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Transcript profiling in peripheral T-cell lymphoma, not otherwise specified, and diffuse large B-cell lymphoma identifies distinct tumor profile signatures

Daruka Mahadevan, Catherine Spier, Kimiko Della Croce, Susan Miller, Benjamin George, Chris Riley, Stephen Warner, Thomas M. Grogan and Thomas P. Miller
DOI: 10.1158/1535-7163.MCT-05-0146 Published December 2005

Abstract

To glean biological differences and similarities of peripheral T-cell lymphoma–not otherwise specified [PTCL-NOS] to diffuse large B-cell lymphoma (DLBCL), a transcriptosome analysis was done on five PTCL-NOS and four DLBCL patients and validated by quantitative real-time reverse transcription-PCR on 10 selected genes. Normal peripheral blood T cells, peripheral blood B cells, and lymph node were used as controls. The resultant gene expression profile delineated distinct “tumor profile signatures” for PTCL-NOS and DLBCL. Several highly overexpressed genes in both PTCL-NOS and DLBCL involve the immune network, stroma, angiogenesis, and cell survival cascades that make important contributions to lymphomagenesis. Inflammatory chemokines and their receptors likely play a central role in these complex interrelated pathways: CCL2 and CXCR4 in PTCL-NOS and CCL5 and CCR1 in DLBCL. Highly overexpressed oncogenes unique to PTCL-NOS are SPI1, STK6, α-PDGFR, and SH2D1A, whereas in DLBCL they are PIM1, PIM2, LYN, BCL2A1, and RAB13. Oncogenes common to both lymphomas are MAFB, MET, NF-κB2, LCK, and LYN. Several tumor suppressors are also down-regulated (TPTE, MGC154, PTCH, ST5, and SUI1). This study illustrates the relevance of tumor-stroma immune trafficking and identified potential novel prognostic markers and targets for therapeutic intervention. [Mol Cancer Ther 2005;4(12):1867–79]

Keywords:
  • leukemias and lymphomas
  • non-Hodgkin's lymphoma
  • gene expression profile
  • peripheral T-cell lymphoma
  • diffuse large B-cell lymphoma

Introduction

The WHO classifies non-Hodgkin's lymphoma into B-cell and T-cell subtypes. B-cell non-Hodgkin's lymphomas comprise ∼85% and T-cell non-Hodgkin's lymphomas comprise ∼15%. Of the T-cell non-Hodgkin's lymphomas, peripheral T-cell lymphoma–not otherwise specified [PTCL-NOS] comprises ∼6% to 10% (1). These are rare tumors whose pathologic diagnosis has been plagued due to the absence of immunophenotypic markers of clonality, morphologic heterogeneity, and poor correlation between cytomorphology and prognosis (2). Large prospective studies have shown that PTCL-NOS have a higher relapse rate and worse survival than diffuse large B-cell lymphoma (DLBCL; refs. 3–5). Regardless of the differences in outcome, both types of lymphoma are largely treated the same, as there are no biological insights to differentiate these tumors. The chemoresistance of PTCL-NOS compared with DLBCL has been attributed to increased expression of multidrug resistance proteins and p53 (6, 7).

The cell of origin of PTCL-NOS is generally CD4+ T cells and very rarely CD8+ T cells. Expression of one or more pan-T-cell antigens (CD45RO, CD2, CD3, CD5, and CD7), absence of pan-B-cell antigens, and histopathologic findings combine to make the diagnosis (8). In the West, nodal PTCL-NOS predominates, whereas in the East extranodal PTCL is more common, particularly the nasal type. The Kiel classification subdivides PTCL-NOS into low-grade and high-grade groups. Within each group, distinct patterns of genetic abnormalities have been identified (9, 10). A second approach analyzed patterns of expression of chemokines and their receptors. Based on the expression of CCR4, CXCR3, CD134, and ST2(L), PTCL-NOS has been subdivided into two prognostic groups (11).

Gene expression profiling using cDNA (12–14) and oligonucleotide microarrays (15) have classified DLBCL into three subgroups: germinal center B-like, activated B-like, and type III. The three subgroups had markedly different survival curves after cyclophosphamide-Adriamycin-vincristine-prednisone (CHOP)–based chemotherapy, providing prognostic information in addition to the international prognostic index and independent of clinical features. A set of 9 genes have been identified that are sufficient to predict the three subgroups and overall survival based on immunohistochemistry (16, 17) and quantitative real-time reverse transcription-PCR (RT-PCR; ref. 18). Such an approach applied to PTCL-NOS would also yield a gene expression-based diagnostic classification, prognostic subgroups, and novel targets that may be amenable to therapeutic intervention.

The aims of the present study were to perform transcript profiling of PTCL-NOS and DLBCL to gain biological insights into the similarities and differences that exist between B-cell and T-cell malignancies. Previous studies (12, 14) used non-Hodgkin's lymphoma cell lines as controls; however, this study used normal peripheral blood B cells, T cells, and lymph node as controls. The classification of the gene expression profile into the six hallmarks of cancer (19) yielded distinct “tumor profile signatures” for PTCL-NOS and DLBCL, respectively. This study has analyzed and evaluated the gene expression profile signature of two phenotypically distinct (T cells versus B cells) non-Hodgkin's lymphomas and provides a framework for more extensive studies for diagnostic classification and prognostic subgroup determinations.

