Originally published as JCO Early Release 10.1200/JCO.2006.09.4474 on June 18 2007

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Identification of a Proliferation Signature Related to Survival in Nodal Peripheral T-Cell Lymphomas

Marta Cuadros, Sandeep S. Dave, Elaine S. Jaffe, Emiliano Honrado, Roger Milne, Javier Alves, Jose Rodríguez, Magdalena Zajac, Javier Benitez, Louis M. Staudt, Beatriz Martinez-Delgado

From the Human Genetics Group and Genotyping Unit, Spanish National Cancer Centre; Department of Pathology, Hospital La Paz, Madrid; Department of Oncology, Hospital Son Dureta, Palma de Mallorca, Spain; and Lymphoid Malignancies Section and Laboratory of Pathology, National Cancer Institute, Bethesda, MD

Address reprint requests to Beatriz Martinez-Delgado, PhD, Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Centre, C/. Melchor Fernández Almagro No. 3, 28029, Madrid, Spain; e-mail: bmartinez{at}cnio.es

	ABSTRACT

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Purpose Nodal peripheral T-cell lymphomas (PTCLs) constitute a heterogeneous group of neoplasms, suggesting the existence of molecular differences contributing to their histologic and clinical variability. Initial expression profiling studies of T-cell lymphomas have been inconclusive in yielding clinically relevant insights. We applied DNA microarrays to gain insight into the molecular signatures associated with prognosis.

Materials and Methods We analyzed the expression profiles of 35 nodal PTCLs (23 PTCLs unspecified and 12 angioimmunoblastic) using two different microarray platforms, the cDNA microarray developed at the Spanish National Cancer Centre and an oligonucleotide microarray.

Results We identified five clusters of genes, the expression of which varied significantly among the samples. Genes in these clusters seemed to be functionally related to different cellular processes such as proliferation, inflammatory response, and T-cell or B-cell lineages. Regardless of the microarray platform used, overexpression of genes in the proliferation signature was associated significantly with shorter survival of patients. This proliferation signature included genes commonly associated with the cell cycle, such as CCNA, CCNB, TOP2A, and PCNA. Moreover the PTCL proliferation signature showed a statistically significant inverse correlation with clusters of the inflammatory response (P < .0001), as well as with the percentage of CD68⁺ cells.

Conclusion Our findings indicate that proliferation could be an important factor in evaluating nodal PTCL outcome and may help to define a more aggressive phenotype.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Nodal peripheral T cell lymphomas (PTCLs) encompass three major categories recognized in the WHO classification¹: PTCL unspecified (PTCLu), angioimmunoblastic T-cell lymphoma (AILT), and anaplastic large-cell lymphoma. Anaplastic large cell, specifically anaplastic lymphoma kinase–positive subgroup, constitutes a well-defined entity, with specific morphologic, histologic, genetic, and clinical characteristics. AILT is also characterized by consistent morphologic features; however, the distinction between AILT and PTCLu is not always clear. The majority of PTCLs are assigned to the PTCLu category, which is morphologically heterogeneous, without evidence that pathologic variants represent true clinicopathologic entities.^2,3

Gene expression profiling is being exploited to improve diagnosis and classification of lymphoma subtypes, and to better define prognosis. In recent years, microarrays have been used to identify new lymphoma categories based on expression patterns.^4-9 Recent studies have begun to show the existence of distinct PTCL subgroups with specific molecular profiles,^10-13 but the use of expression profiling to identify new prognostic factors on PTCL is still lacking.

A number of prognostic factors have been studied for their predictive potential in PTCL, but none have become universally accepted. The International Prognosis Index (IPI) is the most frequently used prognostic model, but sometimes performs poorly in identifying high-risk patients among PTCLs,^14,15 and other clinical scoring systems have been proposed.¹⁶ The influence of immunophenotypic markers and other biologic factors on PTCL outcome is also under investigation. In fact, T-cell phenotype per se is an adverse prognostic factor.^3,17 Among biologic markers, high proliferation rate, mainly measured by Ki-67, has been recognized as a marker of poor prognosis in several lymphoma subtypes.^18-21

