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Clinico-molecular study of dedifferentiation in well-differentiated liposarcoma
BBRC Biochemical and Biophysical Research Communications 315 (2004) 1133–1140 www.elsevier.com/locate/ybbrc
Clinico-molecular study of dedifferentiation in well-differentiated liposarcomaq,qq Takashi Shimoji,a,b,c Hiroaki Kanda,d Tomoyuki Kitagawa,d Koji Kadota,e Ryoichi Asai,e Katsutoshi Takahashi,e Noriyoshi Kawaguchi,c,* Seiichi Matsumoto,c Yoshihide Hayashizaki,b Yasushi Okazaki,b,*,1 and Kenichi Shinomiyaa a Department of Orthopedic Surgery, Tokyo Medical and Dental University, Japan Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), 1-7-22 Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan c Department of Orthopedic Oncology, Japanese Foundation for Cancer Research, Cancer Institute Hospital, Japan d Department of Pathology, Japanese Foundation for Cancer Research, Cancer Institute, Japan Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Japan b
e
Received 26 December 2003
Abstract Well-differentiated liposarcoma (WD) acquires fully malignant potential when the histological progression named dedifferentiation occurs. This progression is supposed to occur in a time-dependent manner but this is still a debated issue. Clinically, the prediction of dedifferentiation for WD is very important from the therapeutic point of view. To identify genes that are predictive of dedifferentiation and to understand the mechanism of dedifferentiation, we investigated clinical information of 50 cases and studied the gene expression profiles of 36 lipomatous tumors using cDNA microarray. The clinical study showed that the dedifferentiation did not always seem to occur in a time-dependent manner. Interestingly, from the gene expression study, unsupervised hierarchical clustering analysis of well-differentiated lesions obtained from dedifferentiated liposarcoma (DD) cases that were indistinguishable from WD pathologically showed a clearly distinct gene expression pattern from WD. Using the pattern-matching program, 1687 genes including 487 known genes were identified, which discriminated WD cases from well-differentiated lipomatous lesions obtained from DD cases. These results suggest that the dedifferentiation may arise from different types of WD that could be distinguished from gene expression profiling but could hardly be classified by the pathological studies. Ó 2004 Elsevier Inc. All rights reserved. Keywords: Liposarcoma; Well-differentiated liposarcoma; Dedifferentiated liposarcoma; Diagnosis; Malignant potential; Dedifferentiation; cDNA microarray; Gene expression profile
Liposarcoma is one of the common soft tissue sarcomas of adults [1]. Clinical behaviors of liposarcoma vary q Abbreviations: WD, well-differentiated liposarcoma; DD, dedifferentiated liposarcoma; MX, myxoid liposarcoma; RC, round cell liposarcoma; LP, lipoma. qq Supplementary data associated with this article can be found in the online version, at doi: 10.1016/j.bbrc.2003.12.203. * Corresponding authors. Fax: +81-429-85-7329 (Y. Okazaki), +81-3-5394-3832 (N. Kawaguchi). E-mail addresses: [email protected] (N. Kawaguchi), [email protected] (Y. Okazaki). 1 Present address: Division of Functional Genomics & Systems Medicine, Research Center for Genomic Medicine, Saitama Medical School, 1397-1 Yamane, Hidaka City, Saitama 350-1241, Japan.
0006-291X/$ - see front matter Ó 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.bbrc.2003.12.203
from non-life-threatening to fully malignant and its histological appearances also vary substantially [2–6]. According to the World Health Organization classification of soft tissue tumors, five pathological subtypes are distinguished; well-differentiated (WD), myxoid (MX), round-cell (RC), pleomorphic (PL), and dedifferentiated (DD) type liposarcomas [7]. Although each subtype is widely disparate in its clinical behavior, it is useful to group them into three classes from a pathological point of view [1,7]. Since a subset of WD type tumors frequently histologically progresses to the DD type, WD and DD comprise one group. MX and RC also comprise another because MX type tumors and RC type tumors
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frequently coexist. Residual PL comprises the last group. Among these three groups, the WD and DD group accounts for over 45% of all liposarcomas, representing the largest subtype of adipocytic malignant neoplasms. Interestingly, this group has a curious aspect in its clinical behaviors. Usually, the most important factor reflecting the prognosis of a malignant tumor would be the various pathological findings. However, in WD cases, tumor localization is the most important factor for the prediction of prognosis. Commonly, WDs located in extremities usually have no tendency of metastasis, so they would be regarded as non-life-threatening lesions. On the other hand, WDs located in the retroperitoneum, where the tumors most frequently occur, tend to recur repeatedly, resulting in the patient’s death as a result of uncontrolled local recurrence. Dedifferentiation also frequently occurs in the retroperitoneum [7]. Once dedifferentiation occurs in WD, which originally has a nonmetastatic tendency, the tumor acquires a metastatic and highly recurrent potential. The progression from WD to DD is supposed to occur in a time-dependent manner rather than in a site-specific manner [1,5–7]. In clinical settings, however, tumor localization is the most reliable parameter for the ultimate risk of dedifferentiation, which is equal to the prediction of prognosis [7]. This dedifferentiation phenomenon has been reported to occur in extremities too, but the frequency is much lower than the case of tumors located in the retroperitoneum [7]. Notwithstanding distinct differences in their clinical behavior, no other factor besides tumor localization and time has been reported to attribute to this phenomenon. If other factors predictive of dedifferentiation are identified, the treatment for WDs such as adjuvant chemotherapy or radiotherapy, or the more eligible surgical treatment, would be impacted. Since gene expression profiles using a microarray have been providing a new level of insight into the diagnosis and prognostic prediction of tumors [8–11], we analyzed 36 lipomatous tumors using the RIKEN 21K human cDNA microarray with the aim of understanding dedifferentiation and searching for novel useful markers for the malignant potential of WD at the molecular level.
Materials and methods Cases and methods of clinical study. Fifty cases in the WD and DD group treated in the Cancer Institute Hospital from 1978 to 2001 were studied. All samples were obtained from surgical specimens or biopsy specimens with no treatment by chemotherapy or radiotherapy. Among the 50 cases, 5 cases had dedifferentiated lesions in the tumors. Clinical information such as localization, surgical margin, tumor size, clinical outcome, and pathological findings were examined for this study. Specimens had been fixed in 10% formalin and pathological findings were obtained from hematoxylin and eosin staining of the formalin-fixed specimens. Each pathological appearance was reviewed to evaluate tumor type according to the World Health Organization classification of tumor.
