Genomic and Proteomic Profiling of Osteosarcoma

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15

Genomic and Proteomic Profiling of Osteosarcoma Tsz-Kwong Man1, Pulivarthi H. Rao1 and Ching C. Lau1 1

Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA.

Contents

osteosarcoma. However, with the advent of high throughput genomic and proteomic technologies, it is now possible to generate comprehensive molecular profiles of tumors by using small quantities of tissue or blood samples. This chapter summarizes the different strategies of molecular profiling and illustrates results of genomic and proteomic characterization of osteosarcoma, how these strategies can refine prognostic assessments, and impact the design of future clinical trials.

I. Introduction 181 II. �������������������������� Ch������������������������ romosome Aberrations in Osteosarcoma ���������������� 181 III. DNA Ploidy 181 IV. �������������������������� Conven�������������������� tional Cytogenetics and ���� Sky 182 V. Genomic Profiling by CGH and Array CGH 184 VI. Molecular ����������������������� Signature of Metastasis ����������������� Based  on Expression profiling 185 VII. Molecular ����������������������� Signature of Chemoresistance ����������������  by Expression Profiling 186 VIII. ����������������������� Proteomic Profiling of Plasma ���������� 188 IX. ��������������� Conclusions 189 References 189

II.  Chromosome aberrations   in osteosarcoma Malignant transformation of a cell requires the accumulation of multiple genetic changes during the process of tumor initiation and progression. In order to understand the underlying biology of cancer, one has to identify as many of the genetic alterations during cancer development as possible. Theodor Boveri’s evaluation that chromosome abnormalities play a central role in the malignant transformation of a normal cell laid the foundation for our current understanding of chromosomal basis of cancer. These chromosomal aberrations can be analyzed using a number of genomic technologies, such as chromosomal banding, multicolor spectral kayotyping (SKY/m-FISH), comparative genomic hybridization (CGH), array based-CGH and fluorescence in situ hybridization (FISH). Several investigators, including our group, have used these technologies to decipher the chromosomal complexity in osteosarcoma (OS).

I.  Introduction The primary treatment of osteosarcoma (OS) uses high dose chemotherapy in both the neoadjuvant and adjuvant settings in association with surgery. Despite such aggressive treatment, up to 40% of osteosarcoma patients eventually succumb to their disease. Many of the survivors also suffer from significant acute and long-term side effects from their therapy. Thus, there is a need to identify biomarkers that could predict the therapeutic response of an individual osteosarcoma prior to the initiation of therapy. This would potentially lead to the customization of therapy for each patient; thus allowing the reduction of therapy and their side effects for patients who have more responsive tumors, while choosing a more aggressive or targeted therapy for the more resistant cases. One strategy to determine such biomarkers is to identify the underlying genetic changes of each tumor, which could then be used to predict biological behavior of the tumor cells and the clinical course of the disease. In the past, this type of approach was considered unpractical because of the limited ability to decipher the complexity of the cancer genome, especially that of  Bone Cancer ISBN: 978-0-12-374895-9

III.  DNA ploidy The DNA content in tumor cells has been reported as a useful way to define the malignant potential of human cancers. Earlier studies showed a correlation between DNA ploidy and histologic grading in OS [1–4]. This was 181

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182      ������������ Section III Bone Markers of Cancer (Genes and Proteins) •

IV.  Conventional cytogenetics   and sky

particularly true in the case of high-grade tumors, of which 96% of them were hyperploid, whereas all lowgrade parosteal osteosarcomas were diploid [1]. Others have examined the association between the degree of aneuploidy and prognosis and response to chemotherapy [4–6]. One study demonstrated that, among hyperploid tumors, the presence of neardiploid stem lines was associated with a better prognosis, including both a lower incidence of pulmonary metastasis and an improved disease-free survival after treatment [6]. The study by Bosing et al. showed a higher frequency of aneuploidy after preoperative chemotherapy among tumors exhibiting a poor response [5]. However, contrary to these earlier reports, a later study reported that patients whose tumors showing non-diploid DNA content had a longer event-free survival after surgical resection and chemotherapy than did those with diploid tumors, suggesting a better response to chemotherapy in the former [4]. Thus, the exact role of aneuploid DNA content in relation to prognosis and response to therapy remains unclear. Serra et al. compared results from DNA ploidy and conventional cytogenetics/FISH studies on the same OS cell lines and found that the ploidy determined by cytogenetics/ FISH analysis was frequently lower than those evaluated by cytometric methods [7]. They concluded that this discrepancy might be due to the presence of unbalanced chromosomal aberrations, including high-level focal amplifications, which may significantly affect the total DNA content with no effect on the total chromosome numbers.