In this study, we applied two different microarray platforms to examine the molecular differences among nodal PTCLs. Our goals were to elucidate oncogenic pathways involved in PTCL, as well as to identify new models for outcome prediction. This allowed us to test the reproducibility of clusters of functionally related genes. We identified clusters that help delineate the variability in nodal PTCL, and identified a proliferation signature associated with survival.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Tumor Samples
Tumor biopsies from 23 PTCLu and 12 AILT patients were included. Most of these samples were also part of the tumor set in previous expression studies using microarrays.^11,22 All tumors were reviewed histologically, characterized using a wide panel of antibodies, and classified according to the WHO criteria. The percentage of tumor cells versus reactive cells was estimated in each sample via visual inspection. Lymphomas were obtained from different hospitals via the Tumor Bank network of the Spanish National Cancer Centre (CNIO). Most patients were treated with similar therapy protocols based on combination chemotherapy with or without anthracyclines.

Microarrays
cDNA microarray experiments were carried out using the CNIO OncoChip, containing 7,657 cDNA sequence-validated clones representing cancer related genes.^23,24 Total RNA was extracted with TriReagent (Molecular Research Center, Cincinnati, OH). Two micrograms of RNA was used to produce double-strand cDNA (Superscript Choice System; Life Technologies Inc, Carlsbad, CA) followed by linear amplification with the Megascript T7 in vitro transcription kit (Ambion, Austin, TX). Amplified RNA (aRNA) was purified further with Rneasy spin columns (Qiagen, Hiden, Germany). Test or reference aRNAs were labeled with fluorophores Cy5 (red) and Cy3 (green), respectively. aRNA from each sample was cohybridized with Universal Human RNA (Stratagene, La Jolla, CA) as reference. Hybridizations were performed at 42°C for 15 hours, as described previously.²²

cDNA microarray slides were scanned using the Agilent Array Scanner (Agilent, Santa Clara, CA). Images were analyzed using GenePix5.0 (Axon Instruments Inc, Berkshire, United Kingdom). The Cy5/Cy3 ratios from each experiment were normalized using DNMAD (http://dnmad.bioinfo.cnio.es/).^25,26 Data were filtered using bioinformatic tools developed at CNIO.²⁷ Unsupervised hierarchical clustering was performed using Gene Cluster and Treeview (http://rana.stanford.edu/software). Matrix data from these array experiments can be seen at ArrayExpress database,²⁸ accession number E-TABM-191.

We also performed gene expression studies in 20 of the PTCLs using a custom oligonucleotide Affymetrix microarray (Affymetrix, Santa Clara, CA) with 2,745 genes that are expressed differentially in non-Hodgkin's lymphoma.²⁹ Total RNA was extracted from tumor biopsies that had been snap-frozen. After first- and second-strand cDNA synthesis (one-cycle cDNA synthesis kit; Affymetrix), biotin-labeled cRNA was obtained by incubation at 37°C for 16 hours with the GeneChip IVT labeling kit (Affymetrix). Hybridization was performed via a 16-hour incubation in a hybridization oven at 45°C.

Immunocytochemical Correlation
Immunohistochemical studies were performed to detect Ki-67 (clone MIB-1; Dako, Glostrup, Denmark), cyclin A (clone 6E6; Novocastra, New Castle, United Kingdom), topoisomerase DNA II alpha (TOP2A; clone Ki-S1; Dako), and CD68 (clone KP1; Dako) in paraffin-embedded tumor material. Cyclin A (CCNA) and TOP2A immunohistochemical analysis was performed in 17 nodal PTCLs. The percentage of Ki-67–positive and CD68⁺ cells was estimated for 33 samples. The CCNA and TOP2A index was calculated by counting the number of CCNA- and TOP2A- positive nuclei.

Statistical Analysis
Correlation coefficients were calculated to describe how the proliferation signature was linearly related to other continuous variables. Univariate Cox analysis was used to compare the overall survival with expression of molecular signatures and the following variables: age, disease stage, lactate dehydrogenase levels, "B" symptoms, and IPI and Ki-67 indices. Multivariate Cox regression models were fit for IPI and proliferation signature expression. Differences in protein expression between proliferation groups were estimated using the Fisher's exact and Mann-Whitney tests. SPSS version 12.0 (SPSS Inc, Chicago, IL) was used for these analyses.

