WO2016135119A1 - Method for the diagnosis of endometrial carcinoma - Google Patents

Method for the diagnosis of endometrial carcinoma Download PDF

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Publication number
WO2016135119A1
WO2016135119A1 PCT/EP2016/053726 EP2016053726W WO2016135119A1 WO 2016135119 A1 WO2016135119 A1 WO 2016135119A1 EP 2016053726 W EP2016053726 W EP 2016053726W WO 2016135119 A1 WO2016135119 A1 WO 2016135119A1
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Prior art keywords
analysis
model
endometrial carcinoma
classification
metabolites
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PCT/EP2016/053726
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English (en)
French (fr)
Inventor
Jacopo TROISI
Giovanni SCALA
Pietro CAMPIGLIA
Fulvio ZULLO
Maurizio Guida
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Hosmotic Srl
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Priority to JP2017563386A priority Critical patent/JP6731957B2/ja
Priority to ES16709979T priority patent/ES2711814T3/es
Priority to US15/552,342 priority patent/US20180038867A1/en
Priority to EP16709979.5A priority patent/EP3262416B1/en
Publication of WO2016135119A1 publication Critical patent/WO2016135119A1/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407Specifically defined cancers
    • G01N33/57442Specifically defined cancers of the uterus and endometrial

Definitions

  • the present invention relates to a method for the diagnosis of endometrial carcinoma based on metabolomic analysis of blood and bioinformatics manipulation of metabolic profiles through classification models.
  • the endometrial carcinoma is the most common invasive cancer of the female genital tract and it is responsible of 7% of all invasive tumours in women (excluding cutaneous tumours).
  • the endometrial carcinoma is rare in women having less than 40 years. The peak of incidence is between 55 and 65 years. Clinical-pathological studies and molecular analysis have supported the classification of endometrial carcinoma into two broad categories: Type I and Type II.
  • the type I is the most frequent, with a percentage of cases higher than 80%, it mines the endometrial proliferative glands and it is so defined with the term endometrioid carcinoma. In general, it arises in a frame of endometrial hyperplasia and, like this one, it is associated with obesity, diabetes, hypertension, infertility and uncontested oestrogenic stimulation. Recent studies have provided further evidence supporting the thesis that endometrial hyperplasia is a precursor of endometrial carcinoma (Muller GL et al. Allelotype mapping of unstable microsatellites establishes direct lineage continuity between endometrial precancers and cancers. Cancers Res 56:4483, 1996).
  • the type II endometrial carcinoma generally affects women ten years later than the type I endometrial carcinoma (65-75 years) and, differently from type I, it most of all develops on a frame of endometrial atrophy.
  • the type II represents less than 15% of endometrial carcinoma cases and it is scarcely differentiated (G3).
  • the most common subtype is the serous one, that is so defined due to the biological and morphological overlapping with the ovarian carcinoma. Less common histological subtypes also belong to this category: clear cell carcinoma and malignant mixed MQIIerian tumour.
  • the present invention solves the above mentioned problems through a non-invasive method for the diagnosis of endometrial carcinoma.
  • a non-invasive method for the diagnosis of endometrial carcinoma Up today, there are no other non-invasive diagnostic methods which allow such a histological distinction of this kind of tumour.
  • Figure 1 shows the result of the analysis OPLS-DA based on data of the metabolomic profile of the patients with endometrial carcinoma and of healthy controls.
  • the scores plots discriminate between the two classes without overlappings.
  • the triangles represent the patients affected by endometrial carcinoma, whereas the small rings the healthy patients.
  • the main components PC1 and PC2 reported on the axes respectively disclose the 16.5% and the 14.9% of the global variance.
  • Figure 2 shows, according to the invention, the histological classification (carcinoma of type I vs carcinoma of type II) obtained with the PLS-DA model.
  • the spots represent the metabolomic profiles of women with endometrial carcinoma of type I, whereas the triangles the ones of the patients with endometrial carcinoma of type II. Only one of these samples is placed by the model in an area which is not univocally attributable to the correct area.
  • metabolites the small molecules derived from the biological processes of anabolic or catabolic type of a cell or of a set of cells are intended. With the term “metabolites” the inventors wish to refer to all the molecules having a molecular weight lower than 1000 Dalton, which are potentially identifiable and measurable within a biological sample.
  • the PLS-DA Partial Least Squares Discriminant Analysis
  • X original variables
  • Y determinate class
  • a permutation test is performed.
  • a PLS-DA model is built from the data (X) and the commuted class labels (Y) by using the optimal numbers of components determinated by cross validation for the model based on the assignment of the original classes.
