From Description to Causality

[Cell Cycle 5:11, 1148-1151, 1 June 2006]; ©2006 Landes Bioscience

From Description to Causality Extra View

Mechanisms of Gene Expression Signatures in Cancer ABSTRACT

*Correspondence to: Howard Y. Chang; Program in Epithelial Biology and Cancer Biology Program; Stanford University School of Medicine; Stanford, California 94305, USA; Email: [email protected]

ABBREVIATIONS

SNP

gene expression signatures stepwise linkage analysis of microarray signatures single nucleotide polymorphisms

.

IEN

GES SLAMS

Over the last several years, a large number of investigators have utilized emerging functional genomics technology to characterize the global gene expression patterns of hundreds of types of cancers.1-6 In many instances, large-scale differences, reflecting the expression pattern of thousands of genes, suggested distinct biological bases for tumors that were previously thought to be homogeneous.1,3,4,7-9 The molecular subtypes are often associated with different clinical outcomes. Global gene expression patterns can also be examined for features that correlate with clinical behavior to create prognostic signatures.5,10,11 For example in breast cancer, a poor-prognosis gene expression signature in the primary tumor can accurately predict the risk of subsequent metastasis, independent of other well-known clinicopathologic risk factors.12 However, because supervised methods are driven by class or outcome prediction, and the complexity of the models considered are necessarily limited, the resulting gene sets may be excellent prognostic markers without revealing much about the underlying biological mechanisms. Gene expression signatures within cancer samples may arise through many means. For example, variations in the composition of cell types or responses to different host environments can lead to gene expression differences.3 Further, GES can arise through the effects of aneuploidy and epigenetic changes (which act in cis) or the responses to altered activities of key transcriptional regulators (which act in trans).13-15 To gain more mechanistic insight into complex cancer GES, we and others have an alternative strategy to infer physiologic mechanisms in human cancers.14-18 We began with a gene expression profile derived from a cell culture model of a physiological process. The in vitro expression profile was used to guide interpretation of gene expression data from human cancers and thereby test a specific hypothesis. In principle, this strategy allows one to connect the controlled and dynamic molecular perturbations possible in vitro with the complex biology of human clinical samples in a comprehensive and quantitative fashion. For example, the transcriptional patterns of cells expressing specific oncogenes can be used to recognize the activation of the cognate oncogenes in vivo.14,16,18 Such patterns allow investigators to assign previously unexplained gene expression patterns to specific pathways, and to evaluate alternative hypotheses of oncogenic mechanisms.16 Moreover, once a match is made between the in vitro transcriptional pattern and a cancer signature in vivo, one can attempt to use this knowledge to gain a greater understanding of the cancer and uncover means to inhibit cancer growth. Because this strategy is analogous to reverse genetics to identify the functions of genes, we term this strategy “reverse genomics” (Fig. 1). We applied this strategy to identify a gene expression signature of a wound response and test its role in cancer progression. Based on the histological similarities between the environments surrounding tumor growth and normal wound healing, Dvorak proposed that tumor growth is “normal

ON

expression profiling, microarray, gene regulation, bioinformatics, MYC, CSN5, ubiquitination

.D

KEY WORDS

CE

Previously published online as a Cell Cycle E-publication: http://www.landesbioscience.com/journals/cc/abstract.php?id=2798

OT D

IST

Original manuscript submitted: 04/11/06 Manuscript accepted: 04/12/06

UT E

Program in Epithelial Biology and Cancer Biology Program; Stanford University School of Medicine; Stanford, California USA

Global gene expression profiles of thousands of cancer samples have been completed, giving rise to hundreds of gene expression signatures (GES). Although many expression signatures show promise in predicting patient prognosis or response to therapies, the usefulness of the signatures in understanding the underlying mechanisms of cancer has not been fully exploited. While “reverse genomic” methods can test specific hypotheses of gene regulation, they fare less well in deciphering novel or combinatorial mechanisms of gene regulation. Recently we described SLAMS (stepwise linkage analysis of microarray signatures), a novel method that can prospectively identify genetic regulators of gene expression signatures in cancer. Applying SLAMS on a poor-prognosis wound signature in human breast cancer, we identified CSN5-mediated ubiquitination of MYC as a novel mechanism to activate a biological program favoring metastasis.

