Rapid Diagnosis and Prognosis of de novo Acute Myeloid Leukemia

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Rapid diagnosis and prognosis of de novo acute myeloid leukemia by serum metabonomic analysis Yihuang Wang, Limin Zhang, Wen-Lian Chen, Jing-Han Wang, Ning Li, Jun-Min Li, Jian-Qing Mi, Wei-Na Zhang, Yang Li, Song-Fang Wu, Jie Jin, Yun-Gui Wang, He Huang, Zhu Chen, Sai-Juan Chen, and Huiru Tang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr400403p • Publication Date (Web): 02 Sep 2013 Downloaded from http://pubs.acs.org on September 10, 2013

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Journal of Proteome Research

Institute of Hematology, Rui Jin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, National Key Laboratory of Medical Genomics Wu, Song-Fang; Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education; Institute of Health Sciences, Shanghai Institutes for Biological Sciences and Graduate School, Chinese Academy of Sciences and Shanghai Institute of Hematology, Rui Jin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, National Key Laboratory of Medical Genomics Jin, Jie; Zhejiang Institute of Hematology, First Hospital affiliated to Zhejiang University School of Medicine, Wang, Yun-Gui; Zhejiang Institute of Hematology, First Hospital affiliated to Zhejiang University School of Medicine, Huang, He; Zhejiang Institute of Hematology, First Hospital affiliated to Zhejiang University School of Medicine, Chen, Zhu; Institute of Health Sciences, Shanghai Institutes for Biological Sciences and Graduate School, Chinese Academy of Sciences and Shanghai Institute of Hematology, Rui Jin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, National Key Laboratory of Medical Genomics; Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education Chen, Sai-Juan; Institute of Health Sciences, Shanghai Institutes for Biological Sciences and Graduate School, Chinese Academy of Sciences and Shanghai Institute of Hematology, Rui Jin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, National Key Laboratory of Medical Genomics; Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine, Ministry of Education Tang, Huiru; Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance

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Rapid diagnosis and prognosis of de novo acute myeloid leukemia by serum metabonomic analysis

Yihuang Wang1,4, Limin Zhang2, Wen-Lian Chen1, Jing-Han Wang1, Ning Li2, Jun-Min Li1, Jian-Qing Mi1, Wei-Na Zhang1, Yang Li1, Song-Fang Wu1,4, Jie Jin3, Yun-Gui Wang3, He Huang3, Zhu Chen1,4*, Sai-Juan Chen1,4*, Huiru Tang2* 1

State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Institutes for Biological

Sciences and Graduate School, Chinese Academy of Sciences and Shanghai Institute of Hematology, Rui Jin Hospital, affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China 2

Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and

Atomic and Molecular Physics, Centre for Biospectroscopy and Metabonomics, Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, Wuhan, 430079, P. R. China 3

Zhejiang Institute of Hematology, First Hospital affiliated to Zhejiang University School of Medicine, Hangzhou,

China. 4

Key Laboratory of Systems Biomedicine(Ministry of Education), Shanghai Center for Systems Biomedicine,

Shanghai Jiao Tong University

*To whom all correspondence should be addressed. Zhu Chen: Tel, +86-21-64377859 ; fax,

+86-21-64743206,

e-mail,

[email protected];

Sai-Juan

Chen:

Tel,

+86-21-64377859 ; fax, +86-21-64743206, e-mail, [email protected]; Huiru Tang: Tel, +86-27-87198430; fax, +86-27-87199291; e-mail, [email protected];


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Abstract Acute myeloid leukemia (AML) is a life threatening hematological disease. Novel diagnostic and prognostic markers will be essential for new therapeutics and for significantly improving the disease prognosis. To characterize the metabolic features associated with AML and search for potential diagnostic and prognostic methods, here we analyzed the phenotypic characteristics of serum metabolite composition (metabonome) in a cohort of 183 patients with de novo acute myeloid leukemia together with 232 age- and gender-matched healthy controls using 1

H-NMR spectroscopy in conjunction with multivariate data analysis. We observed significant

serum metabonomic differences between AML patients and healthy controls, and between AML patients with favorable and intermediate cytogenetic risks. Such differences were highlighted by systems differentiations in multiple metabolic pathways including glycolysis/gluconeogenesis, TCA cycle, biosynthesis of proteins and lipoproteins, metabolisms of fatty acids and cell membrane components especially choline and its phosphorylated derivatives. This demonstrated the NMR-based metabonomics as a rapid and less invasive method for potential AML diagnosis and prognosis. The serum metabolic phenotypes observed here indicated that integration of metabonomics with other techniques will be useful for better understanding the biochemistry of pathogenesis and progression of leukemia.