Tumor samples for microarray experiment. Frozen tumor samples were obtained from surgical specimens or biopsy specimens collected at the Cancer Institute Hospital between 1993 and 2000. Counterparts of each frozen tumor sample were prepared for their pathological diagnosis. Thirty-six samples were used for this study. All samples were diagnosed by pathologists, as 28 liposarcomas and 8 lipomas. The 28 liposarcoma cases were comprised of 7 MX, 7 RC, 11 WD, and 3 DD cases. Samples used for the microarray experiment from the 3 DD cases were obtained from the macroscopically well-differentiated area with no microscopic contaminations of dedifferentiated lesion (Figure not shown). The details of the features of the samples are described in Table 2. All samples were collected with informed consent according to the Japanese Ministry of Health, Labor and Welfare guideline, and the institutional ethics guideline. Reference pool for microarray experiment. Total RNAs from adipose tissues from 10 patients were mixed to provide a reference pool. RNA isolation and amplification for microarray experiment. Total RNAs were extracted with the acid guanidinium–phenol–chloroform method from each frozen tumor sample and normal adipose tissue. The quality of extracted RNA was checked by electrophoresis. Then, the extracted total RNAs were amplified using a modified T7-based amplification for the microarray experiment [12–15]. The amplification method was as follows: 2 lg of total RNA was primed with 1 lg of oligo(dT) T7 primer (50 -AAACGACGGCCAGTGAATTGTAATA CGACTCACTATAGGCGCT15 -30 ). The RNA/primer mixture was incubated at 65 °C for 15 min. Then, 1 ll SuperScript reverse transcriptase (Gibco-BRL, Rockville, MD), 10 ll of first-strand buffer (TaKaRa, 2xLA Taq with GC buffer, TaKaRa Shuzo Ltd; Otsu, Shiga, Japan), 0.5 ll of 10 mM each dNTP mix (Amersham Biosciences; Piscataway, NJ), and 6 ll of trehalose/sorbitol mixture were added and incubated at 42 °C for 30 min, at 50 °C for 10 min, and at 56 °C for 10 min for the first-strand synthesis. Next, 37 ll of 5 secondstrand buffer (100 mM Tris, pH 6.9, 450 mM KCl, 23 mM MgCl2, 0.75 mM nicotine adenine dinucleotide, and 50 mM (NH4 )2 SO4 ), 3.45 ll of 10 mM each dNTP mix (Amersham Biosciences), 6.92 ll of 0.1 M DTT, 0.283 ll Escherichia coli DNA ligase (TaKaRa), 13.28 ll of E. coli DNA polymerase I (TaKaRa), 0.026 ll RNaseH (Promega; Madison, WI), and 100 ll RNase-free water were added and incubated at 16 °C for 2 h for the second-strand cDNA.synthesis. Five microliters of RNaseI (Promega) and a final concentration of 10 mM EDTA were added and incubated at 37 °C for 30 min to stop the reaction. Then, 1 ll of ProteinaseK (Qiagen, Valencia, CA) and a final concentration of 0.2% SDS were added and incubated at 45 °C for 30 min. After adding a final concentration of 2.5 mM ammonium acetate, a phenol/ chloroform extraction was carried out once and chloroform purification was done twice. Ethanol precipitation was carried out overnight. After ethanol precipitation, antisense RNA was synthesized with a MEGA Script T7 kit (Ambion; Woodward, TX) as a provided protocol. Then, the production was purified with an RNeasy mini kit (Qiagen). The quality of antisense RNA was also checked by electrophoresis. RIKEN 21K human cDNA microarray. The target cDNAs used to construct the RIKEN 21K human microarray were sequence-verified clone sets purchased from Research Genetics (http://www.resgen. com/). A total of 20,784 clones and positive and negative controls were printed on the microarray. After plasmid DNAs were extracted, a PCR amplification using universal primers was performed. The PCR products were precipitated with isopropanol and resuspended in 15 ll of 3 SSC. The DNA solution was spotted onto a CMT-GAPS Coated slide (Corning Incorporated, Acton, MA) using a RIKEN DNA Arrayer with 48 pins (SMP3, TeleChem International, Sunnyvale, CA) [15,16]. Fluorescent labeling, hybridization for microarray experiment. We labeled antisense RNA from the tumor samples with Cy5, and aRNA from the reference pool of normal adipose tissues with Cy3, with the double-step labeling method using amino-allyl dUTP (Sigma,
T. Shimoji et al. / Biochemical and Biophysical Research Communications 315 (2004) 1133–1140 St. Louis, MO). Two micrograms of aRNA was used for labeling, and 4 lg of random primer (TaKaRa) was added and incubated at 70 °C for 10 min. Six microliters of 5 first-strand buffer (Gibco-BRL), 3 ll of 0.1% DTT (Gibco-BRL), 1.5 ll of 10 mM each dGTP, dATP, and dCTP mix (Amersham Biosciences), 3.6 ll of 2.5 mM dTTP (Amersham Biosciences), 2.4 ll of 2.5 mM amino-allyl dUTP (Sigma), and 2 ll of SuperScript reverse transcriptase (Gibco-BRL) were added and
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incubated at 42 °C for 1 h to perform a reverse transcription. Next, 1.5 ll of 20 mM EDTA and 1.5 ll of 1 N NaOH were added and incubated at 70 °C for 10 min to hydrolyze the mixture. The sample was then neutralized by adding 1.5 ll of 1 N HCl and purified using a GFX PCR DNA and Band Purification Kit (Amersham Biosciences). After purification, the sample was dried using a Speedvac concentrator and resuspended in 9 ll of 0.1 M sodium bicarbonate buffer (pH
Fig. 1. (A) A specimen dendrogram formed by the 20,784 clone data. LPs and WDs are colored by pink and red, respectively. Whereas MXs, RCs, and DDs are colored by gray, blue, and green, respectively. The scale to the right depicts the correlation coefficient value represented by the length of the dendrogram branches connecting pairs of nodes. Note: All DD samples for the microarray experiment were obtained from a well-differentiated area. (B) Fifty differentially expressed genes between Cluster 1 and Cluster 2. Among the 1687 significant clones from our analysis, 487 clones were known genes. Three hundred and thirty-six clones were up-regulated in Cluster 1 and 151 clones were up-regulated in Cluster 2. The top 25 upregulated known genes in both Cluster 1 and Cluster 2 are shown in this figure. Each row corresponds to a gene and the columns correspond to its expression levels in the samples, respectively. Gene expression ratios are depicted according to the color scale shown at the top right. Gray indicates missing or excluded data.
