Method of detecting lung cancer

Abstract

A biomarker panel for a urine test for detecting lung cancer detects a biomarker selected from the group of biomarkers consisting of DMA, C5:1, C10:1, ADMA, C5-OH, SDMA, and kynurenine, or a combination thereof. A biomarker panel for a serum test for detecting lung cancer detects a biomarker selected from the group of biomarkers consisting of valine, arginine, ornithine, methionine, spermidine, spermine, diacetylspermine, C10:2, PC aa C32:2, PC ae C36:0, and PC ae C44:5; and lysoPC a C18:2, or a combination thereof.

Claims

1. A method for processing a human clinical blood sample, the method comprising obtaining a blood sample from a subject clinically assessed as having or suspected of having lung cancer, and quantifying a panel of metabolites in said blood sample, the panel comprising at least one blood metabolite selected from diacetylspermine, C10:2, PC aa C32:2, PC ae C36:0, and PC ae C44:5, wherein the blood sample is or comprises serum.

2. The method of claim 1, wherein the panel comprises at least two of said blood metabolites.

3. The method of claim 1, wherein the panel comprises at least three of said blood metabolites.

4. The method of claim 1, wherein the at least one blood metabolite comprises diacetylspermine.

5. The method of claim 1, wherein the at least one blood metabolite comprises C10:2.

6. The method of claim 1, wherein the at least one blood metabolite comprises PC aa C32:2.

7. The method of claim 1, wherein the at least one blood metabolite comprises PC ae C36:0.

8. The method of claim 1, wherein the at least one blood metabolite comprises PC ae C44:5.

Description

BRIEF DESCRIPTIONS OF DRAWINGS

(1) The invention will be more readily understood from the following description of the embodiments thereof given, by way of example only, with reference to the accompanying drawings, in which:

(2) FIG. 1 is a box-and-whiskers plot showing the concentrates of metabolites in healthy patients and cancer patients;

(3) FIG. 2 is a partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of urine samples;

(4) FIG. 3 is a variable importance in projection (VIP) analysis plot ranking discriminating urine metabolites in descending order of importance;

(5) FIG. 4 is a receiver operating characteristic (ROC) analysis including the seven most important metabolites from VIP analysis of urine samples shown in FIG. 3;

(6) FIG. 5 is a partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples;

(7) FIG. 6 is a variable importance in projection (VIP) analysis plot ranking discriminating serum metabolites in descending order of importance;

(8) FIG. 7 is a receiver operating characteristic (ROC) including the five most important metabolites from VIP analysis of serum samples shown in FIG. 6;

(9) FIG. 8 is a table showing a univariate analysis of individual metabolites in serum samples at time T1;

(10) FIG. 9 is a table showing a univariate analysis of individual metabolites in serum samples at time T2;

(11) FIG. 10 is a principle component analysis (PCA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T1;

(12) FIG. 11 is a three-dimensional principle component analysis (PCA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T1;

(13) FIG. 12 is a partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T1;

(14) FIG. 13 is a three-dimensional partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T1;

(15) FIG. 14 is a variable importance in projection (VIP) analysis plot ranking discriminating serum metabolites in descending order of importance at time T1;

(16) FIG. 15 is a receiver operating characteristic (ROC) analysis including the five most important metabolites from the VIP analysis of serum samples shown in FIG. 14;

(17) FIG. 16 is a principle component analysis (PCA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T2;

(18) FIG. 17 is a three-dimensional principle component analysis (PCA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T2;

(19) FIG. 18 is a partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T2;

(20) FIG. 19 is a three-dimensional partial least squares discriminant analysis (PLS-DA) plot showing separation between control patients and lung cancer patients based on an analysis of serum samples at time T2;

(21) FIG. 20 is a variable importance in projection (VIP) analysis plot ranking discriminating serum metabolites in descending order of importance at time T2; and

(22) FIG. 21 is a receiver operating characteristic (ROC) analysis including the five most important metabolites from the VIP analysis of serum samples shown in FIG. 20.