Keywords: Metabonomics, NMR, acute myeloid leukemia, diagnosis, prognosis



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Introduction Leukemia is one of the most prevalent cancers for children and young adults in both the developed and developing world.1 In 2000 alone, for instance, approximately 256,000 children and adults around the world developed some forms of leukemia and more than 200,000 died from it. There are two major leukemia cell types, namely, lymphocytic and myeloid (or myelogenous) in acute and chronic forms. Acute myeloid leukemia (AML) accounts for about 80-90% of all acute leukemias in adults and has several subtypes (AML-M0 through M7), representing clonal expansion of malignant hematopoietic cells blocked at distinct differentiation stages along with different lineages (such as myeloblastic, promyelocytic, monoblastic, erythroblastic and megakaryoblastic)2. The incidence of leukemia appears to be population dependent and leukemia is the sixth of the top-ten malignant neoplasms in China with AML being much more common than acute lymphocytic leukemia in adults. One of the prominent clinical manifestations of AML is the presence of blast cells, which are the morphologically immature blood cells, in the hematopoietic organs including bone marrow and blood. The prognosis of AML in general is far from satisfactory. Although the great majority of cases with acute promyelocytic leukemia (APL or AML-M3), which accounts for about 10% of AML, can be cured with a combined chemotherapy with all-trans retinoic acid and arsenic trioxide3, the overall five-year survival rate for all other subtypes of AML is only about 30% in patients under 60 years of age in China4. For those above 60 years of age, the median survival is less than one year and the cure rates are under 10%.5 Although there are great advances in the study of AML biology as well as improvement of therapeutics and clinical care in past several decades, the mainstream initial treatment for AML other than APL is still chemotherapy using a


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combination of cytosine arabinoside (ara-C) with an anthracycline. Such protocol has been used as a standard one of care worldwide for nearly 40 years.5 Therefore, new approaches for both mechanistic studies and treatment of AML are still in urgent need. As a hematopoietic cancer, AML is characterized by malignant cells dispersed in the circulation interacting with almost all organs and exerting holistic influences on the physiology of whole body. In this context, cytogenetic and genomic studies of AML alone may not be sufficient, and the comprehensive research at the systems level is essential for a better understanding of the biology of AML as well as the effective development of new therapeutics. Currently, the classification of AML is based on the morphologic, cytochemical, immunophenotypic, clinical and genetic information.6 The diagnostic markers should be well characterized and be able to predict both the response to therapy and overall survival. In clinical practice, the karyotype of de novo AML is still the most important single prognostic factor predicting the rates of complete remission and overall survival as well as the risk of relapse. Based on the karyotypes of leukemia cells from peripheral blood (PB) or bone marrow (BM) samples, AML patients are classified into favorable, intermediate and unfavorable risk cytogenetic groups.7,8 Those with intermediate risk prognostic factors compose the largest and the most heterogeneous group of AML patients.8,9 Both patients with cytogenetically normal AML (CN-AML) and those with neither favorable nor unfavorable cytogenetic abnormalities have been assigned to this group. Many new prognostic factors, including gene mutations in FLT3 (Fms-like tyrosine kinase 3),10 NPM1 (nucleophosmin 1),11,12 and CEBPA (CCAAT enhancer-binding protein-α)13 have been identified in patients of this group. These new molecular markers make it possible to refine the risk stratification in this group, especially for those with a normal karyotype.



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However, the tests of molecular markers are time consuming and costly processes. Furthermore, the bone marrow biopsy for the cytogenetic and gene mutation analysis is obviously invasive to the patients. Hence, the development of new prognostic factors and less invasive diagnostics for AML will probably not only facilitate better understanding of AML biology but also provide new potential therapeutic targets.14 Metabonomic analysis has been shown as a powerful approach for measuring the global and dynamic metabolic response of patients to diseases and clinical interventions.15,16 Metabonomics has already been applied to investigate the pathogenesis and for diagnosis of various diseases such as cardiovascular diseases,17 diabetes18 and inflammatory bowel diseases19,20. This approach has also been employed in characterizing the metabolic phenotypes (metabotypes) of a variety of cancers such as brain,21,22 prostate,23 and liver cancers.24 It is particularly interesting to note that 1

H NMR analysis has shown the phenotypic differences in the serum metabonomes between

different molecular subgroups of chronic lymphocytic leukemia (CLL).25 This is not surprising since the blood metabolite composition (or metabonome) reflects the real-time expression of all biochemical processes in human body and the abnormal metabonomic changes reflect the biochemistry changes in the whole system. Therefore, metabonomic analysis of blood plasma and serum samples with NMR and/or mass spectrometry (MS) in conjunction with multivariate statistics has shown effectiveness in identifying new cancer markers and has become potentially an important tool for diagnosis and prognosis with little invasiveness.23,25-27 Few metabonomic studies have been reported so far for the large cohorts of AML patients, to the best of our knowledge. The reported metabonomic investigations of AML were mostly focused on the cultured AML cells28,29. Although these well-controlled studies offered excellent molecular


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information for AML pathogenesis, translations of the results from these model systems to clinical settings remain a challenge. Previously published metabonomics results from CLL patients undoubtedly illustrated the usefulness and feasibility of the NMR-based metabonomics methods as potential non-invasive prognosis for CLL25 even though with only limited cases. It is therefore conceivable that serum metabonomic analysis ought to be useful for forwarding our understandings of AML especially with a large cohort by making use of the high-throughput NMR techniques. In this study, we report an NMR-based serum metabonomic analysis of a cohort of 183 de novo AML patients against 232 age- and gender-matched healthy controls. Our objectives are to define the serum metabotypic differences between AML patients and healthy controls and to evaluate the potentials of such metabolic phenotyping techniques in AML diagnosis and prognosis.



