Low-mass-ion discriminant equation: a new concept for colorectal cancer screening

Different Metabolomic and Proteomic Profiles of Cerebrospinal Fluid in Ventricular and Lumbar Compartments in Relation to Leptomeningeal Metastases

The different molecular profiles of cerebrospinal fluid (CSF) between ventricular and lumbar compartments remain elusive, especially in the context of leptomeningeal metastasis (LM), which affects CSF flow. We evaluated CSF metabolomic and proteomic profiles based on the compartments and the diagnosis of spinal LM, proved by MRI from 20 paired ventricular and lumbar CSF samples of LM patients, including 12 spinal LM (+) samples. In metabolome analysis, 9512 low-mass ions (LMIs) were identified-7 LMIs were abundant in all lumbar versus paired ventricular CSF samples, and 3 LMIs were significantly abundant in all ventricular CSF. In comparisons between spinal LM (+) CSF and LM (-) CSF, 105 LMIs were discriminative for spinal LM (+) CSF. In proteome analysis, a total of 1536 proteins were measured. A total of 18 proteins, including complement C3, were more highly expressed in all lumbar CSF, compared with paired ventricular CSF, while 82 proteins, including coagulation factor V, were higher in the ventricular CSF. Of 37 discriminative proteins, including uteroglobin and complement component C8 gamma chain, 4 were higher in all spinal LM (+) CSF versus spinal LM (-) CSF. We further evaluated metabolic pathways associated with these discriminative proteins using the Gene Ontology database. We found that 16/17 spinal LM (+) pathways, including complement activation, were associated with lumbar discriminative proteins, whereas only 2 pathways were associated with ventricular-discriminative proteins. In conclusion, we determined that metabolite and protein profiles differed between paired lumbar and ventricular CSF samples. The protein profiles of spinal LM (+) CSF showed more similarity with the lumbar CSF than the ventricular CSF. Thus, we suggest that CSF LMIs and proteins could reflect LM disease activity and that LM-associated differences in CSF are more likely to be present in the lumbar compartment.

Experimental Assessment of Leptomeningeal Metastasis Diagnosis in Medulloblastoma Using Cerebrospinal Fluid Metabolomic Profiles

Diagnosing leptomeningeal metastasis (LM) in medulloblastoma is currently based on positive cerebrospinal fluid (CSF) cytology or magnetic resonance imaging (MRI) finding. However, the relevance of discordant results has not been established. We evaluated the diagnostic potential of CSF metabolomic profiles in the medulloblastoma LM assessment. A total of 83 CSF samples from medulloblastoma patients with documented MRI and CSF cytology results at the time of sampling for LM underwent low-mass ions (LMIs) analysis using liquid chromatography-mass spectrometry. Discriminating LMIs were selected by a summed sensitivity and specificity (>160%) and LMI discriminant equation (LOME) algorithms, evaluated by measuring diagnostic accuracy for verifying LM groups of different MRI/cytology results. Diagnostic accuracy of LM in medulloblastoma was 0.722 for cytology and 0.889 for MRI. Among 6572 LMIs identified in all sample, we identified 27 discriminative LMIs differentiating MRI (+)/cytology (+) from MRI (-)/cytology (-). Using LMI discriminant equation (LOME) analysis, we selected 9 LMIs with a sensitivity of 100% and a specificity of 93.6% for differentiating MRI (+)/cytology (+) from MRI (-)/cytology (-). Another LOME of 20 LMIs significantly differentiated sampling time relative to treatment (p = 0.007) and the presence or absence of LM-related symptoms (p = 0.03) in the MRI (+)/cytology (-) group. CSF metabolomics of medulloblastoma patients revealed significantly different profiles among LM diagnosed with different test results. We suggest that LM patients could be screened by appropriately selected LOME-generated LMIs to support LM diagnosis by either MRI or cytology alone.

