Proteomics-Based Strategies To Identify Proteins Relevant to Chronic

Article pubs.acs.org/jpr

Proteomics-Based Strategies To Identify Proteins Relevant to Chronic Lymphocytic Leukemia Suliman A. Alsagaby,*,†,‡ Sanjay Khanna,§ Keith W. Hart,§ Guy Pratt,∥ Christopher Fegan,† Christopher Pepper,† Ian A. Brewis,§ and Paul Brennan† †

Institute of Cancer & Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, United Kingdom Department of Medical Laboratory, College of Science, Majmaah University, King Fahd Street, PO Box 1712, Al-Zulfi, Riyadh Region, 11932, Kingdom of Saudi Arabia § TIME Institute, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN, United Kingdom ∥ CRUK Institute for Cancer Studies, University of Birmingham, Vincent Drive, Edgbaston, Birmingham, B15 2TT, United Kingdom ‡

S Supporting Information *

ABSTRACT: Chronic lymphocytic leukemia (CLL), a malignant B-cell disorder, is characterized by a heterogeneous clinical course. Two-dimensional nano liquid chromatography (2D-nano−LC) coupled with matrix-assisted laser desorption/ ionization time-of-flight tandem mass spectrometry (MALDITOF/TOF MS) (LC−MALDI) was used to perform qualitative and quantitative analysis on cellular extracts from 12 primary CLL samples. We identified 728 proteins and quantified 655 proteins using isobaric tag-labeled extracts. Four strategies were used to identify disease-related proteins. First, we integrated our CLL proteome with published gene expression data of normal B-cells and CLL cells to highlight proteins with preferential expression in the transcriptome of CLL. Second, as CLL’s outcome is heterogeneous, our quantitative proteomic data were used to indicate heterogeneously expressed proteins. Third, we used the quantitative data to identify proteins with differential abundance in poor prognosis CLL samples. Fourth, hierarchical cluster analysis was applied to identify hidden patterns of protein expression. These strategies identified 63 proteins, and 4 were investigated in a CLL cohort (39 patients). Thyroid hormone receptor-associated protein 3, Tcell leukemia/lymphoma protein 1A, and S100A8 were associated with high-risk CLL. Myosin-9 exhibited reduced expression in CLL samples from high-risk patients. This study shows the usefulness of proteomic approaches, combined with transcriptomics, to identify disease-related proteins. KEYWORDS: iTRAQ-based quantitative proteomics, CLL, TR150, S100A8, TCL-1, myosin-9

■

INTRODUCTION Chronic lymphocytic leukemia (CLL) is a malignant disorder of B-cells and is characterized by the accumulation of small, mature-appearing CD5+/CD19+ B lymphocytes in peripheral blood, bone marrow, and lymphoid tissues.1 CLL is the most common leukemia in western countries, accounting for 40% of all types of leukemia affecting people over 65 years of age, with a male predominance of approximately 2:1.2,3 Genetic variants that increase the risk of CLL have been identified,4−6 but the cause of CLL is still unclear. The clinical course of CLL is heterogeneous, ranging from an aggressive form with rapid progression to a stable form with no excess age-adjusted risk of mortality.7 Several molecular prognostic markers have been discovered to predict the possible course of the disease. Immunoglobulin gene mutation status (IGHV) is considered to be one of the most accurate prognostic markers; unmutated CLL (UM-CLL) is associated with the aggressive form of CLL. UM-CLL © 2014 American Chemical Society

describes CLL B-cells where the B-cell receptor gene (the IGHV gene) has ≥98% sequence homology to the closest germline sequence. In contrast, in B-CLL cells described as mutated CLL (M-CLL), their IGHV gene sequence is less than 98% homologous to the closest germline sequence. M-CLL is associated with a more favorable outcome.8,9 In addition, ZAP70 has been shown to be upregulated in CLL patients with poor prognosis; high ZAP-70 expression has been reported to be strongly associated with unmutated IGHV.10 Another molecule, which is regarded as an independent prognostic marker, is the surface antigen CD38; high expression correlates with the aggressive form of CLL.11 However, none of these Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: March 17, 2014 Published: July 1, 2014 5051

dx.doi.org/10.1021/pr5002803 | J. Proteome Res. 2014, 13, 5051−5062

Journal of Proteome Research

Article

markers are used to direct clinical treatment, and the mechanism by which they influence disease progression is still under investigation. Although genetic predisposition contributes to the diverse clinical outcomes seen in CLL, differential protein expression has been shown to impact heavily on the clinical progression and response to treatment in this disease. In this regard, antiapoptotic proteins, such as Bcl-2 and Mcl-1, and the NF-κB transcription factor subunits have been shown to be important.12−14 These studies used conventional approaches based on detection of single proteins using specific antibodies. In contrast, proteomic approaches allow the simultaneous identification of a large number of proteins with the potential to identify new molecules expressed specifically in diseased tissue. Of the proteomics studies of CLL cells published to date, most have used gel-based approaches, including an elegant approach using fluorescence labeling of proteins.15−18 However, one group exploited the cleavable isotope-coded affinity tag (cICAT) technology coupled with liquid chromatography electrospray ionization and tandem mass spectrometry (LC− ESI−MS/MS) to compare protein expression of UM-CLL and M-CLL cells, identifying a proteome of 538 proteins.19 These studies reported proteins with a potential role in CLL. For example, nucleophosmin, which stabilizes p53, was found with preferential expression in M-CLL and programmed cell death protein 4, which is a tumor suppressor protein, was detected with reduced expression in stimulated UM-CLL.16,18 In the present study, we chose a complementary approach using 2D nano−LC followed by MALDI-TOF/TOF MS to potentially enable the identification of a greater number of proteins in CLL cells and to perform a quantitative analysis using iTRAQ reagents. This discovery study was performed to identify proteins relevant to CLL. In an attempt to achieve this goal, we applied four different strategies. First, a CLL proteomic data set from 12 primary CLL samples reported in this study was integrated with published Affymetrix gene array data of normal B-cells and CLL cells to identify proteins with preferential expression in the transcriptome of CLL cells. Second, as CLL is characterized by the heterogeneous clinical course, our quantitative proteomic data set was used to indicate proteins with the most heterogeneous expression in CLL samples. Third, the quantitative data set was also used to identify proteins that were differentially expressed in CLL samples from different prognostic subsets of CLL patients. Fourth, hierarchical cluster analysis was utilized to identify hidden patterns of protein expression. Together, these strategies highlighted 63 proteins, of which 10 were linked to cancer and in some cases specifically to CLL. Four of these 63 proteins were subsequently investigated using specific antibodies in a CLL cohort consisting of 39 patients, including those used for proteomic analysis (12 patients).