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Materials and Methods Cohort managements and sample collections. This study was a part of a multi-center investigation and 183 inpatients with de novo acute myeloid leukemia (AML) were recruited from Ruijin Hospital affiliated to Shanghai Jiaotong University School of Medicine and the First Hospital affiliated to Zhejiang University School of Medicine, Hangzhou, China during 2006-2010. The patients with leukemia secondary to other malignancies or transformed from myelodysplastic syndrome were not included in this study. To avoid the age and gender effects, 232 age- and gender-matched healthy controls was also recruited as negative controls. The study was approved by the institutional review board in both hospitals and the use of both serum and bone marrow samples together with the clinical records were conducted with the informed consents in accord with the Helsinki declaration. Serum and bone marrow samples were collected (the latter was only done for patients) prior to any medications. Blood samples were collected from the participants after overnight fasting (for at least 10 hours) and serum samples were collected in a standard manner followed with storage in a -80℃ freezer. Serum samples were then transported in dry-ice boxes to the NMR laboratory at Wuhan Institute of Physics and Mathematics, the Chinese Academy of Sciences, for NMR analysis.

Assays of Cytogenetic and Molecular Marker The cytogenetic analysis for the AML patients was conducted as routine clinical procedure in both hospitals. The assays of molecular markers for all AML patients in this study were performed in the Ruijin Hospital. The cytogenetic and molecular marker analysis was conducted according to the methods described previously30. Some characteristics of this cohort are tabulated in Table S1


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with the overall and event-free survival rates for favorable and intermediate risk subgroups shown in Figure S1. 1

H NMR spectroscopic analysis of serum samples. Each serum sample (200 µL) was mixed with 400 µL saline solution (0.9% NaCl, w/v)

containing 50% D2O (as a field lock). After vortex and 10 min centrifugation (11180 x g, 4 ºC), 550 µL supernatant of each sample was transferred into a 5 mm NMR tube respectively. All 1H NMR spectra were recorded at 298K on a Bruker AVIII 600 spectrometer (Bruker Biospin, Germany) equipped with a cryogenic inverse detection probe with operation frequency of 600.13 MHz for 1H. To attenuate the signals from macromolecules, a 1H NMR spectrum was acquired for each sample using the standard Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence (RD-90°-(τ-180°-τ)n-acquisition) as previously described.31 The 90° pulse length was adjusted to about 10 µs for each sample and water signal was suppressed with a weak continuous wave irradiation during recycle delay (RD). 32 K data points were collected for each spectrum with a spectral width of 20 ppm (12 kHz) and RD of 2 s. The spin-spin relaxation delay, 2nτ, was set to 96 ms. Free induction decays so obtained for all samples were multiplied by an exponential function with a line broadening factor of 1 Hz prior to Fourier transformation. Chemical shifts for all spectra were then manually referenced to the anomeric proton signal of α-glucose (δ5.23). For the purposes of signal assignments, a series of two-dimensional NMR (2D NMR) spectra were recorded and processed for selected samples as previously.32,33 These spectra included 1H-1H correlation spectroscopy (COSY), total correlation spectroscopy (TOCSY), 1H-13C heteronuclear single quantum correlation (HSQC), and 1H-13C heteronuclear multiple bond correlation (HMBC) spectra.



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NMR Data Processing and Multivariate Data Analysis All the 1H NMR spectra were corrected for phase and baseline distortions using Topspin (V2.0, Bruker Biospin) and the spectral region δ 0.5-9.5 were divided into buckets with equal width of 0.004 ppm (2.4 Hz) using AMIX software package (V3.8.3, Bruker Biospin). The regions at δ 4.32-5.17 and δ5.5-6.0 were discarded to eliminate the effects of imperfect water saturation and to remove urea signals. Multivariate data analysis was conducted with SIMCA-P+ package (V12.0, Umetrics, Sweden) following normalization to the volume of serum samples. Principal component analysis (PCA) was carried out on the mean-centered data to generate an overview and check for the outliers. Partial least-squares-discriminant analysis (PLS-DA) and the orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were subsequently performed using the unit-variance scaled data to find metabolites having significant intergroup differences.34 The OPLS-DA models were built with two components calculated and with six-fold cross-validation. These models were further evaluated for their validities with CV-ANOVA method.35 After back-transformation,34 the loadings were plotted using an in-house developed Matlab (V7.1, The Mathworks, MA) script with correlation coefficients color-coded for each variables (or the metabolite signals). The color-coded variables indicate the significance of metabolites contributing to the intergroup differentiation with a “hot” colored (e.g., red) metabolite being more significant than a “cold” colored (e.g., blue) ones. Cutoff values for the correlation coefficients were chosen depending on the number of samples used to extract metabolites having significant intergroup differences based on the discrimination significance (p < 0.05) for the Pearson’s product-moment correlation coefficients.34


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Results Clinical Stratification of AML Patients.