	RESULTS

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cDNA Microarray Gene Clusters
Unsupervised hierarchical clustering analysis of tumors was performed using 2,996 clones showing high expression variability among samples (standard deviation, > 0.4). We identified five major clusters (named A, B, C, D, and E) composed of highly correlated genes (r > 0.75; Fig 1). We then assessed whether the genes in these clusters are functionally related or associated with any common biologic processes.

View larger version (76K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Unsupervised clustering analysis of the 35 nodal peripheral T-cell lymphomas. (A) Tumors were clustered according to the expression of these genes. Five different clusters are shown (A, B, C, D, and E), illustrating significant differences among tumors. (B) Clustering analysis based on the expression of the five selected clusters, named according to functional criteria.

View larger version (50K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Proliferation signature in both array platforms. (A) Peripheral T-cell lymphoma proliferation-core signature from the cDNA microarray platform. Tumors were sorted by the averaged expression of the signature from the less (green) to the most proliferative (red). Estimations of neoplastic cells are represented at the top. (B) Genes included in the proliferation signature in Affymetrix microarray.

There were also two large sets of genes that clustered closely (Fig 1C and 1D), which included 277 and 194 genes, respectively (Fig 1). Both clusters had genes of the nuclear factor-kappa B (NF-B) pathway and resembled an inflammatory response. Cluster C included genes of the NF-B complex (NFKB1, IKBKB, and NFKBIE), NF-B–responsive genes (VCAM1, MMP9, and SELL), cytokines and chemokines (IL15, IL15RA, CCR1, CCL2, and CXCL10), genes of antigen-presenting cells and other infiltrating cells (CD63, CD33, HLA-DM, HLA-DOB, CSF3R, CCL19, CTSS, and CTSL), and many common T-cell genes (CD3G, CD4, IL2RG, and LCP2) were also characteristic. Cluster C was therefore named inflammation-1 cluster. In Cluster D we also found genes related to NF-B (NFKB2, MAP3K14, and IKBKG), macrophages (CSF1, CTSH, CTSF, and CSF2RB), tumor necrosis factor superfamily members (TNFSF10, TNFSF13B, TNFRSF1B, TNFRSF5, and LTBR), signal transduction genes (BTK, FYN, and SYK), transcription factors (JUND, JUNB, and FOS), and some proapoptotic genes (BAK1 and BAX). We named cluster D inflammation-2 cluster.

Finally, we defined a small cluster of genes, cluster E, containing mainly immunoglobulin genes (IGHG1, IGM, and IGLC2), that could reflect a varying percentage of B cells in each tumor. It was defined as B-cell cluster. Given that increased numbers of B cells is characteristic of AILT, we analyzed whether this B-cell cluster was more associated with this type of lymphoma. Although most of the AILT samples (eight of 12) showed high expression of this cluster, this was also found in approximately half of the PTCLu samples (see Appendix Fig A1, online only). This B-cell cluster was similar to the AILT cluster defined by Ballester et al,¹² which also contains immunoglobulin and other B-cell related genes such as XBP1 transcription factor. Although this AILT cluster was clearly associated with AILT, some AILT samples were classified with a subgroup of PTCLu (U2), whereas some PTCLu appeared in the AILT cluster. Similarly, we were not able to specifically separate PTCLu and AILT just with this B-cell gene signature.

Affymetrix Microarray Gene Clusters
Gene expression profiles of 20 PTCLs were also obtained using Affymetrix arrays. Molecular heterogeneity among tumor samples also was evident with this platform, and clusters of genes differentially expressed and with correlation coefficients more than 0.70 were defined. As with the cDNA microarrays, we were able to define a proliferation signature that was highly similar to the proliferation signature found with the OncoChip arrays (Fig 2). Approximately half of the genes included in the proliferation-core signature were also found in the proliferation signature in the Affymetrix arrays (Fig 2B), and both included some of the most characteristic regulators of cell division, CCNA, CCNB, TOP2A, CDC2, STK6, and CHEK1. Affymetrix arrays also revealed a large cluster probably related to an inflammatory response and the presence of infiltrating cells, similar to clusters C and D defined with the cDNA microarrays. This cluster pointed to the presence of cytotoxic molecules, cytokines, and was also related to NF-B pathway. Finally, a clear B-cell cluster was also identified. It included immunoglobulin genes, as well as other B-cell markers and related transcription factors such as CD19 or PAX5 (data not shown).