■

Table 1. Summary of Clinical Details of the CLL Patient Cohorta factor

subset

no.

median age range CD38

71.5 53−87 1.3); 374 of these proteins were quantified in at least five different CLL samples (Supporting Information S9). Proteins with Heterogeneous Expression in CLL Samples

Because CLL is characterized by a heterogeneous clinical outcome, CLL-relevant proteins might be those found among the proteins with the most heterogeneous expression in CLL samples. On the basis of this hypothesis, we used the standard deviation (SD) of relative expression of proteins in five or more CLL samples to indicate whether a protein was heterogeneously expressed. This analysis excluded proteins that were likely to be detected in CLL samples due to contamination. These proteins included different types of keratin, hemoglobin subunits, neutrophil defensin 3, neutrophil elastase, neutrophil cytosol factor 1, and macrophage migration inhibitory factor. For a protein to be reported with variable expression, it must have been identified with two or more peptides (confidence score ≥ 95% for each peptide). The analysis showed that 14 proteins exhibited the most variable expression in CLL samples (z score > 2). Ten proteins were from the NP40 fractions, representing 3.2% of the proteins that were quantified in five or more CLL samples (316 proteins). In addition, four proteins were from the SDS fractions, representing 3.1% of the proteins that were quantified in at least five CLL samples (130 proteins). These proteins, along with their biological functions that were extracted from their Gene Ontology data using Quick GO-EBI tool (http://www.ebi.ac.uk/QuickGO/), are shown in Supporting Information S10. Among these proteins, six (43%) were linked to cancer and in some cases specifically to CLL. For example, vimentin was previously shown to associate with a high-risk form of CLL.29 In addition, S100A8 and S100A9 were shown to be involved in the progression of breast cancer and prostate cancer.30,31 More importantly, TCL-1 was linked to the tumorigenesis of CLL.32 Galectin-1 was also reported with an increased expression in different types of cancer including bladder cancer and pancreatic cancer.33 Finally, heterogeneous nuclear ribonucleoproteins A2/B1 (also known as splicing factor hnRNP A2/B1) was reported to be highly expressed in glioblastoma and correlates with poor prognosis of this cancer.34 This indicates that investigating variably expressed proteins in CLL may help to focus on proteins that contribute CLL progression. S100A9 and S100A8 showed the largest dynamic range of expression in CLL samples, as defined by standard deviation. S100A8 and S100A9 are calcium- and zinc-binding proteins and were shown to play important roles in immune response, inflammation, and tumor promotion through different mechanisms including activation of transcription factors such as NFκB and AP-1.35,36 To investigate whether the variable expression of S100A8 has an impact on CLL, we evaluated the expression of S100A8 using western blotting and antibody detection in the NP40 fractions of 21 CLL samples (Supporting Information S7 shows the utilized samples). The analysis confirmed the variable expression of S100A8 in CLL samples and showed that high expression of S100A8 was associated with a more rapid progression of CLL (p = 0.03, n = 18; Figure 2A; the samples used are shown in Supporting Information S7). On the basis of the association of S100A8 and the progression of CLL, we investigated whether S100A8 expression was informative of requirement for treatment in CLL patients (early stage A0/A). As defined by the median of the normalized S100A8/actin expression in patients analyzed earlier, patients (n = 19) were divided into two groups: a high

Figure 1. Combining CLL proteomic data with Affymetrix microarray data of normal B-cells and CLL cells samples highlights proteins with possible relevance to CLL. A comparison between genes expressed in normal B-cells and CLL cells revealed potential CLL specific genes (A). Proteins of some of the potential CLL specific genes were found in the CLL proteome (B). TR150 was identified with six different peptides (ion score ≥ 95% C.I.). The MS/MS spectra of one peptide, SIFQHIQSAQSQR, is shown to illustrate the assignment of sequence from the MS/MS data (C). TR150 showed variable expression in CLL samples (D). Patients 1, 5, 7, and 8 were treated (TTFT (years) 0.21, 0.78, 2.59, and 3.46, respectively), whereas patients 2, 3, 4, and 6 were untreated (TSD (years) 8.20, 3.20, 0.80, 13.23, respectively). High expression of TR150 was associated with early requirement for treatment in CLL patients; the median of TTFT in the high TR150 group was 0.8 years, whereas it was undefined in the low TR150 group (E). TTFT, time to first treatment; TSD, time since diagnosis.

the relative quantification of proteins found in the SDS fraction from 12 CLL samples (Supporting Information S8). In total, we quantified 655 proteins with ≥95% confidence (unused 5055



Article

different prognostic markers was observed for 13 proteins (six proteins in the NP40 fraction and seven proteins in the SDS fraction). These proteins and their biological processes, as obtained from their Gene Ontology data using Quick GO-EBI tool, are shown in Table 2. Validation of TCL-1 and Myosin-9

From the 13 proteins that exhibited altered expression in good and poor prognosis CLL samples, T-cell leukemia/lymphoma protein 1A (TCL-1) and myosin-9 were chosen for validation because they exhibited the largest expression change among proteins quantified in >6 CLL samples. The iTRAQ data for TCL-1 showed that it was overexpressed in the NP40 fractions of CD38+ CLL samples (poor prognosis) compared with its abundance in CD38− CLL samples (good prognosis) (p = 0.04, n = 11; Table 2) In addition, the iTRAQ data of myosin-9 showed that it had a reduced expression in the SDS fractions of CLL samples from patients in stage B or C (advanced stages of CLL) compared with samples from patients in stage A (early stage of the disease) (p = 0.03, n = 10; Table 2). TCL-1 expression was measured in the NP40 fractions of 24 CLL samples (12 CD38+ CLL samples versus 12 CD38− CLL samples; Supporting Information S7 shows the samples utilized) using western blotting and antibody detection. Consistently, with the iTRAQ data, TCL-1 was detected with an increased expression in CD38+ CLL compared with its abundance in CD38+ CLL (p = 0.03, n = 24; Figure 3A). In addition, TCL-1 was found to have high expression in CLL samples with other poor prognostic markers including ZAP-70+ (p = 0.03, n = 19; Supporting Information S11A; the samples used are shown in Supporting Information S7) and unmutated IGHV gene (p = 0.05, n = 17; Supporting Information S11B; the samples used are shown in Supporting Information S7). Given the association of TCL-1 with poor prognosis in CLL, we investigated whether the expression of TCL-1 in CLL samples is indicative of the need for treatment in CLL patients. On the basis of the median of normalized TCL-1/actin expression in patients investigated earlier, patients (n = 22) were considered to have either high or low expression of TCL1. The median follow-up in the high TCL-1 group was 6.88 years, and it was 4.23 years in the low TCL-1 group. The TTFT of patients in each group was analyzed using the Log-rank test and graphically represented using Kaplan−Meier curves. The analysis showed that patients with high TCL-1 expression are more likely to require treatment earlier than those with low TCL-1 expression. The median TTFT in the high TCL-1 group was 5.4 years and was not reached in the low TCL-1 group (p = 0.01, n = 22; Figure 3B; the samples used are shown in Supporting Information S7). The next step was to validate the altered expression of myosin-9 in CLL samples from patients in late stages of CLL and patients in early stage of the disease. The expression of myosin-9 was assessed using western blotting and antibody detection in the SDS fractions of 16 CLL samples (eight CLL samples stage B or C versus eight CLL samples stage A; Supporting Information S7 shows the utilized samples). The analysis, consistent with the iTRAQ data, demonstrated reduced expression of myosin-9 in CLL samples from patients in advanced stages of CLL compared with those from patients in an early stage of the disease (p = 0.0001, n = 16; Figure 3C). In line with this observation, the analysis also showed that myosin-9 exhibited reduced expression in CLL samples with poor prognostic markers including ZAP-70+ (p = 0.05, n = 16;

