Purpose: Chronic lymphocytic leukemia (CLL) is a B-cell malignancy characterized by a variable clinical course. Several parameters have prognostic capabilities but are associated with altered response to therapy in only a small subset of patients.

Experimental Design: We used gene expression profiling methods to generate predictors of therapy response and prognosis. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemoimmunotherapy were created using cancer cell lines and patient leukemia cell samples. We validated and applied these three signatures to independent clinical data from four cohorts, representing a total of 301 CLL patients.

Results: A genomic signature of prognosis created from patient leukemic cell gene expression data coupled with clinical parameters significantly differentiated patients with stable disease from those with progressive disease in the training data set. The progression signature was validated in two independent data sets, showing a capacity to accurately identify patients at risk for progressive disease. In addition, genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were generated and could accurately distinguish responding and nonresponding CLL patients.

Conclusions: Thus, microarray analysis of CLL lymphocytes can be used to refine prognosis and predict response to different therapies. These results have implications for standard and investigational therapeutics in CLL patients. (Clin Cancer Res 2009;15(22):694755)

Translational Relevance

Chronic lymphocytic leukemia is an incurable malignancy that exhibits clinical heterogeneity. Currently available prognostic markers only partially predict the aggressiveness of disease or response to treatment regimens. In this study, we used gene expression microarray techniques and analyses to create and validate a genomic signature with prognostic capabilities that can be combined with a currently used prognostic marker, with improved performance over either alone. We also generated and validated genomic signatures that predict resistance to two chemotherapeutic regimens. These results show that gene expression profiling can be used to refine prognosis and choose optimal therapies for patients, indicating that this approach could be part of the future personalized medical care of chronic lymphocytic leukemia patients.

The practice of oncology continually faces two major challenges: determining which patients are at risk for progression or recurrence of disease and identifying the most effective therapeutic regimen for the individual patient. Obstacles to address these challenges include the complexity of the disease processes, individual differences and comorbidities, and the paucity of markers to guide the use of available treatments. However, examples such as the use of trastuzumab to treat HER-2positive breast cancer show that selecting therapies for patients based on tumor markers can improve overall response rates. Similarly, identifying predictors of sensitivity to cytotoxic agents that are able to select patients who will respond to these chemotherapeutic agents would directly affect current medical practice, wherein patients are often treated with one of several therapeutic regimens that, on a population basis, have equal efficacy.

Chronic lymphocytic leukemia (CLL) displays a wide spectrum of aggressiveness. Even among those patients with low- or intermediate-risk disease at diagnosis, accurate determination of which patients will progress and require therapy is inexact. For those patients requiring therapy, there are a variety of treatment options, varying in long-term efficacy and toxicity. Multiple factors, such as cytogenetic aberrations, immunoglobulin variable region heavy chain (IgVH) mutational status, and CD38 and ZAP-70 expression, are increasingly used to help refine prognosis and guide patient care in the previously untreated CLL patient (1,5). However, at this time, only the interphase cytogenetic abnormality of 17p13 deletion has been consistently associated with poor response to purine analoguebased therapy (6,8).

Recent advances using genomic technology, particularly the use of gene expression profiling, has provided an opportunity to further address these issues. Previous studies have described the development of gene expressionbased profiles that correlate with clinical outcomes or surrogates of outcome (9,21). These studies on gene expression differences include investigations of CLL and normal B cells, CLL with specific cytogenetic anomalies, and mutated versus unmutated IgVH status. Here, we describe the generation of gene expression signatures with improved capacity to predict which low- or intermediate-risk patients are most likely to progress with CLL and, at the same time, can predict response to a variety of treatment approaches.

Patients and leukemia samples

Two CLL patient cohorts were used in this research: one from Duke University and Durham VA Medical Centers (Duke/VA), and one from Mayo Clinic and Ohio State University (Mayo/Ohio State). The Duke/VA cohort was used to create the genomic signature of progressive disease, and the Mayo/Ohio State cohort was used to generate the genomic signature of response to the chemoimmunotherapy regimen of pentostatin, cyclophosphamide, and rituximab. Clinical data describing these cohorts were previously published (22, 23).