RIB

Adam S. Adler Howard Y. Chang*

SC

ACKNOWLEDGEMENTS

©

20

06

LA

ND

ES

BIO

Supported by grants from the National Institutes of Health (AR050007, CA09302) and the Damon Runyon Cancer Research Foundation.

1148

Cell Cycle

2006; Vol. 5 Issue 11

Mechanisms of Gene Expression Signatures in Cancer

Figure 1. Regulators of gene expression in cancer. (A) A specific gene expression signature is obtained by comparing gene expression profiles of multiple cancer samples. (B) In reverse genomics, gene expression signatures of specific treatments or altered expression of genes are analyzed to determine whether they match the expression pattern obtained from cancer samples in vivo. (C) In forward genomics, cancer samples are first separated based on the presence or absence of a gene expression signature. The samples are analyzed for genetic or epigenetic changes (e.g., DNA copy number, methylation, SNP) that cosegregate with the GES. Genes within the linked region are then filtered, and specific candidates are validated for their ability to induce the signature. In this example, amplification of Gene A and deletion of Gene B are linked to the presence of the signature and would thus be the candidate regulators of the signature.

wound healing gone awry”.19 Wound healing involves vast changes in normal cell behavior, including initiation of cell proliferation, migration, invasion, and angiogenesis—all of these features are characteristic of metastatic tumors.20,21 To further define the role of wound healing in cancer, our laboratory previously identified a gene expression signature, the wound response signature, based on the transcriptional response of fibroblasts to serum,17 the soluble fraction of blood that induces normal wound healing in tissues. Expression of this signature of 512 genes predicted poor survival and increased risk of metastasis in several human cancers, including lung, gastric, prostate, and liver cancer.17 In particular, we found the wound signature to be an extremely strong predictor of death and metastasis in a panel of 295 breast tumors and was a better independent predictor of death and metastasis than classic risk factors, including age, tumor size or grade, and estrogen receptor status.22 Given the ability of the wound signature to accurately predict metastasis, the www.landesbioscience.com

signature further predicted which breast cancer patients would have benefited from chemotherapy, and importantly identified patients that would receive no benefit from chemotherapy (relieving these patients from unnecessary treatment).22 Thus, the wound signature has proven to be a powerful prognostic and diagnostic tool for high-risk breast cancer patients. While the wound signature and other GES may be useful for patient diagnosis and prognosis, deciphering the biological basis of GES will lead to a better understanding of cancer itself and potentially reveal novel therapeutic targets to prevent cancer metastasis and death. Although reverse genomics has the ability to identify regulators of large-scale gene expression signatures, it is better suited to aid in the determination of the role of a specific regulator in cancer progression. For example, Bild and colleagues18 expressed six different oncogenes individually in mammary epithelial cells and obtained GES that were diagnostic of each individual oncogene. They then