Figure 2. Investigation of S100A8 expression in CLL samples. S100A8 was measured in the NP40 fractions of 21 different CLL samples using western blotting followed by antibody detection. S100A8 showed high expression in CLL samples from patients with rapid progression of CLL (A). In addition, S100A8 was associated with an early need for treatment in CLL patients; the median TTFT in the high S100A8 group was 3.2 years, whereas it was undefined in the low S100A8 group (B). PFS, progression-free survival (where it is low in CLL patients with poor prognosis and high in CLL patients with good prognosis); TTFT, time to first treatment.

S100A8 group and a low S100A8 group. The median follow-up was 4.30 years in the high S100A8 group and 10.35 years in the low S100A8 group. TTFT data of the patients in each group were analyzed by using Kaplan−Meier curves. The analysis revealed a significantly different TTFT in the two groups of patients: the median TTFT was 3.2 years in the high S100A8 group and was not reached in the low S100A8 group (p = 0.01, n = 19; Figure 2B; the samples used are shown in Supporting Information S7). Proteins with Differential Abundance in CLL Samples Based on Different Prognostic Markers

Given the clinical data that was available on the patient samples tested, we used the quantitative proteomic data to perform subset analysis based on CD38 expression, ZAP-70 expression, the presence or absence of IGHV gene mutations, and clinical staging. For a protein to be reported as differentially expressed in CLL samples, it must have been identified using at least two peptides with a minimum confidence score 95% and quantified in at least five different CLL samples. In addition, a protein must have had an altered expression ratio (iTRAQ value ≤ 0.80 or ≥ 1.25) representing ≥20% change in protein expression and a significant p value (≤0.05) using unpaired Student’s ttest. Differential expression in CLL samples from patients with 5056



Article

Table 2. Proteins with Differential Abundance in CLL Cells from Patients with Poor or Good Prognosisa protein name

accession no.

mean of peptides count

T-cell leukemia/lymphoma protein 1A Acidic leucine-rich nuclear phosphoprotein 32 family member A DNA-(apurinic or apyrimidinic site) lyase U6 snRNA-associated Sm-like protein LSm3 LIM and SH3 domain protein 1

TCL1A_HUMAN

3.0

AN32A_HUMAN

2.5

APEX1_HUMAN

4.0

LSM3_HUMAN

1.3

LASP1_HUMAN

2.3

LIM and SH3 domain protein 1

LASP1_HUMAN

2.3

14-3-3 protein theta

1433T_HUMAN

5.5

Histone H4

H4_HUMAN

22.8

RNA-binding protein FUS

FUS_HUMAN

2.7

Tropomyosin alpha-4 chain

TPM4_HUMAN

4.0

CD38+/ CD38− CD38+/ CD38− ZAP-70+/ ZAP-70− UM/M

Apoptotic chromatin condensation inducer in the nucleus Splicing factor, arginine/serinerich 2 Myosin-9

ACINU_HUMAN

2.3

UM/M

SFRS2_HUMAN

3.5

MYH9_HUMAN

9.8

CD38+/ CD38− B-C/A

Histone H2B type 2-E

H2B2E_HUMAN

14

change ratio

p value

no. of samples

fraction

biological function

poor/ good poor/ good

1.66

0.04

6 vs 5

NP40

1.30

0.01

2 vs 5

NP40

stem cell maintenance intracellular signal transduction

poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good poor/ good

1.28

0.01

2 vs 4

SDS

1.27

0.02

2 vs 7

NP40

1.27

0.001

3 vs 7

NP40

1.26

0.02

4 vs 4

NP40

0.79

0.05

4 vs 2

NP40

0.74

0.05

6 vs 6

SDS

0.74

0.04

2 vs 5

SDS

0.74

0.05

3 vs 3

NP40

0.73

0.003

3 vs 3

SDS

cellular component movement apoptotic process

0.68

0.01

4 vs 2

SDS

RNA splicing

0.57

0.03

3 vs 7

SDS

cell−cell adhesion

0.48

0.01

2 vs 4

SDS

nucleosome assembly

comparison

prognosis

CD38+/ CD38− ZAP-70+/ ZAP-70− ZAP-70+/ ZAP-70− ZAP-70+/ ZAP-70− ZAP-70+/ ZAP-70− UM/M

CD38+/ CD38−

positive regulation of DNA repair gene expression SH3/SH2 adaptor activity SH3/SH2 adaptor activity protein targeting nucleosome assembly cell death

a Relative quantification was performed using iTRAQ reagents coupled with 2D nano−LC and MALDI-TOF/TOF MS. Differential expression was determined by a relative abundance ratio < 0.8 or > 1.25. CD38+, CLL cells expressing more than 39% CD38 on their surface (poor prognosis); CD38−, CLL cells expressing less than 5% CD38 on their surface (good prognosis); ZAP-70+, CLL cells expressing equal or more than 20% ZAP-70 (poor prognosis); ZAP-70−, CLL cells expressing less than 6% ZAP-70 (good prognosis); UM, unmutated CLL cells (poor prognosis); M, mutated CLL cells (good prognosis); B-C, patients in stage B or C (advanced stages of CLL); A, patients in stage A (an early stage of the disease).

(unpaired Student’s t-tests). Of these proteins, 30 were found with different abundance in group 1 compared with group 2, whereas 16 proteins were detected with altered expression in group 1 compared with group 3 (Supporting Information S15). Ten proteins were differentially expressed in both comparisons (group 1 vs group 2 and group 1 vs group 3). Interestingly, of these 36 proteins, 12 were found to overlap with the 39 proteins of interest that were identified by the three strategies described earlier (shown in Supporting Information S15 with asterisk symbol).