Patients with a diagnosis of CLL and Rai stage 0 to 2 at diagnosis were recruited from Duke/VA for participation in institutional review boardapproved protocols to donate blood for further study. Clinical data were determined according to the National Cancer Institute (NCI) Working Group criteria (24). Blood was collected before therapy; CLL cells were purified by negative selection and frozen in pellets at 80C. Using purified cells, we determined IgVH mutational status by sequencing genomic DNA amplified from the immunoglobulin heavy chain. We determined CD38 status by flow cytometry and ZAP-70 expression by immunoblot and flow cytometry. We used fluorescent in situ hybridization to detect cytogenetic abnormalities of del(13q14), del(11q22.3), trisomy 12, and del(17p13.1). The methods used for these assays were previously described (22). From samples containing 10 to 15 million CLL lymphocytes, total RNA was extracted using Qiashredder and RNeasy Mini Kits (Qiagen, Inc.), and quality was assessed by spectrophotometry and by Agilent 2100 Bioanalyzer (Agilent Technologies).

Patients enrolled in a phase II trial of pentostatin, cyclophosphamide, and rituximab at Mayo/Ohio State had pretreatment CLL lymphocytes collected for a correlative study. All study subjects signed written informed consent to participate in this study in accordance with the Declaration of Helsinki and the Mayo Clinic Institutional Review Board. The description of patient characteristics and response to therapy was previously published (23). Blood from patients enrolled in this trial was drawn into heparin tubes, and peripheral blood mononuclear cells were isolated by Ficoll (Gallard-Schlesinger Industries, Inc.) density gradient centrifugation. CLL patient peripheral blood mononuclear cells that did not exceed 85 CD19+ cells were further purified using CD19 magnetic beads (Miltenyi Biotec). Cells to be purified were suspended with CD19 beads in PBS and 0.5 FCS and 2 mmol/L EDTA for 15 min at 4C. Cells were washed, resuspended in the same buffer at a concentration of 5 to 10 million cells/mL, and passed through the AutoMacs Magnetic Cell sorter (Miltenyi Biotec) to collect the CD19+ cells. Total RNA was isolated from cells using the Trizol reagent (Invitrogen). RNA was quantitated by reading the absorbance at 260/280, and the RNA quality was further tested using an Agilent 2100 Bioanalyzer (Agilent Technologies). Techniques used to determine prognostic markers (ZAP-70, IgVH mutation status, and CD38) were described previously (23).

DNA microarray analysis

RNA samples were prepared and analyzed according to the manufacturer's instructions and as described previously (25). All analyses were done in a minimal information about a microarray experiment compliant fashion. Raw data were normalized using the MAS 5.0 algorithm, using R. Genomic data is found in Gene Expression Omnibus (GSE 10137, 10138, and 10139).

To generate genomic signatures, we had supervised analyses using the Bayesian binary regression algorithm in Matlab (Mathworks), as described previously (26, 27). The signature of progressive disease was created with a training set comprised of all the samples from the Duke/VA cohort. The signature of response to chlorambucil was created using cancer cell lines, and the signature of response to the pentostatin, cyclophosphamide, and rituximab regimen was created with half the samples in the Mayo/Ohio State cohort. The number of probes in a signature that conferred the best accuracy in the training set was selected. Leave-one-out cross validation was done to assess the accuracy of the signature and the supervised analysis method.

A genomic signature of chlorambucil sensitivity was created using the NCI drug-screening panel of cancer cell lines and expression data5

from Affymetrix U133A GeneChips from selected cell lines (Supplementary Table S2), with techniques described previously (25). Cancer cell lines that represent the extremes of sensitivity and resistance to chlorambucil were selected based on in vitro 50 cytotoxic dose and total growth inhibition measurements.

A genomic signature of response to the pentostatin, cyclophosphamide, and rituximab regimen was created using a similar process. From the total of 40 available pretreatment CLL lymphocyte samples, microarray data from 10 long-term responders (time to progression greater than the median time to progression among nonprogressors) and 10 early progressors (time to progression less than the median time to progression among progressors) were used as a training set.

We validated the genomic signatures with external data sets by using the same algorithm. Two independent data sets were used to validate the signature of progressive disease: one from the Spanish National Cancer Centre and the second from the NCI (17, 20). Both validation data sets had been created on cDNA microarrays. We applied the genomic signature of response to chlorambucil using microarray data from a subset of the Duke/VA cohort that were from CLL lymphocytes collected before treatment with chlorambucil. The genomic signature of response to the chemoimmunotherapy regimen of pentostatin, cyclophosphamide, and rituximab was tested using microarray data from the Mayo/Ohio State cohort that was independent of the data used in the training set.