Comparative cerebrospinal fluid metabolites profiling in glioma patients to predict malignant transformation and leptomeningeal metastasis with a potential for preventive personalized medicine

Glioma shows progression presenting as malignant transformation or leptomeningeal metastasis (LM). However, longitudinal biopsy of brain parenchyma is difficult due to its critical location, whereas cerebrospinal fluid (CSF) can be obtained serially with a little invasiveness of puncture. Thus, if we could find a biomarker for glioma progression, we could predict such event and determine therapeutic interventions as early as possible. In this study, we examined whether cerebrospinal fluid (CSF) metabolome profiles can reflect glioma grade, difference with non-glial tumor, and LM status. We selected 32 CSF samples from glioma patients, and compared them with 10 non-tumor control and seven non-glial brain tumor (medulloblastoma) samples. A total of 10,408 low-mass ions (LMIs) were detected as a candidate of metabolites using mass spectrometry, and representative LMIs were identified via the Human Metabolome Database. Grade IV gliomas showed eight LMIs, including acetic acid, of higher levels (summed sensitivity and specificity > 180%) than grade III gliomas. Grade IV gliomas demonstrated more abundant 30 LMIs, including glycerophosphate, compared with medulloblastoma, but none was mutually exclusive. Phospholipid derivatives were significantly more abundant in LM (-) than LM (+) gliomas regardless of glioma grade. LMIs representative of LM (+) gliomas were derivatives of glycolysis. We also verified discriminative LMIs based on mean expression level of each LMI (Student t test, p < 0.05) and evaluated the differences of the above analyses. Over 90% of metabolite pathways indicated from two analytical models were common to each other. Non-targeted mass spectrometry of CSF metabolites revealed significantly different profiles across gliomas that possibly permitted differentiation between glioma grades, LM, and non-glial brain tumors.

Individualized metabolic profiling stratifies pancreatic and biliary tract cancer: a useful tool for innovative screening programs and predictive strategies in healthcare

BACKGROUND

Pancreatic cancer (PC) and biliary tract cancer (BTC) are highly aggressive cancers, characterized by their rarity, difficulty in diagnosis, and overall poor prognosis. Diagnosis of PC and BTC is complex and is made using a combination of appropriate clinical suspicion, imaging and endoscopic techniques, and cytopathological examination. However, the late-stage detection and poor prognosis of this tumor have led to an urgent need for biomarkers for early and/or predictive diagnosis and improved personalized treatments.

WORKING HYPOTHESIS

There are two hypotheses for focusing on low-mass metabolites in the blood. First, valuable information can be obtained from the masses and relative amounts of such metabolites, which present as low-mass ions (LMIs) in mass spectra. Second, metabolic profiling of individuals may provide important information regarding biological changes in disease states that is useful for the early diagnosis of PC and BTC.

MATERIALS AND METHODS

To assess whether profiling metabolites in serum can serve as a non-invasive screening tool for PC and BTC, 320 serum samples were obtained from patients with PC (n = 51), BTC (n = 39), colorectal cancer (CRC) (n = 100), and ovarian cancer (OVC) (n = 30), and from healthy control subjects (control) (n = 100). We obtained information on the relative amounts of metabolites, as LMIs, via triple time-of-flight mass spectrometry. All data were analyzed according to the peak area ratios of discriminative LMIs.

RESULTS AND CONCLUSIONS

The levels of the 14 discriminative LMIs were higher in the PC and BTC groups than in the control, CRC and OVC groups, but only two LMIs discriminated between PC and BTC: lysophosphatidylcholine (LysoPC) (16:0) and LysoPC(20:4). The levels of these two LysoPCs were also slightly lower in the PC/BTC/CRC/OVC groups compared with the control group. Taken together, the data showed that metabolic profiling can precisely denote the status of cancer, and, thus, could be useful for screening. This study not only details efficient methods to identify discriminative LMIs for cancer screening but also provides an example of metabolic profiling for distinguishing PC from BTC. Furthermore, the two metabolites [LysoPC(16:0), LysoPC(20:4)] shown to discriminate these diseases are potentially useful when combined with other, previously identified protein or metabolic biomarkers for predictive, preventive and personalized medical approach.