Supporting Information S12A; the samples used are shown in Supporting Information S7) and unmutated IGHV (p = 0.01, n = 12; Supporting Information 12B; the samples used are shown in Supporting Information S7). Proteins with Differential Abundance Based on Hierarchical Cluster Analysis

In addition to the three strategies applied earlier to identify proteins of interest in CLL, we also used a different statistical analysis to investigate variable expression of proteins in CLL samples. Protein relative quantification (iTRAQ) data from both the NP40 and SDS runs for each patient sample were combined. A box and whisker plot was used to compare the iTRAQ values. Outliers were investigated, and selected proteins were omitted because they were likely to be contaminants from other cells: type I and type II keratins; hemoglobin subunit alpha and beta; neutrophil defensin 3, neutrophil elastase, and neutrophil cytosol factor 1; and macrophage migration inhibitory factor. The box and whisker plot was then redrawn (Supporting Information S13). In an attempt to identify hidden patterns of protein expression in CLL samples, a Euclidian distance matrix was calculated using R and was utilized to plot the hierarchical cluster diagram (Figure 4). Three clusters were identified: one cluster with seven patient samples (P1, P3, P4, P5, P6, P10, and P11, called group 2) and two smaller clusters of two patients samples each (P2 and P8 in group 1; P7 and P9 in group 3). Changes in protein expression (iTRAQ ratio < 0.8 or > 1.25) were observed for 36 proteins with a significant p value

■

DISCUSSION This study reports the largest and most conservative set of proteins identified in primary CLL cells. Of previous CLL proteomics studies, three reported hundreds of protein identifications: 500, 538, and 695 proteins.15,19,37 The concept of false discovery rates (FDR) has been proposed since these studies were published. Furthermore, these studies did not indicate the number of proteins that were detected on the basis of multiple or single peptides. These two factors make it difficult to compare the quality of the reported proteins by these three studies. In contrast, in the current study, 728 proteins with 0% FDR were reported, of which 607 were identified with multiple peptides (ion score ≥ 95% C.I.), and 121 proteins were identified with single peptides (ion score 100% C.I.). Four different strategies were used in the present study to identify proteins of interest. First, our CLL proteomic data was 5057



Article

Figure 4. Hierarchical cluster analysis of iTRAQ data from 12 patients. For each patient sample, data from both the NP40 and SDS runs were combined. Proteins that were likely to be contaminants from other cells were omitted: type I and type II keratins; hemoglobin subunit alpha and beta; and neutrophil defensin 3, neutrophil elastase, and neutrophil cytosol factor 1. A Euclidian distance matrix was calculated using R and was used to plot the cluster diagram.

strategies have not been previously published in other CLL proteomics studies and, to our best knowledge, were not demonstrated in other cancer proteomics studies. These four strategies identified 63 proteins of interest to CLL. Of these proteins, 10 were previously linked to cancer and in some cases specifically to CLL. These proteins included zinc finger protein Aiolos,25 guanine nucleotide-binding protein subunit alpha13,25 stathmin,26−28 vimentin,29 S100A8,30 S100A9,31 TCL-1,32 Galectin-1,33 heterogeneous nuclear ribonucleoproteins A2/ B1,34 and LIM and SH3 domain protein 1.38 These strategies, particularly the first two, may be useful to focus on potentially cancer-relevant proteins that have a dynamic range detectable by mass spectrometry. Interestingly, of the 36 proteins that were identified by the hierarchical cluster analysis, 12 were already identified by the other three strategies used in the present study to discover CLL-related proteins. Some of these proteins, such as vimentin, protein S100A9, galectin-1, and heterogeneous nuclear ribonucleoproteins A2/B1, were previously linked to cancer and in some cases specifically to CLL.29−31,33,34 Furthermore, these 12 proteins included thyroid hormone receptorassociated protein 3 and protein S100A8, which were shown in this study to associate with high-risk CLL and the early need of treatment. TR150 showed potential association with an early need for treatment in CLL patients. No extensive work has been done on TR150 in the context of CLL, but it was reported to play a role in transcriptional coactivation and in mRNA splicing.39,40 More specifically, TR150 is coordinately recruited to the Cyclin D1 gene and cyclin D1 mRNA to control its expression.41 Overexpression of cyclin D1 is a hallmark of mantle cell lymphoma (MCL) and was also found in a percentage (21%) of CLL cases.42 Interestingly, cyclin D1-positive CLL cells were found to localize to the proliferation centers of lymph nodes, suggesting that cyclin D1 is an important factor for CLL proliferation.43 As a result, the high expression of TR150 observed in CLL samples from patients who required earlier treatment is consistent with the increased propensity of these cells to undergo proliferation.

Figure 3. Western blotting validation of differential expression of two proteins in CLL samples. TCL-1 was validated in the NP40 fraction of 24 different CLL samples and demonstrated increased expressed in CD38+ CLL samples (A). TCL-1 was associated with an early requirement for treatment in CLL patients; the median of TTFT in the high TCL-1 group was 5.4 years, whereas it was undefined in the low TCL-1 group (B). Myosin-9 was also validated in the SDS fraction of 16 different CLL samples and showed low expression in CLL samples from patients in advance stages of CLL (C). CD38+ CLL, poor prognosis; CD38− CLL, good prognosis; B-C, patients in stage B or C (advanced stages of CLL); A, patients in stage A (an early stage of the disease); TTFT, time to first treatment.

integrated with previously published trancriptomics data of CLL cells and normal B-cells to identify proteins with preferential expression in the transcriptome of CLL cells. Second, the standard deviation of protein expression in CLL samples was used to identify proteins with the most heterogeneous expression in CLL samples. Third, the proteome of poor prognosis CLL with that of good prognosis CLL was compared to identify differentially expressed proteins in CLL samples. Fourth, hierarchical cluster analysis was utilized to identify hidden patterns of protein expression. The first two 5058



Article

cell lines from apoptosis.61 In addition, acinus, which was detected with reduced expression in UM-CLL compared with its abundance in M-CLL, was shown to be required for apoptotic chromatin condensation following activation by caspase 3.62 In summary, here we have reported a large and conservative set of proteins identified from primary CLL samples. In addition, we applied two novel strategies and two previously described methods to focus on proteins of interest in CLL. Collectively, these methods identified 63 proteins. Four proteins were investigated with specific antibodies: TR150, TCL-1, myosin-9, and S100A8. Of these proteins, TR150, TCL-1, and S100A8 were shown to associate with a high-risk form of CLL and an early requirement for treatment in CLL patients. In contrast, myosin-9 was shown to associate with a low-risk form of CLL. The increased expression of S100A8 protein in poor prognosis patients is consistent with previous work from our laboratory that showed increased transcription of S100A8 in CD38+ subclones.47 Furthermore, the reported link between S100A8 and NF-κB, which is a prognostic marker in CLL, suggests that S100A8 is worthy of further investigation. In addition, the reported proteins of interest, in particular TR150, S100A8, TCL-1, and myosin-9, merit further investigation in a larger CLL cohort in order to characterize their variation and to help us understand the biology of progressive CLL. Overall, this study shows the usefulness of proteomic approaches, especially when combined with transcriptomic data, to identify disease-related proteins.