We joined the training and validation sets for the signature of progressive disease using concordant gene symbols from the cDNA arrays and Affymetrix probes obtained from NetAffx.6

cDNA probes that had null values were eliminated, and only probes that represented shared genes between the two platforms were used, yielding combined data sets with fewer probes than either data set alone. Further processing included quantile normalization and standardization using ComBat (28). When comparing training and validation data for the signatures of response to therapy from Affymetrix U133A and U133 Plus 2.0 GeneChips, we included only shared probe set IDs.

Statistical analysis

We generated and validated genomic signatures using Matlab (Mathworks). Kaplan-Meier analyses, Wilcoxon rank-sum test, Fisher's exact test, log-rank test, and Cox proportional hazards models were calculated using the statistical environment, R.

We used data from cancer cell lines and CLL patient leukemia samples to develop genomic signatures to guide the management of CLL. In particular, the goal was to create gene expression signatures to refine prognosis and predict therapeutic responses for CLL patients, thereby identifying patients who require therapy and determining the best treatment regimen for those patients.

Patient characteristics

We studied low- and intermediate-risk CLL patients who had no immediate indication for therapy at the time of diagnosis to determine if a microarray-based test could be created to help prognosticate need for subsequent therapy.

As patients often live for years to decades after a diagnosis of CLL, we used freedom from progression as our primary clinical outcome. We defined progressive disease as the need for therapy and stable disease as requiring no therapy. In Duke/VA, the 33 patients with stable disease were followed for 2.4 to 26 years from diagnosis. Samples were collected from patients with stable disease 0 to 20.4 years after diagnosis. The 28 patients with progressive disease were followed for 2.3 to 16.1 years from diagnosis and were treated 0.05 to 11.9 years after diagnosis. Samples collected from patients with progressive disease were collected 0 to 10.6 years after diagnosis. Other characteristics of the patients and CLL cell samples are detailed in Table 1 and Supplementary Table S3. Notably, the IgVH mutation status was statistically different between the two groups.

Table 1.

Characteristics of patients and CLL lymphocytes in the genomic signature of progression training set

Stable disease (n = 33)Progressive disease (n = 28)P                  *
Age at diagnosis (y)   0.06 
Median 63 57.5  
Range 40-81 32-79  
Sex   0.40 
Male 25 18  
Female 10  
Race   0.76 
White 29 24  
Black  
Other  
Stage at diagnosis (Rai)   0.48 
24 17  
 
 
IgVH statustblfn2   0.02 
Mutated 24 11  
Unmutated 15  
NA  
Cytogeneticstblfn2   0.10 
Normal  
13q deletion  
Trisomy 12  
11q deletion  
17p deletion  
Complex  
NA 14  
CD38tblfn2   0.07 
Positive  
Negative 28 17  
NA  
ZAP-70tblfn2   1.0 
Positive 21 19  
Negative  
NA  
Stable disease (n = 33)Progressive disease (n = 28)P                  *
Age at diagnosis (y)   0.06 
Median 63 57.5  
Range 40-81 32-79  
Sex   0.40 
Male 25 18  
Female 10  
Race   0.76 
White 29 24  
Black  
Other  
Stage at diagnosis (Rai)   0.48 
24 17  
 
 
IgVH statustblfn2   0.02 
Mutated 24 11  
Unmutated 15  
NA  
Cytogeneticstblfn2   0.10 
Normal  
13q deletion  
Trisomy 12  
11q deletion  
17p deletion  
Complex  
NA 14  
CD38tblfn2   0.07 
Positive  
Negative 28 17  
NA  
ZAP-70tblfn2   1.0 
Positive 21 19  
Negative  
NA  

Abbreviation: NA, not available.

*P value was determined by the Wilcoxon rank-sum test for continuous variables and Fisher's exact test for categorical variables.

ZAP-70, CD38, cytogenetics, and IgVH determination as described in ref. 22.