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9.0) for dye labeling. Cy3 and Cy5 monofunctional reactive dye (Amersham Biosciences) was prepared by diluting with 45 ll DMSO, and 2.8 ll was used for one reaction. The Cye dyes were also dried completely using a Speedvac concentrator. Coupling was performed with the mixture of the dried Cye dye and sodium bicarbonate-diluted sample at room temperature in darkness for 1 h. Then, reactions were quenched by addition of 4.5 ll of 4 M hydroxylamine at room temperature in the dark for 15 min. The Cy3 and Cy5 reactions were combined for each set of reference and sample and purified using a MicroSpin Column S-200 HR Column (Amersham Biosciences). After concentration using a Speedvac to 17 ll, 1 ll Cot1 DNA (GibcoBRL), 3 ll oligo(dA), 3 ll yeast tRNA, 5.1 ll of 20 SSC, and 0.9 ll of 10% SDS were added to make a hybridization mixture and the mixture was denatured at 95 °C for 1 min. After the mixture was cooled to room temperature, hybridization to the RIKEN 21K human cDNA microarray was carried out in a humid chamber at 65 °C overnight. Then, the slide was washed once at room temperature for 5 min in 0.1% SDS, 2 SSC, and then once for 5 min with 1 SSC, and once for 5 min in 0.2 SSC. After these washes, the slide was dried by centrifugation before scanning. Duplicate experiments were performed by using the same template to check the reproducibility of the experiment, but the labeling and hybridization were done separately for each sample. Scanning and data processing for microarray experiment. Intensity of Cy3 and Cy5 channels was measured with a ScanArray5000 confocal laser scanner (Packard Biochip Technologies) and Tiff image files from this scanner were analyzed using ArrayAnalyzer software (Molecular Ware). Each spot was defined automatically and a grid of circles was manually positioned over the array images, and spots deemed unsuitable for accurate quantitation due to array artifacts were flagged and excluded from analysis. The data processing method, the PRIM program, that we developed previously was performed to check the reproducibility of the duplicated experiments and select the reproducible spots from the ArrayAnalyzer output files of duplicate experiments [17]. PRIM outputs the averaged value of log2 (Cy3/Cy5) from the duplicate experiments. In this process, the threshold value for filtering data is optimized by using the product of N and R, where N is the ratio of the number of spots that passed the filtering to the total number of spots, and R is the correlation coefficient for the results obtained from the duplicate experiments. Hierarchical clustering analysis and pattern-matching program to identify genes discriminating the two major Clusters. We applied a hierarchical clustering using the “Cluster” program to clarify the 36 lipomatous tumors in gene expression patterns from the output data of the PRIM processing program [18]. As we could observe 2 major clusters from the result of hierarchical clustering, we used a patternmatching program to identify genes that discriminate the two major clusters [19]. Detection of genes with a p value was performed using a patternmatching program. According to the original procedure [19], the template was set as a binary vector of length m with a value of 0 corresponding to one expression value and a value of 1 corresponding to a contrasting expression value. Then, genes were searched for by comparing each gene expression profile with the following template at a threshold of p value < 6.58 E-09 (19 cases in Cluster 1 and 17 cases in Cluster 2) (Fig. 1). The Pearson’s correlation coefficient (r) of the profile with the template was used as a simple measure of the agreement of the profile with the pattern. To effectively identify both “matches” and “anti-matches,” the absolute value of r was used. The p values were calculated using a t test on r. Then, a random permutation test was performed to assess the significant p value based on its ability to differentiate the two groups tested. The clone selection procedure with the random permutation test (with 1000 permutations) was applied to the 2 major clusters and the subclusters in the major clusters.
Results Behavior of 50 well-differentiated/dedifferentiated type liposarcomas and relationship between dedifferentiation and tumor size Fifty cases of well-differentiated and dedifferentiated type liposarcomas were studied. Out of the 5 DD cases, 2 had tumors located in the extremities and groin, respectively. The other 3 DD cases had tumors located in the retroperitoneum and died of disease after lung metastasis and local recurrences (Table 1A). The dedifferentiation of all 5 cases was aroused as a “de novo” lesion. No DD cases were seen with recurred lesions. On the other hand, almost all WD cases had tumors located in the extremities and groin (Table 1A). There was only one in the 45 WD cases that showed a local recurrence after 13 years had passed from the initial marginal resection without showing dedifferentiation. From a study of the relationship between dedifferentiation and tumor size, 4 out of the 5 DD tumors were within the size of 15 cm, but dedifferentiated lesions were not observed in huge tumors over 20 cm in diameter (Table 1B). On the other hand, 8 WD tumors grew to sizes of over 20 cm without dedifferentiation (Table 1B) resulting in no tendency of dedifferentiation depending on the tumor size. Gene expression study Unsupervised hierarchical clustering analysis The processing program, PRIM [17], showed that each result of the correlation coefficient r, indicating the reproducibility of the experiments, was over 0.7 and the average r was 0.89. The reproducible filtered data were subsequently analyzed. Unsupervised hierarchical clustering using 20,784 genes produced 2 major clusters of tumors (Fig. 1). Cluster 1 consisted of 11 WD cases and 8 LP. Cluster 2 was composed of relatively high-grade liposarcomas including 3 well-differentiated lesions from DD cases and 14 MX/RC cases. Interestingly, from unsupervised hierarchical clustering, well-differentiated lesions from DD cases showed a distinctly different gene expression pattern from those from WD. On the other hand, WD cases and LP cases in Cluster 1 were mingled without correlating to their pathological diagnosis. Identification of genes predictive of dedifferentiation using pattern matching program After unsupervised hierarchical clustering, we selected clones which were remarkably differentially expressed between Cluster 1 and Cluster 2. We set a significant p value as 6.58E-09 because that p value was the lowest in a random permutation test (1000 permutations; see Materials and methods) (Table 3). We
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Table 1A Clinical behaviors of the 50 well-differentiated/dedifferentiated liposarcoma cases Location
No. of cases
Dedifferentiation
Recurrence
Metastasis
D.O.Da
Follow-up (mean)
Extremity Lower extremity Upper extremity Groin Retroperitoneum Trunk
30 3 12 4 1
1 case None 1 case 3 casesb None
None None None 3 casesb None
None None None 3 casesb None
None None None 3 casesb None
2–18 years (8) 4–16 years (9) 3–12 years (8) 2–5 years (3) 3 years
a b
D.O.D: died of disease. Cases are all from dedifferentiated liposarcoma, all dedifferentiated liposarcoma cases aroused as ‘de novo’ lesions.
Table 1B Relationship between dedifferentiation and tumor size Location
No. of cases
Tumor size
Recurrence
Metastasis
Tumor size Tumor type Well-differentiated lesion Dedifferentiated lesion
<10 cm 17 cases 2 cases
10–15 cm 7 cases 2 cases
15–20 cm 13 cases 1 case
>20 cm 8 cases None
9 cases
14 cases
Total
19 cases
8 cases
Table 2 Clinical features of the 36 liposarcoma cases for the microarray experiment Case
Anatomical Location
Surgical margin
Outcome
Follow-up (year)
WD1 WD2 WD3 WD4 WD5 WD6 WD7 WD8 WD9 WD10 WD11 DD1 DD2 DD3 ML1 ML2 ML3 ML4 ML5 ML6 ML7 RC1 RC2 RC3 RC4 RC5 RC6 RC7
Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Trunk Retroperitoneum Retroperitoneum Retroperitoneum Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity
Wide Marginal Wide Wide Marginal Wide Wide Marginal Marginal Wide Wide Marginal Marginal Marginal Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide
CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF DOD DOD DOD CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF DOD
3 4 5 6 2 2 2 2 2 4 4 2 2 3 8 2 2 2 3 2 3 8 7 4 3 4 3 1
WD, well-differentiated type; DD, dedifferentiated type; MX, myxoid type; RC, round-cell type; CDF, complete disease-free; and DOD, died of disease, are used as non-standard abbreviations on this table. Regarding a section of surgical margin, wide, marginal, intra are also non-standard abbreviations meaning wide resection, marginal resection, and intra-lesional resection, respectively. Three dedifferentiated cases died of disease showing local recurrence and metastasis, respectively. Four extremity cases with inappropriate surgical treatment showed no local recurrence.