  • Two types of statistical tests are performed to measure the discrimination power between the classes. The first one is based on the prediction accuracy in the training phase of the model.
  • the second one is based on the separation distance according to the ratio between the sum of the quadratic distances within the classes and among the classes (B/W- ratio).
  • the OPLS-DA Orthogonal Partial Least Squares - Discriminant Analysis
  • OPLS-DA increases the classification performances of the models PLS-DA.
  • the performances of classification are estimated on the basis of "k-fold cross validation" by dividing the data matrix in k random subsets. For each calculation cycle, one of the subsets of F is kept aside as a test set and the remaining k-1 subsets act as trainers. Each of the K subsets is used one time as a test set, generating K precision values.
  • the accuracy of the classification is calculated as the average of the accuracy rates in k subsets.
  • the model is subjected to cross validation with the method "leave one out cross validation" (LOOCV) in order to be validated.
  • LOOCV leave one out cross validation
  • the data matrix is scaled to the mean and the unit variance, before being submitted to the division into k subsets.
  • the average and the standard deviation of the training data are used to indicate the center and to scale the test data.
  • the model is used to check whether the data have generated an "overfitting".
  • a validation set with known class labels is created and it is thus checked whether it gives an accuracy rate comparable to that of the training data.
  • Another method is a plot validation R 2 /Q 2 which helps to assess the risk that the current model is spurious, that is, the model fits well only to subsets set but does not predict Y just as well for the new observations.
  • the value of R 2 is the percentage variation of the training set that can be explained by the model.
  • Q 2 is a cross-validated measure of R 2 .
  • This validation compares the goodness of fit of the original model with the goodness of fit of different models based on the data in which the order of observations Y is permuted randomly, while the matrix is kept intact.
  • the criteria for the validity of the model are the following:
  • the regression line (the line joining the actual point Q 2 to the centroid of the cluster of Q 2 permuted values) has a negative value of the y-axis intercept.
  • Support Vector Machines are machine learning supervised techniques relatively new for classification uses.
  • the SVMs were proposed for the first time in 1982 by Vapnik (Vapnik, V. Estimation of Dependences Based on Empirical Data; Springer Verlag: New York, 1982).
  • the basic principle of SVMs which are essentially binary classifiers is the following: given a set data with two classes, a linear classifier is constructed in the form of a hyperplane, which has the maximum margin in the simultaneous minimization of the empirical classification error and the maximization of the geometric margin.
  • the original data are mapped into a higher dimensional feature space and a linear classifier is built in this new space (this is known as the "kernel").
  • SVM determines the hyperplane whose parameters are given by (w,b) as obtained by the solution of the following convex optimization problem: subjected to the following conditions:
  • c is the regularization parameter, which is a compromise between the learning accuracy and the term prediction, and ⁇ is a measure of the number of classification errors.
  • regularization reduces the problem of overfitting.
  • Decision trees build classification models based on recursive partitioning of data.
  • an algorithm of the decision tree begins with the entire set of data, the data are divided into two or more subgroups based on the values of one or more attributes, and then each subset is repeatedly divided into smaller subsets until the size of each subset reaches an appropriate level.
  • the entire modeling process can be represented in a tree structure, and the generated model can be summarized as a set of rules "if-then”.
  • Decision trees are easy to interpret, computationally undemanding, and able to cope with noisy data. Most of the decision trees tackles the classification problems, such as for example the object of this invention.
  • the technique is also referred to as classification tree.
  • a knot represents a set of data, and the entire set of data is represented as a knot at the root.
  • the present invention relates to a method for the diagnosis of endometrial carcinoma, based on metabolomic analysis of blood and on an integration of the obtained results through a multivariate analysis using models of discriminant analysis selected in the group consisting of PLS-DA and OPLS-DA, or models of computer learning selected in the group consisting of SVM and decision tree.
  • the object of the present invention is a method for the diagnosis of the endometrial carcinoma based on metabolomic analysis of blood, said method comprising the following phases:
  • model of computer learning selected from the group consisting of: SVM and decision tree;
  • said training phase (I) the samples derived from patients affected by endometrial carcinoma and from healthy women with similar physical (BMI, age, co-morbidity) and social (level of education, socio-economic condition) characteristics are analysed, and in this way the classification models are trained.
  • This training phase is aimed at creating and delimiting the characteristics of the metabolic profile present in the blood of the two groups.
  • a number of blood samples derived from patients with endometrial carcinoma and from healthy controls equal to at least 80% of the number of the identified variables of metabolic profiles, such samples belonging to at least 2 different classes.