Forty patients of the total 64 patients in the Mayo/Ohio State cohort were included in the analysis to create a genomic signature of response to therapy. Patients were included if they gave a pretreatment blood sample, and microarray analysis was successfully done. The clinical and laboratory parameters and the response data for the entire cohort were described previously (23). There was no significant difference between the group from which microarrays were done compared with the remaining patients with regard to progression-free survival, CD38, ZAP-70, or IgVH mutational status (Fisher's exact test; data not shown); however, there were more patients that had microarrays done that achieved complete or nodular partial responses (P = 0.007; Fisher's exact test).

Genomic signature of progression

By applying the supervised analysis method of Bayesian binary regression to the gene expression data from the 33 samples from patients with stable disease and the 28 samples from patients with progressive disease, a gene expression signature comprised of 180 gene probes was generated (Fig. 1A; Supplementary Table S1). Figure 1B shows that leave-one-out cross-validation distinguished stable and progressive disease states. Using a cutoff of 0.5, the sensitivity of this signature was 64, the specificity was 67, the positive predictive value was 62, and the negative predictive value was 69 (Supplementary Table S2). The association of the signature with the phenotype is independent of the prognostic markers IgVH mutational status, CD38 and ZAP-70 expression, and cytogenetic aberrations.

Fig. 1.

Genomic signature of disease progression. A, heat map of the 180-gene signature differentiating CLL samples from patients who did not require therapy (stable disease; n = 33 samples) from those who required therapy (progressive disease; n = 28 samples). Red, upregulated genes; blue, downregulated genes. B, leave-one-out cross-validation score of fitting the 180-gene signature for the 33 samples in the stable group and the 28 samples in the progressive group. The median values for the stable and progressive groups were 0.157 and 0.840 (horizontal lines), respectively. P value was determined by Wilcoxon rank-sum test.

Fig. 1.

Genomic signature of disease progression. A, heat map of the 180-gene signature differentiating CLL samples from patients who did not require therapy (stable disease; n = 33 samples) from those who required therapy (progressive disease; n = 28 samples). Red, upregulated genes; blue, downregulated genes. B, leave-one-out cross-validation score of fitting the 180-gene signature for the 33 samples in the stable group and the 28 samples in the progressive group. The median values for the stable and progressive groups were 0.157 and 0.840 (horizontal lines), respectively. P value was determined by Wilcoxon rank-sum test.

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We then compared the predictive ability of the genomic signature to that of IgVH mutational status, another parameter that is increasingly used clinically. Previous evaluation of the entire Duke/VA cohort showed that the sensitivity and specificity of unmutated IgVH status for predicting need for therapy was 48 and 77, with a positive predictive value of 71 and a negative predictive value of 56 (22). Thus, compared with the currently used parameter, IgVH mutation status, the genomic signature has a higher sensitivity but lower specificity.

Combining prognostic markers

Using Kaplan-Meier analysis, two groups of patients defined by the genomic signature had significantly different times to treatment need (P = 0.0008; log-rank test; Fig. 2A). IgVH mutational status could also define two groups with different times to progression of disease (P = 0.001; log-rank test; Fig. 2B). We then evaluated the utility of combining the genomic signature with other markers associated with clinical outcomes using a multivariate Cox proportional hazards model. The inclusion of prognostic markers other than IgVH mutational status did not contribute significantly to the model. The genomic signature score and IgVH mutational status had no significant interaction in the multivariate model. As seen in Table 2, the multivariate model combining genomic signature score and IgVH mutational status outperformed each individual factor to identify a low-risk population. The hazard ratio of not needing therapy in the group of patients with high genomic signature score and mutated IgVH was 0.157 compared with the hazard ratios of 0.29 for either factor alone. In addition, the combination of a low progressive disease signature score and mutated IgVH status conferred the best prognosis and was greatest at selecting this low-risk group of patients (Fig. 2C).

Fig. 2.