Materials and Methods

Patients

A total of nine patients [four DLBCL and five PTCL-NOS] were studied. The tumor tissue were fixed as snap-frozen samples and stored at −80°C. They were also fixed with buffered formalin, embedded in paraffin, and stained with H&E. All slides were reviewed (C.M.S.) and the initial histologic diagnosis was confirmed. These studies were approved by the patients or their legal guardians as well as appropriate written consents before biopsy. This study is institutional review board exempt.

Immunohistochemistry

Immunohistochemistry was done to determine tumor lineage on either snap-frozen or formalin fixed, paraffin-embedded tissue samples using a battery of monoclonal antibodies directed against both B cells (CD19, CD20, CD21, and CD22) and T cells (CD1a, CD2, CD3, CD4, CD5, and CD8). Immunoglobulin light (κ and λ; Becton Dickinson Corp., San Jose, CA) and heavy (IgM, IgG, IgA, and IgD; Becton Dickinson) chain status was also determined. In addition, Ki-67 (DAKO, Carpinteria, CA; for proliferation) and CD30 (anaplastic T-cell lymphoma and activated B cells; DAKO) were used. For frozen tissues, an indirect immunoperoxidase method using 3,3′-diaminobenzidine as the chromogen was used (20), and for paraffin sections, the Ventana ES or a benchmark instrument (Ventana Medical Systems, Tucson, AZ; ref. 21) was used. All patient samples had sufficient testing to assure lineage and clonal status, however. Aberrant loss of pan-T lineage antigens and/or T-cell subsets was used in lieu of receptor gene rearrangements for T-cell lesions when such testing was not available or there was tissue insufficient for study.

RNA Isolation and Oligonucleotide Microarrays

Total RNA was extracted from each frozen PTCL-NOS and DLBCL specimen using the RNeasy Mini kit (Qiagen, Valencia, CA). The amount of total RNA isolated from the cells was quantified using spectrophotometric A260 measurements with yields ≥25 μg/sample. Control RNA was obtained from normal peripheral B cells (AllCells, Berkeley, CA), normal peripheral T cells (AllCells), and a snap-frozen normal lymph node (Department of Pathology, University of Arizona). mRNA (5 μg) was used to generate first-strand cDNA by using a T7-linked oligo(dT) primer. After second-strand synthesis, in vitro transcription (Ambion, Austin, TX) was done with biotinylated UTP and CTP (Enzo Diagnostics, Farmingdale, NY), resulting in 40- to 80-fold linear amplification of RNA. Biotinylated RNA (40 μg) was fragmented to 50- to 150-nucleotide size before overnight hybridization at 45°C to HG-U133A 2.0 Affymetrix (Santa Clara, CA) array comprising ∼18,400 transcripts and 22,000 probe sets. After washing, arrays were stained with streptavidin-phycoerythrin (Molecular Probes, Invitrogen, Carlsbad, CA) and scanned on a Hewlett-Packard (Raleigh, NC) scanner. Intensity for each feature of the array was captured using GeneChip software (Affymetrix), and a single raw expression level for each gene was derived from the 10 to 20 probe pairs representing each gene by using a trimmed mean algorithm. Intensity values were scaled such that overall intensity for each chip of the same type was equivalent. The mean ± SD difference between the scaling factors of all GeneChips was 0.75 ± 0.15. Other additional measures of quality, the percentage of gene present (55.0 ± 2.5), the ratio of actin 3′ to 5′ (1.25 ± 0.15), and the ratio of glyceraldehyde-3-phosphate dehydrogenase 3′ to 5′ (1.02 ± 0.10) indicated a high overall quality of the samples and assays. Well-measured genes were defined genes that had a ratio of signal intensity to background noise of >2 in >80% of the samples hybridized.

Data Analysis and Visualization

For each patient PTCL-NOS or DLBCL compared with the control sample (normal B cells or T cells or normal lymph node), lists of “robust increasers” or “robust decreasers” were generated using the Affymetrix Data Analysis Program (MAS 5.0). Fundamentals guide was used to import these lists into GeneSpring version 5.0 and obtain the intersection of robust increasers or decreasers across all patients PTCL-NOS or DLBCL, respectively. Increasers or decreasers common to normal T cells and lymph node or normal B cells and lymph node were compared with PTCL-NOS or DLBCL patients. Further, increasers or decreasers common to normal T cells and B cells or lymph node to PTCL-NOS and DLBCL were also compared. Gene expression profiles for PTCL-NOS or DLBCL were further classified according to the Hallmarks of Cancer (19) to determine lymphoma-specific tumor profile signatures. These are genes involved in (a) self-sufficiency in growth signals, including oncogenes; (b) insensitivities to growth inhibitory signals, including absence of tumor suppressors; (c) evasion apoptosis; (d) limitless replicative potential; (e) sustained angiogenesis; and (f) invasion and metastasis. Finally, searches were conducted for potential therapeutic targets by keywords (e.g., protein kinases to generate gene lists for each desired target type). Intersection of the gene lists for each target with the commonly up-regulated and down-regulated genes across patient samples were obtained. This was also done on the intersects of the up-regulated and down-regulated gene lists with the GeneSpring “Simplified GO Ontology” as another method of classifying genes. For the four DLBCL patients, the measurement of 9 prognostic genes (LMO2, BCL6, FN1, SCYA3, BCL2, CD10, MUM1, FOXP1, and CYCLIN D2; refs. 22, 23) were applied to ascertain which subclass they belonged to based on a comparison with the normal B cells or lymph node.