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Table 3 Numbers of differentially expressed clones between the two groups p value
Cluster 1–Cluster 2 distinction Randomized
1.00E-03 5.00E-04 1.00E-04 5.00E-05 1.00E-05 5.00E-06 1.00E-06 5.00E-07 1.00E-07 5.00E-08 1.00E-08 5.00E-09
LP–WD distinction Observed believability
Mean
99%
30.86 18.38 6.40 3.99 1.09 0.58 0.10 0.05 0.01 0.01 0.00 0.00
660.01 500.24 259.93 180.61 56.08 31.70 5.46 2.50 0.51 0.34 0.17 0.00
9295 8646 7273 6717 5536 5063 4055 3683 2868 2544 1859 1574
Randomized
99.67% 99.79% 99.91% 99.94% 99.98% 99.99% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
Observed believability
Mean
99%
17.30 8.81 2.03 1.15 0.32 0.19 0.04 0.02 0.00 0.00 0.00 0.00
210.85 146.65 62.48 43.36 16.04 9.86 2.51 1.10 0.17 0.08 0.00 0.00
157 78 16 6 0 0 0 0 0 0 0 0
88.98% 88.71% 87.29% 80.85% — — — — — — — —
In the distinction of Cluster 1 and Cluster 2, the lowest p value showed 6.58E-09 by random permutation. The number of clones in the observed section below p value of 6.58E-09 was 1687.
defined 1687 clones below the p value as truly differentially expressed. We used the leave-one-out cross-validation to reconfirm the reliability of these genes. With analysis by k-nearest neighbors, accuracy of these 1687 genes was 100%. Of the 1687 genes, the 50 differentially expressed known genes between Cluster 1 and Cluster 2 are shown in Fig. 1B. We also tried to find divergently expressed clones between LP and WD in the Cluster 1 cases. As can be expected from the dendrogram of Cluster 1, there was no significant clone to separate the LP from the WD, since the lowest p value (1.07E-05) observed was not below the significant p value (3.05E-8) defined by the permutation test (Table 3).
Discussion It is an interesting result that the well-differentiated lesions obtained from the DD cases showed a quite different gene expression profile from those of the WD cases by the unsupervised hierarchical clustering analysis. Although well-differentiated lesions from DD and WD are pathologically indistinguishable, our gene expression studies showed that the expression profiles are divergent. The histological progression named dedifferentiation occurring in WD has been proposed to occur in a timedependent manner [1,5–7]. However, a line of clinical evidence from our clinical study suggested that there are some additional factors affecting the progression besides time course. From clinical studies elucidating the relationship between dedifferentiation and tumor size, dedifferentiation did not always correlate to tumor size (Table 1B). If time course is the only factor for progression, dedifferentiation should be observed in accordance with tumor
growth. Other clinical evidence that denies the possibility of dedifferentiation occurring only in dependence of time is that dedifferentiation usually arises as a ‘de novo’ lesion [7], which is in agreement with our clinical study results. Here, again, if time course is the only factor for tumor progression, dedifferentiation should have occurred more in recurred lesions. This is because the duration of the affected period in recurred lesions is usually longer than that in primary lesions. These lines of clinical evidence strongly suggest that this progression of dedifferentiation would be not only a time-dependent phenomenon but dependent upon an additional factor that could not be described by previously established clinico-pathological studies. The different gene expression profiles studied here using the well-differentiated lesions obtained from DD and those from WD can be interpreted as additional factors for dedifferentiation or the characters not having been described by conventional studies. Hence, we speculate that the genes we have identified would reveal not only the accumulated genetic alterations associated with tumor progression but also the genetic background from which the tumors have originated. These genes have potential value for a more conclusive and accurate diagnosis of well-differentiated lipomatous lesions. The number of genes we identified was 1687. Over 1000 genes were ESTs. 487 genes were known genes. Among them, 336 genes were up-regulated in Cluster 1 and the other 151 genes were up-regulated in Cluster 2. Among the up-regulated known genes in Cluster 2 that consisted of relatively high-grade malignant cases, there were genes reflecting the malignant nature of this cluster. Of the 151 genes, 24 genes show some involvement in cancer. Among these 24 genes, some genes play an important role in various cancer tumorigenesis: FGFR1 for chronic myeloid leukemia, IRF4 for multiple myeloma, BRCA2 for breast cancer, and CEACAM5 for colon
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cancer [20–24]. Among them, the CEACAM5 (CEA) immunoassay is known to be useful in the diagnosis and serial monitoring of cancer patients for recurrence or response to therapy, particularly in the case of colonic cancers. Further study to validate the usefulness of the CEACAM5 (CEA) immunoassay for liposarcoma is necessary. The remaining cancer-related genes showed apparent relation to cancer growth or differentiation, proliferation, and so on. There were also genes such as ANKH, FXC1, GHR, and RPS6KA3, which are known for growth and proliferation of mesenchymal tissues [25–28]. Regarding the origin of WD/DD, up-regulation of these 4 genes would be a reasonable explanation. Intriguingly, ABCC5 is also listed, which is a good marker for the resistance to anti-cancer drugs [29]. As an explanation of the clinical features of liposarcoma that provide resistance for any anti-cancer drug, up-regulation of ABCC5 would be feasible. From the gene ontology analysis of these 151 genes, 26 genes were involved in cell growth, 11 genes in apoptosis, and most of the remaining genes in metabolism or regulation of transcription. Further study using the genes we identified would lead us to elucidate the mechanism of dedifferentiation and, we hope, useful differential diagnosis markers for WD and DD would be confirmed in relation to the clinical courses, and new therapeutic targets will be further investigated from these genes.
Acknowledgments This study has been supported by a Research Grant for the RIKEN Genome Exploration Research Project from the Ministry of Education, Culture, Sports, Science and Technologies of the Japanese Government and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japanese Government. We thank Manabe J, Kuroda K, Ae K, Tanizawa K, Tomaru Y, Hoshikawa Y, and Nagasaki K for helpful discussion.