Refining prognosis by combining genomic signature score with IgVH mutation status and validating in independent data sets. A, Kaplan-Meier analysis of freedom from therapy in patients predicted to have stable disease by the genomic signature with a score of <0.5 (n = 39; blue) compared with patients predicted to have progressive disease by the genomic signature with a score of 0.5 (n = 29; red). B, Kaplan-Meier analysis of freedom from therapy in patients with mutated IgVH (n = 35; blue) versus patients with unmutated IgVH (n = 23; red). C, Kaplan-Meier analysis of freedom from therapy in patients subdivided based on a Cox proportional hazards model. A high microarray score (0.5) combined with unmutated IgVH conferred the worst prognosis (n = 14; red), whereas a low microarray score (<0.5) combined with mutated IgVH conferred the best prognosis (n = 22; blue). A low microarray score with unmutated IgVH (n = 9; light blue) or high microarray score with mutated IgVH (n = 13; pink) conferred intermediate prognoses. P values were calculated using the log-rank test. D, Kaplan-Meier analysis of freedom from therapy in patients from the Spanish National Cancer Centre predicted to have stable disease (prediction score, <0.5; n = 87; blue) compared with patients predicted to have progressive disease (prediction score, 0.5; n = 73; red). E, Kaplan-Meier analysis of freedom from therapy in patients from the NCI predicted to have stable disease (prediction score, <0.5; n = 58; blue) compared with patients predicted to have progressive disease (prediction score, 0.5; n = 49; red). F, combination of IgVH mutational status with genomic prediction of progressive disease reveals improved prognostic capabilities in the NCI cohort. A low microarray score (<0.5) combined with mutated IgVH conferred the best prognosis (n = 44; blue). A high microarray score (0.5) combined with unmutated IgVH (n = 14; red), a low microarray score with unmutated IgVH (n = 14; pink), and a high microarray score with mutated IgVH (n = 35; light blue) are also displayed. P values were calculated using the log-rank test.

Fig. 2.

Refining prognosis by combining genomic signature score with IgVH mutation status and validating in independent data sets. A, Kaplan-Meier analysis of freedom from therapy in patients predicted to have stable disease by the genomic signature with a score of <0.5 (n = 39; blue) compared with patients predicted to have progressive disease by the genomic signature with a score of 0.5 (n = 29; red). B, Kaplan-Meier analysis of freedom from therapy in patients with mutated IgVH (n = 35; blue) versus patients with unmutated IgVH (n = 23; red). C, Kaplan-Meier analysis of freedom from therapy in patients subdivided based on a Cox proportional hazards model. A high microarray score (0.5) combined with unmutated IgVH conferred the worst prognosis (n = 14; red), whereas a low microarray score (<0.5) combined with mutated IgVH conferred the best prognosis (n = 22; blue). A low microarray score with unmutated IgVH (n = 9; light blue) or high microarray score with mutated IgVH (n = 13; pink) conferred intermediate prognoses. P values were calculated using the log-rank test. D, Kaplan-Meier analysis of freedom from therapy in patients from the Spanish National Cancer Centre predicted to have stable disease (prediction score, <0.5; n = 87; blue) compared with patients predicted to have progressive disease (prediction score, 0.5; n = 73; red). E, Kaplan-Meier analysis of freedom from therapy in patients from the NCI predicted to have stable disease (prediction score, <0.5; n = 58; blue) compared with patients predicted to have progressive disease (prediction score, 0.5; n = 49; red). F, combination of IgVH mutational status with genomic prediction of progressive disease reveals improved prognostic capabilities in the NCI cohort. A low microarray score (<0.5) combined with mutated IgVH conferred the best prognosis (n = 44; blue). A high microarray score (0.5) combined with unmutated IgVH (n = 14; red), a low microarray score with unmutated IgVH (n = 14; pink), and a high microarray score with mutated IgVH (n = 35; light blue) are also displayed. P values were calculated using the log-rank test.

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Table 2.

Effect of combining prognostic markers to define need for therapy

Duke/VA cohortHazard ratio (95 CI)P                  *
Univariate model with microarray score of 0.5 0.287 (0.131-0.625) 0.0017 
Univariate model with mutated IgVH status 0.291 (0.132-0.638) 0.002 
Multivariate model with microarray score of 0.5 and mutated IgVH status 0.157 (0.054-0.461) 0.0007 
Duke/VA cohortHazard ratio (95 CI)P                  *
Univariate model with microarray score of 0.5 0.287 (0.131-0.625) 0.0017 
Univariate model with mutated IgVH status 0.291 (0.132-0.638) 0.002 
Multivariate model with microarray score of 0.5 and mutated IgVH status 0.157 (0.054-0.461) 0.0007 

Abbreviation: 95 CI, 95 confidence interval.

*P value determined by the Wald test.

