Proteomic-based prediction of clinical behavior in adult acute lymphoblastic leukemia

Why Do Children with Acute Lymphoblastic Leukemia Fare Better Than Adults?

It is a new and exciting time for acute lymphoblastic leukemia (ALL). While nearly 50 years ago, only one in nine children with ALL survived with chemotherapy, nowadays nearly 90% of children have a chance of long-term survival. Adults with ALL, as well as the special category of adolescents and young adult (AYA) patients, are catching up with the new developments seen in children, but still their prognosis is much worse. A plethora of factors are regarded as responsible for the differences in treatment response, such as age, ethnicity, disease biology, treatment regimens and toxicities, drug tolerance and resistance, minimal residual disease evaluation, hematopoietic stem cell transplantation timing and socio-economic factors. Taking these factors into account, bringing pediatric-like protocols to adult patient management and incorporating new agents into frontline treatment could be the key to improve the survival rates in adults and AYA.

Advances of proteomics technologies for multidrug-resistant mechanisms

Multidrug resistance (MDR) is a vital issue in cancer treatment. Drug resistance can be developed through a variety of mechanisms, including increased drug efflux, activation of detoxifying systems and DNA repair mechanisms, and escape of drug-induced apoptosis. Identifying the exact mechanism related in a particular case is a difficult task. Proteomics is the large-scale study of proteins, particularly their expression, structures and functions. In recent years, comparative proteomic methods have been performed to analyze MDR mechanisms in drug-selected model cancer cell lines. In this paper, we review the recent developments and progresses by comparative proteomic approaches to identify potential MDR mechanisms in drug-selected model cancer cell lines, which may help understand and design chemical sensitizers.

Application of oncoproteomics to aberrant signalling networks in changing the treatment paradigm in acute lymphoblastic leukaemia

Oncoproteomics is an important innovation in the early diagnosis, management and development of personalized treatment of acute lymphoblastic leukaemia (ALL). As inherent factors are not completely known - e.g. age or family history, radiation exposure, benzene chemical exposure, certain viral exposures such as infection with the human T-cell lymphoma/leukaemia virus-1, as well as some inherited syndromes may raise the risk of ALL - each ALL patient may modify the susceptibility of therapy. Indeed, we consider these unknown inherent factors could be explained via coupling cytogenetics plus proteomics, especially when proteins are the ones which play function within cells. Innovative proteomics to ALL therapy may help to understand the mechanism of drug resistance and toxicities, which in turn will provide some leads to improve ALL management. Most important of these are shotgun proteomic strategies to unravel ALL aberrant signalling networks. Some shotgun proteomic innovations and bioinformatic tools for ALL therapies will be discussed. As network proteins are distinctive characteristics for ALL patients, unrevealed by cytogenetics, those network proteins are currently an important source of novel therapeutic targets that emerge from shotgun proteomics. Indeed, ALL evolution can be studied for each individual patient via oncoproteomics.

Serum peptidome based biomarkers searching for monitoring minimal residual disease in adult acute lymphocytic leukemia

BACKGROUND

The persistence of minimal residual disease (MRD) during therapy is the strongest adverse prognostic factor in acute lymphocytic leukemia (ALL). This study was to identify serum candidate peptides for monitoring MRD in adult ALL.

RESULTS

A total of 33 peptides in the molecular weight range of 1000-10000 Da were detected using ClinProt system and statistically different between adult patients with ALL and healthy controls. Quick classifier (QC) algorithm was used to obtain a diagnostic model consisting of five peptides that could discriminate patients with ALL from controls with a high sensitivity (100%) and specificity (96.67%). The peptides in the QC model were identified as fibrinogen alpha chain (FGA), glutathione S-transferase P1 (GSTP1), isoform 1 of fibrinogen alpha chain precursor, platelet factor 4 (PF4) by high pressure/performance liquid chromatography mass spectrometry/mass spectrometry. Relative intensities of the five peptides were compared among ALL different groups for the potential importance of MRD evaluation in ALL. The peptides with increased relative intensities in newly diagnosed (ND) ALL patients were found to be decreased in their relative intensities after complete remission (CR) of adult ALL. When ALL patients were refractory & relapsed (RR), relative intensities of the peptides were elevated again. Peptides with decreased relative intensities in ND and RR ALL patients were found to be increased in their relative intensities when ALL patients achieved CR. The findings were validated by ELISA and western blot. Further linear regression analyses were performed to eliminate the influence of platelet and white blood cell counts on serum protein contents and indicated that there were no correlations between the contents of all four proteins (PF4, connective tissue active peptide III, FGA and GSTP1) and white blood cell or platelet counts in ALL different groups and healthy control.