The stratification of AML patients in this

study was based on the WHO criteria and NCCN clinical practice guideline,8 respectively (Table 1). Concluded from clinical observations, AML patients with chromosome abnormalities of t(15;17), t(8;21) and inv(16) tend to have better clinical outcomes, which means better response to treatment, higher rates of complete remission and long term survival. In contrast, patients with chromosome abnormalities in t(9;22), inv (3)/t(3;3), 11q23 -5, -7, del(5q), del(7p) and complex chromosome translocations usually have deteriorated clinical outcomes. Patients with neither above mentioned chromosome abnormalities nor 11q23 related chromosome translocations have clinical outcomes in between. Therefore, AML patients can be divided into favorable, unfavorable and intermediate risk subgroups based on their characteristics of karyotypes. In this study, AML patients (n=183) were divided into the three risk subgroups, favorable (n=58), unfavorable (n=6) and intermediate (n=119) groups respectively. 1

H NMR Spectroscopy of Serum Samples.

In order to emphasizing small

metabolites in human serum, the T2-edited NMR spectra from the CPMG sequence were employed for metabonomic analysis. Typical 1H NMR spectra (Figure 1) showed that a set of metabolite signals was observable for a healthy control and an AML patient. The NMR signals were assigned to individual metabolites based on the published data,31,36,37 publically available and in-house databases. The assignments were further confirmed individually with the 2D NMR data (Table 2). Visual inspection showed clear differences in glucose-to-lactate ratio (observable from δ5.23 and δ4.13) between the spectra of the control and AML patient (Figure 1).

Multivariate data analysis revealed significant serum metabonomic



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differences between AML patients and healthy controls.

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Multivariate data

analysis of NMR spectra was performed to reveal the different metabolic patterns between the healthy controls and AML patients. In OPLS-DA modeling, both age- and gender-matched healthy controls and AML patients were respectively divided into two subgroups, namely, the training and validation sets (Table S1) so as to further ensure the model qualities. Two separate models were calculated with the data from the training sets (Figure 2A) and validation sets (Figure 2B) so that the latter was also considered as an independent validation to the former. The results from both CV-ANOVA and permutation tests (Figure S2) showed good qualities for these OPLS-DA models. The validities of these OPLS-DA models indicated that significant differences were present in the serum metabonomic phenotypes between healthy controls and AML patients. Corresponding loadings plots (Figure 2) revealed that a number of serum metabolites had significant differences (Table 3) between these two groups. Compared with the healthy controls, AML patients had higher levels in phenylalanine, tyrosine, N-acetyl-glycoproteins (NAG), citrate, mannose and glucose but lower levels in choline, glycerophosphorylcholine (GPC), phosphorylcholine (PC), acetylcarnitine, some unsaturated fatty acids, HDL, valine, isoleucine, leucine, lysine, arginine, glutamine, alanine, histidine, scyllitol, and lactate.

Metabonomic analysis indicated some potential for AML prognosis. We further tested the possible potentials of metabonomic analysis for AML prognosis. OPLS-DA was conducted to explore metabonomic differences in sera of AML patients between three subgroups. The results showed that significant metabonomic differences were detectable between the subgroups with favorable and intermediate cytogenetic risks (Figure 3). However, only limited


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cases were available for AML with unfavorable cytogenetic risk (n=6) in this study. The metabolic differences between those with intermediate and unfavorable cytogenetic risks remain to be further investigated. Compared with the favorable cytogenetic risk group, the intermediate group had significantly higher serum levels in some amino acids (e.g., isoleucine, leucine, valine, glutamine, glutamate, lysine, arginine, phenyalanine and histidine), myo-inositol, choline, PC/GPC, lactate and HDL but lower in VLDL and LDL (Table 4).



