann MH, Borowsky AD, Ruebner B, Cardiff RD: MYC inactivation uncovers pluripotent differentiation and tumour dormancy in hepatocellular cancer. Nature. 2004, 431 (7012): 1112-1117. 10.1038/nature03043View ArticlePubMedGoogle Scholar
- Giuriato S, Ryeom S, Fan AC, Bachireddy P, Lynch RC, Rioth MJ, van Riggelen J, Kopelman AM, Passegue E, Tang F: Sustained regression of tumors upon MYC inactivation requires p53 or thrombospondin-1 to reverse the angiogenic switch. Proc Natl Acad Sci USA. 2006, 103 (44): 16266-16271. 10.1073/pnas.0608017103PubMed CentralView ArticlePubMedGoogle Scholar
- Wu C-H, van Riggelen J, Yetil A, Fan AC, Bachireddy P, Felsher DW: Cellular senescence is an important mechanism of tumor regression upon c-Myc inactivation. Proceedings of the National Academy of Sciences of the United States of America. 2007, 104 (32): 13028-13033. 10.1073/pnas.0701953104PubMed CentralView ArticlePubMedGoogle Scholar
- Tran TP, Fan AC, Bendapudi PK, Koh S, Komatsubara K, Chen J, Horng G, Bellovin DI, Giuriato S, Wang CS: Combined Inactivation of MYC and K-Ras oncogenes reverses tumorigenesis in lung adenocarcinomas and lymphomas. PloS one. 2008, 3 (5): e2125- 10.1371/journal.pone.0002125PubMed CentralView ArticlePubMedGoogle Scholar
- Xiao C, Srinivasan L, Calado DP, Patterson HC, Zhang B, Wang J, Henderson JM, Kutok JL, Rajewsky K: Lymphoproliferative disease and autoimmunity in mice with increased miR-17-92 expression in lymphocytes. Nature immunology. 2008, 9 (4): 405-414. 10.1038/ni1575PubMed CentralView ArticlePubMedGoogle Scholar
- Akao Y, Nakagawa Y, Kitade Y, Kinoshita T, Naoe T: Downregulation of microRNAs-143 and -145 in B-cell malignancies. Cancer Science. 2007, 98 (12): 1914-1920. 10.1111/j.1349-7006.2007.00618.xView ArticlePubMedGoogle Scholar
- Karlsson A, Deb-Basu D, Cherry A, Turner S, Ford J, Felsher DW: Defective double-strand DNA break repair and chromosomal translocations by MYC overexpression. Proc Natl Acad Sci USA. 2003, 100 (17): 9974-9979. 10.1073/pnas.1732638100PubMed CentralView ArticlePubMedGoogle Scholar
- Karlsson A, Giuriato S, Tang F, Fung-Weier J, Levan G, Felsher DW: Genomically complex lymphomas undergo sustained tumor regression upon MYC inactivation unless they acquire novel chromosomal translocations. Blood. 2003, 101 (7): 2797-2803. 10.1182/blood-2002-10-3091View ArticlePubMedGoogle Scholar
- Shachaf CM, Perez OD, Youssef S, Fan AC, Elchuri S, Goldstein MJ, Shirer AE, Sharpe O, Chen J, Mitchell DJ: Inhibition of HMGcoA reductase by atorvastatin prevents and reverses MYC-induced lymphomagenesis. Blood. 2007, 110 (7): 2674-2684. 10.1182/blood-2006-09-048033PubMed CentralView ArticlePubMedGoogle Scholar
- Kistner A, Gossen M, Zimmermann F, Jerecic J, Ullmer C, Lubbert H, Bujard H: Doxycycline-mediated quantitative and tissue-specific control of gene expression in transgenic mice. Proc Natl Acad Sci USA. 1996, 93 (20): 10933-10938. 10.1073/pnas.93.20.10933PubMed CentralView ArticlePubMedGoogle Scholar
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