Real-time Quantitative RT-PCR

Total RNA (100 ng) was used for reverse transcription reactions (20 mL total volume) carried out using SuperScript III Platinum Reverse Transcriptase (Invitrogen, Carlsbad, CA). Reactions were incubated at 42°C for 50 minutes followed by incubation at 37°C with RNase H for 20 minutes. An Opticon DNA Engine (MJ Research, Reno, NV) was used to perform real-time fluorescence detection PCR. cDNA (1 μL) produced from reverse transcription reactions was added to 12.5 μL Platinum SYBR Green quantitative PCR SuperMix-UDG (Invitrogen), 1 μL gene-specific or β-actin-specific primer pair (see below for primer design), and 10.5 μL distilled H2O (final volume of 25 μL). Amplification (95°C for 15 seconds, 55°C for 30 seconds, and 72°C for 30 seconds) was repeated for 44 cycles. Following the PCR reaction, a melting curve assay was done to determine the purity of the amplified product. Data were provided as a threshold cycle value (CT) for each sample, which indicated the cycle at which a statistically significant increase in fluorescence was first detected. These data were then normalized to β-actin, which served as an unaffected control gene, for each data point and compared with a normal T-cell control to determine relative expression ratios. Each measurement was done in triplicate.

Primer Design

PCR primers were designed using MacVector (Accelrys, San Diego, CA) to produce amplicons with lengths ranging from 80 to 250 bp to optimize the efficiency of quantitative PCR. The sequences are 5′-TGGGCTCTTTTGAAGGATTGG-3′ and 5′-GCAGTCTGGCTATCTGATTGAAGC-3′ for Lumican, 5′-CTCCTGGCAGATTCCACAAAAG-3′ and 5′-CACTTCTTATTGGGGTCAGCACAG-3′ for CCL18, 5′-ACTCCCTCAATCTGTCGTTCGC-3′ and 5′-TTCCCGTCCAGTGTCAGGTTATCC-3′ for CD14, 5′-TGTCCCCCTCAAAAGTCATCC-3′ and 5′-TTGCTCAGTTCATACACCTTCCG-3′ for CD54, 5′-GAATGGTGCTACTTGAAGACTCTGG-3′ and 5′-CACTCTCCCGCTACACTTGTTTTC-3′ for CD163, 5′-CACACACAGGTGGGACACAAATAAG-3′ and 5′-GCAAGTCAATGAGACGGAGTCAC-3′ for CD106, 5′-TGAAGTCCAAGTTCCTCCAGGTC-3′ and 5′-TGTTGCTGTTGTGGCTATTAGGC-3′ for HCK, 5′-TGCGTAAGAGCAAAAAGCGAAG-3′ and 5′-AAATAAGCCCCCCCTCAACCTTGG-3′ for α-PDGFR, 5′-CGCTTTGCTCCTTGTTTTTTCC-3′ and 5′-CGTCTCTTTCTCCTACCCCTTGAC-3′ for ABCA1, and 5′-TGCTACCACAAGAAATACCGCTC-3′ and 5′-ATCCTCCTTCCCAAACACCAGC-3′ for TEM6. β-actin primers from QuantumRNA β-Actin Internal Standards (Ambion) were used to normalize the quantitative PCR data.

Results

Morphology and Immunohistochemistry Analysis

The five PTCL-NOS patients were all large cell lymphomas. All were de novo. Each sample contained >50% tumor cells. Four were of nodal origin, whereas one originated as a soft tissue mass in the knee. All PTCL-NOS patients were negative for CD1 and CD5 and positive for CD2, CD3, and CD7. The Ki-67 ranges from 30% to 50%. Of the four DLBCL patients, three were nodal in origin and one occurred as a soft tissue mass in the right breast. All four did not show any follicular areas; had large cell morphology, uniform expression of CD20, and monoclonal light chain immunoglobulin; and contained >50% tumor cells. Proliferation as judged by Ki-67 for the four patients was 10%, 35%, 60%, and 76%, respectively. No aberrant antigen expression, specifically no CD5 coexpression, was detected. Figure 1A shows a representative patient with PTCL-NOS and Fig. 1B shows a patient with DLBCL with immunohistochemistry analysis, distinguishing the two lymphoma subtypes and providing a measure of tumor involvement.

Figure 1.
Figure 1.

A, a representative patient with PTCL-NOS showing H&E staining for morphology and immunohistochemistry to delineate T-cell phenotype (CD2) is CD8 negative (staining is due to tumor-infiltrating lymphocytes) with tumor involvement (>50%) and aggressiveness (Ki-67). B, a representative patient with DLBCL showing H&E staining for morphology and immunohistochemistry to delineate B-cell phenotype (CD20) is CD8 negative (staining is due to tumor-infiltrating lymphocytes) with tumor involvement (>50%) and aggressiveness (Ki-67).

Gene Expression Profiling of PTCL-NOS and DLBCL

The Affymetrix MAS 5.0 software was used to calculate the average, SD, median, and interquartile range calculations [e.g., the top 25 increases or decreases; PTCL-NOS versus normal T cells or normal lymph node]. The intersample variability correlated well with the quantitative real-time RT-PCR data. For example the five patients with PTCL-NOS for CD14 gene expression signals were 1,034.6, 3,371.4, 4,720.3, 3,451.1, and 3,130.5. The average was 3,141.5, SD was 1,330.46, median was 3,371.4, and interquartile range was 320.6. Based on these data, the average fold change was calculated to be 88-fold compared with normal peripheral blood T-cell RNA. Well-measured genes were defined as genes that had a ratio of signal intensity to background noise of >2 in >80% of the samples hybridized.