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With the exception of osteosarcoma and leiomysarcoma, all other sarcomas are cytogenetically simple and exhibit reciprocal translocations resulting in the formation of fusion genes. These translocations not only serve as diagnostic markers but also hold the key to insights into the biology of these sarcomas. On the other hand, osteosarcomas often show complex chromosomal rearrangements and numerous gene amplifications and deletions, suggesting that genomic instability is linked to the development of this tumor (Figures 15.1 and 15.2; see also Plate 4). Although several reciprocal translocations were reported in OS, none of them were present more than once (Table 15.1). Cytogenetic evidence of gene amplification (21%) in the form of homogeneously staining regions (hsr) and double minutes (dmin) was reported in 5 and 16% of the cases, respectively [8]. Ring chromosomes, hsrs and dmins are more frequently seen in conventional OS than in parosteal OS. The ring chromosomes in parosteal OS are usually accompanied by numerous complex chromosomal abnormalities. Based on G-banding analysis of previously published clonally abnormal karyotypes of 168 specimens of OS, a number of recurrent breakpoints have been identified at 1p11-13,1q12-12,11p15,12p13,17p12-p13,19q13 and 20q1113 and trisomy for chromosome 1 and total or partial monosomy for chromosomes 6q, 9, 10, 13, and 17 (8; http://cgap. nci.nih.gov/Chromosomes/Mitelman). Because of the high

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Figure 15.1  A representative G-banded karyotype obtained from short-term culture derived from a patient with osteosarcoma.

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Chapter 15 Genomic and Proteomic Profiling of Osteosarcoma       183 •

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Table 15.1  Reciprocal translocations identified  in osteosarcoma* Translocation

Figure 15.2  An example of metaphase chromosomes from osteosarcoma after hybridization with 24 differently labeled whole-chromosome painting probes. (A) Spectra-based display colors; (B) spectral classification from the same metaphase.

percentage of unidentified marker chromosomes, the accurate identification of these markers is extremely difficult in OS. A combined approach of G-banding, spectral karyotyping/m-FISH, CGH/arrayCGH and FISH would enhance the sensitivity of detection of chromosomal aberrations in OS. We and others used a similar approach and identified several novel sites of chromosomal rearrangements at Xp11, Xp22, Xq22, 1q11, 1q32, 2p11, 2p13, 3p11, 3p13, 4q12, 4q21, 4q27, 6p12, 6p21, 8p11, 8q11, 8q22, 8q23, 8q24, 9p10, 9p13, 9q11, 9q22, 10p11, 10q11, 11q23, 12p11, 12q24, 13q11, 13q14, 13q32, 14p11, 14q24, 15p11, 15q15, 16p11.2, 17q11, 17q21, 17q25, 18q21, 20p13, 20p11, 21q22 and 22q11 [9–10]. Telomere maintenance is a key mechanism in overcoming cellular senescence in tumor cells and, in most cases, is achieved by the activation of telomerase. However, there is at least one alternative mechanism of telomere lengthening (ALT) which is characterized by heterogeneous and elongated telomeres in the absence of telomerase activity (TA). A comprehensive study of telomere maintenance mechanisms in 62 OS specimens found that a subset of cases which lacked both telomerase activity and evidence of ALT had the most favorable prognosis [11]. Scheel et al. evaluated the prevalence of TA, gene expression of telomerase subunits and ALT in relation to telomere morphology and function in matrix producing bone tumors and in osteosarcoma cell lines [12]. They presented evidence of a direct association of ALT with telomere dysfunction and chromosomal instability. They concluded that ALT in association with telomere dysfunction and chromosomal instability might have major implications for tumor progression. Gisselsson et al. generated chromosomal breakpoint profiles of 140 OS specimens to investigate the role of breakpoints in genomic instability. Based on their study, they concluded that the cases with few chromosomal alterations tend to have alterations clustering to the terminal chromosomal bands whereas tumors with many changes showed abnormalities preferentially clustering to the interstitial or centromeric regions [13]. Furthermore, they showed that the terminal breakpoint frequency was negatively correlated to

t(1;5)(q21; p15.3) t(1;9)(p32;q22) t(1;11)(p31;q23-q25) t(1;11)(q44;q12) t(1;12)(p11;p11) t(1;14)(q21;q32) t(1;19)(p22;p13-3) t(1;19)(q21;q13) t(2;4)(p23;q21) t(2;12)(q13;q13) t(2;16)(q24;q23) t(3;13;19)(p22;q22;q13.1) t(4;6)(q27;q21) t(4;20)(p13;p11-2) t(5;9)(q22;q21) t(5;10)(p13;p14-p15) t(5;17)(q11;p11) t(5;18)(q13;q22) t(6;7)(q15;q22) t(6;13)(p21;q33) t(7;15)(q32;24) t(7;20)(p13:p11-2) t(8;11)(q11;q11) t(8;12)(q24;q13) t(8;21)(q12;q11) t(9;15)(q34;q22) t(10;17)(p11-2;q21.3) t(11;16)(q13;p13-3) t(11;17)(q21;q24) t(12;12)(q10:q24.3) t(12;21)(p11;q11) t(16; 19)(q12;q13.2) t(16;19)(q12;q13.2) t(X;7)(p22.3;q11) *