Cell Cycle

1149


used these signatures to predict the activated oncogenes in human tumors with known genetic alterations or specific mouse models of cancer that were derived from the dysregulation of one of these genes. Further, the GES were used to predict sensitivity to drugs that target specific oncogenic pathways. Lamb and colleagues16 used a similar approach, though rather than using a gene expression signature to predict regulators of other cancers, they used a GES to further define the mechanism of action of a well-known oncogene. After obtaining a GES by overexpressing cyclin D1 in mammary epithelial cells, a computational method was used to find other genes that are coexpressed with the cyclin D1 GES in cancer, isolating the C/EBPβ transcription factor as a candidate coregulator of cyclin D1 action. Sweet-Cordero and colleagues15 used a different but complementary approach, where a GES derived from a KRAS2-driven mouse model of cancer led to the detection of KRAS2 mutant human cancers. While reverse genomic experiments predicted the oncogenes responsible for well-characterized cancers and can be used to further define mechanisms of oncogene activity, it would be difficult to use these approaches to tackle novel GES with limited a priori biological knowledge observed in sporadic human cancers. The power and limitation of reverse genomics both lie in its specificity. We note three limitations. First, because reverse genomics is hypothesis driven, based on prior knowledge of the relevant biological pathways, one is less able to identify novel regulators of cancer GES. Second, each hypothesis must be tested individually, and it is not feasible to test all possible hypotheses at one time. Third, given that cancer usually occurs through the disruption of multiple independent or converging pathways, it would be difficult to fully characterize these cancers through a reverse genomic approach. For example, to identify the regulator(s) driving a reproducible gene expression signature from multiple cancers (Fig. 1A), one would have to activate (or silence) all candidate regulators through genetic or pharmacologic approaches before one could discover that Gene A and Gene B together induced the signature (Fig. 1B). However, if one of the candidate regulators was not previously known to play a role in cancer, one would never be able to find the regulators. As previously stated, this approach is better suited as a hypothesis-driven method to further understand specific aspects of cancer based on prior knowledge. To overcome some of these limitations, we recently devised a “forward genomic” approach to identify regulators of gene expression signatures in cancer, termed SLAMS for stepwise linkage analysis of microarray signatures (Fig. 1C).23 Rather than starting with potential regulators, we use the expression signature as our starting point and then use genetic linkage strategy to relate GES activity with specific genetic alternations. Since we do not begin with individual, previously characterized regulators of cancer, our method allows the identification of both known and novel regulators of cancer. Briefly, in SLAMS tumors are first separated into two classes, one that expresses the GES and one that does not. Secondly, we identify specific genetic alterations (in this case DNA copy number) that cosegregate with the GES, mapping prospective regulators to large chromosomal regions. Thirdly, the list of genes within each linked genomic region is filtered by their transcriptional regulation in appropriate expression data sets. For example, if amplification of Gene A is associated with an expression signature, then increased expression levels of Gene A should also be associated with the GES. Other sources of information may also be utilized, such as the literature or additional expression profiles from relevant in vitro experiments. Further validation of candidate regulators is achieved by the ability 1150

of their expression levels to predict the signature in additional tumor samples. The end result is the prospective identification of one or more regulators that drive GES, with no bias towards known oncogenes or tumor suppressors. In the example shown in Figure 1C, expression of the signature is linked to an amplification of a region that contains Gene A and a deletion of a region that contains Gene B. We note that the method is general, whereby linkage analysis can be completed using other types of genetic or epigenetic data, such as single nucleotide polymorphisms (SNP)24 or DNA methylation maps.25 To illustrate our method, we applied SLAMS to identify the regulators of the wound signature in human breast cancer.23 We found significant linkage between expression of the wound signature and amplification of chromosome 8q, specifically the amplification of MYC on 8q24 and CSN5 on 8q13. MYC encodes a well-known oncogenic transcription factor,26 and CSN5 is the catalytic component of the COP9 signalosome, an essential activator of Cullin-RING ubiquitin ligases.27 Expression of MYC and CSN5 were predictive of wound signature activation in human tumor samples, and coexpression of MYC and CSN5 fully induced the wound signature in nontransformed breast epithelial cells to a level seen in authentic human tumors.23 Because we were able to induce the wound signature in untransformed cells, it gave us the unique opportunity to observe specific changes caused by expression of the wound signature. We found that MYC and CSN5 synergistically increased cell proliferation, disrupted cell shape and adhesion, and increased cellular invasiveness, alterations commonly found in metastatic cancers.20,21 We next explored the mechanism by which MYC and CSN5 function together. The transcriptional activity of MYC was known to be coupled to ubiquitination, but the mechanisms of control were unclear.28 The stability and activity of MYC protein is regulated by at least two SCF (SKP1-CUL1-F-box protein) ubiquitin ligase complexes. SCFFBW7 inhibits MYC function by increasing its rate of turnover.29,30 Conversely, SCFSKP2 increases MYC transcriptional activity followed by rapid turnover.31,32 We found that expression of CSN5 induced the ubiquitination of MYC and increased its transcriptional activity, followed by SCFSKP2-mediated MYC degradation.23 Using microarray analysis, we identified several MYC target genes involved in cancer progression, including MDM2, INSR, and HIF1α, to require CSN5 coexpression for full induction by MYC. Conversely, silencing of endogenous CSN5 by RNA interference led to destabilization of the SKP2 F-box adaptor protein, increased stability of MYC protein, and decreased transcriptional potency of MYC. Cope and Deshaies33 also independently demonstrated the requirement of CSN5 for MYC protein turnover. In summary, SLAMS led to the identification of MYC and CSN5 as the collaborating genetic lesions responsible for a prognostic GES of metastatic breast cancer and revealed a biochemical pathway, comprised of CSN5 → SKP2 → MYC, in the regulation of select MYC target genes. Future studies will be needed to dissect the mechanisms whereby CSN5 controls the biological activities of MYC. Knowledge of genetic regulators of cancer GES can potentially improve the diagnosis and treatment of human cancers. For instance, we found that the same prognostic information can be obtained by measuring the expression level of the two regulators of the wound signature, MYC and CSN5, rather than all 512 genes of the signature,23 substantially simplifying the use of the wound signature in the clinic and the likely associated cost. We were able to assess the functional consequences of wound signature activation through experimental induction of the GES, altering the specific