Genes that are robustly up-regulated and common to all five patients with PTCL-NOS are compared with normal peripheral blood T cells or normal lymph node (Table 1 ). It seems that the comparison of PTCL-NOS patients to normal peripheral blood T cells shows that most of the genes that are overexpressed are involved in tumor invasion (COL1A1, FN1, CTGF, COL1A2, CCL2, CLU, C1S, LUM, and LYS), antiapoptosis (CD14), proliferation (CCL18, CXCL13, and IGFBP7), and angiogenesis (TEM6). Similarly, when PTCL-NOS are compared with the normal lymph node, most of the genes that are up-regulated and overexpressed are involved in tumor invasion (CHI3L1, CCL5, and CORO1A) and proliferation (SH2D1A, FLI1, AIF1, CRIP1, MAP4K1, and Rac2). Genes known to be causative in tumorigenesis are prominently expressed, indicating that peripheral blood T cells and lymph node controls both provide valuable information regarding the evolution of the tumor from normal T cells and lymph nodes. The robustly down-regulated genes are not included here but will be discussed below with regard to the tumor profile signature.

View this table:
Table 1.

Highly overexpressed genes in PTCL-NOS compared with normal peripheral blood T cells or lymph node

Genes that are robustly up-regulated and common to all four patients with DLBCL are compared with normal peripheral blood B cells or normal lymph node (Table 2 ). The comparison of DLBCL patients to normal peripheral blood B cells shows that the genes that are up-regulated and overexpressed are also involved in tumor invasion (LYS, CCL5, CHI3L1, CHIT1, MMP9, and ADAMDEC1), immune cell trafficking or host cell response to tumor (CCL18, RGS13, CXCL9, and VNN1), and proliferation (PLA2G2D and PLA2G7). Similarly, when DLBCL patients are compared with the normal lymph node, most of the genes that are up-regulated and overexpressed are involved in tumor invasion (CCL5, CORO1A, and CHI3L1), antiapoptosis (BCL2A1), immune trafficking (CCL18, CD79B, CD52, and POU2AF1), and proliferation (CD20, MAP4K1, and Rac2). Tumor tissues express genes that promote and maintain the oncogenic process, and the profiles obtained based on normal peripheral blood B cells and lymph node will help guide the elucidation of the mechanism initiation and progression. The robustly down-regulated genes are not included and will be discussed with regard to the tumor profile signature.

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Table 2.

Highly overexpressed genes in DLBCL compared with normal peripheral blood B cells or lymph node

Table 3 shows the highly up-regulated genes that are common to both PTCL-NOS and DLBCL when compared with normal peripheral blood T cells and B cells or lymph node, respectively. There are only three genes common to PTCL-NOS and DLBCL when compared with normal peripheral blood T cells and B cells and they are CCL18, UBD, and RARRES1. However, there are 11 genes common to PTCL-NOS and DLBCL when compared with the lymph node and they are CCL18, CD52, MAP4K1, CORO1A, PPP1R16B, PSMB8, UBD, LYZ, TCRβ, RAC2, and CCL5. These comparisons provide insights into the importance of differential gene expression profiles that are common to both types of lymphoma. By including a single cell (peripheral blood B cells or T cells) as well as a complex tissue (lymph node), insights into the relative importance of the relationships between tumor-stroma immune trafficking cells can now be gleaned.

View this table:
Table 3.

Overexpressed genes common to PTCL-NOS and DLBCL compared with normal peripheral blood T cells, peripheral blood B cells, and lymph node

Tumor Profile Signatures of PTCL-NOS and DLBCL

We next did an analysis based on the six hallmarks of cancer (19) in which a tumor has the following acquired capabilities: (a) self-sufficiency in growth signals, (b) insensitivity to antigrowth signals, (c) evasion of apoptosis, (d) limitless replicative potential, (e) sustained angiogenesis, and (f) tissue invasion and metastasis. Genes were sorted into each of these categories common to all five PTCL-NOS patients compared with peripheral blood T cells, lymph node, and/or both (Table 4 ). A similar analysis was done for the four DLBCL patients compared with peripheral blood B cells, lymph node, and/or both (Table 5 ). This provides a tumor profile signature for PTCL-NOS and DLBCL. PTCL-NOS identified genes associated with proliferation (IGFBP7, SH2D1A, RAC2, and TCIRG1), tumor suppressors (RARRES3, GAS1, and CDKN1A), invasion (COL1A, FN1, and CCL2), unlimited replication (CTGF, CYR61, and NEK2), angiogenesis (VEGF, PDGFR, CSF1R, and HGF), antiapoptosis (CD14 and NF-κB), and oncogenes (FLI1, MAFB, SPI1, STK6, and MET). DLBCL identified genes associated with proliferation (IGFBP7, CD20, CD72, CRIP1, and RAB13), tumor suppressors (GPNMB, CD63, BTG2, and RARRES3), invasion (LUM, FN1, MMP1, and COL6A3), unlimited replication (CCR1 and CCL5), angiogenesis (VEGF, PDGF, PDGFR, and FGF13), and antiapoptosis (CD14 and BCL2A1). Therefore, it is apparent that normal peripheral blood T cells or B cells and lymph node in combination provides consistent gene expression findings that now can be evaluated in detail and attempts be made to position these in signaling pathways that make the relationships more obvious.

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Table 4.

Hallmarks of cancer for DLBCL: DLBCL vs peripheral blood B cells and lymph node

View this table:
Table 5.