S100A8 was found to associate with rapid progression of CLL and with the early need for treatment in CLL patients. S100A8 has been reported to be upregulated in different types of cancer44 and has been linked to tumor cell proliferation and metastasis.45,46 In the context of CLL, S100A8 mRNA was 6fold overexpressed in CLL CD38-positive subclones.47 Furthermore, a relationship between S100A8 and nuclear factor kappa B (NF-κB), a transcription factor that is important in CLL, was reported in which overexpression of S100A8 leads to greater activity of NF-κB.12,48 Thus, high expression of S100A8 may be indicative of high levels of NF-κB activation, which is a poor prognostic marker in CLL.49 This study also showed an association of TCL-1 with poor prognosis CLL as well as an early need for treatment. Overexpression of TCL-1 in transgenic mice leads to a CLLlike illness, indicating a primary role for TCL-1 in tumorigenesis.21 TCL-1 has been recently identified to interact with ATM, to enhance NF-κB activation, and to inhibit DNA methylation in CLL cells.50,51 Furthermore, high expression of TCL-1 in CLL samples has been reported to be associated with poor prognostic markers including unmutated IGHV gene, ZAP-70 positivity, and the cytogenetic 11q deletion.52 Consistently high gene expression of TCL-1 was reported to indicate an early requirement for treatment in CLL patients.53 Our data support the observations that TCL-1 is associated with poor prognosis CLL and indicates an early need for treatment. Myosin-9 was detected with reduced expression in poor prognosis CLL. Myosin-9 plays an essential role in mediating uropodal detachment from highly adhesive molecules such as intercellular adhesion molecules.54,55 Loss of myosin-9 in Tcells leads to a prolonged contact with high-endothelial venules (HEVs).56 Interestingly, these HEVs express large amounts of CD31.57 Therefore, the low expression of myosin-9 in subgroup of CLL samples may facilitate a long-lasting interaction between CD38 on CLL cells and its ligand, CD31, on endothelial cells. This interaction was demonstrated to promote CLL survival and proliferation.58,59 Moreover, in vivo analysis reported that mysoin-9 deficient T-cells accumulate in lymph nodes for a longer time period compared with that of control T-cells.52 Thus, the low expression of myosin-9 observed in CLL cells from patients in advanced stages of CLL may result in more CLL cells being retained in lymph nodes, allowing them to receive more prosurvival and proliferation signals, which leads to proliferation and survival of CLL cells. Our data agree with previously published CLL studies. For example, variably active NF-κB pathway was reported in CLL and was associated with short TTFT and short survival time.12,46 In line with this, our data showed that S100A8, S100A9, and Galectin-1 are among the most variably expressed proteins in CLL samples. These proteins were reported to positively regulate NF-κB activation, suggesting that the variable activity of NF-κB in CLL patients can be partly driven by the heterogeneous expression of these proteins.48,60 In addition, CLL has been historically characterized by a defective apoptosis system in the malignant cells. In agreement with this view, the strategies that were used in this study to focus on potentially CLL-related proteins highlighted proteins with apoptotic activity, as demonstrated in their Gene Ontology data. For example, 3-ketoacyl-CoA thiolase, mitochondrial, which was detected in the proteome and transcriptome of CLL cells but not in the transcriptome of normal B-cells, was reported to protect hepatocellular carcinoma and osteosarcoma

■

ASSOCIATED CONTENT

S Supporting Information *

S1: Clinical details of the CLL patient cohort. S2: Quality control of CLL samples. S3: Full list of protein identifications. S4: Summary of the 20 LC−MALDI runs and the utilized samples. S5: Full list of peptides that were identified in CLL samples and were used for protein identification. S6: Proteins whose cognate gene expression is restricted to CLL cells but not normal B-cells. S7: CLL samples used for proteomic analysis and subsequent investigations S8: Schematic of using a reference sample to perform quantitative proteomic analysis in CLL samples. S9: iTRAQ quantification of proteins found in the CLL samples. S10: Proteins found with the most heterogeneous expression in CLL samples. S11: Different expression of TCL-1 in CLL samples with different prognostic markers. S12: Different expression of myosin-9 in CLL samples with different prognostic markers. S13: Boxplot showing the variation in iTRAQ data from 12 patients. S14: Proteins with differential abundance based on cluster analysis. This material is available free of charge via the Internet at http://pubs.acs.org.

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: +966 54 2552 749 or +44 29 2068 7087. Fax: +966 16 422 7484. Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank the Saudi Ministry of High Education and Majmaah University for funding this project. We also thank the Cardiff University Central Biotechnology Service (CBS), Proteomic 5059