CONCLUSIONS

We speculate the five peptides, FGA, isoform 1 of fibrinogen alpha chain precursor, GSTP1, PF4 and connective tissue active peptide III would be potential biomarkers for forecasting relapse, monitoring MRD and evaluating therapeutic response in adult ALL.

Predicting early intrahepatic recurrence of hepatocellular carcinoma after microwave ablation using SELDI-TOF proteomic signature

BACKGROUND/AIMS

Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature.

METHODS

After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm.

RESULTS

According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%.

CONCLUSIONS

Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.

Potential biomarkers for adult acute myeloid leukemia minimal residual disease assessment searched by serum peptidome profiling

BACKGROUND

Post treatment minimal residual disease (MRD) determination contributes to impending relapse prediction, chemotherapy response and clinical outcomes assessment, guiding clinicians to develop reasonable and effective individual chemotherapy options after induction/consolidation. This study was to identify serum candidate peptides for monitoring adult acute myeloid leukemia (AML) MRD.

RESULTS

47 statistically different expressed peptide peaks were obtained in the molecular weight range of 700-10000 Da. Quick classifier (QC) model had optimal distinction efficiency, in the training set with a sensitivity of 90% and a specificity of 93.33%. Peptides were identified as ubiquitin-like modifier activating enzyme 1(UBA1), isoform 1 of fibrinogen alpha chain precursor and platelet factor 4(PF4). The peptide up-regulated in newly diagnosed AML patients were decreased to the normal level after CR. When refractory & relapsed, relative intensity was elevated again. Results were contrary to down-regulated peptide peaks. Western blot demonstrated that levels of the UBA1 protein did not differ between the leukemia and normal cells. Levels of isoform 1 of fibrinogen alpha chain precursor protein and PF4 protein were both decreased in leukemia cells comparing with normal cells. The serum levels of the PF4 in the newly diagnosed AML patients and healthy controls were significantly different. Further correlation analysis did not indicate the correlated relation between platelet counts and PF4 content, the correlation coefficient was 0.097. Kaplan-Meier analyses of overall survival showed that relative intensity of peptides was correlated with patient's clinical outcome.

CONCLUSIONS

We speculate the peptides can be used as potential markers for monitoring minimal residual disease and clinical outcome assessment.

Apolipoprotein A1: A new serum marker correlated to JAK2 V617F proportion at diagnosis in patients with polycythemia vera

Polycythemia vera (PV) is a myeloproliferative disorder (MPD) characterized by an acquired gain-of-function mutation of the JAK2 protein (JAK2 V617F). Allele-specific quantitative PCR has showed a JAK2 V617F dosage effect on haematological and clinical parameters of PV at diagnosis, but it is unknown whether the level of certain serum proteins might correlate with the proportion of mutated JAK2. Taking into account that such proteins could represent useful prognostic marker, we investigated the serum protein profile of PV patients by SELDI-TOF MS. We identified apolipoprotein A1 (Apo-A1) as a serum marker correlated to the percentage of JAK2 V617F alleles; Apo-A1 expression being the highest for PV patients with more than 75% of mutated alleles. Immuno-assay on an automated random immuno-analyser confirmed the correlation between Apo-A1 concentrations and JAK2 V617F percentages, and showed that serum Apo-A1 assay allowed the specific discrimination of PV patients with high levels of mutated alleles (≥75%). These data suggest that Apo-A1 assay could be a useful assay for the stratification of PV patients at diagnosis.