  • the method of diagnosis of the endometrial carcinoma of the present invention is not based on the measurement of the concentration of each metabolite, but the whole cluster of metabolites is considered as biomarker (metabolic profile), which, for being present according to different proportions in the 2 groups, allow the insertion into two different classes of pertinence.
  • said training phase (I) further comprises the following sub-phases:
  • classification models can be used according to the present invention; preferably said classification models are selected from the group consisting of: PLS- DA, OPLS-DA, SVM and Decision Tree.
  • assignment phase (II) further comprises the following sub-phases: - extraction and derivatization of metabolites from at least an unknown blood sample;
  • the method of the present invention envisages a classification model trained for a dichotomous classification "Healthy Patient” or "Patient affected by endometrial carcinoma". Even more preferably, said classification model is also trained for a histolological classification of "type I" or "type II” cancer.
  • said extraction is carried out using an extraction mixture consisting of an aqueous mixture of an alcohol and of an aprotic polar solvent, preferably CH3OH/H2O/CHCI3, even more preferably with a volume ratio 2-3/0.5-0.5/0.5-1.
  • said extraction and derivatization sub-phase comprises: i) stirring of the sample obtained from addition of an extraction mixture;
  • BSTFA N-methyl-N-(trimethylsilyl) trifluoroacetamide
  • MSTFA esamethyl disilazane
  • HMDS 1 -(trimethylsilyl) imidazole
  • TMSI N-tert-butyldimethylsilyl-N- methyltrifluoroacetamide
  • TDMSIM l-(tert-butyldimethylsilyl) imidazole
  • said extraction of metabolites is carried out after having added to the sample a known aliquot of a reference compound; preferably said reference compound is ribitol.
  • each peak is identified on the basis of one signal m/z of quantization and at least 2 signals m/z of qualification.
  • the quantification with the method of normalized percentages areas is carried out.
  • the obtained results from this quantization (normalized percentages areas) are transferred to a matrix wherein each sample represents a line and the columns are represented by various metabolites univocally identified by means of their gas chromatographic retention time, compared to the retention time of the reference compound.
  • the first column of the matrix is used to define the class of pertinence of the sample. In the easiest case only two classes can be envisaged "Healthy Patient" and "Patient affected by endometrial carcinoma", further on are reported evidences of the working of the invention on the basis of this dichotomous classification.
  • the multivariate statistical analysis of data (PLS-DA and OPLS-DA) and the automatic learning (SVM and decision tree) are carried out on normalized and corrected chromatograms (based on the peak area of ribitol) using SIMPCA-P 13.0 (Umetrics), RapidMiner 5.3 (Rapid-I) and R (Foundation for Statistical Computing, Vienna). The values are centered on the average and the variance is normalized.
  • Figure 1 shows the separation between classes obtained with OPLS-DA model.
  • the diagnostic methodology object of the present invention was developed starting from metabolomic analysis, carried out on blood samples collected from patients with certain diagnosis of endometrial carcinoma, before the intervention of hysterectomy and from a group of control women having similar physical and socio- economic characteristics but with a healthy uterus.
  • the information about the isotype and the neoplasia stage were collected after the hysterectomy on the basis of the anatomopathological evidences obtained by the analysis of the explanted organ.
  • the samples were taken from 88 women with endometrial carcinoma and 80 healthy women, who voluntary gave samples of blood.
  • the samples of blood were taken just before the hysterectomy intervention using vials BD Vacutainer ® , the serum was frozen at -80°C till the time of analysis.
  • the diagnostic suspect of endometrial carcinoma after the hysterectoscopic test with biopsy of the endometrial lesion was confirmed by the anatomopathological test of the uterus after the hysterectomy intervention.
  • a control group was also arranged taking blood samples from women having no signs of endometrial carcinoma and with similar physical and socio-economic characteristics (weight, height, BMI, age, civil status, level of education and so).
  • the lyophilized sample was treated with 50 ⁇ _ of 20 mg/mL methoxyamine hydrochloride in pyridine. The reaction was carried out at 37°C under stirring (350 rpm) for 90 minutes. At the end, 50 ⁇ di N,0-bis(trimethyllsilyl)trifluoroacetamide (BSTFA) with 1 % of trimethylchlorosilane were added to each ampoule and the silanization reaction was carried out at 37°C for 60 minutes under stirring (350 rpm). MDGCMS analysis
  • a BPX-50 5,0 m x 0,50 mm ID with 0.25 ⁇ of thickness of the film was bound to the position 7 of the interface.
  • a BPX-50 1 .5 m x 0.25 mm ID, 0.25 ⁇ was set to position 6 and connected to a flame ionisation detector (FID) set at 320°C, while the analytical column of 5.0 m (chemically identical to the one connected to FID) was connected to system qMS.