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Clinico-molecular study of dedifferentiation in well-differentiated liposarcomaq,qq Takashi Shimoji,a,b,c Hiroaki Kanda,d Tomoyuki Kitagawa,d Koji Kadota,e Ryoichi Asai,e Katsutoshi Takahashi,e Noriyoshi Kawaguchi,c,* Seiichi Matsumoto,c Yoshihide Hayashizaki,b Yasushi Okazaki,b,*,1 and Kenichi Shinomiyaa a Department of Orthopedic Surgery, Tokyo Medical and Dental University, Japan Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), 1-7-22 Suehirocho, Tsurumi-ku, Yokohama 230-0045, Japan c Department of Orthopedic Oncology, Japanese Foundation for Cancer Research, Cancer Institute Hospital, Japan d Department of Pathology, Japanese Foundation for Cancer Research, Cancer Institute, Japan Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Japan b
e
Received 26 December 2003
Abstract Well-differentiated liposarcoma (WD) acquires fully malignant potential when the histological progression named dedifferentiation occurs. This progression is supposed to occur in a time-dependent manner but this is still a debated issue. Clinically, the prediction of dedifferentiation for WD is very important from the therapeutic point of view. To identify genes that are predictive of dedifferentiation and to understand the mechanism of dedifferentiation, we investigated clinical information of 50 cases and studied the gene expression profiles of 36 lipomatous tumors using cDNA microarray. The clinical study showed that the dedifferentiation did not always seem to occur in a time-dependent manner. Interestingly, from the gene expression study, unsupervised hierarchical clustering analysis of well-differentiated lesions obtained from dedifferentiated liposarcoma (DD) cases that were indistinguishable from WD pathologically showed a clearly distinct gene expression pattern from WD. Using the pattern-matching program, 1687 genes including 487 known genes were identified, which discriminated WD cases from well-differentiated lipomatous lesions obtained from DD cases. These results suggest that the dedifferentiation may arise from different types of WD that could be distinguished from gene expression profiling but could hardly be classified by the pathological studies. Ó 2004 Elsevier Inc. All rights reserved. Keywords: Liposarcoma; Well-differentiated liposarcoma; Dedifferentiated liposarcoma; Diagnosis; Malignant potential; Dedifferentiation; cDNA microarray; Gene expression profile
Liposarcoma is one of the common soft tissue sarcomas of adults [1]. Clinical behaviors of liposarcoma vary q Abbreviations: WD, well-differentiated liposarcoma; DD, dedifferentiated liposarcoma; MX, myxoid liposarcoma; RC, round cell liposarcoma; LP, lipoma. qq Supplementary data associated with this article can be found in the online version, at doi: 10.1016/j.bbrc.2003.12.203. * Corresponding authors. Fax: +81-429-85-7329 (Y. Okazaki), +81-3-5394-3832 (N. Kawaguchi). E-mail addresses: [email protected] (N. Kawaguchi), [email protected] (Y. Okazaki). 1 Present address: Division of Functional Genomics & Systems Medicine, Research Center for Genomic Medicine, Saitama Medical School, 1397-1 Yamane, Hidaka City, Saitama 350-1241, Japan.
0006-291X/$ - see front matter Ó 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.bbrc.2003.12.203
from non-life-threatening to fully malignant and its histological appearances also vary substantially [2–6]. According to the World Health Organization classification of soft tissue tumors, five pathological subtypes are distinguished; well-differentiated (WD), myxoid (MX), round-cell (RC), pleomorphic (PL), and dedifferentiated (DD) type liposarcomas [7]. Although each subtype is widely disparate in its clinical behavior, it is useful to group them into three classes from a pathological point of view [1,7]. Since a subset of WD type tumors frequently histologically progresses to the DD type, WD and DD comprise one group. MX and RC also comprise another because MX type tumors and RC type tumors
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frequently coexist. Residual PL comprises the last group. Among these three groups, the WD and DD group accounts for over 45% of all liposarcomas, representing the largest subtype of adipocytic malignant neoplasms. Interestingly, this group has a curious aspect in its clinical behaviors. Usually, the most important factor reflecting the prognosis of a malignant tumor would be the various pathological findings. However, in WD cases, tumor localization is the most important factor for the prediction of prognosis. Commonly, WDs located in extremities usually have no tendency of metastasis, so they would be regarded as non-life-threatening lesions. On the other hand, WDs located in the retroperitoneum, where the tumors most frequently occur, tend to recur repeatedly, resulting in the patient’s death as a result of uncontrolled local recurrence. Dedifferentiation also frequently occurs in the retroperitoneum [7]. Once dedifferentiation occurs in WD, which originally has a nonmetastatic tendency, the tumor acquires a metastatic and highly recurrent potential. The progression from WD to DD is supposed to occur in a time-dependent manner rather than in a site-specific manner [1,5–7]. In clinical settings, however, tumor localization is the most reliable parameter for the ultimate risk of dedifferentiation, which is equal to the prediction of prognosis [7]. This dedifferentiation phenomenon has been reported to occur in extremities too, but the frequency is much lower than the case of tumors located in the retroperitoneum [7]. Notwithstanding distinct differences in their clinical behavior, no other factor besides tumor localization and time has been reported to attribute to this phenomenon. If other factors predictive of dedifferentiation are identified, the treatment for WDs such as adjuvant chemotherapy or radiotherapy, or the more eligible surgical treatment, would be impacted. Since gene expression profiles using a microarray have been providing a new level of insight into the diagnosis and prognostic prediction of tumors [8–11], we analyzed 36 lipomatous tumors using the RIKEN 21K human cDNA microarray with the aim of understanding dedifferentiation and searching for novel useful markers for the malignant potential of WD at the molecular level.
Materials and methods Cases and methods of clinical study. Fifty cases in the WD and DD group treated in the Cancer Institute Hospital from 1978 to 2001 were studied. All samples were obtained from surgical specimens or biopsy specimens with no treatment by chemotherapy or radiotherapy. Among the 50 cases, 5 cases had dedifferentiated lesions in the tumors. Clinical information such as localization, surgical margin, tumor size, clinical outcome, and pathological findings were examined for this study. Specimens had been fixed in 10% formalin and pathological findings were obtained from hematoxylin and eosin staining of the formalin-fixed specimens. Each pathological appearance was reviewed to evaluate tumor type according to the World Health Organization classification of tumor.