Detection of serum tumor markers in multiple myeloma using the CLINPROT system

The discovery of biomarkers unique to multiple myeloma (MM) is of great importance to clinical practice. This study was designed to identify serum tumor marker candidates of MM in the mass range of 700-10000 Da. Serum samples from 48 MM patients and 74 healthy controls were collected and classified into a training dataset (MM/controls: 26/26) and a testing dataset (MM/controls: 22/48). Weak cation exchange magnetic beads, MALDI-TOF MS and analytic software in the CLINPROT system were used to do serum sample pre-fractionation, data acquisition and data analysis. Peak statistics were performed using Welch's t test. Mass spectra from the two model generation cohorts in the training dataset were analyzed by the Supervised Neural Network Algorithm (SNNA) in ClinProTools((TM)) to identify the mass peaks with the highest separation power. The resulting diagnostic model was subsequently validated in the testing dataset. A total of 89 discriminating mass peaks were detected by ClinProTools((TM)) in the range of 700-10000 Da using a signal to noise threshold of 3.0. Of these, 49 peaks had statistical significance (P < 0.0001) and four peaks with the highest separation power were picked up by SNNA to form a diagnostic model. This model achieved high sensitivity (86.36 %) and specificity (87.5 %) in the validation in the testing dataset. Using CLINPROT system and MB-WCX we found four novel biomarker candidates. The diagnostic model built by the four peaks achieved high sensitivity and specificity in validation. CLINPROT system is a powerful and reliable tool for clinical proteomic research.

Apolipoprotein A1 and C-terminal fragment of α-1 antichymotrypsin are candidate plasma biomarkers associated with acute renal allograft rejection

BACKGROUND

Current diagnostic methods of renal allograft rejection are neither sensitive nor specific. Needle biopsies are invasive and associated with patient morbidity. Thus, it is desirable to develop noninvasive tests to predict and diagnose rejection.

METHODS

Using a case-control approach, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry was used to identify plasma proteins associated with renal allograft rejection. From each rejection patient (n=16), two plasma samples (one near the biopsy date and the other at a time postbiopsy) were compared. Biopsy-confirmed nonrejection patients (n=48) were further analyzed as controls. Antibody-based quantitative enzyme-linked immunosorbent assay was performed to validate candidate biomarker apolipoprotein A1 (Apo A1) in a subset of the original and a second cohort of biopsy-confirmed rejection (n=40) and nonrejection (n=70) patients.

RESULTS

Twenty-two proteins/peptides showed significant differences between rejection and postrejection samples. Peptides 5191 Da and 4467 Da detected rejection with 100% sensitivity and 94% specificity. The 4467 Da peptide was identified as the C-terminal fragment of α-1 antichymotrypsin and a 28 kDa protein was determined as Apo A1. Both protein levels were significantly lower at rejection compared with postrejection. Protein levels of nonrejection patients were similar to the postrejection samples. Apo A1 enzyme-linked immunosorbent assay results showed significantly lower Apo A1 levels (P=0.001 for the original and P=4.14E-11 for the second cohort) at the time of rejection compared with nonrejection which coincides with the SELDI findings.

CONCLUSIONS

Together α-1 antichymotrypsin, Apo A1, and the unidentified 5191 Da peptide provide a plasma molecular profile, and this is associated with acute cellular renal allograft rejection.

Expression of S100A8 in leukemic cells predicts poor survival in de novo AML patients

Cytogenetic stratification remains insufficient for almost half of the acute myeloblastic leukemia (AML) cases, with AML patients requiring subsequent molecular investigation. In our study, we used mass spectrometry (MS)-based proteomic approaches to characterize de novo AML. Fifty-four samples (mononuclear cells from bone marrow or peripheral blood mononuclear cells collected and frozen before treatment) from two independent cohorts of newly diagnosed AML patients were analyzed. We showed that the protein signature of leukemic cells defined two clusters that displayed significant variation for overall and disease-free survival (P=0.001 and 0.0004, respectively). This proteomic classification refines the cytogenetic classes. AML patients with intermediate and unfavorable cytogenetic classifications could be subdivided according to their protein profiles into subgroups with significantly different survival rates. Among the proteins expressed by leukemic cells, we isolated a 10,800-Da marker that retained the highest discriminative value between living and deceased patients. The 10,800-Da marker was identified by MS peptide sequencing as S100A8 (also designated MRP8 or calgranulin A). Western blot analysis confirmed its expression mainly in AML patients with the worst prognosis, arguing for a selective deregulation associated with poor prognosis. These results suggest that the expression of S100A8 in leukemic cells is a predictor of low survival.