Article

Disease Progression in Chronic Lymphocytic Leukemia. N. Engl. J. Med. 2004, 351, 893−901. (11) Durig, J.; Naschar, M.; Schmucker, U.; Renzing-Kohler, K.; Holter, T.; Huttmann, A.; Duhrsen, U. CD38 Expression Is an Important Prognostic Marker in Chronic Lymphocytic Leukaemia. Leukemia 2002, 16, 30−35. (12) Hewamana, S.; Alghazal, S.; Lin, T. T.; Clement, M.; Jenkins, C.; Guzman, M. L.; Jordan, C. T.; Neelakantan, S.; Crooks, P. A.; Burnett, A. K.; Pratt, G.; Fegan, C.; Rowntree, C.; Brennan, P.; Pepper, C. The NF-κB Subunit Rel A Is Associated with in Vitro Survival and Clinical Disease Progression in Chronic Lymphocytic Leukemia and Represents a Promising Therapeutic Target. Blood 2008, 111, 4681−4689. (13) Otake, Y.; Soundararajan, S.; Sengupta, T. K.; Kio, E. A.; Smith, J. C.; Pineda-Roman, M.; Stuart, R. K.; Spicer, E. K.; Fernandes, D. J. Overexpression of Nucleolin in Chronic Lymphocytic Leukemia Cells Induces Stabilization of BCL2 mRNA. Blood 2007, 109, 3069−3075. (14) Buggins, A. G.; Pepper, C. J. The Role of Bcl-2 Family Proteins in Chronic Lymphocytic Leukaemia. Leuk. Res. 2010, 34, 837−842. (15) Boyd, R. S.; Adam, P. J.; Patel, S.; Loader, J. A.; Berry, J.; Redpath, N. T.; Poyser, H. R.; Fletcher, G. C.; Burgess, N. A.; Stamps, A. C.; Hudson, L.; Smith, P.; Griffiths, M.; Willis, T. G.; Karran, E. L.; Oscier, D. G.; Catovsky, D.; Terrett, J. A.; Dyer, M. J. Proteomic Analysis of the Cell-Surface Membrane in Chronic Lymphocytic Leukemia: Identification of Two Novel Proteins, BCNP1 and MIG2B. Leukemia 2003, 17, 1605−1612. (16) Cochran, D. A.; Evans, C. A.; Blinco, D.; Burthem, J.; Stevenson, F. K.; Gaskell, S. J.; Whetton, A. D. Proteomic Analysis of Chronic Lymphocytic Leukemia Subtypes with Mutated or Unmutated Ig V(H) Genes. Mol. Cell Proteomics. 2003, 2, 1331−1341. (17) Voss, T.; Ahorn, H.; Haberl, P.; Dohner, H.; Wilgenbus, K. Correlation of Clinical Data with Proteomics Profiles in 24 Patients with B-Cell Chronic Lymphocytic Leukemia. Int. J. Cancer 2001, 91, 180−186. (18) Perrot, A.; Pionneau, C.; Nadaud, S.; Davi, F.; Leblond, V.; Jacob, F.; Merle-Beral, H.; Herbrecht, R.; Bene, M. C.; Gribben, J. G.; Bahram, S.; Vallat, L. A Unique Proteomic Profile on Surface IgM Ligation in Unmutated Chronic Lymphocytic Leukemia. Blood 2011, 118, e1−e15. (19) Barnidge, D. R.; Jelinek, D. F.; Muddiman, D. C.; Kay, N. E. Quantitative Protein Expression Analysis of CLL B Cells from Mutated and Unmutated IgV(H) Subgroups Using Acid-Cleavable Isotope-Coded Affinity Tag Reagents. J. Proteome Res. 2005, 4, 1310− 1317. (20) Brennan, P.; Shore, A. M.; Clement, M.; Hewamana, S.; Jones, C. M.; Giles, P.; Fegan, C.; Pepper, C.; Brewis, I. A. Quantitative Nuclear Proteomics Reveals New Phenotypes Altered in Lymphoblastoid Cells. Proteomics: Clin. Appl. 2009, 3, 359−369. (21) A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013; http:// www.R-project.org/. (22) Huttmann, A.; Klein-Hitpass, L.; Thomale, J.; Deenen, R.; Carpinteiro, A.; Nuckel, H.; Ebeling, P.; Fuhrer, A.; Edelmann, J.; Sellmann, L.; Duhrsen, U.; Durig, J. Gene Expression Signatures Separate B-Cell Chronic Lymphocytic Leukaemia Prognostic Subgroups Defined by ZAP-70 and CD38 Expression Status. Leukemia 2006, 20, 1774−1782. (23) Hutcheson, J.; Scatizzi, J. C.; Siddiqui, A. M.; Haines, G. K., 3rd; Wu, T.; Li, Q. Z.; Davis, L. S.; Mohan, C.; Perlman, H. Combined Deficiency of Proapoptotic Regulators Bim and Fas Results in the Early Onset of Systemic Autoimmunity. Immunity 2008, 28, 206−217. (24) Billot, K.; Soeur, J. R.; Chereau, F.; Arrouss, I.; Merle-Beral, H. l.; Huang, M.-E.; Mazier, D.; Baud, V. R.; Rebollo, A. Deregulation of Aiolos Expression in Chronic Lymphocytic Leukemia Is Associated with Epigenetic Modifications. Blood 2011, 117, 1917−1927. (25) Rasheed, S. A.; Teo, C. R.; Beillard, E. J.; Voorhoeve, P. M.; Casey, P. J. MicroRNA-182 and MicroRNA-200a Control G-Protein Subunit α-13 (Gna13) Expression and Cell Invasion Synergistically in Prostate Cancer Cells. J. Biol. Chem. 2013, 288, 7986−7995.

Facility. P.B., C.P., and C.F. are funded by a Specialist Programme from Leukaemia and Lymphoma Research, UK.

■

ABBREVIATIONS CLL, chronic lymphocytic leukemia; IGHV, immunoglobulin heavy chain variable region genes; ZAP-70, tyrosine-protein kinase ZAP-70; UM-CLL, CLL with unmutated immunoglobulin heavy chain variable region genes; M-CLL, CLL with mutated immunoglobulin heavy chain variable region genes; TR150, thyroid hormone receptor-associated protein 3; BCA assay, bicinchoninic acid assay; CHCA, α-cyano-4-hydroxycinnamic acid; RP, reverse phase; TCL-1, T-cell leukemia/ lymphoma protein 1A; LASP-1, LIM and SH3 domain protein 1

■

REFERENCES

(1) Dighiero, G. CLL Biology and Prognosis. Hematology 2005, 278−284. (2) Rozman, C.; Montserrat, E. Chronic Lymphocytic Leukemia. N. Engl. J. Med. 1995, 333, 1052−1057. (3) Herishanu, Y.; Polliack, A. Chronic Lymphocytic Leukemia: A Review of Some New Aspects of the Biology, Factors Influencing Prognosis and Therapeutic Options. Transfus. Apheresis Sci. 2005, 32, 85−97. (4) Allan, J. M.; Sunter, N. J.; Bailey, J. R.; Pettitt, A. R.; Harris, R. J.; Pepper, C.; Fegan, C.; Hall, A. G.; Deignan, L.; Bacon, C. M.; Pointon, J. C.; Houlston, R. S.; Broderick, P.; Mainou-Fowler, T.; Jackson, G. H.; Summerfield, G.; Evans, P. A.; Strefford, J. C.; Parker, A.; Oscier, D.; Pratt, G.; Allsup, D. J. Variant IRF4/MUM1 Associates with CD38 Status and Treatment-Free Survival in Chronic Lymphocytic Leukaemia. Leukemia 2010, 24, 877−881. (5) Crowther-Swanepoel, D.; Broderick, P.; Di Bernardo, M. C.; Dobbins, S. E.; Torres, M.; Mansouri, M.; Ruiz-Ponte, C.; Enjuanes, A.; Rosenquist, R.; Carracedo, A.; Jurlander, J.; Campo, E.; Juliusson, G.; Montserrat, E.; Smedby, K. E.; Dyer, M. J.; Matutes, E.; Dearden, C.; Sunter, N. J.; Hall, A. G.; Mainou-Fowler, T.; Jackson, G. H.; Summerfield, G.; Harris, R. J.; Pettitt, A. R.; Allsup, D. J.; Bailey, J. R.; Pratt, G.; Pepper, C.; Fegan, C.; Parker, A.; Oscier, D.; Allan, J. M.; Catovsky, D.; Houlston, R. S. Common Variants at 2q37.3, 8q24.21, 15q21.3 and 16q24.1 Influence Chronic Lymphocytic Leukemia Risk. Nat. Genet. 2010, 42, 132−136. (6) Enjuanes, A.; Benavente, Y.; Bosch, F.; Martin-Guerrero, I.; Colomer, D.; Perez-Alvarez, S.; Reina, O.; Ardanaz, M. T.; Jares, P.; Garcia-Orad, A.; Pujana, M. A.; Montserrat, E.; de Sanjose, S.; Campo, E. Genetic Variants in Apoptosis and Immunoregulation-Related Genes Are Associated with Risk of Chronic Lymphocytic Leukemia. Cancer Res. 2008, 68, 10178−10186. (7) Pepper, C.; Majid, A.; Lin, T. T.; Hewamana, S.; Pratt, G.; Walewska, R.; Gesk, S.; Siebert, R.; Wagner, S.; Kennedy, B.; Miall, F.; Davis, Z. A.; Tracy, I.; Gardiner, A. C.; Brennan, P.; Hills, R. K.; Dyer, M. J.; Oscier, D.; Fegan, C. Defining the Prognosis of Early Stage Chronic Lymphocytic Leukaemia Patients. Br. J. Haematol. 2012, 156, 499−507. (8) Hamblin, T. J.; Davis, Z.; Gardiner, A.; Oscier, D. G.; Stevenson, F. K. Unmutated Ig V(H) Genes Are Associated with a More Aggressive Form of Chronic Lymphocytic Leukemia. Blood 1999, 94, 1848−1854. (9) Damle, R. N.; Wasil, T.; Fais, F.; Ghiotto, F.; Valetto, A.; Allen, S. L.; Buchbinder, A.; Budman, D.; Dittmar, K.; Kolitz, J.; Lichtman, S. M.; Schulman, P.; Vinciguerra, V. P.; Rai, K. R.; Ferrarini, M.; Chiorazzi, N. Ig V Gene Mutation Status and CD38 Expression as Novel Prognostic Indicators in Chronic Lymphocytic Leukemia. Blood 1999, 94, 1840−1847. (10) Rassenti, L. Z.; Huynh, L.; Toy, T. L.; Chen, L.; Keating, M. J.; Gribben, J. G.; Neuberg, D. S.; Flinn, I. W.; Rai, K. R.; Byrd, J. C.; Kay, N. E.; Greaves, A.; Weiss, A.; Kipps, T. J. ZAP-70 Compared with Immunoglobulin Heavy-Chain Gene Mutation Status as a Predictor of 5060



