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From Centro Nacional de Investigaciones Oncológicas (CNIO), Madrid; Universidad de Granada,* Granada; Universidad de Jaén, Jaén; Universidad de Santiago de Compostela, Santiago de Compostela; Hospital Verge de la Cinta, ¶ Tortosa; Hospital 12 de Octubre, || Madrid; Hospital Virgen de la Salud,** Toledo; and Centro Nacional de Epidemiologia, Instituto de Salud Carlos III, Madrid, Spain
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Abstract |
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Outcome with DLBCL, as in other types of cancer, is the result of interactions between the genetic abnormalities in the tumor and the clinical status of the patients. Information concerning the molecular abnormalities present in DLBCL, derived from genome-wide expression analysis, allows us to identify multiple markers that suggest the existence of a vast number of underlying genetic events in all of the major cell pathways involved in control of proliferation, apoptosis, signal transduction, DNA repair, and other processes.2-4 Nevertheless, until recently, outcome-predictor systems have been based on single genetic abnormalities, or the integration of clinical data into predictive models, such as the International Prognostic Index (IPI).5
Tissue microarrays (TMAs) are a powerful and reproducible technique for demonstrating the biological variability inherent in cancer and, when applied to lymphoma samples, are capable of identifying multiple alterations in the regulation of critical genes and pathways.6,7
In the present study we have investigated the expression of a large number (52) of markers in a DLBCL series using TMAs. The results yield information concerning the variety of molecular markers that predict clinical response. These can be integrated into a single predictive model that identifies the probability of failure with 78% accuracy. This biological score can be used to complement the information obtained by the use of the IPI, allowing patients to be stratified into different risk categories.
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Materials and Methods |
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235 cases of DLBCL were collected. These were diagnosed between 1990 and 1999, the stages being evaluated according to standard protocols. Patients were treated with regimes including polychemotherapy (mainly adriamycin-based) with or without adjuvant radiotherapy and/or surgery. Diagnostic paraffin blocks were selected on the basis of the availability of suitable formalin-fixed paraffin-embedded tissue, containing enough remaining tissue as for a minimum of 60 sections. Histological confirmation of DLBCL was achieved in all cases by central review using standard tissue sections. Histological criteria used for diagnoses and classification of cases were those described in the World Health Organization classification.8 Paraffin-embedded blocks from reactive lymphoid tissue, cell lines and different B- and T-cell lymphoma samples, used for control purposes, were obtained from the tissue archives of the CNIO Tumor Bank.
Tissue Microarray Design
We used a Tissue Arrayer device (Beecher Instruments, Sun Prairie, WI) to construct three different TMA blocks, containing 502 cylinders in total, according to conventional protocols.7 All cases were histologically reviewed and the most tumor-rich areas were marked in the paraffin blocks. Two selected 0.6-mm-diameter cylinders from two different areas were included in each case, along with 16 separate controls to ensure the quality, reproducibility and homogenous staining of the slides. Selected controls include reactive lymph nodes and tonsils, and paraffin-embedded cell lines.
Immunohistochemical staining was performed and evaluated for the 50 different antibodies using standard procedures.7 The selected markers correspond to sets of key proteins involved in cell cycle, apoptosis (extrinsic and intrinsic pathways), and B-cell differentiation, additionally including a large majority of the markers previously identified as survival predictors in DLBCL.
Staining of TMA sections was evaluated by three different pathologists (A.S., J.F.G., F.C.), using uniform criteria. To guarantee the reproducibility of this method, we decided to employ straightforward and clear-cut criteria. After initial analysis, the pattern of staining for each Ab was recorded as positive versus negative, or high versus low level of expression, taking into account the expression in reactive and tumoral cells and specific cut-offs for each marker. Specific details of the threshold used in each case are given in Table 1
. As a general criterion, these thresholds were preferentially selected on the basis of their reproducibility and, when possible, their ability to correlate with previous findings using these markers and/or specific biological events.
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B expression can generally be found in normal lymphoid cells and lymphomas, we have considered as positive cases only those showing distinct nuclear expression in the tumoral cells, thereby indicating the activated form of these proteins.9 Discrepancies between the two cylinders included for each case were resolved through a reviewed joint analysis of both cores. The same procedure was applied to discrepancies among pathologists.