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Discussions The importance of glycolysis for cancer cells even in the present of oxygen has been realized since 1920s and is now known as “Warburg effect”. Warburg then proposed it as a possible cause of many cancers,38,39 which could therefore be regarded as metabolic diseases. However, this theory appeared to be neglected by and large in the light of rapid developments in molecular and genetic cancer biology in the late 20th century. Nonetheless, recent studies showed that several cancerous signaling pathways also regulated metabolic pathways for biomass production for cancer cells whilst certain cancer-associated mutations could turn on glycolysis.40-42 These led to a revival of interests in cancer metabolism, and the reprogramming of energy metabolism was included as one of the “emerging hallmarks” of cancer.43 This study presents the first serum metabolic pattern in the context of AML. Here, the 1H NMR-based metabonomics approach was employed to distinguish the metabonomic differences between the AML patients and healthy controls together with such differences between different AML subgroups. Our results revealed that AML patients had very different serum metabolic signatures from the age- and gender-matched healthy controls. Furthermore, such metabonomic analysis also distinguished the patients with favorable cytogenetic risk from these with intermediate risk. The metabolic differences were highlighted in multiple metabolic pathways involving energy metabolism, protein biosynthesis, metabolisms of fatty acids and choline which probably involved biosynthesis of cell membranes. Differential metabolite levels in sera of AML patients against the healthy controls clearly indicated a shift of energy metabolism under the condition of leukemia. Cancer cells require energies largely produced from glycolysis consuming significant amount of blood glucose. When


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glucose from glycogenolysis cannot meet such demands for glucose, gluconeogenesis becomes the secondary route of glucose supply by consuming glucogenic amino acids and circulation lactate. Our observation of higher citrate level in AML patients than in controls (Figure 2, Table 3) indicated that AML enhanced TCA cycle probably fed by amino acids. Higher blood glucose level in AML patients (Table 3) suggested that AML patients had enhanced glucogenic processes to ensure homeostasis for circulation glucose. Such notion is further supported by the reduced levels of lactate and glucogenic amino acids including alanine, glutamine, histidine, valine, and isoleucine in AML patients’ sera (Table 3). These changes may also be related to the demands of the biomass production in cancerous cells in patients with AML.40,41 Phenylalanine (Phe) is an essential amino acid for all mammals and its dietary intakes are essential for protein biosynthesis. Phe can then be metabolized into tyrosine by hydroxylation. Our results showed that serum levels for both phenylalanine and tyrosine were significantly higher in AML patients than in healthy controls; the level change for Phe was much greater than for tyrosine (Table 3). This implies that the degradation of proteins from the host is also enhanced under cancer burden, which is supported by significant lower serum albumin levels (Table S1), to generate amino acids needed for gluconeogenesis and catabolism to feed TCA cycle.44,45 Glutamine is not an essential amino acid, but has a diverse physiological function. It is a nitrogen carrier in the body and a major fuel source for the rapidly proliferating cells, such as lymphocytes, monocytes and tumor cells. Glutamine can be converted to glucose as an energy source, and also be used in the biosynthesis of purines and pyrimidines as building blocks for DNA and RNA biosynthesis. In normal state, the serum glutamine is maintained at a fairly constant level. In cancer patients, the rapid proliferating tumor cells are the major consumer of



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glutamine, and cause a gradually depletion of glutamine in host blood along with the disease progression.46,47 The decreased level of glutamine in the blood of AML patients is in agreement with previously observations in other types of cancer.48,49 Significant lower levels of HDL, acetylcarnitine and some unsaturated fatty acids were found in the blood of AML patients than in controls. This is in accordance with previous findings48,50-52 and indicates the variations of fatty acid metabolisms associated with AML. This situation, together with the HDL depletion from blood, may be related to the excessively increased demands for lipids and cholesterols in tumor cell proliferation. Choline plays important roles in phospholipid metabolism related to cell membranes and an elevation of phosphocholine (PC) has been reported together with the elevation of total choline containing metabolites (tCho) in numerous in vitro and in vivo NMR studies53-55 of tumor cells. This is now considered as a characteristic feature for aberrant choline phospholipid metabolisms in cancers.55 Lower levels for choline and PC/GPC observed in the AML patients’ serum in this study probably result from the excessive needs for choline and its derivatives during leukemic cell proliferation. In general, the trend of alterations in levels of glucose, phenylalanine, lactate, citrate, alanine, lysine, leucine, isoleucine, and valine are in agreement with the observations in oral cancer.27 However, serum tyrosine level was decreased whereas choline level showed an increase in oral cancer.27 Nonetheless, sera of AML patients shared most of the significantly changed metabolites with those observed in the case of human hepatocellular carcinoma (HCC)56 with only the difference in regard to glutamine changes, which was decreased in AML but increased in HCC56. Such differences are not surprising given the different metabolic behavior of different cancer cells