Hallmarks of cancer for PTCL-NOS: PTCL-NOS vs peripheral blood T cells and lymph node

Nine-Gene Predictive Model of DLBCL

Tissue microarray using immunoperoxidase staining for predictive markers to accurately subdivide DLBCL into prognostically relevant subgroups has been done and correlated with cDNA microarray as the gold standard (22). Six proteins were identified: CD10, BCL2, BCL6, MUM1/IRF4, CYCLIN D2, and FOXP1. Similarly, a quantitative real-time RT-PCR analysis identified six genes to have prognostic significance: BCL2, BCL6, FN1, LMO2, CCND2, and SCYA3/CCL3 (23). In this study, we combined the genes that were used in the tissue microarray and RT-PCR to evaluate the four DLBCL and five PTCL-NOS patients. When the four DLBCL patients were compared with the normal peripheral blood B cells, three of nine genes were up-regulated (FN1 140-fold, CCL3 4.9-fold, and BCL6 2.2-fold), and when compared with the lymph node, seven of nine genes were up-regulated (MUM1 28-fold, CCL3 11.4-fold, CCND2 10.4-fold, BCL6 6.7-fold, LMO2 5.3-fold, BCL2 4.6-fold, and FN1 2.3-fold). These data place these four unselected DLBCL patients into the non-germinal center activated B-cell subgroup. Further, the importance of the control RNA is highlighted, implying that the lymph node may be more informative due to its complex nature and that a tumor does follow the hallmarks of cancer.

Discovery of New Therapeutic Targets

The study also analyzed certain key therapeutic targets that may be amenable to targeting either with small-molecule inhibitors and/or monoclonal antibodies. For patients with PTCL-NOS, the following are novel targets: small-molecule inhibitors to protein kinases (STK6, NEK2, c-MET, SYK, and α-PDGFR), monoclonal antibodies to circulating ligands (CCL2, VEGF, HGF, CTGF, PDGF, FGF7, and IGFBP7), and a small-molecule inhibitor to Rac2, a member of the Ras family of small GTP-binding proteins. For patients with DLBCL, the following are novel targets: small-molecule inhibitors to protein kinases (LCK, LYN, PIM1, PIM2, MET, and SYK), small-molecule inhibitors to novel targets (PSMA, BCL2A1, and NF-κB2), monoclonal antibodies to circulating ligands (CCL2, CCL5, and VEGF), and cell surface markers (CD72, ICAM2, and CD52).

Validation with Quantitative Real-time RT-PCR

Quantitative real-time RT-PCR on 10 selected, overexpressed, functionally relevant genes (Lumican, CCL18, CD14, CD54, CD106, CD163, α-PDGFR, HCK, ABCA1, and TEM6) common to all five PTCL-NOS patients validated the gene expression profile data (Fig. 2 ). The expression level for each patient is shown and averaged over three runs of RT-PCR. These correlated well with the intersample variability observed from the gene expression signals obtained from the DNA microarray.

Figure 2.
Figure 2.

Quantitative real-time RT-PCR of 10 selected genes common to five PTCL-NOS patients (1–5). Y axis, level of expression compared with control.

Discussion

Patients with PTCL-NOS have a worse outcome compared with patients with DLBCL (24). To distinguish biological properties of these two phenotypic variants of large cell lymphoma, we undertook a gene expression profile study of PTCL-NOS and DLBCL using an Affymetrix platform. The study identified several key elements in lymphoma biology and has led to hypothesis-generating research that should guide elucidation of the underlying oncogenic events in PTCL-NOS and DLBCL. The complexity of these tumors is represented by 25 to 30 up-regulated cancer genes and several down-regulated tumor suppressors that provide a growth advantage and enhance the ability to invade and metastasize.

PTCL-NOS Tumor Profile Signature

A predictive model for the PTCL-NOS tumor profile is delineated based on the six hallmarks of cancer (Fig. 3 ). Chemokines cause the directed migration of leukocytes along a chemical gradient of ligand. Many cancers, including lymphomas, have a complex chemokine network that influences immune cell infiltration, tumor cell growth, survival, migration, and angiogenesis. These cells themselves express chemokine receptors and can respond to chemokine in an autocrine and/or paracrine mechanism (25). Many different cancers, including PTCL-NOS (26), have different profiles of chemokine and chemokine receptor expression, but CXCR4 is most commonly found.

Figure 3.
Figure 3.

A predictive model delineating the tumor profile signature of PTCL-NOS. Activation of oncogenic transcription factors (MAFB, FLI1, and SPI1) promotes overexpression of chemokines (CCL2 and CCL5) and chemokine receptors (CCR1 and CXCR4). These acting in an autocrine and paracrine manner recruit stromal fibroblasts, inflammatory immune cells, and endothelial cells to form a complex network of tumor tissue. The overexpression of integrins (LFA1 and VLA4) on the tumor cells and their binding partners on stromal cells (ICAM1, ICAM2, and VCAM1) promotes tumor-stroma cross-talk that activates proliferative signaling pathways (JAK/STAT), ECM formation (FN1, COL1A1, COL1A2, CHI3L1, and emilin), and stromal-derived cytokines/growth factors (CTGF and CYR61). Several tumor-derived angiogenesis promoters (VEGF, PDGF-CC, FGF7, PDGFR, and CSF1R) are overexpressed, which helps maintain a complex angiogenic process. The recruitment of inflammatory immune cells that enhance tumor growth and the production of invasive factors (HGF, MET, CTGF, and IGFBP7) bode for an aggressive T-cell lymphoma. Moreover, the overexpression of NF-κB-driven proliferative factors (TCIRG1, CD14, CD52, TNFRSF25, and CCR1) inhibits many forms of apoptosis and contributes to chemoresistance. The overexpression of NEK2 and STK6 contributes to cell cycle dysregulation and resultant aneuploidy. Several markers of proliferation are also overexpressed (SH2D1A, LCK, Rac2, and MAP4K1), which contributes to the overall survival and antiapoptotic pathways.