Tumor samples for microarray experiment. Frozen tumor samples were obtained from surgical specimens or biopsy specimens collected at the Cancer Institute Hospital between 1993 and 2000. Counterparts of each frozen tumor sample were prepared for their pathological diagnosis. Thirty-six samples were used for this study. All samples were diagnosed by pathologists, as 28 liposarcomas and 8 lipomas. The 28 liposarcoma cases were comprised of 7 MX, 7 RC, 11 WD, and 3 DD cases. Samples used for the microarray experiment from the 3 DD cases were obtained from the macroscopically well-differentiated area with no microscopic contaminations of dedifferentiated lesion (Figure not shown). The details of the features of the samples are described in Table 2. All samples were collected with informed consent according to the Japanese Ministry of Health, Labor and Welfare guideline, and the institutional ethics guideline. Reference pool for microarray experiment. Total RNAs from adipose tissues from 10 patients were mixed to provide a reference pool. RNA isolation and amplification for microarray experiment. Total RNAs were extracted with the acid guanidinium–phenol–chloroform method from each frozen tumor sample and normal adipose tissue. The quality of extracted RNA was checked by electrophoresis. Then, the extracted total RNAs were amplified using a modified T7-based amplification for the microarray experiment [12–15]. The amplification method was as follows: 2 lg of total RNA was primed with 1 lg of oligo(dT) T7 primer (50 -AAACGACGGCCAGTGAATTGTAATA CGACTCACTATAGGCGCT15 -30 ). The RNA/primer mixture was incubated at 65 °C for 15 min. Then, 1 ll SuperScript reverse transcriptase (Gibco-BRL, Rockville, MD), 10 ll of first-strand buffer (TaKaRa, 2xLA Taq with GC buffer, TaKaRa Shuzo Ltd; Otsu, Shiga, Japan), 0.5 ll of 10 mM each dNTP mix (Amersham Biosciences; Piscataway, NJ), and 6 ll of trehalose/sorbitol mixture were added and incubated at 42 °C for 30 min, at 50 °C for 10 min, and at 56 °C for 10 min for the first-strand synthesis. Next, 37 ll of 5 secondstrand buffer (100 mM Tris, pH 6.9, 450 mM KCl, 23 mM MgCl2, 0.75 mM nicotine adenine dinucleotide, and 50 mM (NH4 )2 SO4 ), 3.45 ll of 10 mM each dNTP mix (Amersham Biosciences), 6.92 ll of 0.1 M DTT, 0.283 ll Escherichia coli DNA ligase (TaKaRa), 13.28 ll of E. coli DNA polymerase I (TaKaRa), 0.026 ll RNaseH (Promega; Madison, WI), and 100 ll RNase-free water were added and incubated at 16 °C for 2 h for the second-strand cDNA.synthesis. Five microliters of RNaseI (Promega) and a final concentration of 10 mM EDTA were added and incubated at 37 °C for 30 min to stop the reaction. Then, 1 ll of ProteinaseK (Qiagen, Valencia, CA) and a final concentration of 0.2% SDS were added and incubated at 45 °C for 30 min. After adding a final concentration of 2.5 mM ammonium acetate, a phenol/ chloroform extraction was carried out once and chloroform purification was done twice. Ethanol precipitation was carried out overnight. After ethanol precipitation, antisense RNA was synthesized with a MEGA Script T7 kit (Ambion; Woodward, TX) as a provided protocol. Then, the production was purified with an RNeasy mini kit (Qiagen). The quality of antisense RNA was also checked by electrophoresis. RIKEN 21K human cDNA microarray. The target cDNAs used to construct the RIKEN 21K human microarray were sequence-verified clone sets purchased from Research Genetics (http://www.resgen. com/). A total of 20,784 clones and positive and negative controls were printed on the microarray. After plasmid DNAs were extracted, a PCR amplification using universal primers was performed. The PCR products were precipitated with isopropanol and resuspended in 15 ll of 3 SSC. The DNA solution was spotted onto a CMT-GAPS Coated slide (Corning Incorporated, Acton, MA) using a RIKEN DNA Arrayer with 48 pins (SMP3, TeleChem International, Sunnyvale, CA) [15,16]. Fluorescent labeling, hybridization for microarray experiment. We labeled antisense RNA from the tumor samples with Cy5, and aRNA from the reference pool of normal adipose tissues with Cy3, with the double-step labeling method using amino-allyl dUTP (Sigma,
T. Shimoji et al. / Biochemical and Biophysical Research Communications 315 (2004) 1133–1140 St. Louis, MO). Two micrograms of aRNA was used for labeling, and 4 lg of random primer (TaKaRa) was added and incubated at 70 °C for 10 min. Six microliters of 5 first-strand buffer (Gibco-BRL), 3 ll of 0.1% DTT (Gibco-BRL), 1.5 ll of 10 mM each dGTP, dATP, and dCTP mix (Amersham Biosciences), 3.6 ll of 2.5 mM dTTP (Amersham Biosciences), 2.4 ll of 2.5 mM amino-allyl dUTP (Sigma), and 2 ll of SuperScript reverse transcriptase (Gibco-BRL) were added and
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incubated at 42 °C for 1 h to perform a reverse transcription. Next, 1.5 ll of 20 mM EDTA and 1.5 ll of 1 N NaOH were added and incubated at 70 °C for 10 min to hydrolyze the mixture. The sample was then neutralized by adding 1.5 ll of 1 N HCl and purified using a GFX PCR DNA and Band Purification Kit (Amersham Biosciences). After purification, the sample was dried using a Speedvac concentrator and resuspended in 9 ll of 0.1 M sodium bicarbonate buffer (pH
Fig. 1. (A) A specimen dendrogram formed by the 20,784 clone data. LPs and WDs are colored by pink and red, respectively. Whereas MXs, RCs, and DDs are colored by gray, blue, and green, respectively. The scale to the right depicts the correlation coefficient value represented by the length of the dendrogram branches connecting pairs of nodes. Note: All DD samples for the microarray experiment were obtained from a well-differentiated area. (B) Fifty differentially expressed genes between Cluster 1 and Cluster 2. Among the 1687 significant clones from our analysis, 487 clones were known genes. Three hundred and thirty-six clones were up-regulated in Cluster 1 and 151 clones were up-regulated in Cluster 2. The top 25 upregulated known genes in both Cluster 1 and Cluster 2 are shown in this figure. Each row corresponds to a gene and the columns correspond to its expression levels in the samples, respectively. Gene expression ratios are depicted according to the color scale shown at the top right. Gray indicates missing or excluded data.