These translocations are based on the review of 168 published  G-banded karyotypes. Source: http://cgap.nci.nih.gov/Chromosomes/Mitelman;[8].

telomeric TTAGGG repeat length. This is consistent with the theory of breakage-fusion-bridge (BFB) cycle, in which cycles are initiated by teleomeric fusion, followed by a gradual shift of breakpoints from the chromosomal ends towards central regions as recurring anaphase breakage erodes the chromosomal arms [14]. Previous studies have shown that these BFB events contribute to genetic heterogeneity in a number of aggressive epithelial and mesenchymal tumors [15].

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Similar to some other primary human tumors, the mutation of p53 correlates significantly with the presence of high levels of genomic instability in osteosarcomas. Overholtzer et al. examined the relationship between p53 mutation and genomic instability in human osteosarcomas [16] using genomic instability index scores based on the total genomic imbalances identified by CGH. Osteosarcomas harboring an amplification of the HDM2 oncogene, which inhibits the tumor-suppressive properties of p53, do not display high levels of genomic instability. They concluded that the inactivation of p53 in osteosarcomas directly by mutation versus indirectly by HDM2 amplification might have different cellular consequences with respect to the stability of the genome.

V.  Genomic profiling by CGH   and array CGH CGH is one of the most powerful global assays for detecting losses and gains in a given genomic complement. This method involves competitive hybridization of differently labeled test DNA (tumor) and reference DNA (green) to normal human metaphase chromosome spreads. Based on the relative intensity of the two fluorescent colors, chromosomal regions with gain or loss can be identified in a single

hybridization. One of the advantages of CGH is that it can make use of archival materials when frozen tissues are not available, thus greatly expanding the number of cases that can be analyzed. Several groups have used this technique to identify genomic imbalances in OS [16–24]. Increased DNA copy numbers have been reported on every autosome and the X chromosome in OS by CGH. Regions most frequently involved in chromosomal losses are 2q34-qter, 3p, 3qcen-q22, 5q, 6q16-qter, 8p12-pter, 9p13-pter, 10p12, 10q23, 11p12-pter, 13qcen-q21, 14q, 15qcen-q21, 16p, 17p and 18q. The most common chromosomal gains have been detected at 1p21-p31, 1p35-p36, 1q21-q31, 2p, 2q31-32, 3q25-qter, 4q12-q13, 4q27-q32, 5p13-p15.2, 5q, 7q31-q32, 8q21.3-q22, 8qcen-q13, 9q21-22, 11q14, 14q24-qter, 16p, 17p, 19(p), 20q, 21q, Xpcen-p21 and Xq25-qter. The most frequently gained or amplified chromosomal regions were 1p22, 1p31, 1q21, 1q23, 2q24, 3p25, 3q26, 6q24.3, 4q12, 5p14-p15, 5q33, 6p12-p21, 6q24.3, 7p21-p22, 8q12-q23, 10p21, 10q11.1, 10q22, 11q13, 11q23, 12p13, 12q12-q15, 17p11.2, 17q21, 18q22, 19p13.1 and 20p11.2 (Figure 15.3; see Plate 5). Of these, amplification of 6p12-p21 (15–26%) and 17p11.2 (13–29%) appeared to be an early event in pathogenesis of OS because it was observed in all specimen types, including those from biopsies, definitive surgery, and metastatic lesions [10]. These chromosomal bands are also

Figure 15.3  Various chromosomal amplifications identified in osteosarcoma by CGH. Partial CGH karyotype for individual chromosomes (left) and corresponding ratio profiles showing high-level amplifications. The vertical red and green bars on the right of the ideogram indicate the threshold values of 0.80 and 1.20 for loss and gain, respectively.