Reduced levels of N'-methyl-2-pyridone-5-carboxamide and lysophosphatidylcholine 16:0 in the serum of patients with intrahepatic cholangiocarcinoma, and the correlation with recurrence-free survival

We searched for metabolic biomarkers that may predict the prognosis of patients with intrahepatic cholangiocarcinoma (IHCC). To this end, a total of 237 serum samples were obtained from IHCC patients (n = 87) and healthy controls (n = 150), and serum metabolites were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Two stratified algorithms were used to select the metabolites, the levels of which predicted the prognosis of IHCC patients. We performed MS/MS and multiple-reaction-monitoring MS analyses to identify and quantify the selected metabolites. Continuous biomarker levels were dichotomized based on cutoffs that maximized between-group differences in recurrence-free survival (RFS) in terms of the log-rank test statistic. These RFS differences were analyzed using the log-rank test, and survival curves were drawn with the aid of the Kaplan–Meier method. Six metabolites (l-glutamine, lysophosphatidylcholine [LPC] 16:0, LPC 18:0, N’-methyl-2-pyridone-5-carboxamide [2PY], fibrinopeptide A [FPA] and uric acid) were identified as candidate metabolic biomarkers for predicting the prognosis of IHCC patients. Of these metabolites, levels of l-glutamine, uric acid, LPC 16:0, and LPC 18:0 were significantly lower in the serum from IHCC patients, whereas levels of 2PY and FPA were significantly higher (p < 0.01). 2PY and LPC 16:0 showed significantly better RFS at low level than high level (2PY, median RFS: 15.16 months vs. 5.90 months, p = 0.037; LPC 16:0, median RFS: 15.62 months vs. 9.83 months, p = 0.035). The findings of this study suggest that 2PY and LPC 16:0 identified by metabolome-based approaches may be useful biomarkers for IHCC patients.

Cerebrospinal fluid metabolomic profiles can discriminate patients with leptomeningeal carcinomatosis from patients at high risk for leptomeningeal metastasis

Purpose

Early diagnosis of leptomeningeal carcinomatosis (LMC) is necessary to improve outcomes of this formidable disease. However, cerebrospinal fluid (CSF) cytology is frequently false negative. We examined whether CSF metabolome profiles can be used to differentiate patients with LMC from patients having a risk for development of LMC.

Results

A total of 10,905 LMIs were evaluated using PCA-DA. The LMIs defined Group 2 with a sensitivity of 85% and a specificity of 91%. After selecting 33 LMIs, including diacetylspermine and fibrinogen fragments, the CSF metabolomics profile had a sensitivity of 100% and a specificity of 93% for discriminating Group 1b from the other groups. After selecting 21 LMIs, including phosphatidylcholine, the CSF metabolomics profile differentiated LMC (Group 2) patients from the high-risk groups of Group 3 and Group 4 with 100% sensitivity and 100% specificity.

Materials and Methods

We prospectively collected CSF from five groups of patients: Group 1a, systemic cancer; Group 1b, no tumor; Group 2, LMC; Group 3, brain metastasis; Group 4, brain tumor other than brain metastasis. All metabolites in the CSF samples were detected as low-mass ions (LMIs) using mass spectrometry. Principal component analysis-based discriminant analysis (PCA-DA) and two search algorithms were used to select the LMIs that differentiated the patient groups of interest from controls.

Conclusions

Analysis of CSF metabolite profiles could be used to diagnose LMC and exclude patients at high-risk of LMC with a 100% accuracy. We expect a future validation trial to evaluate CSF metabolic profiles supporting CSF cytology.

Profiling of Serum Metabolites Using MALDI-TOF and Triple-TOF Mass Spectrometry to Develop a Screen for Ovarian Cancer

Purpose

We sought to develop a matrix assisted laser desorption ionization-time of flight (MALDI-TOF)-based, ovarian cancer (OVC), low-mass-ion discriminant equation (LOME) and to evaluate a possible supportive role for triple-TOF mass analysis in identifying metabolic biomarkers.

Materials and Methods

A total of 114 serum samples from patients with OVC and benign ovarian tumors were subjected to MALDI-TOF analysis and a total of 137 serum samples from healthy female individuals and patients with OVC, colorectal cancer, hepatobiliary cancer, and pancreatic cancer were subjected to triple-TOF analysis. An OVC LOME was constructed by reference to the peak intensity ratios of discriminatory low-mass ion (LMI) pairs. Triple-TOF analysiswas used to select and identify metabolic biomarkers for OVC screening.