Cell Cycle

2006; Vol. 5 Issue 11


expression pattern of hundreds of genes, in a nontransformed cell line; this also provided us with a model for testing targeted therapies. Further, identification of the regulators led to the description of a novel biochemical pathway attributed to high-risk breast cancers, thus revealing new proteins and pathways that could be amenable to pharmacologic inhibition in cancers that express the wound signature. Thus, identification of regulators of cancer GES may be a useful first step in finding treatments for newly defined molecular subtypes of cancer. We note that SLAMS is not suitable to address environmental or nongenetic factors that activate GES in cancer. Regardless, oncogenes and tumor suppressors are often modified on a DNA level though genetic or epigenetic changes.34-36 Using SLAMS, multiple types of genetic or epigenetic data can be used to link specific alterations with the presence of a gene expression signature. To increase the usefulness of SLAMS, we are in the process of automating the SLAMS method. This will allow one to test any cancer samples for the presence of all known (and unknown) gene expression signatures, preventing the need to first identify which samples posses a specific expression signature, and then identify prospective regulators of these gene expression patterns. An automated version of SLAMS will bring this technology to everyone, providing a unique approach to understanding mechanisms of cancer progression. References 1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson Jr J, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403:503-11. 2. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999; 286:531-7. 3. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature 2000; 406:747-52. 4. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, Borresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98:10869-74. 5. van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415:530-6. 6. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet 2003; 33:49-54. 7. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA 2004; 101:811-6. 8. Glinsky GV. Death-from-cancer signatures and stem cell contribution to metastatic cancer. Cell Cycle 2005; 4:1171-5. 9. Klein CA. Gene expression signatures, cancer cell evolution and metastatic progression. Cell Cycle 2004; 3:29-31. 10. Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF, Bild A, Iversen ES, Liao M, Chen CM, West M, Nevins JR, Huang AT. Gene expression predictors of breast cancer outcomes. Lancet 2003; 361:1590-6. 11. Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, Muir B, Mohapatra G, Salunga R, Tuggle JT, Tran Y, Tran D, Tassin A, Amon P, Wang W, Wang W, Enright E, Stecker K, Estepa-Sabal E, Smith B, Younger J, Balis U, Michaelson J, Bhan A, Habin K, Baer TM, Brugge J, Haber DA, Erlander MG, Sgroi DC. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 2004; 5:607-16. 12. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347:1999-2009.