Correlation Among Gene Clusters
We next analyzed the correlation between the different clusters defined with the cDNA arrays (Fig 3). We observed a significant inverse correlation between the proliferation-core signature and clusters related to inflammatory response, inflammation-1 (r² = –0,577; P < .0001) and inflammation-2 (r² = –0,702; P < .0001). Tumors expressing higher levels of the proliferation genes displayed a reduced expression of inflammation clusters and T-cell markers, suggesting that they likely represent tumors with a low proportion of infiltrating reactive cells. None of the other clusters showed significant correlation to the proliferation signature (Fig 3).

View larger version (24K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. Correlation among clusters. The averaged expression of each cluster in the samples was used. Expression of Ki-67 and CD68 is represented at the bottom. Tumor samples were sorted according to the proliferation-core signature. Correlation coefficients and significance between the proliferation signature and the other clusters, Ki-67 and CD68, are shown at the right. Statistically significant results appear in bold font. PTCL, peripheral T-cell lymphoma.

Association With Survival
To evaluate whether any of the clusters were associated with patient survival, the average expression of each was calculated and used in Cox regression analysis. Higher expression of the proliferation-core signature was associated significantly with poorer survival (P = .027; Table 1). Mean overall survival of PTCL in the lower and higher proliferative PTCL groups was 31 and 15 months, respectively. None of the other clusters were found associated with outcome.

Although correlation between overexpression of the proliferation-core signature and the immunohistochemically measured Ki-67 expression was found, Ki-67 expression by itself was not associated with outcome (P = .2). In the study by Went et al,³⁰ a poor outcome was related to a high number of Ki-67–positive cells (> 80%). In our study, only six samples were estimated to have more than 80% Ki-67–positive cells, and they showed a trend but not significant association (P = .21) to poor survival.

Immunohistochemical Analysis
If PTCLs are divided into two groups by the median expression of the proliferation-core signature, a statistically significant increase in the expression of CCNA (P = .019), TOP2A (P = .014), and Ki-67 (P = .043) was found in the most proliferative PTCLs. As observed in the microarray analysis, high correlation between CCNA and TOP2A immunohistochemical expression was found (r = 0.754; P = .001). TOP2A expression also showed significant correlation with Ki-67 expression (r = 0.718; P = .009).

Given that macrophage infiltration is a significant event in inflammatory responses, we evaluated CD68 in tumors to correlate it with the inflammation clusters. A significant correlation was found between CD68 and expression of both the inflammation-1 and inflammation-2 clusters (r = 0.51; P = .002 and r = 0.42; P = .014, respectively). Moreover, CD68 expression was inversely correlated with the proliferation-core signature (Fig 3). The percentages of CD68 varied largely in the samples, with a median expression of 30%. Histologic review of these lymphomas indicated that the group of high-proliferation PTCLs demonstrated less cellular polymorphism without a prominent inflammatory component (nontumoral cells; Fig 4). In fact, the percentage of CD68 differed significantly in the two groups of samples with low or high proliferation (Table 2). Thus, the median expression of CD68 was 50% (range, 15% to 80%) in PTCLs with low proliferation, compared with 17% (range, 5% to 60%) in those with high proliferation.

View larger version (100K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. Immunohistochemical analysis of the expression of different markers. Expression of CD3, CD68, CD20, TOP2A, and Ki-67 are shown in representative cases of both low expression and high expression of the proliferation signature divided by the median expression of the proliferation signature.

	DISCUSSION

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We did not observe any significant association between gene clusters and the histologic subtypes PTCLu and AILT. Most of the AILT samples showed expression of the B-cell cluster, but it does not appear to be exclusive to this subgroup, given that some PTCLu samples also showed high expression of this cluster. A supervised analysis comparing these two entities would provide a better molecular definition of these groups.