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and different nature of AML from the tissue tumors. Nevertheless, our observations seem to be more consistent with the observations in oral cancer and HCC than those in chronic lymphocytic leukemia (CLL)25 and therapy-related myelodyspasia/acute myeloid leukemia (t-MDS/AML).57 It is particularly interesting to note that the results of these studies on hematological malignancies differ not only in the contributing metabolites separating the patients from healthy controls, but also in the changes of certain metabolites. These differences may be attributable to the intrinsically heterogeneous nature of the hematological malignances, the differences in disease stages when sampling, and the clinical features of the controls. Unlike the other two studies,25,57 we used age- and gender-matched controls to eliminate the effects of some confounding factors in this study. However, it remains unknown whether and how these confounding factors contribute to the aforementioned metabolic differences. It is more striking to observe that the patterns of serum metabolite changes have some similarities for AML patients and type 2 diabetes mellitus (T2DM) compared with controls. Except for phenylalanine and tyrosine (increased in AML and decreased in T2DM), the levels of glucose, citrate, alanine, isoleucine, leucine, valine, glutamine, lysine, histidine, HDL and choline had the same trends of alterations from controls31 for both groups of patients. It is possible that these two diseases, though having completely different pathophysiology, affect the same metabolic pathways related to glucose, amino acids and lipid metabolisms. Hence, we postulate that AML may interfere with the insulin-mediated regulation of glucose, protein and energy metabolisms. In fact, a surge of researches on exploiting metabolic pathways as anti-caner targets has already been reported41,58 and emerging evidences suggest the use of drugs for type 2 diabetes as potential



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anti-cancer therapeutics.59,60 This clearly warrants further detailed investigations. We further tested the potential prognostic power of NMR-based metabonomics in the stratification of cytogenetic risks in AML patients. Significant metabonome-related phenotypic differences were detected between AML patients with favorable and intermediate cytogenetic risks. The significantly changed metabolites (Table 4) between these two groups were fairly similar to those contributing a metabonomic separation of the AML patients from healthy controls. However, levels of most significantly changed metabolites were higher in patients with intermediate cytogenetic risk than those in patients with favorable cytogenetic risk. The exceptions were observed for LDL, VLDL, unsaturated fatty acids and triglycerides which were higher in the latter group. This suggests that the leukemic cells in patients with intermediate cytogenetic risk are not at the full-wing state of development. Therefore, they are poised to be more resistant to therapies and need shorter time to accrue mutations facilitating further malignant transformation compared with their counterparts in patients with favorable cytogenetic risk. With only limited cases (n=6) available for the unfavorable risk group in this study, OPLS-DA modeling failed to yield conclusive results on whether metabonomic phenotypes for AML patients with unfavorable cytogenetic risk differed from these with intermediate risk. Further studies are clearly needed with much more cases for the latter. Conclusions This study has demonstrated the feasibility for the NMR-based metabonomics approach to distinguish the serum metabolic profiles of AML patients from those of healthy controls. This approach is also useful for stratifying risk groups in AML patients. Compared with controls, AML patients showed obvious serum metabonomic differences involving aberrant metabolism pathways


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including glycolysis/gluconeogenesis, TCA cycle, protein biosynthesis, lipoprotein changes, choline and fatty acid metabolisms. It is obvious that larger cohort studies are warranted in future with an integrative approach using metabonomics, genomics, epigenomics, transcriptomics and proteomics. It will be particularly interesting to test the possible applicability of the NMR-based metabonomics in the prognosis of AML with unfavorable risk. Nevertheless, with the non-invasive and high throughput nature of 1H NMR techniques, the present results have already indicated the great potential of this approach in rapid and noninvasive diagnosis and prognosis of AML in clinical settings.



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Abbreviations:

AML: acute myeloid leukemia; CLL: chronic lymphocytic leukemia;

COSY, correlation spectroscopy; CPMG, Carr-Purcell-Meiboom-Gill; FID, free induction decay; FT, Fourier Transformation; NMR, nuclear magnetic resonance; OPL-SDA, orthogonal partial least-squares-discriminant analysis; PCA, principal components analysis; PLS, partial least-squares; TOCSY, total correlation spectroscopy; T2DM, type 2 diabetes.

Acknowledgement.

We acknowledge financial supports from Ministry of Science and

Technology of China (2010CB529200, 2010CB912501, 2012AA02A505, 2013CB966800 and 2013ZX09303302), Ministry of Health of China (201202003), National Natural Science Foundation of China (21175149 and 21221064), Chinese Academy of Sciences (KJCX2-YW-W13) and Shanghai Municipal Natural Science Foundation (12ZR1418400).

Supporting Information Available:

Table S1. Some characteristics of 232 healthy

controls and 183 AML patients in the training and validation sets; Figure S1. The overall and event free survival rates for favorable and intermediate risk subgroups; Figure S2. Permutation test results for OPLS-DA models. This material is available free of charge via the Internet at http://pubs.acs.org.


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Figure captions Figure 1. Typical 1H CPMG NMR spectra of plasma from healthy controls and acute myeloid leukemia (AML) patients. Metabolite keys: 1. high-density lipoprotein (HDL); 2. low-density lipoprotein (LDL); 3. very low-density lipoprotein

(VLDL); 4. isoleucine; 5. leucine; 6. valine;

7. D-3-hydroxybutyrate (3-HB); 8. lactate; 9. alanine; 10. lysine; 11. arginine; 12. acetate; 13. N-acetyl-glycoproteins (NAG); 14. glutamine; 15. glutamate; 16. citrate; 17. succinimide; 18. lipids; 19. choline; 20. phosphorylcholine; 21. glycerophosphocholine; 22. scyllitol; 23. glucose and α-protons of amino acids; 24. glycine; 25. β-glucose; 26. α-glucose; 27. unsaturated fatty acids (UFA); 28. tyrosine; 29. phenylalanine; 30. histidine; 31. formate. 32. α-mannose; 33. acetylcarnitine; 34. myo-inositol; 35. triglycerides.