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9.0) for dye labeling. Cy3 and Cy5 monofunctional reactive dye (Amersham Biosciences) was prepared by diluting with 45 ll DMSO, and 2.8 ll was used for one reaction. The Cye dyes were also dried completely using a Speedvac concentrator. Coupling was performed with the mixture of the dried Cye dye and sodium bicarbonate-diluted sample at room temperature in darkness for 1 h. Then, reactions were quenched by addition of 4.5 ll of 4 M hydroxylamine at room temperature in the dark for 15 min. The Cy3 and Cy5 reactions were combined for each set of reference and sample and purified using a MicroSpin Column S-200 HR Column (Amersham Biosciences). After concentration using a Speedvac to 17 ll, 1 ll Cot1 DNA (GibcoBRL), 3 ll oligo(dA), 3 ll yeast tRNA, 5.1 ll of 20 SSC, and 0.9 ll of 10% SDS were added to make a hybridization mixture and the mixture was denatured at 95 °C for 1 min. After the mixture was cooled to room temperature, hybridization to the RIKEN 21K human cDNA microarray was carried out in a humid chamber at 65 °C overnight. Then, the slide was washed once at room temperature for 5 min in 0.1% SDS, 2 SSC, and then once for 5 min with 1 SSC, and once for 5 min in 0.2 SSC. After these washes, the slide was dried by centrifugation before scanning. Duplicate experiments were performed by using the same template to check the reproducibility of the experiment, but the labeling and hybridization were done separately for each sample. Scanning and data processing for microarray experiment. Intensity of Cy3 and Cy5 channels was measured with a ScanArray5000 confocal laser scanner (Packard Biochip Technologies) and Tiff image files from this scanner were analyzed using ArrayAnalyzer software (Molecular Ware). Each spot was defined automatically and a grid of circles was manually positioned over the array images, and spots deemed unsuitable for accurate quantitation due to array artifacts were flagged and excluded from analysis. The data processing method, the PRIM program, that we developed previously was performed to check the reproducibility of the duplicated experiments and select the reproducible spots from the ArrayAnalyzer output files of duplicate experiments [17]. PRIM outputs the averaged value of log2 (Cy3/Cy5) from the duplicate experiments. In this process, the threshold value for filtering data is optimized by using the product of N and R, where N is the ratio of the number of spots that passed the filtering to the total number of spots, and R is the correlation coefficient for the results obtained from the duplicate experiments. Hierarchical clustering analysis and pattern-matching program to identify genes discriminating the two major Clusters. We applied a hierarchical clustering using the “Cluster” program to clarify the 36 lipomatous tumors in gene expression patterns from the output data of the PRIM processing program [18]. As we could observe 2 major clusters from the result of hierarchical clustering, we used a patternmatching program to identify genes that discriminate the two major clusters [19]. Detection of genes with a p value was performed using a patternmatching program. According to the original procedure [19], the template was set as a binary vector of length m with a value of 0 corresponding to one expression value and a value of 1 corresponding to a contrasting expression value. Then, genes were searched for by comparing each gene expression profile with the following template at a threshold of p value < 6.58 E-09 (19 cases in Cluster 1 and 17 cases in Cluster 2) (Fig. 1). The Pearson’s correlation coefficient (r) of the profile with the template was used as a simple measure of the agreement of the profile with the pattern. To effectively identify both “matches” and “anti-matches,” the absolute value of r was used. The p values were calculated using a t test on r. Then, a random permutation test was performed to assess the significant p value based on its ability to differentiate the two groups tested. The clone selection procedure with the random permutation test (with 1000 permutations) was applied to the 2 major clusters and the subclusters in the major clusters.
Results Behavior of 50 well-differentiated/dedifferentiated type liposarcomas and relationship between dedifferentiation and tumor size Fifty cases of well-differentiated and dedifferentiated type liposarcomas were studied. Out of the 5 DD cases, 2 had tumors located in the extremities and groin, respectively. The other 3 DD cases had tumors located in the retroperitoneum and died of disease after lung metastasis and local recurrences (Table 1A). The dedifferentiation of all 5 cases was aroused as a “de novo” lesion. No DD cases were seen with recurred lesions. On the other hand, almost all WD cases had tumors located in the extremities and groin (Table 1A). There was only one in the 45 WD cases that showed a local recurrence after 13 years had passed from the initial marginal resection without showing dedifferentiation. From a study of the relationship between dedifferentiation and tumor size, 4 out of the 5 DD tumors were within the size of 15 cm, but dedifferentiated lesions were not observed in huge tumors over 20 cm in diameter (Table 1B). On the other hand, 8 WD tumors grew to sizes of over 20 cm without dedifferentiation (Table 1B) resulting in no tendency of dedifferentiation depending on the tumor size. Gene expression study Unsupervised hierarchical clustering analysis The processing program, PRIM [17], showed that each result of the correlation coefficient r, indicating the reproducibility of the experiments, was over 0.7 and the average r was 0.89. The reproducible filtered data were subsequently analyzed. Unsupervised hierarchical clustering using 20,784 genes produced 2 major clusters of tumors (Fig. 1). Cluster 1 consisted of 11 WD cases and 8 LP. Cluster 2 was composed of relatively high-grade liposarcomas including 3 well-differentiated lesions from DD cases and 14 MX/RC cases. Interestingly, from unsupervised hierarchical clustering, well-differentiated lesions from DD cases showed a distinctly different gene expression pattern from those from WD. On the other hand, WD cases and LP cases in Cluster 1 were mingled without correlating to their pathological diagnosis. Identification of genes predictive of dedifferentiation using pattern matching program After unsupervised hierarchical clustering, we selected clones which were remarkably differentially expressed between Cluster 1 and Cluster 2. We set a significant p value as 6.58E-09 because that p value was the lowest in a random permutation test (1000 permutations; see Materials and methods) (Table 3). We
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Table 1A Clinical behaviors of the 50 well-differentiated/dedifferentiated liposarcoma cases Location
No. of cases
Dedifferentiation
Recurrence
Metastasis
D.O.Da
Follow-up (mean)
Extremity Lower extremity Upper extremity Groin Retroperitoneum Trunk
30 3 12 4 1
1 case None 1 case 3 casesb None
None None None 3 casesb None
None None None 3 casesb None
None None None 3 casesb None
2–18 years (8) 4–16 years (9) 3–12 years (8) 2–5 years (3) 3 years
a b
D.O.D: died of disease. Cases are all from dedifferentiated liposarcoma, all dedifferentiated liposarcoma cases aroused as ‘de novo’ lesions.
Table 1B Relationship between dedifferentiation and tumor size Location
No. of cases
Tumor size
Recurrence
Metastasis
Tumor size Tumor type Well-differentiated lesion Dedifferentiated lesion
<10 cm 17 cases 2 cases
10–15 cm 7 cases 2 cases
15–20 cm 13 cases 1 case
>20 cm 8 cases None
9 cases
14 cases
Total
19 cases
8 cases
Table 2 Clinical features of the 36 liposarcoma cases for the microarray experiment Case
Anatomical Location
Surgical margin
Outcome
Follow-up (year)
WD1 WD2 WD3 WD4 WD5 WD6 WD7 WD8 WD9 WD10 WD11 DD1 DD2 DD3 ML1 ML2 ML3 ML4 ML5 ML6 ML7 RC1 RC2 RC3 RC4 RC5 RC6 RC7
Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Trunk Retroperitoneum Retroperitoneum Retroperitoneum Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity Extremity
Wide Marginal Wide Wide Marginal Wide Wide Marginal Marginal Wide Wide Marginal Marginal Marginal Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide Wide
CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF DOD DOD DOD CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF CDF DOD
3 4 5 6 2 2 2 2 2 4 4 2 2 3 8 2 2 2 3 2 3 8 7 4 3 4 3 1
WD, well-differentiated type; DD, dedifferentiated type; MX, myxoid type; RC, round-cell type; CDF, complete disease-free; and DOD, died of disease, are used as non-standard abbreviations on this table. Regarding a section of surgical margin, wide, marginal, intra are also non-standard abbreviations meaning wide resection, marginal resection, and intra-lesional resection, respectively. Three dedifferentiated cases died of disease showing local recurrence and metastasis, respectively. Four extremity cases with inappropriate surgical treatment showed no local recurrence.