Chapter 15 Genomic and Proteomic Profiling of Osteosarcoma       185 •

shown to be involved in chromosomal rearrangements [10]. This high frequency of increased copy numbers at these regions suggests the selection for oncogenes that remain uncharacterized. In addition, DNA sequence loss has also been observed at 2q, 6p, 8p, 10p, and 17p13 [16–24]. In the first genome-wide array comparative genomic hybridization on osteosarcoma, we reported frequent deletions at the chromosomal regions 2q31.1, 3p14.1, 4p16.2, 6q12, 6q21, 7q35, 10p15.1, 10q22-q23, 10q25-q26, 11q25, 13q12.2, 13q14.3, 13q22.1, 17p13.3 and 17q12 and seven recurrent homozygous deletions at 1q25.1, 3p14.1, 4p15.1, 6q12, 6q13 and 13q12.22 [25]. The chromosomal regions that represented gains were mapped to 1p36, 4p16, 6p12-p21, 8q21, 8q23-24, 12q14.3, 16p13.3, 17p11-p12, 19p13.3 and 21q22.3. This study also refined the 6p amplicon to 9.4 Mb with amplification peak for clone RP11-81F7. Previous study by the same group demonstrated the origin of 6p amplicon as a consequence of tandem duplication of clones RP11-81F7 and RP11-79F13 [10]. Based on combined array CGH and FISH analysis we identified CDC5L, HSPCB, NFKBIE, HGNC and MRPL14 as candidate genes from the 6p12-p21 amplicon [25]. A more recent study established the correlation between amplification of 6p12p21 and the overexpression of CDC5L, a G2-M transition regulatory gene. We further corroborated this observation using a functional assay, demonstrating that CDC5L possesses oncogenic properties [26]. The 17p amplicon identified by CGH and microsatellite marker analysis [10,17,18,20,23,27,28] is also a common structural abnormality identified by cytogenetic techniques [10,29]. Several candidate genes were suggested from the 17p amplicon in OS. Man et al. have identified by array CGH three clones with high-level amplifications that spans 3.7 Mb region on 17p11.2 [25]. Several candidate genes were identified within these clones (TPP3A, SMCR5, DRG2, FL11, MYCD, SOX 17, ELAC2, and PMP22). Other studies have shown the amplification of PMP22 and TOP3A from 17p11.2-p12 in high-grade OS by semi-quantitative PCR and cDNA microarrays [23,30]. We note that, despite its overall amplification, segments of DNA loss have been identified within the 17p amplicon [27,31], indicating that not just gene amplification, but also loss of genetic material, may be an important factor within this amplicon for the development of OS. Using a different method (representational difference analysis (RDA)) to isolate DNA fragments deleted, amplified, or rearranged in tumor cells, Simons et al. identified in conjunction with CGH, two regions of amplification, 17p11.2-p12 and 19q12-q13 (32). Yan et al. identified COPS3 as a putative oncogene from the 17p amplicon in OS and reported amplification of this gene in 31% of the osteosarcomas [33]. Amplification of COPS3 was strongly associated with a large tumor size, but was not associated with age at diagnosis, site, gender, and tumor necrosis. A genomewide high-resolution gene copy number analysis of 22 osteosarcoma samples using comparative genomic hybridization

on a cDNA microarray that contained cDNA clones of about 13,000 genes had amplifications that on average spanned more than 1 Mb and contained more than 10 genes [34]. The study identified two candidate genes—TOM1L2 and CYP27B1 from 17p and 12q amplicons, respectively. Tarkkanen et al. reported that the increased copy number at 8q13 and/or 8q21.3-q22 was associated with statistically significant poorer distant disease-free survival and a trend towards short overall survival [18]. The same group reported that patients with a copy number increase at 1q21 also showed a trend towards shorter overall survival. In contrast, Stock et al. [21] found that copy number increases at 8q and 1q21 did not have an unfavorable impact on prognosis.

VI.  Molecular signature of metastasis based on expression profiling The most common cause of death among osteosarcoma patients is pulmonary metastasis. Thus, understanding the genetic determinants of metastasis in osteosarcoma and therapeutically modulating these factors should improve the outcome of these patients. Several genomics studies have implicated the involvement of pathways in cell motility, adherence, angiogenesis and bone differentiation in osteosarcoma metastasis. Using cDNA microarrays, Khanna et al. compared the expression profiles of two orthotopic ostoesarcoma xenograft mouse models [35]. One was developed from a less aggressive K12 cell line originating from a spontaneous BALB/c osteosarcoma which is primarily nonmetastatic. The other model was derived from a clonally related K7M2 cell line, which is much more aggressive and highly metastatic. Fifty-three unique genes were differently expressed between these two types of tumors. These differently expressed genes were divided into six functional categories, including proliferation and apoptosis, motility and cytoskeleton, invasion, immune surveillance, adherence and angiogenesis. Based on several criteria, such as previously identified functions in several metastasis-associated ­processes, novelty to osteosarcoma, and not previously described in mesenchymal tumors, they selected a membrane-cytoskeleton organizer protein, Ezrin, for further analysis and confirmed its increased expressions in metastatic osteosarcoma at both the RNA and protein levels. The same group later reported that Ezrin expression provided an early survival advantage for cancer cells that reach the lung [36]. This early survival characteristic was partially dependent on the activation of MAPK, but not AKT. They also found that Ezrin expression was associated with an early development of metastases in a canine model as well as a poor outcome in human osteosarcoma patients. By examining over 5000 human cancers and normal tissue of various types, it was shown that Ezrin was expressed higher in sarcomas than in carcinomas [37]. Ezrin expression was shown