The reactivity of most of the antibodies used here has been validated in previous studies.7
In situ detection of apoptosis and EBER in situ hybridization (ISH) were performed using standard procedures,7 using the appropriate controls. Apoptosis was detected using the ApopTag Peroxidase In Situ Apoptosis Detection Kit (Intergen Co., Oxford, UK). Epstein-Barr virus (EBV) was detected by ISH with fluorescein-conjugated Epstein-Barr Virus (EBER) PNA probe (DAKO, Glostrup, Denmark). EBV-positive cases were considered to be those showing EBER nuclear expression in a majority of the tumoral cells.
Validation of the Technique
The reproducibility of the results obtained was confirmed by comparing them with those from whole sections from 42 randomly selected cases that had been stained using the same procedures for a selection of markers including CD20, bcl-2, and bcl-6.
Statistical Study
The Pearson 
2
statistic and the Spearman correlation coefficient were used as appropriate to analyze relationships between the 52 markers studied.
Survival analyses were performed on all patients for whom follow-up information was available for a minimum of 24 months (approximately 70% of the overall series) and who had complete expression analysis data. HIV-positive patients9 were excluded from the outcome analysis. The final number of patients included in the survival analysis was 152, all of them treated with curative intention.
Failure was defined as the absence of complete remission, progression, or death attributable to the tumor. The series was divided into a training group of 103 cases for the purpose of building the predictor, and a second, smaller group of 49 cases, to validate the model.
Overall Survival (OS) and Failure-Free Survival (FFS) curves were plotted using the Kaplan-Meier method. Statistical significance of associations between individual variables and OS or FFS was determined using the log-rank test.
Cox’s univariate proportional hazard analysis was also performed independently for each variable. Results were validated by multiple testing and the random permutation test.
For multivariate analysis, the series was divided into a training group of 103 cases for the purpose of building the predictor, and a second, smaller group of 49 cases, to validate the model.
A logistic regression model was used to predict failure. Only variables identified in the univariate analysis associated with FFS with values of P < 0.2 and in which at least 5 cases were considered positive or negative were included. Highly variable components in the model were excluded, since they could have introduced uncertainty in predictions. For comparative purposes, multivariate models using step-up (forward) variable selection and other heuristic procedures were also fitted. The final model estimates values of the odds ratio (OR), 95% confidence interval (CI) and P for each variable. General applicability of the model was tested by leave-one-out cross-validation. The stability of the model was evaluated by influence statistics (DfBeta). Different predictor models were found, when using the leave-one-out cross-validation, but these showed only small variations in the weight of each marker, or selection of markers. Accuracy was also tested by the Receiver Operating Characteristic (ROC) curve, which allows the discriminating ability of the model to be estimated.
To demonstrate the predictive capacity of the model, patients were ranked according to this score and then divided into four equal groups, or quartiles. To validate the model overall, the specific weight or coefficient assigned to each gene, as determined in the preliminary group, was applied to calculate the outcome-predictor score in the validation group. Once the model had been validated, a final logistic regression model was fitted to the entire data, allowing adjustment of the coefficients. Statistical analyses were performed using the SPSS program and the tools at http://bioinfo.cnio.es/ for random permutation tests.
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Results |
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Results of the overall DLBCL series are summarized in Table 2
. Figure 1
shows the expression of the markers found to predict failure after the multivariate analysis.
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The Pearson test revealed a large number of significant associations between the different markers analyzed. Full details of the correlation between markers are given in Supplementary Appendix 2 at http://bioinfo.cnio.es/data/DLBCL_TMA.
The most striking findings were as follows:
Correlation between Protein-RNA Expression and Outcome in DLBCL
To detect any possible selection bias, the 152 included patients (Table 3)
were compared with those who had been excluded due to insufficient follow-up. Comparison of age, gender, clinical stage and IPI revealed no significant differences.
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. Ten variables (cyclin E, CDK1, SKP2, bcl-6, p21, Oct-2, BOB1, EMA, Bax, bcl-2) were significantly correlated with FFS (P < 0.05) and nine showed a non-significant trend (P < 0.2). All of the significantly FFS-correlated variables, except Rb-P, were also associated with OS probability (P < 0.05) (data not shown). Furthermore, EBER, which showed a non-significant trend in the FFS analysis, was found to be associated with OS (P < 0.05) (data not shown). The result of the Cox proportional hazard analysis was then validated using multiple testing and random permutation tests (n = 1000).