Validation in external data sets

Although the leave-one-out cross-validation provides evidence for a model that can distinguish patients with progressive disease from those with stable disease, the utility of such a model requires validation in independent cohorts of patients. To address the utility of the model, we examined the signature and methods in two external data sets, one from the Spanish National Cancer Centre (ref. 17; Array Express E-TABM-80; n = 160) and the other from the NCI7

(ref. 20; n = 107). Clinical characteristics of the CLL patients included in these cohorts were published previously (17, 20).

Because these two validation data sets were generated using cDNA microarrays, we compared cDNA ratios with MAS5 calculated expression values. Because of the reduced number of probes in the merged data sets compared with the training set (3,576 probes with the Spanish data set and 6,279 probes with the NCI data set), it was necessary to alter the parameters in the binary regression algorithm from those used initially (Supplementary Table S2). Thus, this analysis is a validation of the ability of the genomic data from the Duke/VA training group of patients to predict the outcomes of two separate independent cohorts rather than testing the accuracy of a specific set of genes.

Scores reflecting the probability of progression in the two validation sets were calculated by applying the genomic signature created from the training set to gene expression data from the two independent cohorts. We used Kaplan-Meier analyses to show associations between the classifications of stable or progressive disease determined from the genomic signature and time to therapy (Fig. 2D and E). This showed the ability of the Duke/VA genomic data and signature to discriminate between stable and progressive disease in these cohorts in a statistically significant manner. The full predictive accuracy to predict need for therapy in these validation sets is shown in Supplementary Table S2.

A multivariate Cox proportional hazards model was created that combined the genomic signature prediction and the IgVH mutational status for the NCI data set (Fig. 2F). As in the Duke/VA cohort, the combination of the genomic signature and the IgVH mutation status was superior to either alone.

Genomic signatures of resistance to chemotherapy

The selection of chemotherapy for a CLL patient depends in part on the patient's performance status and comorbidities. Chlorambucil has been used, particularly in the elderly, because of its low toxicity profile. However, this agent rarely induces complete remissions and it must be administered for as long as 1 year to control the disease (29). To help identify patients who would respond to chlorambucil, we used a genomic approach (25).

We used gene expression data coupled with drug sensitivity data from the NCI-60 data set to create a 140-probe genomic signature that reflects resistance to chlorambucil in cancer cell lines (Supplementary Table S1). As seen in Fig. 3A, the leave-one-out cross-validation predictions for the training set were statistically different, with an accuracy of 92 to predict chlorambucil resistance, sensitivity of 100, and specificity of 92 (Supplementary Table S2). We next evaluated the applicability of the signature to patients' clinical responses by evaluating a group of 14 Duke/VA patients treated with chlorambucil. These patients' characteristics are described in Table 3. The genomic signature could define two groups of patients: those who had a meaningful response to chlorambucil and those who did not (Fig. 3A).

Fig. 3.

Genomic signatures of chemotherapy sensitivity applied to clinical data. A, top, heat map of the 140-gene signature of resistance to chlorambucil, created from gene expression profiling of cell lines sensitive and resistant to this agent. Red, upregulated genes; blue, downregulated genes. Middle, the leave-one-out cross-validation values for the training data set. Horizontal lines, median predicted values for the sensitive and resistant groups (0.025 and 0.969, respectively). Bottom, when applied to genomic data of CLL cells from patients subsequently treated with chlorambucil, this signature can discriminate patients based on clinical response to therapy. Blue, a prediction of more durable response (prediction score, <0.5; n = 8); red, a prediction of less durable response (prediction score, 0.5; n = 6). P value was determined by log-rank test. B, top, the heat map of the 60-gene signature of resistance to pentostatin, cyclophosphamide, and rituximab, created from genomic data from patients who progressed early or were long-term responders. Red, upregulated genes; blue, downregulated genes. Middle, the leave-one-out cross-validation values for the training data set. Horizontal lines, median predicted values for the long-term responder and early-progression groups (0.329 and 0.822, respectively). Bottom, when applied to the genomic data from an additional 20 patients treated with this regimen and using a cutoff of 0.5, this signature can separate patients based on response. Blue, a prediction of long-term response (n = 15); red, a prediction of early progression (n = 5). P value was determined by log-rank test.

Fig. 3.