Figure 2. OPLS-DA scores (left) and loadings plots for (A) the training set with healthy controls (HC, n=118, black) and AML patients (AML, n=95, red) (p=2.1x10-6 from CV-ANOVA); (B) the validation set with healthy controls (HC, n=114, black) and AML patients (AML, n=47, red) (p=0 from CV-ANOVA). Results were from the six-fold cross-validated models and colored scales were for the correlation coefficients (|r|) of variables.

Figure 3. OPLS-DA scores (left) and loadings plots for AML patients with favorable (Good, black) and intermediate (Medium, red) cytogenetic risks (p=0 from CV-ANOVA). Results were from the six-fold cross-validated models and colored scales were for the correlation coefficients (|r|) of variables.



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Table 1. Clinical Stratification of AML Patients Variables

AML (n=183)

WHO subtype

n (%)

AML with t(15;17)

33 (18.03)

AML with t(8;21)

20 (10.93)

AML with t(16;16)

5 (2.73)

AML with minimal differentiation

1 (0.55)

AML without maturation

5 (2.73)

AML with maturation

22 (12.02)

Acute myelomonocytic leukemia

48 (26.23)

Acute monoblastic/monocytic leukemia

44 (24.04)

Acute erythroid leukemias

5 (2.73)

Cytogenetic risk group Favorable

58 (31.69)

Intermediate

119 (65.03)

Unfavorable

6 (3.28)


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Table 2. NMR data and assignments for the metabolites in human serum. key

metabolites

moieties

δ 1H (ppm) and multiplicitya

δ 13C (ppm)

1

HDL

CH3

0.82(m)

#

2

LDL

CH3

0.85(m)

#

3

VLDL

CH3

0.88(m)

#

4

Isoleucine

αCH, βCH, γCH3, δCH3

3.65(d), 1.95(m), 0.99(t), 1.02(d)

62.6, 38.8, 17.8, 13.9

5

Leucine

αCH, βCH2, γCH3, δCH3

0.94(d), 3.72(t), 1.69(m), 0.91(d)

24.5, 42.8, 27.3, 24.5

6

Valine

αCH, βCH, γCH3

3.6(d), 2.26(m), 0.98(d), 1.04(d)

63.4, 31.9, 19.5, 20.9

7

D-3-hydroxybutyrate

CH, CH2, γCH3, CH2

4.16(dt),2.41(dd),1.20(d), 2.31(dd)

68.8, 49.5, 24.4, 49.5

8

Lactate

αCH, βCH3

4.11(q), 1.32(d)

63.4, 71.1

9

Alanine

αCH, βCH3

3.77(q), 1.48(d)

53.9/178.9, 19.3

10

Lysine

αCH, βCH2, γCH2, δCH2

3.76(t), 1.89(m), 1.72(m), 3.01(t)

57.4, 33.0, 29.4, 42.4

11

Arginine

CH2, CH2, CH2, CH

1.68(m), 1.90(m), 3.23(t), 3.76(t)

26.6, 30.3, 57.2, 160.1

12

Acetate

CH3

1.91(s)

26.5/184.4

13

N-acetyl-glycoproteins

CH3

2.04(s)

#

14

Glutamate

αCH, βCH2, γCH2

2.06(m),2.11(m), 2.36(m)

28.9,33.4, 57.1

15

Glutamine

αCH, βCH2, γCH2

2.15(m),2.44(m), 3.77(m)

30.1,30.1,36.4

16

Citrate

CH2(1/2), CH2(1/2)

2.55(d), 2.65(d)

48.5, 78.2, 181.7, 184.6

17

Succinimide

CH

2.75(s)

#

CH3, (CH2)n, CH2-C=C, CH2-C=O,

0.89(m), 1.27(m), 2.0(m),

18

Lipid =C-CH2-C=, -CH=CH-

2.3(m), 2.78(m), 5.3(m)

#

19

Choline

N(CH3)3, OCH2, NCH2

3.2(s), 4.05(t), 3.51(t)

56.5, 58.1, 7.01

20

Phosphocholine(PC)

N(CH3)3, OCH2, NCH2

3.22(s), 4.21(t), 3.61(t)

57.1, 74.9

21

Glycerophosphocholine

N(CH3)3, OCH2, NCH2

3.22(s), 4.32(t), 3.68(t)

57.1, 74.9

22

Scyllitol

CH

3.36

#

23

Glucose/amino acids

α-CH resonances

3.3-3.9

#

24

Glycine

CH2, COOH

3.56(s)

44.3, 175.3

25

β-Glucose

1-CH

4.66(d)