Proteomic analysis of haptoglobin and amyloid A protein levels in patients with vivax malaria

Advancements in the field of proteomics have provided great opportunities for the development of diagnostic and therapeutic tools against human diseases. In this study, we analyzed haptoglobin and amyloid A protein levels of vivax malaria patients with combinations of depletion of the abundant plasma proteins, 2-dimensional gel electrophoresis (2-DE), image analysis, and mass spectrometry in the plasma between normal healthy donors and vivax malaria patients. The results showed that the expression level of haptoglobin had become significantly lower or undetectable in the plasma of vivax malaria patients due to proteolytic cleavage when compared to healthy donors on 2-DE gels. Meanwhile, serum amyloid A protein was significantly increased in vivax malaria patient's plasma with high statistical values. These 2 proteins are common acute phase reactants and further large scale evaluation with a larger number of patient's will be necessary to establish the possible clinical meaning of the existential changes of these proteins in vivax malaria patients. However, our proteomic analysis suggests the feasible values of some plasma proteins, such as haptoglobin and serum amyloid A, as associating factor candidates for vivax malaria.

Proteomics-based prediction of clinical response in acute myeloid leukemia

OBJECTIVE

Response to chemotherapy is achieved in 60% to 70% of patients with acute myeloid leukemia. The ability to predict responders may help in stratifying patients and exploring different therapeutic approaches for nonresponders. Proteomics methods were used to search for predictive factors or combinations of factors.

MATERIALS AND METHODS

Peripheral blood plasma samples from 41 patients with confirmed acute myeloid leukemia with intermediate or poor cytogenetics were obtained prior to induction therapy for proteomic analysis. For each plasma sample, four fractions eluted from a strong anion column were applied to 3 different ProteinChip array surfaces and 12 surface-enhanced laser desorption/ionization spectra were generated. Peaks that correlated with response were identified, and decision trees incorporating these peaks along with various clinical and laboratory findings were constructed to predict response.

RESULTS

Multiple decision trees were constructed. One peak, when combined with age, provided strong positive prediction of responders with 83% accuracy. A second tree, which combined one peak with both cytogenetics and the percent of monocytes in peripheral blood, detected responders with 95% accuracy. A third peak was adequate to predict responders in the intermediate cytogenetic group with 86% accuracy.

CONCLUSIONS

Proteomic analysis should be further explored to define factors important in predicting clinical response in patients with acute myeloid leukemia.

Serum proteomic profiling and haptoglobin polymorphisms in patients with GVHD after allogeneic hematopoietic cell transplantation

We studied serum proteomic profiling in patients with graft versus host disease (GVHD) after allogeneic hematopoietic cell transplantation (allo-HCT) by two-dimensional gel electrophoresis (2-DE) and mass spectrometry analysis. The expression of a group of proteins, haptoglobin (Hp), alpha-1-antitrypsin, apolipoprotein A-IV, serum paraoxonase and Zn-alpha-glycoprotein were increased and the proteins, clusterin precursor, alpha-2-macroglobulin, serum amyloid protein precursor, sex hormone-binding globulin, serotransferrin and complement C4 were decreased in patients with extensive chronic GVHD (cGVHD). Serum haptoglobin (Hp) levels in patients with cGVHD were demonstrated to be statistically higher than in patients without cGVHD and normal controls (p < 0.01). We used immunoblotting and PCR in combination with 2-DE gel image analysis to determine Hp polymorphisms in 25 allo-HCT patients and 16 normal donors. The results demonstrate that patients with cGVHD had a higher incidence of HP 2-2 phenotype (43.8%), in comparison to the patients without cGVHD (0%) and normal donors (18.7%), suggesting the possibility that specific Hp polymorphism may play a role in the development of cGVHD after allo-HCT. In this study, quantitative serum Hp levels were shown to be related to cGVHD development. Further, the data suggest the possibility that specific Hp polymorphisms may be associated with cGVHD development and warrant further investigation.