Article

(26) Sherbet, G. V.; Cajone, F. Stathmin in Cell Proliferation and Cancer Progression. Cancer Genomics Proteomics 2005, 2, 227−237. (27) Price, D. K.; Ball, J. R.; Bahrani-Mostafavi, Z.; Vachris, J. C.; Kaufman, J. S.; Naumann, R. W.; Higgins, R. V.; Hall, J. B. The Phosphoprotein Op18/Stathmin Is Differentially Expressed in Ovarian Cancer. Cancer Invest. 2000, 18, 722−730. (28) Curmi, P. A.; Nogues, C.; Lachkar, S.; Carelle, N.; Gonthier, M. P.; Sobel, A.; Lidereau, R.; Bieche, I. Overexpression of Stathmin in Breast Carcinomas Points out to Highly Proliferative Tumours. Br. J. Cancer. 2000, 82, 142−150. (29) Nowakowski, G. S.; Hoyer, J. D.; Shanafelt, T. D.; Geyer, S. M.; LaPlant, B. R.; Call, T. G.; Jelinek, D. F.; Zent, C. S.; Kay, N. E. Using Smudge Cells on Routine Blood Smears to Predict Clinical Outcome in Chronic Lymphocytic Leukemia: A Universally Available Prognostic Test. Mayo Clin. Proc. 2007, 82, 449−453. (30) Cormier, K. A.; Fournier, S.; O’Brien, J.; Tessier, P.; Robichaud, G. A. Role of S100A8 and S100A9 Proteins on Breast Cancer Aggressivity. FASEB J. 2009, 23, LB276. (31) Hermani, A.; Hess, J.; De Servi, B.; Medunjanin, S.; Grobholz, R.; Trojan, L.; Angel, P.; Mayer, D. Calcium-Binding Proteins S100A8 and S100A9 as Novel Diagnostic Markers in Human Prostate Cancer. Clin. Cancer Res. 2005, 11, 5146−5152. (32) Bichi, R.; Shinton, S. A.; Martin, E. S.; Koval, A.; Calin, G. A.; Cesari, R.; Russo, G.; Hardy, R. R.; Croce, C. M. Human Chronic Lymphocytic Leukemia Modeled in Mouse by Targeted TCL1 Expression. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 6955−6960. (33) Balan, V.; Nangia-Makker, P.; Raz, A. Galectins as Cancer Biomarkers. Cancers 2010, 2, 592−610. (34) Golan-Gerstl, R.; Cohen, M.; Shilo, A.; Suh, S. S.; Bakcs, A.; Coppola, L.; Karni, R. Splicing Factor HNRNP A2/B1 Regulates Tumor Suppressor Gene Splicing and Is an Oncogenic Driver in Glioblastoma. Cancer Res. 2011, 71, 4464−4472. (35) Gebhardt, C.; Nmeth, J.; Angel, P.; Hess, J. S100A8 and S100A9 in Inflammation and Cancer. Biochem. Pharmacol. 2006, 72, 1622− 1631. (36) Vogl, T.; Gharibyan, A. L.; Morozova-Roche, L. A. ProInflammatory S100A8 and S100A9 Proteins: Self-Assembly into Multifunctional Native and Amyloid Complexes. Int. J. Mol. Sci. 2012, 13, 2893−2917. (37) Barnidge, D. R.; Tschumper, R. C.; Jelinek, D. F.; Muddiman, D. C.; Kay, N. E. Protein Expression Profiling of CLL B Cells Using Replicate Off-Line Strong Cation Exchange Chromatography and LC−MS/MS. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2005, 819, 33−39. (38) Grunewald, T. G.; Kammerer, U.; Kapp, M.; Eck, M.; Dietl, J.; Butt, E.; Honig, A. Nuclear Localization and Cytosolic Overexpression of LASP-1 Correlates with Tumor Size and Nodal-Positivity of Human Breast Carcinoma. BMC Cancer 2007, 7, 198−207. (39) Fondell, J. D.; Ge, H.; Roeder, R. G. Ligand Induction of a Transcriptionally Active Thyroid Hormone Receptor Coactivator Complex. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 8329−8333. (40) Lee, K.-M.; Hsu, I.-W.; Tarn, W.-Y. TRAP150 Activates PremRNA Splicing and Promotes Nuclear mRNA Degradation. Nucleic Acids Res. 2010, 38, 3340−3350. (41) Bracken, C. P.; Wall, S. J.; Barre, B.; Panov, K. I.; Ajuh, P. M.; Perkins, N. D. Regulation of Cyclin D1 RNA Stability by Snip1. Cancer Res. 2008, 68, 7621−7628. (42) Jain, P.; Giustolisi, G. M.; Atkinson, S.; Elnenaei, M. O.; Morilla, R.; Owusu-Ankomah, K.; Rafiq-Mohammed, F.; Matutes, E.; Wotherspoon, A.; Catovsky, D. Detection of Cyclin D1 in B Cell Lymphoproliferative Disorders by Flow Cytometry. J. Clin. Pathol. 2002, 55, 940−945. (43) Abboudi, Z.; Patel, K.; Naresh, K. N. Cyclin D1 Expression in Typical Chronic Lymphocytic Leukaemia. Eur. J. Haematol. 2009, 83, 203−207. (44) Hermani, A.; De Servi, B.; Medunjanin, S.; Tessier, P. A.; Mayer, D. S100A8 and S100A9 Activate MAP Kinase and NF-κB Signaling Pathways and Trigger Translocation of Rage in Human Prostate Cancer Cells. Exp. Cell Res. 2006, 312, 184−197.