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Logistic regression analysis was used to find a DLBCL outcome predictor, making it possible to recognize which patients could be cured by the application of chemotherapeutic regimes. The group of 103 cases was used to build the predictor. Only variables identified in the univariate analysis associated with FFS with values of P < 0.2, and in which at least five cases were considered positive or negative, were included (19 variables, excluding EMA, Oct-2, BOB1). The final logistic regression model included the following markers: cyclin E, CDK1, SKP2, EBER, MUM1, CDK2, bcl-6, and Rb-P (Figure 1)
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The predictor is a biological score, the probability of "failure" for one patient, which is calculated as
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The percentage of correct classification for this model, using the training series, was 78.64% (81.13% for predicting FFS and 76% for patients with treatment failure).
In a second step, patients were ranked according to their protein-expression-based score (0 to 1) and divided into four different quartiles, according to their specific risk. Stratifying patients according to these quartiles, 92.3% of patients beneath the 25 percentile were accurately predicted as "failure-free" by the score, and 96.2% of the patients above the 75 percentile were correctly predicted as belonging to the group of "fatal or refractory disease". Between the 25 and 75 percentiles the accuracy of prediction fell below 90% for both categories (64% in the second quartile and 53.8% in the third quartile). Thus, when assigning each patient a specific risk, the capacity for predicting the upper and lower quartile is much higher than for patients with intermediate quartiles.
Validating the Biological Score for Failure in DLBCL
A Kaplan-Meier survival analysis, classifying patients according to the quartile of assigned probability, confirmed that the patients predicted to be cured had significantly improved long-term survival compared with those predicted to have fatal/refractory disease (5-year OS: 91.97% below the 25 percentile, vs. 25.45% above the 75 percentile; P < 0.0001) (Figure 2A)
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. Different predictor models were found, when using the leave-one-out cross-validation, showing only small variations in the weight of each marker, or selection of markers. Although the majority of the patients of this series received anthracycline-based chemotherapy, 12 of 103 (11.6%) patients were treated with different drugs. To examine whether the biological model was independent of the treatment regimes used, treatment was included as a new variable. The specific weight of each variable in the model remained similar (3.064 x cyclin E + 2.499 x CDK1 + 2.364 x SKP2 + 2.264 x EBER + 1.391 x MUM1 + 1.088 x CDK2 + 0.898 x bcl-6 + 0.828 x Rb-P). Moreover, the correct classification percentage in this new model with the variable "treatment" decreased imperceptibly (77.2% for the overall prediction). Correct prediction percentage in the different quartiles was 92% (quartile 1 for failure-free) vs. 96.2% (quartile 4 for failure). These percentages are very similar to those obtained previously.
Integration of Protein-Expression-Based Score and IPI
This biological score yielded a 13.616-fold odds ratio (OR) [95% CI (5.288, 35.063), P < 0.0001] for failure of treatment (percentile 50). IPI (low risk versus high risk), the standard clinical score for predicting the outcome in DLBCL,5 in this series yielded a 10.151-fold OR [95% CI (3.159, 32.616), P < 0.0001] for failure. A multivariate analysis including both the IPI and the protein-expression-based score showed that the significance of the biological score for failure [percentile 50; OR = 18.983; 95% CI (5.988, 60.180); P < 0.0001] seemed to be superior to and independent of the IPI [OR = 15.359; 95% CI (3.672, 64.244); P < 0.0001].
To determine whether the information contained in the protein and RNA-expression-based model was the same as or additional to the variables included in the IPI, patients were classified into low-risk (IPI: 0–2) and high-risk groups (IPI: 3–5), and then the protein-expression-based score quartiles were used in both groups. Low-risk IPI patients were accurately stratified by the protein-expression-based score into groups with a failure probability of 95.24% (quartile 4), 81.89% (quartiles 3 and 2) and 31.59% (quartile 1), P < 0.00001. High-risk IPI patients were also discriminated into two main groups using the protein-expression-based score, although the difference was not significant. These results suggest that an integrated use of the IPI and the protein-expression-based score could improve the predictive capacity of the model (Figure 2, C and D)
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The joint predictive capacity of the protein-expression-based score and IPI was analyzed in a multivariate model. The specific weight of each component of the biological score in this new model remained quite similar (Table 4)
, confirming that the biological and clinical scores contain at least partially independent information. The predictive capacity of the model incorporating the IPI and the variables integrated in this biological score was slightly higher than that based purely on the protein and RNA- expression-based model, with 83% overall correct classification of failure (92% for quartile 1 and 96% for quartile 4).