It has been shown that c-MAF may alter the function of multiple myeloma cells by up-regulating CCR1, a receptor for the chemokine MIP-1. Three MAF target genes overexpressed in multiple myeloma are CYCLIN D2, integrin β7, and CCR1. Increased expression of CYCLIN D2 enhances proliferation, whereas increased integrin β7 promotes adhesion to bone marrow stroma and increases VEGF elaboration (27). In PTCL-NOS, MAFB is 80-fold overexpressed and is important for IL-4 gene expression by Th2 cells. Several of the target genes activated by c-MAF in multiple myeloma are also up-regulated in PTCL-NOS, which include VEGF (84-fold), ITGA4 (88-fold), and CCR1 (39-fold), implying aberrant proliferation, increased adhesion to surrounding stroma, tumor angiogenesis, and immune dysregulation (Fig. 3). Several other chemokines are also overexpressed and are likely driven by MAFB and/or two other highly overexpressed oncogenic transcription factors FLI1 (100-fold) and SPI1 (29-fold). FLI1 promotes cell cycle progression by suppressing the expression of CDK inhibitor p27kip1 (28), whereas SPI1 is normally expressed in all hematopoietic cell lineages, except in T-cell lines (29), but its aberrant expression may be an additional mechanism that maintains the malignant lymphocyte to proliferate.

CCL2 is 100-fold overexpressed and a potent chemoattractant for monocytes, memory T lymphocytes, and natural killer cells. CCL2 is a tumor promoter (30), is able to recruit leukocytes, and provides growth signals for tumor angiogenesis. Several angiogenic receptors and ligands are overexpressed in PTCL-NOS, which includes VEGF (84-fold), α-PDGFR (26-fold), CSF1R (19-fold), β-PDGFR (19-fold), c-MET (7-fold), HGF (24-fold), PDGF-CC (5-fold), and FGF7 (2.5-fold), all seem to contribute to tumor growth. CCL5 is a 120-fold overexpressed chemoattractant that binds to several receptors, including CCR1 (39-fold), CCR3, CCR4, and CCR5. CCL5 up-regulates transcription of MMP9, which can contribute to angiogenesis. Further, tumor-derived CCL5 can inhibit the T-cell response and enhance the in vivo growth of tumors (31). CXCR4 is 79-fold overexpressed and involved in lymphocyte trafficking. When activated, CXCR4 promotes invasion of lymphoma cells into tissues through activated LFA1, which in turn activates the JAK kinases and shown to be essential for lymphoma invasion and metastasis (32).

Tumor-stroma interactions play an integral role in lymphoma cell survival. In PTCL-NOS, IGFBP7 is ∼130-fold overexpressed, enhances IGF activity, and influences cell adhesion and migration (33), a potential mechanism of tumor invasion. CTGF/CCN2 is >100-fold and CYR61/CCN1 is >70-fold overexpressed and belongs to a cysteine-rich secreted protein of the CCN family (34) that binds to integrins and modulates invasive behavior of cancer cells. Several components of the extracellular matrix (ECM) are elevated and include CHI3L1 (200-fold), COL1A1 (177-fold), FN1 (108-fold), COL1A2 (103-fold), and emilin (44-fold), most likely mediated by CTGF/CYR61 (Fig. 3). CYR61 has been shown to enhance tumorigenicity through activated integrin-linked kinase to stimulate β-catenin-TCF/Lef and Akt signaling pathways (34).

Aberrant cellular proliferation due to dysregulated cell cycle control seems to be mediated by the serine/threonine kinase NEK2, which is 58-fold overexpressed (Fig. 3) and localizes to the centrosome. Overexpression of active NEK2 induces splitting of centrosomes, whereas prolonged expression of either active or inactive NEK2 leads to dispersal of centrosomal material and loss of a focused microtubule-nucleating activity (35). NEK2 has also been shown to be overexpressed in transformed follicular lymphoma and de novo DLBCL (36). STK6 (Aurora kinase-A) is 17-fold overexpressed and plays a role in cell cycle regulation during anaphase and/or telophase, in relation to the function of the centrosome/spindle pole region during chromosome segregation. Defect in STK6 is responsible for numerical centrosome aberrations, including aneuploidy, which is related to its role in microtubule formation and/or stabilization (37).

Several markers of proliferation are represented, of which SH2D1A is 120-fold overexpressed and binds to a specific motif in the cytoplasmic domains of several members of the CD2 subfamily of cell surface receptors, where it blocks recruitment of the tyrosine phosphatase SHP-2 and prevents inhibition of tyrosine kinase activated signaling pathways (38). Rac2 is 107-fold overexpressed and regulates a diverse array of cellular events, such as cell growth, cytoskeletal reorganization, and activation of protein kinases. Rac2 is expressed in thymocytes and activated T cells and could play a role in control of growth and death of T cells (39). LCK is 35-fold overexpressed and phosphorylates mitogen-activated protein kinase and L-selectin with subsequent association of Grb2/Sos with L-selectin. This association correlates with an activation of p21ras, mitogen-activated protein kinase, and Rac2 and a transient increase of O2− synthesis. A final common path could involve a L-selectin-associated CD3 ζ-chain-driven T-cell antigen receptor-induced cell proliferation (40). TCIRG1/TIRC7 is 54-fold overexpressed, a novel seven-transmembrane spanning vacuolar proton pump protein that is up-regulated during the early stages of T-cell activation in response to alloantigens. Inhibiting TIRC7 suppresses proliferation of activated T cells (41) and supports the notion that it may play an important role in PTCL proliferation.