Results

Three OVC LOMEs were finally constructed using discriminatory LMI pairs (137.1690 and 84.4119 m/z; 496.5022 and 709.7642 m/z; and 524.5614 and 709.7642 m/z); all afforded accuracies of > 90%. The LMIs at 496.5022 m/z and 524.5614 m/z were those of lysophosphatidylcholine (LPC) 16:0 and LPC 18:0. Triple-TOF analysis selected seven discriminative LMIs; each LMI had a specificity > 90%. Of the seven LMIs, fourwith a 137.0455 m/z ion atretention times of 2.04-3.14 minuteswere upregulated in sera from OVC patients; the ion was identified as that derived from hypoxanthine.

Conclusion

MALDI-TOF–based OVC LOMEs combined with triple-TOF–based OVC metabolic biomarkers allow reliable OVC screening; the techniques are mutually complementary both quantitatively and qualitatively.

C1QBP is upregulated in colon cancer and binds to apolipoprotein A-I

The present study aimed to investigate the expression of complement component 1, q subcomponent-binding protein (C1QBP) in colon cancer cells, and identify proteins that interact with C1QBP. Total proteins were extracted from both the tumor and normal tissues of 22 patients with colon cancer and analyzed using liquid chromatography-mass spectrometry (LC-MS) to identify proteins that were differentially-expressed in tumor tissues. C1QBP overexpression was induced in 293T cells using a pFLAG-CMV2 expression vector. Overexpressed FLAG-tagged C1QBP protein was then immunoprecipitated using anti-FLAG antibodies and C1QBP-interacting proteins were screened using LC-MS analysis of the immunoprecipitates. The C1QBP-interacting proteins were confirmed using reverse-immunoprecipitation and the differential expression of C1QBP in tissues and cell lines was confirmed using western blot analysis. LC-MS analysis revealed that C1QBP exhibited a typical tumor expression pattern. Two immune-reactive signals (33 and 14 kDa) were detected in normal and tumor tissues from 19 patients. Furthermore, 14 kDa C1QBP protein was upregulated in the tumors of 15 patients. In total, 39 proteins were identified as candidate C1QBP-interacting proteins, and an interaction between C1QBP and apolipoprotein A-I was confirmed. The present study indicates that C1QBP is involved in colon cancer carcinogenesis, and that the mechanisms underlying the established anti-tumor properties of apolipoprotein A-I may include interacting with and inhibiting the activity of C1QBP.

Pattern recognition for predictive, preventive, and personalized medicine in cancer

Predictive, preventive, and personalized medicine (PPPM) is the hot spot and future direction in the field of cancer. Cancer is a complex, whole-body disease that involved multi-factors, multi-processes, and multi-consequences. A series of molecular alterations at different levels of genes (genome), RNAs (transcriptome), proteins (proteome), peptides (peptidome), metabolites (metabolome), and imaging characteristics (radiome) that resulted from exogenous and endogenous carcinogens are involved in tumorigenesis and mutually associate and function in a network system, thus determines the difficulty in the use of a single molecule as biomarker for personalized prediction, prevention, diagnosis, and treatment for cancer. A key molecule-panel is necessary for accurate PPPM practice. Pattern recognition is an effective methodology to discover key molecule-panel for cancer. The modern omics, computation biology, and systems biology technologies lead to the possibility in recognizing really reliable molecular pattern for PPPM practice in cancer. The present article reviewed the pathophysiological basis, methodology, and perspective usages of pattern recognition for PPPM in cancer so that our previous opinion on multi-parameter strategies for PPPM in cancer is translated into real research and development of PPPM or precision medicine (PM) in cancer.

Low-mass-ion discriminant equation (LOME) for ovarian cancer screening

Background

A low-mass-ion discriminant equation (LOME) was constructed to investigate whether systematic low-mass-ion (LMI) profiling could be applied to ovarian cancer (OVC) screening.