This was correlated with a better discrimination of patients with different outcomes. Thus, patients allocated above the 50 percentile of the integrated score had 91.73% 5-year OS versus 29.71% for patients predicted for "failure" (Figure 2E)
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Blind Test for Validation of the Predictor
The leave-one-out cross-validation confirmed the high predictive capacity of this integrated model, with a probability of failure in each respective quartile of 12%, 24%, 68%, and 88%, reflected in the overall survival probability (Figure 2F)
. The discriminating ability of this model was better than that of the protein and RNA-expressed-based model [ROC curve area: 0.901; P < 0.0001, 95% CI (0.840, 0.961)].
As this evaluation was based on the same training set of patients from which the predictive model was derived, we decided to estimate the accuracy of the classifier with an additional cohort of 49 patients who had not previously been included. In this independent series, the failure prediction and the outcome were evaluated by the model integrating the 8 markers and IPI, using the threshold from the training set of patients. The immunostaining and evaluation of these tumors were performed independently of the previous cases. The predictive capacities of the validation and preliminary group were comparable with respect to the assigned score for each patient by the model (76.9% and 83.3% of correct classification into quartiles 1 and 4, P < 0.001). Furthermore, values for 5-year OS were closely related with the assigned failure probability for each patient (5-year OS: 100%, 81.48%, 75%, and 25% for each quartile of the score; P < 0.0001).
Once the model had been validated, a final model with the 8 biological markers and IPI was fitted to the entire data (training + validation series). Finally, the biological-IPI score allowed assignment of a case-specific probability of failure, as can be observed in Figure 3
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Discussion |
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Some of the observed changes affect the large majority of cases analyzed here, such as the expression of bcl-6. The hypothetical relevance of bcl-6 in DLBCL pathogenesis is underlined by the increasing number of bcl-6 targets that are being described in B cells, and for its capacity to contribute to oncogenesis by rendering cells unresponsive to antiproliferative signals from the p19(ARF)-p53 pathway, as demonstrated by Shvarts et al.12 In this respect, it is noteworthy that in this series bcl-6 expression appears to be associated with down-regulation of p21 and overexpression of MDM2. The potential role of bcl-6 as a promoter of cell-cycle progression beyond the G1/S restriction point is suggested by the existence of an additional significant relationship with increased phosphorylated Rb. Our data also confirm the prognostic significance of bcl-6 expression in DLBCL, as previously pointed out, when taking into account bcl-6 mRNA expression levels.13
According to the results of this study, Skp2 expression, which increased in one-fifth of the cases analyzed, is associated with many changes in apoptosis and cell-cycle regulators. Protein degradation throughout the ubiquitin pathway thus seems to be indicated as a potential contributory factor in the deregulation of proliferation and apoptosis in DLBCL.14,15 In addition to the confirmed role of Skp2 for inducing the degradation of p27 and Cdk2-unbound cyclin E, an accelerated degradation of unknown additional substrates is likely to play a role in oncogenic events mediated by Skp2.15
Cyclin E overexpression is highlighted by the uni- and multivariate analyses as a clinically highly relevant adverse prognostic marker, thus confirming previous observations in specific lymphoma types16,17 and other tumors.18 A possible explanation for these findings is provided by the recent demonstration that overexpression of cyclin E leads to increased chromosome instability and impaired S-phase progression.19
In general, the results of the univariate analysis confirm those previously published concerning single markers, such as the case for bcl-2 or others.20,21 Nevertheless, some of the significant markers in the univariate analysis, can prove not significant in the multivariate analysis.