Discovery and identification of potential biomarkers of pediatric acute lymphoblastic leukemia

Background

Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods

Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results

A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion

Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.

How will haematologists use proteomics?

Proteomics technologies are emerging as a useful tool in the identification of disease biomarkers, and in defining and characterising both normal physiological and disease processes. Many cellular changes in protein expression in response to an external stimulus or mutation can only be characterised at the proteome level. In these cases protein expression is often controlled by altered rates of translation and/or degradation, making proteomics an important tool in the analysis of biological systems. In the leukaemias, post-translational modification of proteins (e.g. phosphorylation, acetylation) plays a key role in the molecular pathology of the disease: such modifications can now be detected with novel proteomic methods. In a clinical setting, serum remains a relatively un-mined source of information for prognosis and response to therapy. This protein rich fluid represents an opportunity for proteomics research to benefit hematologists and others. In this review, we discuss the technologies available for the study of the proteome that offer realistic opportunities in haematology.

Differentiation of tumour-stage mycosis fungoides, psoriasis vulgaris and normal controls in a pilot study using serum proteomic analysis

BACKGROUND

Serum proteomic analysis is an analytical technique utilizing high-throughput mass spectrometry (MS) in order to assay thousands of serum proteins simultaneously. The resultant 'proteomic signature' has been used to differentiate benign and malignant diseases, enable disease prognosis, and monitor response to therapy.

OBJECTIVES

This pilot study was designed to determine if serum protein patterns could be used to distinguish patients with tumour-stage mycosis fungoides (MF) from patients with a benign inflammatory skin condition (psoriasis) and/or subjects with healthy skin.

METHODS

Serum was analysed from 45 patients with tumour-stage MF, 56 patients with psoriasis, and 47 controls using two MS platforms of differing resolution. An artificial intelligence-based classification model was constructed to predict the presence of the disease state based on the serum proteomic signature.

RESULTS

Based on data from an independent testing set (14-16 subjects in each group), MF was distinguished from psoriasis with 78.6% (or 78.6%) sensitivity and 86.7% (or 93.8%) specificity, while sera from patients with psoriasis were distinguished from those of nonaffected controls with 86.7% (or 93.8%) sensitivity and 75.0% (or 76.9%) specificity (depending on the MS platform used). MF was distinguished from unaffected controls with 61.5% (or 71.4%) sensitivity and 91.7% (or 92.9%) specificity. In addition, a secondary survival analysis using 11 MS peaks identified significant survival differences between two MF groups (all P-values <0.05).

CONCLUSIONS

Serum proteomics should be further investigated for its potential to identify patients with neoplastic skin disease and its ability to determine disease prognosis.

Building the power house: recent advances in mitochondrial studies through proteomics and systems biology

The emerging field of systems biology seeks to develop novel approaches to integrate heterogeneous data sources for effective analysis of complex living systems. Systemic studies of mitochondria have generated a large number of proteomic data sets in numerous species, including yeast, plant, mouse, rat, and human. Beyond component identification, mitochondrial proteomics is recognized as a powerful tool for diagnosing and characterizing complex diseases associated with these organelles. Various proteomic techniques for isolation and purification of proteins have been developed; each tailored to preserve protein properties relevant to study of a particular disease type. Examples of such techniques include immunocapture, which minimizes loss of posttranslational modification, 4-iodobutyltriphenylphosphonium labeling, which quantifies protein redox states, and surface-enhanced laser desorption ionization-time-of-flight mass spectrometry, which allows sequence-specific binding. With the rapidly increasing number of discovered molecular components, computational models are also being developed to facilitate the organization and analysis of such data. Computational models of mitochondria have been accomplished with top-down and bottom-up approaches and have been steadily improved in size and scope. Results from top-down methods tend to be more qualitative but are unbiased by prior knowledge about the system. Bottom-up methods often require the incorporation of a large amount of existing data but provide more rigorous and quantitative information, which can be used as hypotheses for subsequent experimental studies. Successes and limitations of the studies reviewed here provide opportunities and challenges that must be addressed to facilitate the application of systems biology to larger systems.