In PTCL-NOS, several tumor suppressors are down-regulated. TPTE encodes a PTEN-related tyrosine phosphatase, is 99-fold down-regulated, and plays an important role in suppressing signaling via tyrosine-phosphorylated proteins (42). MGC15419 is 20-fold down-regulated and may be critical for the controlled degradation of cellular regulatory proteins (43). PTCH is 6-fold down-regulated and is the receptor for sonic hedgehog, implicated in the formation of embryonic structures and in tumorigenesis. Mutations of this gene have been associated with nevoid basal cell carcinoma syndrome, squamous cell carcinoma, trichoepitheliomas, and transitional cell carcinoma (44). ST5 is 5-fold down-regulated and contains a COOH-terminal region that shares similarity with the Rab3 family of small GTP-binding proteins. This protein preferentially binds to the SH3 domain of c-Abl kinase and acts as a regulator of mitogen-activated protein kinase 1/extracellular signal-regulated kinase 2 kinase, which may contribute to its ability to reduce the tumorigenic phenotype in cells (45).

Several antiapoptotic proteins are represented in PTCL-NOS. CD14 is 88-fold overexpressed and acts via MyD88, TIRAP, and TRAF6, leading to NF-κB activation, cytokine secretion, and inflammatory response (46). NF-κB2 is 44-fold overexpressed and is a transcription factor containing rel-polyG-ankyrin domains. NF-κB2/p52 gene rearrangements have been observed in patients with lymphoma and have been shown to contribute to tumorigenesis by suppressing apoptosis and increasing proliferation in response to inflammatory cytokines (47–49). A recent expression profiling study showed that the NF-κB signaling pathway is deregulated in PTCL-NOS (50), implicating this inflammatory transcription factor in maintaining cell survival pathways. IL-18 is 39-fold overexpressed and induces IFN-γ production in T cells, and dysregulated IL-18 and/or IL-18R in chronic B-cell lymphoproliferative disorders may contribute to tumor escape from the host immune system by reducing the expression of Fas ligand. Moreover, it has been shown to function as a novel angiogenic mediator by promoting endothelial cell migration (51).

Several proapoptotic genes are down-regulated. c-Jun is a 27-fold down-regulated oncoprotein that blocks the ubiquitination of tumor suppressor p53 and thus increases the stability of p53 in nonstressed cells. Studies of the mouse counterpart of this gene suggest a key role in T-cell differentiation (52). CED-6 is 22-fold down-regulated and is an evolutionarily conserved adaptor protein required for efficient engulfment of apoptotic cells and that promotes programmed cell death. Inactivating mutations in engulfment genes enhance the frequency of cell survival (53, 54). TNFRSF25 is 12-fold down-regulated, is expressed preferentially in tissues enriched in lymphocytes, and plays a role in regulating lymphocyte homeostasis. This receptor has been shown to stimulate NF-κB activity and regulate cell apoptosis. The alternative splicing of this gene in B cells and T cells encounters a programmed change on T-cell activation, which predominantly produces full-length, membrane-bound isoforms and is thought to be involved in controlling lymphocyte proliferation induced by T-cell activation (55). Therefore, a loss of function would lead to uncontrolled T-cell proliferation.

DLBCL Tumor Profile Signature

A predictive model for the DLBCL tumor profile is delineated based on the six hallmarks of cancer (Fig. 4 ). A chemokine network seems to be in operation and is likely modulated by transcription factors MAFB (83-fold overexpressed), FLI1 (86-fold overexpressed), and CRIP1 (67-fold overexpressed), a cysteine-rich intestinal protein 1 that contains a double zinc finger motif. Overexpression of CRIP in mice produces an imbalance in cytokine pattern favoring Th2 cytokines: low IFN-γ and high IL-6 and IL-10, which alter the immune response and likely favor escape from immune attack (56). CCL5 is 147-fold overexpressed and binds its receptor CCR1, which is 82-fold overexpressed. Tumor-derived CCL5 can inhibit the inhibitory T-cell response and enhance in vivo lymphoma growth. Several proangiogenic ligands and receptors are overexpressed, which include VEGF (66-fold), PDGF-AA (15-fold), CSF1R (14-fold), β-PDGFR (11-fold), MET (7-fold), FGF13 (6-fold), and PSMA (4-fold), and are likely to be induced by CCL5-CCR1. Tumor stroma–derived factors, such as IGFBP7, which is 107-fold overexpressed (33) and likely elaborates several components of the ECM, such as CHI3L1 (84-fold), FN1 (60-fold), COL6A3 (49-fold), COL18A1 (47-fold), and ICAM2 (30-fold).

Figure 4.
Figure 4.