Several studies have associated a high IPI index with poor survival^35-37; however, the relationship between genotype and phenotype, prognostic factors, and treatment for PTCLs requires better definition if improvements in therapy are to be achieved. We found that the proliferation signature is significantly associated with survival in PTCL. The usefulness of proliferation markers as prognostic tools has been reported in immunohistochemical studies of different neoplasms^38-41 and recently in PTCL.³⁰ Another proliferation signature was identified by expression profiling and described as the best predictor of survival in mantle cell lymphoma.⁴² The mantle cell lymphoma and PTCL proliferation signatures are similar but show also some differences. Therefore, there could be genes associated specifically with the proliferation signature of PTCL that should be investigated to identify new oncogenic events in these tumors.

Ki-67 is the most commonly used marker to study proliferation,^21,43 but the relationship between Ki-67 and the outcome in PTCL is still unclear. We observed a significant correlation between high expression of Ki-67 and the proliferation signature, but not between Ki-67 and survival, although the trend was toward poorer survival. This is consistent with Went et al,³⁰ who reported association between a high percentage of Ki-67–positive cells (> 80%) and poorer outcome in PTCL. In our study, the lack of association between Ki-67 and survival may be due to the low number of samples with Ki-67 more than 80%. It is also possible that other genes within the proliferation signature could contribute in a more significant way to survival in PTCL.

The proliferation signature was associated with high TOP2A expression by immunohistochemistry. Given the clinical significance of the proliferation signature and high expression of TOP2A in our study, it is likely that inhibitors of TOP2A such as doxorubicin could play an important therapeutic role. Therefore, it would be worthwhile to explore dose-intense regimens with shortened periods of administration in the treatment of highly proliferative tumors.⁴⁴

In summary, our results indicate the importance of the proliferation signature in nodal PTCL. A higher proliferation signature was inversely correlated with genes associated with an inflammatory response and genes regulating the T-cell–specific program.

	AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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The author(s) indicated no potential conflicts of interest.

	AUTHOR CONTRIBUTIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Conception and design: Javier Benitez, Beatriz Martinez-Delgado

Financial support: Javier Benitez

Provision of study materials or patients: Sandeep S. Dave, Javier Alves, Louis M. Staudt

Collection and assembly of data: Marta Cuadros, Sandeep S. Dave, Elaine S. Jaffe, Emiliano Honrado, Roger Milne, Javier Alves, Magdalena Zajac, Louis M. Staudt

Data analysis and interpretation: Marta Cuadros, Sandeep S. Dave, Elaine S. Jaffe, Emiliano Honrado, Roger Milne, Javier Alves, Jose Rodriguez, Magdalena Zajac, Javier Benitez, Louis M. Staudt, Beatriz Martinez-Delgado

Manuscript writing: Marta Cuadros, Elaine S. Jaffe, Jose Rodriguez, Beatriz Martinez-Delgado

Final approval of manuscript: Javier Benitez, Louis M. Staudt, Beatriz Martinez-Delgado

	Appendix

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View larger version (32K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A1. Cluster E: B-cell cluster.

	ACKNOWLEDGMENTS

 
We thank all of the people involved in the Spanish Cooperative Group for the Study of T-Cell Lymphomas for their collaboration, Victoria Fernandez and the CNIO Histology and Immunohistochemistry and Bioinformatics Units for their excellent technical assistance, and the CNIO Tumor Bank for help in providing tumor samples.

	NOTES

 
published online ahead of print at www.jco.org on June 18, 2007.

Supported by the Comunidad Autonoma de Madrid (Grant No. CAM GR/SAL/0203/2004) and Fondo de Investigación Sanitaria. (Grant No. FIS G03/179). M.C. is a fellow of the Fondo de Investigación Sanitaria.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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Submitted October 17, 2006; accepted May 3, 2007.

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• Gene Expression Profiling Does Not Identify Molecular Subgroup Among Nodal Peripheral T-Cell Lymphomas
  Vincenzo Pitini, Carmela Arrigo, and Giuseppe Altavilla
  JCO 2007 25: 4851 [Full Text]

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