Genomic signatures of chemotherapy sensitivity applied to clinical data. A, top, heat map of the 140-gene signature of resistance to chlorambucil, created from gene expression profiling of cell lines sensitive and resistant to this agent. Red, upregulated genes; blue, downregulated genes. Middle, the leave-one-out cross-validation values for the training data set. Horizontal lines, median predicted values for the sensitive and resistant groups (0.025 and 0.969, respectively). Bottom, when applied to genomic data of CLL cells from patients subsequently treated with chlorambucil, this signature can discriminate patients based on clinical response to therapy. Blue, a prediction of more durable response (prediction score, <0.5; n = 8); red, a prediction of less durable response (prediction score, 0.5; n = 6). P value was determined by log-rank test. B, top, the heat map of the 60-gene signature of resistance to pentostatin, cyclophosphamide, and rituximab, created from genomic data from patients who progressed early or were long-term responders. Red, upregulated genes; blue, downregulated genes. Middle, the leave-one-out cross-validation values for the training data set. Horizontal lines, median predicted values for the long-term responder and early-progression groups (0.329 and 0.822, respectively). Bottom, when applied to the genomic data from an additional 20 patients treated with this regimen and using a cutoff of 0.5, this signature can separate patients based on response. Blue, a prediction of long-term response (n = 15); red, a prediction of early progression (n = 5). P value was determined by log-rank test.

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Table 3.

Characteristics of patients and CLL lymphocytes in the genomic signatures of treatment response

Chlorambucil test set (n = 14)PCR training set (n = 20)PCR test set (n = 20)
Sex 
Male 10 15 16 
Female 
Race 
White 12 19 20 
Other 
NA 
Median age at treatment (range; y) 64 (42-82) 61 (40-78) 65 (49-76) 
Stage at treatment (Rai) 
11 
Median progression-free survival (range), d 135 (51-969) 822 (45-1803) 923 (243-1546) 
IgVH status* 
Mutated 
Unmutated 12 13 
Cytogenetics* 
Normal 
13q deletion 
Trisomy 12 
11q deletion 
17p deletion 
Complex or other 
NA 
CD38* 
Positive 
Negative 12 13 13 
ZAP-70* 
Positive 10 
Negative 10 11 
NA  
Chlorambucil test set (n = 14)PCR training set (n = 20)PCR test set (n = 20)
Sex 
Male 10 15 16 
Female 
Race 
White 12 19 20 
Other 
NA 
Median age at treatment (range; y) 64 (42-82) 61 (40-78) 65 (49-76) 
Stage at treatment (Rai) 
11 
Median progression-free survival (range), d 135 (51-969) 822 (45-1803) 923 (243-1546) 
IgVH status* 
Mutated 
Unmutated 12 13 
Cytogenetics* 
Normal 
13q deletion 
Trisomy 12 
11q deletion 
17p deletion 
Complex or other 
NA 
CD38* 
Positive 
Negative 12 13 13 
ZAP-70* 
Positive 10 
Negative 10 11 
NA  

Abbreviation: PCR, pentostatin, cyclophosphamide, and rituximab.

*ZAP-70, CD38, cytogenetics, and IgVH determination as described in refs. 22 and 23.

To broaden the possibilities of using genomic signatures to predict patterns of response, we evaluated a larger cohort of progressive CLL patients who were treated with chemoimmunotherapy. Triple-agent chemoimmunotherapy is increasingly used in the treatment of CLL (23, 30, 31). The recent phase II study on treatment with pentostatin, cyclophosphamide, and rituximab in previously untreated patients showed an overall response rate of 91 and a median progression-free survival of 32.6 months (23). Twenty of 40 CLL patient samples collected before therapy were used to create a 60-gene signature of sensitivity to this regimen that significantly differentiated the long-term responders from the early progressors (Fig. 3B). There was no statistically significant difference in type of clinical response obtained by these two groups. Notably, the probes that comprise this signature are distinct from those that make up the signatures for progressive disease and chlorambucil resistance (Supplementary Table S1). We used leave-one-out cross-validation to assess the accuracy of this signature and found that the sensitivity and specificity were both 80 when a cutoff of 0.5 was used (Supplementary Table S2). As seen in Fig. 3B, the prediction scores on leave-one-out cross-validation in the long-term responders versus early progressors in this training set were statistically different (P = 0.0089 by Wilcoxon rank-sum test), whereas there was no significant difference using IgVH, CD38, or ZAP-70.