www.landesbioscience.com

13. Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA 2002; 99:12963-8. 14. Huang E, Ishida S, Pittman J, Dressman H, Bild A, Kloos M, D’Amico M, Pestell RG, West M, Nevins JR. Gene expression phenotypic models that predict the activity of oncogenic pathways. Nat Genet 2003; 34:226-30. 15. Sweet-Cordero A, Mukherjee S, Subramanian A, You H, Roix JJ, Ladd-Acosta C, Mesirov J, Golub TR, Jacks T. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 2005; 37:48-55. 16. Lamb J, Ramaswamy S, Ford HL, Contreras B, Martinez RV, Kittrell FS, Zahnow CA, Patterson N, Golub TR, Ewen ME. A mechanism of cyclin D1 action encoded in the patterns of gene expression in human cancer. Cell 2003; 114:323-34. 17. Chang HY, Sneddon JB, Alizadeh AA, Sood R, West RB, Montgomery K, Chi JT, van de Rijn M, Botstein D, Brown PO. Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. PLoS Biol 2004; 2:E7. 18. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, Olson Jr JA, Marks JR, Dressman HK, West M, Nevins JR. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006; 439:353-7. 19. Dvorak HF. Tumors: Wounds that do not heal. Similarities between tumor stroma generation and wound healing. N Engl J Med 1986; 315:1650-9. 20. Bissell MJ, Radisky D. Putting tumours in context. Nat Rev Cancer 2001; 1:46-54. 21. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100:57-70. 22. Chang HY, Nuyten DS, Sneddon JB, Hastie T, Tibshirani R, Sorlie T, Dai H, He YD, van’t Veer LJ, Bartelink H, van de Rijn M, Brown PO, van de Vijver MJ. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 2005; 102:3738-43. 23. Adler AS, Lin M, Horlings H, Nuyten DS, van de Vijver MJ, Chang HY. Genetic regulators of large-scale transcriptional signatures in cancer. Nat Genet 2006; 38:421-30. 24. Wang ZC, Lin M, Wei LJ, Li C, Miron A, Lodeiro G, Harris L, Ramaswamy S, Tanenbaum DM, Meyerson M, Iglehart JD, Richardson A. Loss of heterozygosity and its correlation with expression profiles in subclasses of invasive breast cancers. Cancer Res 2004; 64:64-71. 25. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schubeler D. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 2005; 37:853-62. 26. Pelengaris S, Khan M, Evan G. c-MYC: More than just a matter of life and death. Nat Rev Cancer 2002; 2:764-76. 27. Cope GA, Deshaies RJ. COP9 signalosome: A multifunctional regulator of SCF and other cullin-based ubiquitin ligases. Cell 2003; 114:663-71. 28. Adhikary S, Eilers M. Transcriptional regulation and transformation by Myc proteins. Nat Rev Mol Cell Biol 2005; 6:635-45. 29. Welcker M, Orian A, Jin J, Grim JE, Harper JW, Eisenman RN, Clurman BE. The Fbw7 tumor suppressor regulates glycogen synthase kinase 3 phosphorylation-dependent c-Myc protein degradation. Proc Natl Acad Sci USA 2004; 101:9085-90. 30. Yada M, Hatakeyama S, Kamura T, Nishiyama M, Tsunematsu R, Imaki H, Ishida N, Okumura F, Nakayama K, Nakayama KI. Phosphorylation-dependent degradation of c-Myc is mediated by the F-box protein Fbw7. Embo J 2004; 23:2116-25. 31. Kim SY, Herbst A, Tworkowski KA, Salghetti SE, Tansey WP. Skp2 regulates Myc protein stability and activity. Mol Cell 2003; 11:1177-88. 32. von der Lehr N, Johansson S, Wu S, Bahram F, Castell A, Cetinkaya C, Hydbring P, Weidung I, Nakayama K, Nakayama KI, Soderberg O, Kerppola TK, Larsson LG. The F-box protein Skp2 participates in c-Myc proteosomal degradation and acts as a cofactor for c-Myc-regulated transcription. Mol Cell 2003; 11:1189-200. 33. Cope GA, Deshaies RJ. Targeted silencing of Jab1/Csn5 in human cells downregulates SCF activity through reduction of F-box protein levels. BMC Biochem 2006; 7:1. 34. Robertson KD. DNA methylation and human disease. Nat Rev Genet 2005; 6:597-610. 35. Baylin SB, Ohm JE. Epigenetic gene silencing in cancer - A mechanism for early oncogenic pathway addiction? Nat Rev Cancer 2006; 6:107-16. 36. Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet 2006; 7:85-97.

Cell Cycle

1151