98.6

26

α-Glucose

1-CH

5.23(d)

94.8

27

unsaturated fatty acids

CH

6.53(s)

137.8

28

Tyrosine

CH, CH

6.89(dd), 7.18(dd)

119.2, 133.5

29

Phenylalanine

Ring-CH

7.40(m), 7.33(m), 7.35(m)

132.4, 132.6. 131.3

30

Histidine

2-CH, 4-CH, CH2

7.75(t), 7.08(d), 6.05(d)

118.2, 136.2, 52.9

31

Formate

CH

8.45(s)

151.8

32

α-Mannose

1-CH, 2-CH

5.18 (d), 3.93(m)

97.1, 75.3

33

Acetylcarnitine

CH3C=O,α−CH, α−CH’, γ-CH2

2.13(s), 2.46(m), 2.63(m), 3.90 (m)

#

34

Myo-inositol

1,3-CH, 2-CH, 4,6-CH

3.65(m), 3.29(m), 3.57(m)

#

a #

Key: s, singlet; d, doublet; t, triplet; q, quartet; m, multiplet; dd, doublet of doublet. The signals or the multiplicities were not determined.



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Page 28 of 33

Table 3. Serum metabolites having significant differences between AML patients and healthy controls Metabolites Phenylalanine Scyllitol PC/GPC Choline HDL Histine Isoleucine Leucine Citrate Lysine Arginine Glutamine NAG Valine Alanine Unsaturated fatty acids α-Glucose Succinate Lactate Tyrosine α-mannose acetylcarnitine

Changes in AML patients against healthy controls

correlation coefficients (R2X=0.38 Q2=0.84)

↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ ↑ ↓ ↓ ↓ ↑ ↑ ↓ ↑ ↑ ↓

0.78 -0.75 -0.71 -0.68 -0.62 -0.62 -0.58 -0.57 0.60 -0.53 -0.51 -0.50 0.48 -0.46 -0.43 -0.31 0.38 0.29 -0.28 0.25 0.33 -0.81


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Table 4. Significantly differentiated serum metabolites in the AML patients between favorable (good) and intermediate cytogenetic (medium) risks. Metabolites Isoleucine Leucine Valine Glutamine Glutamate LDL VLDL Unsaturated fatty acids Triglycerides Phenylalanine Lysine Arginine PC/GPC Lactate Citrate Histidine HDL Choline Myo-inositol

Medium VS Good

Correlation coefficients (R2X=0.38, Q2=0.19)

↑ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑

0.59 0.56 0.53 0.47 0.45 -0.43 -0.43 -0.41 -0.32 0.41 0.35 0.35 0.30 0.27 0.34 0.27 0.26 0.26 0.52



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

TOC graphical


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1 2 360 4 5 6 7 8 9 10 0 11 12 13 14 15 16 -60 17 18 19 20 -40 21 22 23 40 24 25 26 27 28 29 30 310 32 33 34 35 36 37 -40 38 39 40 41-4 0 42 43 44 45 46 47

TOC Graphic


AML

23

0.8

13 17

0.6 0.4

Control

10

8 0

40

Medium 8

9

24 20,21 19 23 10

7

1 4,5,6

0.2

8

19,20,21

22

t[1]P

16 14 10,11

15 14,15 14

10,11 9

0 0.5

8,17 4,5,6

0.42

1

0.2

9 9

Good 0 t[1]P

9

0.14

2,3

40

4

3.5

3

2.5


2

1.5

1

0.5

0


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

X8

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30

30

20,21

23 26 35 32

AML 31

14,15 1733 33 13

24

25

34

10

9

4,5,6 3 2

10,11

1616

28 8 29

22 2,3,18 8 27

HC

8.0 7.5

ppm

18

19 27

7 12

18

5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5

Figure 1


1

ppm

Page 33 of 33

29

60

t[2]O

AML

23 28

training

28

0.8

13

17

29

0.6

16

0

26

0.4 32

27

-60 -40

0

40

HC

30

8

t[1]P

30

7.5

5.3

33 33 10

8 22

5.2

7

4

3.5

10 33 10,11 9

2.5

0.2

8

19,20,21

3

1 7 4,5,6

2

1.5

1

validation

0

0.8

AML 40

0.6

t[2]O

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.4

0

0.2

HC

-40 -40

0 t[1]P

40

5.3

8

7.5

7

5.2

4

3.5

3

Figure 2


2.5

2

1.5

1

0


Medium

40

8

29 28

28

20,21 19 34 34 10 24

30

30 t[2]O

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

29

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15 14,15 16 14

9

10,11

0.56

8,17 4,5,6 1

0

0.42 0.28

9 9

-40 -40

0 t[1]P

40

Good 8

27 5.3

7.5

9

0.14

35 2,3

5.2

7

4

3.5

3

Figure 3


2.5

2

1.5

1

0 0.5

Rapid Diagnosis and Prognosis of de novo Acute Myeloid Leukemia

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