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Table 3 Numbers of differentially expressed clones between the two groups p value
Cluster 1–Cluster 2 distinction Randomized
1.00E-03 5.00E-04 1.00E-04 5.00E-05 1.00E-05 5.00E-06 1.00E-06 5.00E-07 1.00E-07 5.00E-08 1.00E-08 5.00E-09
LP–WD distinction Observed believability
Mean
99%
30.86 18.38 6.40 3.99 1.09 0.58 0.10 0.05 0.01 0.01 0.00 0.00
660.01 500.24 259.93 180.61 56.08 31.70 5.46 2.50 0.51 0.34 0.17 0.00
9295 8646 7273 6717 5536 5063 4055 3683 2868 2544 1859 1574
Randomized
99.67% 99.79% 99.91% 99.94% 99.98% 99.99% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
Observed believability
Mean
99%
17.30 8.81 2.03 1.15 0.32 0.19 0.04 0.02 0.00 0.00 0.00 0.00
210.85 146.65 62.48 43.36 16.04 9.86 2.51 1.10 0.17 0.08 0.00 0.00
157 78 16 6 0 0 0 0 0 0 0 0
88.98% 88.71% 87.29% 80.85% — — — — — — — —
In the distinction of Cluster 1 and Cluster 2, the lowest p value showed 6.58E-09 by random permutation. The number of clones in the observed section below p value of 6.58E-09 was 1687.
defined 1687 clones below the p value as truly differentially expressed. We used the leave-one-out cross-validation to reconfirm the reliability of these genes. With analysis by k-nearest neighbors, accuracy of these 1687 genes was 100%. Of the 1687 genes, the 50 differentially expressed known genes between Cluster 1 and Cluster 2 are shown in Fig. 1B. We also tried to find divergently expressed clones between LP and WD in the Cluster 1 cases. As can be expected from the dendrogram of Cluster 1, there was no significant clone to separate the LP from the WD, since the lowest p value (1.07E-05) observed was not below the significant p value (3.05E-8) defined by the permutation test (Table 3).
Discussion It is an interesting result that the well-differentiated lesions obtained from the DD cases showed a quite different gene expression profile from those of the WD cases by the unsupervised hierarchical clustering analysis. Although well-differentiated lesions from DD and WD are pathologically indistinguishable, our gene expression studies showed that the expression profiles are divergent. The histological progression named dedifferentiation occurring in WD has been proposed to occur in a timedependent manner [1,5–7]. However, a line of clinical evidence from our clinical study suggested that there are some additional factors affecting the progression besides time course. From clinical studies elucidating the relationship between dedifferentiation and tumor size, dedifferentiation did not always correlate to tumor size (Table 1B). If time course is the only factor for progression, dedifferentiation should be observed in accordance with tumor
growth. Other clinical evidence that denies the possibility of dedifferentiation occurring only in dependence of time is that dedifferentiation usually arises as a ‘de novo’ lesion [7], which is in agreement with our clinical study results. Here, again, if time course is the only factor for tumor progression, dedifferentiation should have occurred more in recurred lesions. This is because the duration of the affected period in recurred lesions is usually longer than that in primary lesions. These lines of clinical evidence strongly suggest that this progression of dedifferentiation would be not only a time-dependent phenomenon but dependent upon an additional factor that could not be described by previously established clinico-pathological studies. The different gene expression profiles studied here using the well-differentiated lesions obtained from DD and those from WD can be interpreted as additional factors for dedifferentiation or the characters not having been described by conventional studies. Hence, we speculate that the genes we have identified would reveal not only the accumulated genetic alterations associated with tumor progression but also the genetic background from which the tumors have originated. These genes have potential value for a more conclusive and accurate diagnosis of well-differentiated lipomatous lesions. The number of genes we identified was 1687. Over 1000 genes were ESTs. 487 genes were known genes. Among them, 336 genes were up-regulated in Cluster 1 and the other 151 genes were up-regulated in Cluster 2. Among the up-regulated known genes in Cluster 2 that consisted of relatively high-grade malignant cases, there were genes reflecting the malignant nature of this cluster. Of the 151 genes, 24 genes show some involvement in cancer. Among these 24 genes, some genes play an important role in various cancer tumorigenesis: FGFR1 for chronic myeloid leukemia, IRF4 for multiple myeloma, BRCA2 for breast cancer, and CEACAM5 for colon
T. Shimoji et al. / Biochemical and Biophysical Research Communications 315 (2004) 1133–1140
cancer [20–24]. Among them, the CEACAM5 (CEA) immunoassay is known to be useful in the diagnosis and serial monitoring of cancer patients for recurrence or response to therapy, particularly in the case of colonic cancers. Further study to validate the usefulness of the CEACAM5 (CEA) immunoassay for liposarcoma is necessary. The remaining cancer-related genes showed apparent relation to cancer growth or differentiation, proliferation, and so on. There were also genes such as ANKH, FXC1, GHR, and RPS6KA3, which are known for growth and proliferation of mesenchymal tissues [25–28]. Regarding the origin of WD/DD, up-regulation of these 4 genes would be a reasonable explanation. Intriguingly, ABCC5 is also listed, which is a good marker for the resistance to anti-cancer drugs [29]. As an explanation of the clinical features of liposarcoma that provide resistance for any anti-cancer drug, up-regulation of ABCC5 would be feasible. From the gene ontology analysis of these 151 genes, 26 genes were involved in cell growth, 11 genes in apoptosis, and most of the remaining genes in metabolism or regulation of transcription. Further study using the genes we identified would lead us to elucidate the mechanism of dedifferentiation and, we hope, useful differential diagnosis markers for WD and DD would be confirmed in relation to the clinical courses, and new therapeutic targets will be further investigated from these genes.
Acknowledgments This study has been supported by a Research Grant for the RIKEN Genome Exploration Research Project from the Ministry of Education, Culture, Sports, Science and Technologies of the Japanese Government and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japanese Government. We thank Manabe J, Kuroda K, Ae K, Tanizawa K, Tomaru Y, Hoshikawa Y, and Nagasaki K for helpful discussion.
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