A predictive model delineating the tumor profile signature of DLBCL. Activation of oncogenic transcription factors (MAFB, FLI1, and CRIP1) promotes overexpression of chemokines (CCL3, CCL5, and CXCL9) and chemokine receptor (CCR1). The chemokine network acting in an autocrine and paracrine manner recruit stromal fibroblasts, inflammatory immune cells, and endothelial cells to form a complex network of tumor tissue. The overexpression of integrin LFA1 on the tumor cells and their binding partners on stromal cells (ICAM2 and ICAM3) promotes tumor-stroma cross-talk which activates proliferative signaling pathways (JAK/STAT) and ECM formation (Lumican, FN1, COL6A3, COL18A1, and CHI3L1). Several tumor-derived angiogenesis promoters (VEGF, PDGF-AA, FGF13, PDGFR, CSF1R, and PSMA) are overexpressed, which helps maintain a complex angiogenic process. The recruitment of inflammatory immune cells that enhance tumor growth and the production of invasive factors (MET and IGFBP7) bode for an activated B-like DLBCL. Moreover, the overexpression of NF-κB-driven proliferative factors (CD14, CD20, CD52, CD72, and CCR1) inhibits many forms of apoptosis and contributes to chemoresistance. The overexpression of CCND2 and MUM1 contributes to cell cycle dysregulation and uncontrolled proliferation. Several markers of proliferation are also overexpressed (PIM1, PIM2, BCL2A1, RAB13, and MAP4K1) that contribute to the overall survival and antiapoptotic pathways.

Markers of proliferation represented include CD20 (99-fold), a member of the membrane-spanning 4A gene family that functions as a Ca2+ channel resident on B lymphocytes and plays a role in the development and differentiation of B cells into plasma cells. It is the target for rituximab in B-cell non-Hodgkin's lymphoma and shows excellent correlation between gene expression profile and immunohistochemistry (57). CD72 is a 83-fold overexpressed type II membrane protein of the C-type lectin superfamily and is the ligand for CD5. It plays a role in pre-B-cell and B-cell proliferation and differentiation. Among 83 malignant non-Hodgkin's lymphomas evaluated by immunohistochemistry, all 54 B-cell lymphomas, including precursor B-cell lymphomas, were CD72 positive, but staining was not observed in T-cell lymphomas. Therefore, CD72 is a proliferative marker that cooperates with CD20 in DLBCL (58). RAB13, a GTPase, is 29-fold overexpressed and regulates the assembly of functional epithelial tight junctions. Expression of the GTP-bound form of RAB13 inhibits protein kinase A–dependent phosphorylation and tight junction recruitment of the vasodilator-stimulated phosphoprotein, an actin remodeling protein and thus may play a role in invasion and metastasis (59).

In DLBCL, two tumor suppressors are down-regulated. TPTE is a 100-fold down-regulated PTEN-related tyrosine phosphatase that, if active, would switch off tyrosine kinase–driven oncogenic pathways (42). SUI1/MOF2 is 25-fold down-regulated and functions as a modulator of accuracy at both initiation and elongation phases of translation (60). Transfection of human SUI1 into HepG2 hepatocarcinoma cells inhibited cell growth in culture in soft agar and partially inhibited tumor formation in nude mice (61).

Of the antiapoptotic proteins that are overexpressed, CD14 is 141-fold overexpressed and is known to activate NF-κB leading to inflammatory cytokine secretion (46) and an inflammatory response that likely helps avoid immune attack. BCL2A1 is 107-fold overexpressed, forms heterodimers or homodimers, and is involved in a wide variety of cellular activities, such as embryonic development, homeostasis, and tumorigenesis. BCL2A1 is a direct transcription target of NF-κB in response to inflammatory mediators and is up-regulated by different extracellular signals, such as granulocyte macrophage colony-stimulating factor, CD40, and inflammatory cytokines (TNF and IL-1), which provides a cytoprotective function that may be essential for lymphocyte activation and cell survival (62). Mice with BCL2A1 overexpression showed extended survival with altered B lineage development that led to expansion of the pro-B-cell subset at the expense of pre-B cells, suggesting an impairment of the pro-B-cell to pre-B-cell transition (63). Several genes are implicated in the pathogenesis of DLBCL, which seem to have prognostic significance. Patients with germinal center B-like DLBCL have the most favorable cure rate, whereas patients with activated B-like or type III DLBCL have a worse survival and this may be due in part to the ability of the NF-κB pathway to inhibit intrinsic apoptosis and/or chemotherapy-mediated apoptosis (64).

A transcriptosome analysis of PTCL-NOS and DLBCL patients has identified a molecular tumor signature profile that distinguishes B-cell from T-cell non-Hodgkin's lymphoma. In both types of non-Hodgkin's lymphoma, MAFB-driven inflammatory chemokine signaling pathways play critical roles in promoting proliferation, angiogenesis, tumor-stroma interactions, and immune escape albeit with different key players most likely reflecting lineage specificity. Phase I clinical trials are now feasible targeting the PDGFR (Gleevec), MET (Mab to HGF), STK6 (AK inhibitors), and CD52 (CAMPATH-1H) in this setting.

Acknowledgments

We thank Ellen Chase for coordinating this project and Nikhil Shirahatti for technical help with article preparation.

Footnotes

  • Grant support: Southwest Oncology Group grant CA-32102, subcontract 03041.

  • The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

    • Accepted September 27, 2005.
    • Received May 10, 2005.
    • Revision received July 21, 2005.

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Molecular Cancer Therapeutics: 4 (12)
December 2005
Volume 4, Issue 12

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Transcript profiling in peripheral T-cell lymphoma, not otherwise specified, and diffuse large B-cell lymphoma identifies distinct tumor profile signatures
Daruka Mahadevan, Catherine Spier, Kimiko Della Croce, Susan Miller, Benjamin George, Chris Riley, Stephen Warner, Thomas M. Grogan and Thomas P. Miller
Mol Cancer Ther December 1 2005 (4) (12) 1867-1879; DOI: 10.1158/1535-7163.MCT-05-0146
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