Results of this study, not based on previous hypotheses of DLBCL subclassification, are difficult to match with the three DLBCL subgroups defined by Rosenwald et al4 : germinal-center B-cell-like, activated B-cell-like, and type 3 diffuse large B-cell lymphoma. Instead, it seems that the tumors accumulate alterations in critical pathways stochastically, leading to the increased proliferation and loss of apoptosis observed here. The existence of a large group of double bcl-6+ MUM1+ cases demonstrates that the mutual exclusion of these markers, as observed in reactive germinal centers, is not preserved in DLBCLs.22 Tumoral cells probably take advantage of the simultaneous expression of both proteins.
The technique used here is based on large-scale analysis of protein expression, detected by immunohistochemistry. The use of tissue microarrays is limited by the relatively small number of markers chosen (52 in this case), although it has the advantage of using protein profiling, which probably reflects more closely the characteristics of the tumoral cells than does RNA detection.
The integration of these markers into a single model allows the assignment of a specific probability of failure to each patient, according to the biological and clinical characteristics of each case. This information could eventually be used for individualized treatments, in which patients are stratified into therapeutic groups. A clinical application of this and other studies should, nevertheless, first fulfill the necessity of demonstrating the reproducibility of immunohistochemistry techniques among different groups, which would be facilitated by the application of automated systems for scoring immunohistochemical expression.
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Acknowledgements |
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Footnotes |
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Supported by grants from the Fondo de Investigaciones Sanitarias (FIS 98/993, 01/0035–01, 02/0201), Ministerio de Sanidad y Consumo; from the Ministerio de Ciencia y Tecnología (SAF2001–0060); and from Xunta de Galicia (XUGA20810B96), Spain. A.I. Sáez was supported by a grant from the Ministerio de Sanidad y Consumo, Spain. F. Camacho was supported by a grant from the Madrid City Council and the CNIO.
Accepted for publication October 24, 2003.
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References |
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B maintains high expression of a characteristic gene network, including CD40, CD86, and a set of antiapoptotic genes in Hodgkin/Reed-Sternberg cells. Blood 2001, 97:2798-2807This article has been cited by other articles:
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  R. Malumbres, J. Chen, R. Tibshirani, N. A. Johnson, L. H. Sehn, Y. Natkunam, J. Briones, R. Advani, J. M. Connors, G. E. Byrne, et al. Paraffin-based 6-gene model predicts outcome in diffuse large B-cell lymphoma patients treated with R-CHOP Blood, June 15, 2008; 111(12): 5509 - 5514. [Abstract] [Full Text] [PDF]
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S. Montes-Moreno, G. Roncador, L. Maestre, N. Martinez, L. Sanchez-Verde, F. I. Camacho, J. Cannata, J. L. Martinez-Torrecuadrada, Y. Shen, W. C. Chan, et al. Gcet1 (centerin), a highly restricted marker for a subset of germinal center-derived lymphomas Blood, January 1, 2008; 111(1): 351 - 358. [Abstract] [Full Text] [PDF]
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G. W. van Imhoff, E.-J. G. Boerma, B. van der Holt, E. Schuuring, L. F. Verdonck, H. C. Kluin-Nelemans, and P. M. Kluin Prognostic Impact of Germinal Center-Associated Proteins and Chromosomal Breakpoints in Poor-Risk Diffuse Large B-Cell Lymphoma J. Clin. Oncol., September 1, 2006; 24(25): 4135 - 4142. [Abstract] [Full Text] [PDF]
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R. Agrelo, F. Setien, J. Espada, M. J. Artiga, M. Rodriguez, A. Perez-Rosado, A. Sanchez-Aguilera, M. F. Fraga, M. A. Piris, and M. Esteller Inactivation of the Lamin A/C Gene by CpG Island Promoter Hypermethylation in Hematologic Malignancies, and Its Association With Poor Survival in Nodal Diffuse Large B-Cell Lymphoma J. Clin. Oncol., June 10, 2005; 23(17): 3940 - 3947. [Abstract] [Full Text] [PDF]
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Y. Natkunam, I. S. Lossos, B. Taidi, S. Zhao, X. Lu, F. Ding, A. S. Hammer, T. Marafioti, G. E. Byrne Jr, S. Levy, et al. Expression of the human germinal center-associated lymphoma (HGAL) protein, a new marker of germinal center B-cell derivation Blood, May 15, 2005; 105(10): 3979 - 3986. [Abstract] [Full Text] [PDF]
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