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to have a significant association with advanced histological grade in sarcomas and poor outcome in breast cancer. A similar study using tissue microarrays and immunohistochemical staining has also shown that Ezrin expression predicted disease-free survival in osteosarcoma [38]. Instead of mouse models, Nakano et al. used a more focused microarray containing 637 cancer-related genes to compare the expression profiles of the highly metastatic sublines and less metastatic sublines from a human osteosarcoma cell line, HuO9 [39]. From their analysis, only seven genes were differently expressed between the highly metastatic and less metastatic sublines. Four genes (AXL, TGFA, COLLA7A and WNT5A) were upregulated and three genes (IL-16, MKK6, and BRAG) were down-regulated in the highly metastatic osteosarcoma sublines. Some of these genes are associated with adherence, motility, and invasiveness pathways. Other than the cDNA microarray approach, representational difference analysis has also been used by Gillette et al. to analyze human primary osteosarcomas and metastatic lung lesions [40]. Several genes were identified as differently expressed between the two types of tumors. L-Ferritin and ANXA2 were downregulated while MDM2 was up-regulated in the metastatic lesions. Further functional assays suggested that ANXA2 did not affect motility, adhesion, or proliferation. Instead, it was hypothesized that since anxA2 affects the mineralization process of osteoblastic cells in vitro, the higher expression of ANXA2 in the less metastatic tumors might indicate a more differentiated osteoblastic pheotype in osteosarcoma with less aggressiveness.

VII.  Molecular signature of chemoresistance by expression profiling More than 40% of osteosarcomas showed relative chemoresistance in various clinical trials. After diagnosis is made by an initial biopsy, treatment typically consists of pre-operative chemotherapy followed by definitive surgery when the tumor is removed and evaluated for histologic responses to preoperative chemotherapy. The degree of necrosis in the tumor specimen in response to pre-operative chemotherapy is a reliable prognostic factor for localized osteosarcoma. Patients with tumors which show at least 90% necrosis are considered to be good responders who have an excellent prognosis (80% overall survival), while those with less than 90% necrosis are considered as poor responders who have a significantly poorer outcome (40% overall survival). Previous attempts to improve the outcome of the poor responders by modifying the postoperative chemotherapy have not been successful. We attribute the failure of such therapeutic strategies partly to the fact that the degree of necrosis is known only after 8–10 weeks of pre-operative therapy. It is possible that resistant tumor cells have ­additional time either to

metastasize to the lungs or to evolve further during the period when ineffective pre-operative chemotherapy is given. Therefore, we believe there is a need to identify, at the time of initial diagnosis, the patients who are likely to have a poor response to standard pre-operative therapy and therefore a poor outcome eventually. Therapies tailored to improve the outcome for those patients identified, at the time of diagnosis, to have a poor outcome can then be instituted at the outset when the chance for success is potentially higher. To achieve this goal, we used cDNA microarrays to analyze a series of osteosarcomas from patients enrolled in a single treatment protocol and identified a molecular signature that can predict chemoresistance of osteosarcoma [41]. In our study, we hypothesized that the definitive surgery (post-treatment) samples from the poor responders should be enriched for resistant tumor cells. Therefore, using expression profiles from these post-treatment samples would enhance the sensitivity and likelihood of detecting the difference between sensitive and resistant cell populations; as opposed to using initial biopsy (pre-treatment) samples, because resistant cells may only constitute a small fraction in the pre-treatment tumors. To test this hypothesis, we used various classification algorithms to develop classifiers that could distinguish good and poor responders using post-treatment tumors as the training set. Based on a leave-one-out cross-validation method, Support Vector Machine (SVM) algorithm was one of the best performing algorithms (70% correct classification). The predictive power of the SVM classifier was tested on 14 initial biopsy (IB) samples that consisted of paired and independent samples. The patients of the paired samples had corresponding definitive surgery specimens used in the training of the classifier. The independent samples did not have corresponding samples included in the training set. The SVM classifier misclassified one sample out of six in the paired samples, with a correct classification rate of 83%. In the independent group, the classifier correctly predicted eight out of eight samples (100% correct). These results further indicated that the gene expression signature of the resistant cells in the post-treatment samples was already present in the pretreatment samples at the time of diagnosis, which is consistent with the notion proposed by Ramaswamy et al. that the metastatic signature of metastatic tumors is already present in the primary tumor [42]. The high accuracy of the multigene classifier to identify poor responders from two separate groups of pre-treatment samples suggests that response to chemotherapy can potentially be predicted at the time of diagnosis. The SVM classifier consisted of 45 predictor genes and most of them (91%) were upregulated in tumors from the poor responders (Table 15.2). Based on gene ontology classification, the majority of these genes belong to pathways of nucleobase, nucleoside, nucleotide and nucleic acid metabolism, protein metabolism, and cell proliferation. Many of the predictor genes in the classifier are related to bone development, cell cycle, or drug resistance. For instance, TWIST1