We then applied the signature to the remaining 20 patients' data to determine if classification based on the genomic signature was associated with clinical outcome. The clinical characteristics of the patients in the training and the test sets are shown in Table 3. Using Kaplan-Meier analysis, we showed that the genomic signature defined two groups that differed based on clinical response of time to progression (P = 0.0003; log rank test; Fig. 3B). Comparing the progressors to the nonprogressors in this group, there was no statistically significant difference in pathologic response, IgVH mutation status, or CD38 or ZAP-70 expression. The genomic signatures of progressive disease and of chlorambucil sensitivity could not accurately classify patients by response (Supplementary Fig. S1). These results indicate that, unlike most current prognostic markers, a specific genomic signature seems capable of identifying CLL patients who will have rapid progression after treatment with pentostatin, cyclophosphamide, and rituximab, an effective chemoimmunotherapy regimen.

Like many cancers, CLL is a heterogeneous malignancy in which there is uncertainty about optimal treatment selection and the identification of patients likely to have progressive disease. We used gene expression data from CLL patients from Duke University and Durham VA Medical Centers, Mayo Clinic, and Ohio State University, as well as published and publicly available data sets to show that genomic data from CLL patients can be used to address these critical issues.

We found that a genomic signature classifies patients into those likely to progress and those likely to have stable disease. Compared with IgVH mutational status in the entire Duke/VA cohort, the genomic signature of progressive disease was at least as good at predicting outcomes. There were insufficient numbers of patients with specific cytogenetic aberrations to evaluate a contribution from fluorescent in situ hybridization analyses. The combination of the genomic score of progression and IgVH mutational status provided improved prognostic capability over either marker alone. Others have created models and nomograms that combine prognostic factors into one score (22, 32). As genomic-based tests enter clinical use, understanding how to use this information in conjunction with other prognostic markers in a simple and efficient manner will be important.

Our goal with this work was to create and validate genomic signatures that might be used clinically in the future, irrespective of the genes that make up those signatures. However, analysis of the gene probes that constitute the genomic signature of progressive disease show several genes involved in the cytoskeleton (RDX, TNS3) or tumor necrosis factor family members and genes involved in tumor necrosis factor cleavage (TNFSF13, ADAM17). Others have previously noted the prognostic significance of cytoskeletal genes and the tumor necrosis factor in CLL (33,35). Notably, probes for ZAP-70 did not constitute this genomic signature, although mean expression for ZAP-70 probes in the samples from patients with progressive disease was higher than those from patients with stable disease. In part, this may be due to the different statistical methodology for supervised analysis of genomic data compared with other groups.

We found clinical heterogeneity and variable length of follow-up in our training and validation data sets, as well as methodologic differences in sample collection and research techniques. Despite these substantial limitations, we could create a genomic signature that reflects prognosis, and this signature and methodology could be independently validated with other published cohorts. This points toward the potential of this tool to do well in situations that are much better controlled than what was possible for this analysis.

The choice of optimal therapy in CLL is a balance between the efficacy of multidrug regimens and the lower toxicity of less effective single agents. Although no currently available biomarkers predict resistance to treatment, the deletions of chromosomes 17p13.1 or 11q22.3 are associated with incomplete and inferior responses to purine analoguebased therapy (6,8). Using gene expression profiling, we found that we could predict response of a patient's leukemia to chlorambucil, an agent with less toxicity than other agents but lower long-term efficacy. The same genomic data could be tested against a distinct genomic signature of response to a more effective but more toxic regimen of chemoimmunotherapy with pentostatin, cyclophosphamide, and rituximab. Given the limited ability of available prognostic factors to predict responses to therapy, these findings suggest that it will be ultimately possible to make rational selections of therapies for CLL patients, moving toward the goal of personalized medicine.

In conclusion, gene expression profiling not only assists in refining prognosis of CLL but may also be used in determining optimal therapies. Our results have potential applicability in guiding clinical and investigational medicine and may direct future care of CLL patients.

J.R. Nevins has an ownership interest with Expression Analysis. The other authors disclosed no potential conflicts of interest.

We thank the clinicians responsible for sample collection at the sites contributing to training data and Neil Kay, John Byrd, Diane Jelinek, Renee Tschumper, Jeanette Eckel-Passow, and Adrian Wiestner for the data and input on data analysis.

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Competing Interests

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.

Supplementary data