Results

Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry was performed to obtain mass spectral data on metabolites detected as LMIs up to a mass-to-charge ratio (m/z) of 2500 for 1184 serum samples collected from healthy individuals and patients with OVC, other types of cancer, or several types of benign tumor. Principal component analysis-based discriminant analysis and two search algorithms were employed to identify discriminative low-mass ions for distinguishing OVC from non-OVC cases. OVC LOME with 13 discriminative LMIs produced excellent classification results in a validation set (sensitivity, 93.10 %; specificity, 100.0 %). Among 13 LMIs showing differential mass intensities in OVC, 3 metabolic compounds were identified and semi-quantitated. The relative amount of LPC 16:0 was somewhat decreased in OVC, but not significantly so. In contrast, _D,L-glutamine and fibrinogen alpha chain fragment were significantly increased in OVC compared to the control group (p = 0.001 and 0.002, respectively).

Conclusion

The present study suggested that OVC LOME might be a useful non-invasive tool with high sensitivity and specificity for OVC screening. The LOME approach could enable screening for multiple diseases, including various types of cancer, based on a single blood sample. Furthermore, the serum levels of three metabolic compounds—_D,L-glutamine, LPC 16:0 and fibrinogen alpha chain fragment—might facilitate screening for OVC.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0111-7) contains supplementary material, which is available to authorized users.

Time for paradigm change in management of hepatocellular carcinoma: is a personalized approach on the horizon?

Hepatocellular carcinoma (HCC) is the fifth most frequent cancer form but the second leading cause of all cancer-related deaths. There are several reasons for high mortality in the HCC cohort: lack of effective screening programs and consequently late diagnosis, multifactorial origin with cumulative risk factors, complex carcinogenesis, tumor heterogeneity, unpredictable impacts of individual microenvironment on tumor development and progression, and, as the consequence, frequently untargeted therapy and cancer resistance toward currently applied treatment approaches. The currently applied 'treat and wait' approach is inappropriate in the overall HCC management. Urgent need in paradigm change toward predictive, preventive and personalized medicine is discussed in this review article. Innovative strategies for an advanced predictive, preventive and personalized medicine approach in the overall HCC management benefiting the patient are presented.

Investigation of early and advanced stages in ovarian cancer using human plasma by differential scanning calorimetry and mass spectrometry

Ovarian cancer is recognized with high mortality due to asymptomatic nature of the disease and difficulties in diagnosing early stage of the cancer. The present study evaluates the use of differential scanning calorimetry (DSC) in differentiating the severity of ovarian cancer from healthy women. 47 diseased women were subdivided into four stages with respect to clinical relevance and severity. Stages I-II were regarded as early stages and stages III-IV were regarded as advanced stages. The two average transition temperatures (T m ) increased with disease severity from 64.84 and 70.32 °C (healthy) to 68.46 and 75.24 °C (stage IV), respectively. T m were increased depending on clinical groups. In addition, the change in heat capacity was also dependent on the disease severity. To further support and investigate the nature of the proposed interactions, matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis is employed. The results suggest the differences in peptide expression between early and advanced stage of ovarian cancer, affected abundant proteins in plasma. The combined DSC and MS approach was supportive in identifying a unique signature of ovarian cancer stages, and demonstrates the potential of DSC as a complementary diagnostic tool in the evaluation of early stage ovarian cancer.

EPMA position paper in cancer: current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision.

Cancer screenee cohort study of the National Cancer Center in South Korea

The Cancer Screenee Cohort Study was first established in 2002 by the National Cancer Center in South Korea to investigate all possible risk factors related to cancers and to expand biological specimen banking for the development of effective methodologies for cancer detection, diagnosis, and prevention. As of July in 2014, total 41,105 participants were enrolled in this cohort. Data were collected via questionnaire, clinical examination, cancer screening, and biological specimen testing including blood, urine, and exfoliated cervical cells. The highest incidence was found to be thyroid cancer, according to a nested case-control study that was linked to the National Cancer Registry information as of December 31, 2011. Case-control, cross-sectional, and cohort studies have been published using these data since 2009. Diet and nutrition was the most published topic, followed by genetics, hepatitis B virus and liver cancer screening, methodologies, physical activity, obesity, metabolic syndrome, smoking and alcohol consumption, and blood type. Evidence from the Cancer Screenee Cohort Study is highly anticipated to reduce the burden of cancer in the Korean population and aid in the detection, diagnosis, and prevention of cancer.