Chapter 15 Genomic and Proteomic Profiling of Osteosarcoma       187 •

Table 15.2  F  orty-five predictor genes in the chemoresistance prediction model Ubiquitin specific protease 32 CHK1 checkpoint homolog (S. pombe) Ras homolog gene family, member Q Twist homolog 1 (acrocephalosyndactyly 3; Saethre-Chotzen syndrome) (Drosophila) Cell division cycle 2-like 2 (PITSLRE proteins) Sec1 family domain containing 1 Zinc finger protein 184 (Kruppel-like) Matrin 3 Thymopoietin LIM domain only 6 Ligase I, DNA, ATP-dependent SWI/SNF related, matrix associated, actin dependent regulator    of chromatin, subfamily a, member 1 RNA binding motif protein 25 CDC-like kinase 3 Ubiquitin-conjugating enzyme E2D 2 (UBC4/5 homolog, yeast) Hydroxyacyl-coenzyme A dehydrogenase, type II Adenosine monophosphate deaminase 2 (isoform L) Interleukin-6 signal transducer (gp130, oncostatin M receptor) Chromosome 6 open reading frame 68 Dimethylarginine dimethylaminohydrolase 1 Succinate dehydrogenase complex, subunit B, iron sulfur (Ip) Transmembrane protein 1 Programmed cell death 5 MCM2 minichromosome maintenance deficient 2, mitotin    (S. cerevisiae) GDP dissociation inhibitor 1 SMC4 structural maintenance of chromosomes 4-like 1 (yeast) Chromosome 10 open reading frame 7 CDC20 cell division cycle 20 homolog (S. cerevisiae) Praja 2, RING-H2 motif containing PTD008 protein 3-oxoacid CoA transferase 1 SNRPN upstream reading frame Centromere protein A, 17 kDa Heat shock 70 kDa protein 4 SWI/SNF related, matrix associated, actin dependent regulator    of chromatin, subfamily e, member 1 NADH dehydrogenase (ubiquinone) Fe-S protein 5, 15 kDa    (NADH-coenzyme Q reductase) Ubiquitin-conjugating enzyme E2A (RAD6 homolog) Fetal Alzheimer antigen Mature T-cell proliferation 1 RNA binding motif protein, X-linked Tubulin tyrosine ligase Transcribed locus, moderately similar to XP_509796.1 similar    to CGI-145 protein [Pan troglodytes] Ephrin-B2 Triggering receptor expressed on myeloid cells 2 Membrane-spanning 4-domains, subfamily A, member 3    (hematopoietic cell-specific)

encodes a helix-loop-helix transcriptional factor [43] and has been implicated in the Saethre-Chotzen syndrome, radial aplasia, Robinow-Sorauf syndrome and craniosynostosis [44–47]. Mice with heterozygous TWIST1 mutation showed defects in craniofacial and limb development [48], which resemble those found in Saethre-Chotzen syndrome patients. It has been reported that TWIST1 affects CBFA1/ RUNX2 expression [49] and DNA binding ability of RUNX2 [50], suggesting that TWIST1 is important for osteoblast differentiation and bone formation. Thus, dysregulation of TWIST1 expression may play a role in the pathogenesis of osteosarcoma [51,52]. In addition, several studies indicated the function of TWIST1 as a potential oncogene in c-myc- and N-myc-induced, p53-dependent apoptotsis pathways [53,54]. Other findings have also shown that TWIST1 was involved in Taxol resistance and metastasis [55,56], further implicating the important role of this gene in chemoresistance and tumor invasion. Another predictor gene in the chemoresistance signature is PDCD5 which has been implicated in the regulation of apoptosis [57]. The expression of PDCD5 was higher in cells undergoing apoptosis as compared to normal cells. During apoptosis, PDCD5 was shown to translocate rapidly from cytoplasm to nucleus [58]. PDCD5 is upregulated in pancreatic ductal carcinoma cells treated with trichostain-A, suggesting that PDCD5 may play a role in drug response [59]. CDC2L2 is involved in cell cycle progression, RNA processing and apoptosis [60]. It is a part of CDK11 protein kinases and may have tumor suppression function as demonstrated in melanoma [61]. TMPO belongs to a group of ubiquitously expressed nuclear proteins, which play an important role in nuclear envelope organization and cell cycle control. Upregulation of TMPO was seen in medulloblastoma when compared to normal cerebellum [62]. TMPO-beta transcript was expressed highly in human neuroblastoma cell lines, which may correlate with the occurrence of the cancer [63]. UBE2D2 encodes a member of the E2 ubiquitin-conjugating enzyme family. Inhibition of UBE2D2 (UBC4) inhibits E6-stimulated p53 [64]. MCM2 is a member of the family of mini-chromosome maintenance proteins (MCM) involved in the initiation of eukaryotic genome replication. The function of MCM2 is to regulate the helicase activity of the complex. A high expression of MCM2 has been shown to be associated with poor survival in prostate cancer [65]. Using a similar cDNA microarray approach, Ochi et al. employed a drug response scoring system to distinguish the good and poor responders in osteosarcoma [66]. The scoring system was based on the expression values of 60 genes that were significantly associated with the response to preoperative chemotherapy. The results of cross validation in the training set of 19 samples and five additional independent test samples showed that the scoring system classified the good and poor responders perfectly. However, the small sample size used in the test set [5] diminished the confidence of the results. Among the 60 genes used in the