(45) Cross, S. S.; Hamdy, F. C.; Deloulme, J. C.; Rehman, I. Expression of S100 Proteins in Normal Human Tissues and Common Cancers Using Tissue Microarrays: S100A6, S100A8, S100A9 and S100A11 Are All Overexpressed in Common Cancers. Histopathology 2005, 46, 256−269. (46) Salama, I.; Malone, P. S.; Mihaimeed, F.; Jones, J. L. A Review of the S100 Proteins in Cancer. Eur. J. Surg. Oncol. 2008, 34, 357−364. (47) Pepper, C.; Ward, R.; Lin, T. T.; Brennan, P.; Starczynski, J.; Musson, M.; aRowntree, C.; Bentley, P.; Mills, K.; Pratt, G.; Fegan, C. Highly Purified CD38+ and CD38− Sub-Clones Derived from the Same Chronic Lymphocytic Leukemia Patient Have Distinct Gene Expression Signatures Despite Their Monoclonal Origin. Leukemia 2007, 21, 687−696. (48) Benedyk, M.; Sopalla, C.; Nacken, W.; Bode, G.; Melkonyan, H.; Banfi, B.; Kerkhoff, C. HaCaT Keratinocytes Overexpressing the S100 Proteins S100A8 and S100A9 Show Increased NADPH Oxidase and NF-κB Activities. J. Invest. Dermatol. 2007, 127, 2001−2011. (49) Hewamana, S.; Lin, T. T.; Rowntree, C.; Karunanithi, K.; Pratt, G.; Hills, R.; Fegan, C.; Brennan, P.; Pepper, C. Rel A Is an Independent Biomarker of Clinical Outcome in Chronic Lymphocytic Leukemia. J. Clin. Oncol. 2009, 27, 763−769. (50) Gaudio, E.; Spizzo, R.; Paduano, F.; Luo, Z.; Efanov, A.; Palamarchuk, A.; Leber, A. S.; Kaou, M.; Zanesi, N.; Bottoni, A.; Costinean, S.; Rassenti, L. Z.; Nakamura, T.; Kipps, T. J.; Aqeilan, R. I.; Pekarsky, Y.; Trapasso, F.; Croce, C. M. TCL1 Interacts with ATM and Enhances Nf-κB Activation in Hematologic Malignancies. Blood 2012, 119, 180−187. (51) Palamarchuk, A.; Yan, P. S.; Zanesi, N.; Wang, L.; Rodrigues, B.; Murphy, M.; Balatti, V.; Bottoni, A.; Nazaryan, N.; Alder, H.; Rassenti, L.; Kipps, T. J.; Freitas, M.; Croce, C. M.; Pekarsky, Y. TCL1 Protein Functions as an Inhibitor of De Novo DNA Methylation in B-Cell Chronic Lymphocytic Leukemia (CLL). Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 2555−2560. (52) Herling, M.; Patel, K. A.; Khalili, J.; Schlette, E.; Kobayashi, R.; Medeiros, L. J.; Jones, D. TCL1 Shows a Regulated Expression Pattern in Chronic Lymphocytic Leukemia That Correlates with Molecular Subtypes and Proliferative State. Leukemia 2006, 20, 280−285. (53) Mansouri, M. R.; Sevov, M.; Aleskog, A.; Jondal, M.; Merup, M.; Sundstrom, C.; Osorio, L.; Rosenquist, R. IGHV3-21 Gene Usage Is Associated with High TCL1 Expression in Chronic Lymphocytic Leukemia. Eur. J. Haematol. 2010, 84, 109−116. (54) Morin, N. A.; Oakes, P. W.; Hyun, Y. M.; Lee, D.; Chin, Y. E.; King, M. R.; Springer, T. A.; Shimaoka, M.; Tang, J. X.; Reichner, J. S.; Kim, M. Nonmuscle Myosin Heavy Chain IIA Mediates Integrin LFA1 De-adhesion During T Lymphocyte Migration. J. Exp. Med. 2008, 205, 195−205. (55) Smith, A.; Bracke, M.; Leitinger, B.; Porter, J. C.; Hogg, N. LFA1-Induced T Cell Migration on ICAM-1 Involves Regulation of MLCK-Mediated Attachment and ROCK-Dependent Detachment. J. Cell Sci. 2003, 116, 3123−3133. (56) Jacobelli, J.; Friedman, R. S.; Conti, M. A.; Lennon-Dumenil, A. M.; Piel, M.; Sorensen, C. M.; Adelstein, R. S.; Krummel, M. F. Confinement-Optimized Three-Dimensional T Cell Amoeboid Motility Is Modulated Via Myosin IIA-Regulated Adhesions. Nat. Immunol. 2010, 11, 953−961. (57) Clark, R. A.; Fuhlbrigge, R. C.; Springer, T. A. L-Selectin Ligands That Are O-Glycoprotease Resistant and Distinct from MECA-79 Antigen Are Sufficient for Tethering and Rolling of Lymphocytes on Human High Endothelial Venules. J. Cell Biol. 1998, 140, 721−731. (58) Deaglio, S.; Vaisitti, T.; Bergui, L.; Bonello, L.; Horenstein, A. L.; Tamagnone, L.; Boumsell, L.; Malavasi, F. CD38 and CD100 Lead a Network of Surface Receptors Relaying Positive Signals for B-CLL Growth and Survival. Blood 2005, 105, 3042−3050. (59) Deaglio, S.; Aydin, S.; Grand, M. M.; Vaisitti, T.; Bergui, L.; D’Arena, G.; Chiorino, G.; Malavasi, F. CD38/CD31 Interactions Activate Genetic Pathways Leading to Proliferation and Migration in Chronic Lymphocytic Leukemia Cells. Mol. Med. 2010, 16, 87−91. 5061



Article

(60) Matsuda, A.; Suzuki, Y.; Honda, G.; Muramatsu, S.; Matsuzaki, O.; Nagano, Y.; Doi, T.; Shimotohno, K.; Harada, T.; Nishida, E.; Hayashi, H.; Sugano, S. Large-Scale Identification and Characterization of Human Genes That Activate NF-κB and MAPK Signaling Pathways. Oncogene 2003, 22, 3307−3318. (61) Cao, W.; Liu, N.; Tang, S.; Bao, L.; Shen, L.; Yuan, H.; Zhao, X.; Lu, H. Acetyl-Coenzyme a Acyltransferase 2 Attenuates the Apoptotic Effects of BNIP3 in Two Human Cell Lines. Biochim. Biophys. Acta 2008, 1780, 873−880. (62) Sahara, S.; Aoto, M.; Eguchi, Y.; Imamoto, N.; Yoneda, Y.; Tsujimoto, Y. Acinus Is a Caspase-3-Activated Protein Required for Apoptotic Chromatin Condensation. Nature 1999, 401, 168−173.

5062


Proteomics-Based Strategies To Identify Proteins Relevant to Chronic

Recommend Documents