DESCRIPTIONS OF THE PREFERRED EMBODIMENTS

(23) Serum ampler from control patients, early stage cancer patients, and late stage cancer patients were analyzed using a combination of direct injection mass spectrometry and reverse-phase LC-MS/MS. An AbsoluteIDQ® p180 Kit obtained from Biocrates Life Sciences AG of Eduard-Bodem-Gasse 8 6020, Innsbruck, Austria was used in combination with an API4000 Qtrap® tandem mass spectrometer obtained from Applied Biosystems/MDS Sciex of 850 Lincoln Centre Drive, Foster City, Calif., 94404, United States of America, for the targeted identification and quantification of up to 180 different endogenous metabolites including amino acids, acylcarnitines, biogenic amines, glycerophospholipids, sphingolipids and sugars.

(24) The method used combines the derivatization and extraction of analytes, and the selective mass-spectrometric detection using multiple reaction monitoring (MRM) pairs. Isotope-labeled internal standards and other internal standards are integrated in AbsoluteIDQ® p180 Kit plate filter for metabolite quantification. The AbsoluteIDQ® p180 Kit contains a 96 deep-well plate with a filter plate attached with sealing tape as well as reagents and solvents used to prepare the plate assay. First 14 wells in the AbsoluteIDQ® p180 Kit were used for one blank, three zero samples, seven standards and three quality control samples provided with each AbsoluteIDQ® p180 Kit. All the serum samples were analyzed with the AbsoluteIDQ® p180 Kit using the protocol described in the AbsoluteIDQ® p180 Kit User Manual.

(25) Serum samples were thawed on ice and were vortexed and centrifuged at 2750×g for five minutes at 4° C. 10 μL of each serum sample was loaded onto the center of the filter on the upper 96-well kit plate and dried in a stream of nitrogen. 20 μL of a 5% solution of phenyl-isothiocyanate was subsequently added for derivatization. The filter spots were then dried again using an evaporator. Extraction of the metabolites was then achieved by adding 300 μL methanol containing 5 mM ammonium acetate. The extracts were obtained by centrifugation into the lower 96-deep well plate. This was followed by a dilution step with MS running solvent from the AbsoluteIDQ® p180 Kit.

(26) Mass spectrometric analysis was performed on the API4000 Qtrap® tandem mass spectrometer which was equipped with a solvent delivery system. The serum samples were delivered to the mass spectrometer by either a direct injection (DI) method or liquid chromatography method. The Biocrates MetIQ™ software, which is integral to the AbsoluteIDQ® p180 Kit, was used to control the entire assay workflow, from sample registration to automated calculation of metabolite concentrations to the export of data into other data analysis programs. A targeted profiling scheme was used to quantitatively screen for known small molecule metabolites using multiple reaction monitoring, neutral loss, and precursor ion scans.

(27) First Study

(28) Metabolites were detected and quantified in urine samples collected from 10 control patients and 12 lung cancer patients undergoing chemotherapy treatment using LC-MS/MS-based assay. In particular, the following polyamine pathway metabolites: spermidine, spermine, methionine, putrescine, methylthioadenosine (MTA), S-adenosyl-L-methionine (SAMe), ornithine, arginine, N-acetylspermine, and N-acetylspermidine were detected and quantified in urine samples.

(29) The results of this study, shown in FIG. 1, indicate that four metabolites have been identified as putative biomarkers for cancer, namely, spermidine, ornithine, arginine and methionine. The results from this study revealed a preliminary picture of the polyamine metabolome in cancer patients and healthy subjects.

(30) Second Study

(31) Metabolites were detected in urine and serum samples collected from 15 control patients and 31 lung cancer patients (including 7 early stage cancer patients). The samples were analyzed using a combined direct injection mass spectrometry (MS) and reverse-phase LC-MS/MS as described above. Statistical analysis was performed using MetaboAnalyst and ROCCET.