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scoring system, several of them have been previously implicated in the resistance of chemotherapy. For example, ALR1C4 is a member of the aldo-keto reductase family, which may be involved in multi-drug resistance [67]. GPX1 can protect cells from oxidative stress [68] while GSTTLp28 belongs to the family of glutathione-S-transferase, which plays an important role in cellular detoxification and drug resistance [69,70]. In addition to cDNA microarrays, oligonucleotide arrays were also used for genomic analyses of osterosarcomas. Mintz et al. used Affymetrix oligonucleotide arrays (U95Av2) to identify a prognostic signature that was based on a histologic response in osteosarcoma [71]. Using both p-value and fold change criteria, they identified 104 genes that were differently expressed between Huvos Grade I/II and Grade III/IV osteosarcomas. In the Huvos system, Grade I/II tumors show a poor response, whereas Grade III/IV tumors show a good response towards the pre-operative chemotherapy. Different from the other two studies, Mintz et al. directly used the expressions of these differently expressed genes to perform hierarchical clustering on the original tumors that were used to derive the significant genes and three additional xenograft samples. These xenograft tumors were derived from aggressively growing and chemoresistant primary tumor cells. The clustering result showed that the xenograft tumors were clustered together with tumors from poor responders. Based on the functions of the 104 genes, they suggested that the poor prognosis tumors are associated with osteoclastogenesis and bone resorption, ECM remodeling, tumor progression, and resistance to apoptosis. Despite addressing a similar question using comparable approaches, genes identified from these three microarray studies shared very little in common. While such discord­ ance could be partly explained by the potential differences in microarray platforms and analytical strategies, one of the most important issues that ultimately needs to be addressed is the small sample size used in each of these studies. Although genes used in different classifiers need not be the same as long as they are accurate in classifying samples in different datasets, small sample sizes may pose a serious bias in the results given the high dimensionality in the data [72]. Given the relatively low incidence rate of osteosarcoma and differences in treatment protocol at different institutions, a large-scale, multi-institutional collaborative effort using the same treatment protocol is needed to evaluate the validity of these signatures and classifiers. Such a study will be carried out with the ongoing joint European/ North American Osteosarcoma Study (EURAMOS).

VIII.  Proteomic profiling   of plasma Although transcriptomic profiling is the most common platform used in the molecular classification of human cancers,