  • FID flame ionisation detector
  • the column connected to FID was used to reduce the flux in the second dimension and to check that the scarcely representative compound was not due to a random fluctuation of the chromatography.
  • the thermal program equal for the two ovens was: 80°C for 1 minute then heating till 320°C at 3°C/minute and maintained for 4 minutes.
  • the starting pressure of helium was set at 129.6 kPa.
  • the auxiliary starting pressure of helium of the APC (advanced control of pressure), which also works in constant linear velocity conditions was set at 90.4 kPa.
  • the modulation period was set at 4.1 s (accumulation period 4.0 seconds, injection period 0.1 seconds).
  • the conditions of the quadrupole mass spectrometer were: ionization mode: electronic impact (70 eV), mass range:40-600 m/z, scanning rate: 10.000 amu/second.
  • the thermal program of GC envisaged a starting temperature of 100°C per 1 minute then heating till 320°C at 4°C/minute and 4 minutes of hold time for a total running time of 60 minutes.
  • the starting pressure of helium was set at 83.7 kPa.
  • the injecton volume at 2 ⁇ _ with a split ratio: 1 :5.
  • the conditions of the quadrupole mass spectrometer were: ionization mode: electronic impact (70 eV), mass range: 35-600 m/z, scanning rate: 3.333 amu/second with a solvent cut time of 4.5 minutes.
  • Gas chromatograms obtained in SCAN mode were integrated so as to identify all the peaks having an area greater than 10 times the background noise of the gas chromatogram trace. Each peak was identified on the basis of signal m/z of quantization and at least two signals m/z of qualification. After the integration, the quantification with the method of normalized percentages areas was carried out, the ribitol peak was used as reference both for quantitative analysis and to center the retention times.
  • the other models of classification have shown good (even if lower than OPLS-DA) classification abilities.
  • Different approaches are possible for the final assignment of the class of pertinence of the unknown sample.
  • the answer of a sole model can be used or the answers of the various models can be integrated in a more complex decisional algorithm.
  • Table 3 reports some indexes of the assessment of diagnostic performances used to evaluate the investigated models.
  • the sensitivity was calculated as TP/(TP+FN), wherein TP represents the number of true positives, namely correctly diagnosticated samples as affected by endometrial carcinoma by the proposed model, and FN is the number of false negatives, namely the samples erroneously identified as negatives.
  • the specificity was calculated as TN/(TN+FP), wherein TN represents the number of true negatives, namely samples correctly diagnosticated as healthy and FP represents the false positives, namely the number of people erroneously diagnosticated as healthy.
  • the ratio of positive likelihood (PLR) was calculated as Sensitivity/(1 -Specificity), while the negative one (NLR) as (1 -Sensitivity )/Specificity.
  • the predictive value (NPV) was calculated as TN/(TN+FN), while the positive (VPP) as TP/(TP+FP).
  • the accuracy represents the percentage of all the correct assignments and was calculated as (TP+TN)/(TP+FP+TN+FN) while the repeatability as the numbers of correct reassignments in 10 replications of the analysis of a sample. Table 3 - Diagnostic performance of the investigated models
  • VIP scores represent the weighted sum of the squares of loading of the pis, considering the amount of y-variance in any dimension. Two peaks show a VIP score greater than 2 in both the models PLS-DA and OPLS-DA (both in the classification of endometrial carcinoma vs control and in the classification of type I vs type II. These were identified as important knots also in the decision tree, these observations suggest a great importance of these variables in the classification processes (not reported data).
PCT/EP2016/053726 2015-02-27 2016-02-23 Method for the diagnosis of endometrial carcinoma WO2016135119A1 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2017563386A JP6731957B2 (ja) 2015-02-27 2016-02-23 子宮内膜癌の診断方法
ES16709979T ES2711814T3 (es) 2015-02-27 2016-02-23 Método para el diagnóstico de carcinoma endometrial
US15/552,342 US20180038867A1 (en) 2015-02-27 2016-02-23 Method for the diagnosis of endometrial carcinoma
EP16709979.5A EP3262416B1 (en) 2015-02-27 2016-02-23 Method for the diagnosis of endometrial carcinoma

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ITUB2015A000390A ITUB20150390A1 (it) 2015-02-27 2015-02-27 Metodo per la diagnosi di carcinoma endometriale

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JP2007057309A (ja) * 2005-08-23 2007-03-08 Yoshikimi Kikuchi 子宮体癌の検出方法
JP2007147459A (ja) * 2005-11-28 2007-06-14 Kazusa Dna Kenkyusho 情報処理装置、プログラム、及びコンピュータ読み取り可能な記録媒体
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