(32) The following metabolites were identified and quantified using the Biocrates Absolute p180IDQ™ Kit:

(33) TABLE-US-00001 Metabolite Serum Urine Amino Acids 21 21 Acylcarnitines 23 35 Biogenic amines 13 17 Glycerophospholipids 85 32 (PCs & LysoPCs) Sphingolipids 15 6 Hexose 1 1

(34) PLS Discriminant Analysis (PLS-DA) resulted in detectable separation of lung cancer patients and control patients based on seven metabolites in urine, as shown in FIG. 2, and five metabolites in serum, as shown in FIG. 5.

(35) Total dimethylarginine in asymmetric and symmetric forms (DMA), tiglylcarnitine (C5:1), decenoylcarnitine (C10:1), asymmetric dimethylarginine (ADMA), hydroxyvalerylcarnitine (C5-OH), symmetric dimethylarginine (SDMA), and kynurenine appear to be the seven most important urinary metabolites for distinguishing lung cancer based on variable importance in projection (VIP) analysis as shown in FIG. 3. A receiver operating characteristic (ROC) analysis including the seven most important metabolites from VIP analysis of urine samples is shown in FIG. 4.

(36) Valine, decadienylcarnitine (C10:2), glycerophosopholipids (PC aa C32:2; PC ae C36:0, and PC ae C44:5) appear to be the five most important serum metabolites for distinguishing lung cancer based on variable importance in projection (VIP) analysis as shown in FIG. 6. A receiver operating characteristic (ROC) analysis including the five most important metabolites from VIP analysis of serum samples is shown in FIG. 7.

(37) Seven putative urinary biomarkers and five putative serum biomarkers have accordingly been identified for diagnosis of lung cancer and may be used in a biomarker panel for a urine test or serum test to detect lung cancer.

(38) Third Study

(39) Metabolites were detected in serum samples collected from 26 late stage lung cancer patients and 15 control patients at times T1 and T2. In particular, the following polyamine pathway metabolites: valine, arginine, ornithine, methionine, spermidine, spermine, diacetylspermine, decadienylcarnitine (C10:2), glycerophosopholipids (PC aa C32:2 and PC ae C36:0), lysoPC a C18:2, methylthioadenosine, and putrescine were detected and quantified in the serum samples at times T1 and T2.

(40) The samples were analyzed using a combined direct injection mass spectrometry (MS) and reverse-phase LC-MS/MS as described above. Statistical analysis was performed using MetaboAnalyst and ROCCET. Methylthioadenosine and putrescine were however excluded from the analysis because the missing rates were greater than 50%. FIGS. 8 and 9 respectively show the results of a univariate analysis of the remaining individual metabolites at times (T1) and (T2).

(41) Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) at time T1 resulted in a detectable separation of lung cancer patients and control patients based on eleven metabolites in serum as shown in FIGS. 10 to 13.

(42) Total valine, diacetylspermine, spermine, lysoPC a C18.2, and decadienylcarnitine (C10:2) appear to be the five most important serum metabolites for distinguishing late stage lung cancer based on variable importance in projection (VIP) analysis as shown in FIG. 14. A receiver operating characteristic (ROC) analysis including the five most important metabolites from VIP analysis of serum samples is shown in FIG. 15.

(43) Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) at time T2 resulted in a detectable separation of lung cancer patients and control patients based on eleven metabolites in serum as shown in FIGS. 16 to 19.

(44) Total valine, diacetylspermine, spermine, lysoPC a C18.2, and decadienylcarnitine (C10:2) again appear to be the five most important serum metabolites for distinguishing late stage lung cancer based on variable importance in projection (VIP) analysis as shown in FIG. 20. A receiver operating characteristic (ROC) analysis including the five most important metabolites from VIP analysis of serum samples is shown in FIG. 21.

(45) Eleven putative serum biomarkers have accordingly been identified for diagnosis of late stage lung cancer and may be used in a biomarker panel for a serum test to detect lung cancer.

(46) It will be understood by a person skilled in the art that many of the details provided above are by way of example only, and are not intended to limit the scope of the invention which is to be determined with reference to the following claims.