it is less suitable than proteomic profiling for analyzing cell-free body fluids, such as plasma due to very limited quantities of intact RNA present in such fluids. Because of post-transcriptional and post-translational regulations and modifications, RNA and protein expression measurements for the same gene may not correlate. In addition, plasma is much easier to access than a tumor biopsy, thus providing a means to monitor the disease progression and perform early detection of cancer. Individuals with retinoblastoma and RB mutation [73], Li-Fraumeni syndrome and p53 mutation [74], Werner syndrome [75], or Rothmund-Thomson syndrome and REQL4 mutation [76] have a higher risk of developing osteosarcoma. Therefore, an accurate but minimally invasive assay will be useful to monitor these patients for the development of osteosarcoma. We have previously used proteomic profiles generated from patients’ plasma to test if we could develop a classifier that can distinguish osteosarcoma from benign osteochondroma [77]. The proteomic profiles were generated by the Surface Enhanced Laser Desorption/Ionization (SELDI) technique. SELDI is a high-throughput proteomic platform that combines solid phase chromatography with time-of-flight(TOF) mass spectrometry to detect protein peaks in many samples simultaneously. Solid phase chromatography is accomplished using microliter quantities of samples on protein chips with various surface chemistries to capture different types of proteins. Based on these plasma-based proteomic profiles, we developed a 3-nearest neighbor classifier to distinguish malignant osteosarcomas from benign osteochondromas. The ultimate goal is to develop a proteomic signature in plasma that could be used to screen the high risk population for early detection of osteosarcoma. In the pilot study, however, we chose to use the plasma of osteochondroma patients, to control for non-specific host reactions that might manifest as plasma protein changes in response to bone lesions. The classifier achieved 97% specificity and 80% sensitivity to classify osteosarcoma using an external leaveone-out cross validation. The classifier consisted of 19 protein peaks of various m/z ratios. One of the protein peaks was identified by peptide mass fingerprinting to be serum amyloid protein A (SAA), which is a known biomarker in various cancers [78–81]. Consistent with our proteomic analysis, Song et al. has independently shown that SAA was increased in the sera of osteosarcoma patients when compared to sera from normal individuals [82]. They first performed two-dimensional fluorescence difference gel electrophoresis (2-D DIGE) on sera from four osteosarcoma patients and four age- and sexmatched healthy controls. 2-D DIGE is a modification of ­ 2-D gel electrophoresis, which can analyze multiple samples at the same time [83]. From this analysis, they identified the increase of 18 and decrease of 25 proteins in osteosarcoma sera relative to normal sera using matrixassisted laser deadsorption/ionization (MALDI)-TOF mass spectrometry. Topping the list of proteins increased in

Chapter 15 Genomic and Proteomic Profiling of Osteosarcoma       189 •

osteosarcoma sera were SAA, ceruloplasmin, DGKG protein, and human complement component C3. Proteins that were decreased in osteosarcoma sera included fibronectin 1 isoform 6 preprotein, MTA2 protein, inter-alpha-trypsin inhibitor heavy chain H4 precursor, and fibrinogen gamma. Most interesting, when these investigators tested the abundance of SAA in another set of patients’ sera, they found that the abundance of SAA was lower in the samples after chemotherapy and after surgical removal of primary tumors, but its level was higher in the samples from relapsed patients. Therefore, they suggested that serum SAA might be a relapse biomarker for osteosarcoma. The consistency of these two proteomic studies suggests that SAA may be useful for detecting the occurrence of osteosarcoma in highrisk individuals and recurrence in previously diagnosed osteosarcoma patients.

IX.  Conclusions Genomic profiling approaches should advance our knowledge of the pathogenesis of osteosarcoma and refine the management of this disease, although the optimal application of these data requires that several conditions be met. First and foremost is that these clinically relevant profiles need to be validated with independent data sets by multiple institutions. Although the currently available microarray platforms are proving to be technically robust and reliable, subtle differences in sample collection and preparation can have a profound impact on the final data. This is evidenced by the often discordant results by different investigators in the published literature. Moreover, the bioinformatic and statistical methods used in analyzing these data sets are far from uniform or adequate, making it difficult to perform comparisons between data sets. Another important step in the utilization of these profiles to guide clinical decisionmaking involves translating these technologies from the research setting into clinically applicable diagnostic studies that can be performed rapidly and reliably on surgically obtained tumor specimens. It is questionable whether it is practical or necessary to perform genome-wide profiling of every tumor in a clinical laboratory setting. The requirement of standardized laboratories with standardized procedures for performance and analysis of genomic profiling is far too cumbersome. Alternatively, it is possible to narrow down the number of clinically relevant genes from a genome-wide profile into a more manageable number, which can then be assayed using more conventional methods such as immunohistochemistry and quantitative PCR that are already established and employed in clinical laboratories. Thirdly, the validity of these prognostic correlates must be confirmed in an independent, preferably prospective, cohort of patients. Studies designed to address these challenges for osteosarcoma are currently either in progress or under development within the Children’s Oncology

Group and several European cooperative groups. The ultimate challenge involves demonstrating that biologic stratification can support risk-based therapeutic stratification that will improve the outcome of osteosarcoma patients. The realization of this long-range goal will also require the identification of novel therapeutic strategies that promise to improve the outcome in tumor subgroups that have been resistant to conventional therapies. We believe that molecular profiling will provide important clues regarding critical pathways that the tumor cells depend on to maintain their malignant phenotype. Such pathways will be ideal therapeutic targets for tumor-specific therapies. An equally important aspect of this goal will involve the continuation of ongoing efforts to cautiously decrease the intensity of potentially toxic therapies in order to reduce the morbidity of treatment in tumors that have particularly favorable risk factors based on genomic and proteomic profiling. In conclusion, despite all the limitations described, genomic and proteomic profiling offers an exciting possibility for refining the diagnosis, stratification and therapy of osteosarcoma. It is not too hard to imagine that in the near future predictive individualized care based on molecular classification and targeted therapy will become a reality for this type of cancer.

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