TWI716093B - A method for predicting the risk of recurrence of breast cancer based on metabolic biomarkers - Google Patents

Abstract

The present invention provides a method for predicting the risk of recurrence of breast cancer, comprising: (a) providing a sample; (b) measuring the concentrations _of at least six metabolites in a sample, wherein the metabolites species selected from the group of Betaine, Creatine, Methionine, 2-Methylbutyryl-L-carnitine, Linoleyl-L-carnitine, D-mannose, L-valine, Tyrosine, Norvaline, Tagatose and Linoleic acid; (c) converting the concentrations of these metabolites to test values and comparing with these values that correspond to non-recurring patients in the step (b); and (d) assessing the risk of recurrence of breast cancer.

Description

Method for predicting the risk of breast cancer recurrence based on metabolite markers

The disclosure of the present invention is a method for assessing the recurrence of breast cancer patients; more specifically, the metabolites are used to assess the difference in the types of metabolites in the plasma of patients with recurrence of breast cancer compared with patients without recurrence.

Breast cancer has always been one of the main cancers in women in the world. It is the most common and serious type of cancer among women all over the world. Its recurrence rate is very high. The burden of breast cancer in the world is currently increasing. According to WHO's 2014 World Cancer Report It is shown that breast cancer is the most common cancer among women. For every 100,000 people, 43.3 suffer from breast cancer, and 12.9 of them die of breast cancer. According to statistics from the National Health Agency of Taiwan, the age-standardized incidence rate of breast cancer in 2003 was 42.42% (per 100,000 population), compared with 57.69% in 2008 and 70.74% in 2014, showing a multiple increase.

Due to the improvement of early diagnosis and treatment effect, the survival rate of patients with breast cancer has been greatly improved, but it is expected that more and more women will experience ipsilateral breast tumor recurrence (IBTR) or suffer from the contralateral side The risk of breast cancer (contralateral breast cancer, CBC), and recurrence can be associated with a poor prognosis, including the increased risk of distant metastasis and increased breast cancer mortality. However, the risk of individual recurrence is difficult to predict, because breast cancer recurrence rates are often Research is conducted in different environments, different patient structures, and different time frames, resulting in different recurrence rates in the literature, including occurrences of 2% to 24% IBRT ranging from 1% to 12% CBC, so it is urgent to pay attention and seek better tools to detect and monitor the prognosis of breast cancer.

Metabolomics is the use of unique small molecules in body fluids and their concentrations to form a "fingerprint". This fingerprint can be held by individuals, including health and disease states, and is a dynamic picture of the metabolic state in biological systems, including small molecules The regulation of metabolites and the physiological process of signal transmission. Because small changes in the human body can lead to large changes in metabolites, metabolomics is considered to be the amplification of biological systems, so the fluctuations of certain specific metabolites in body fluids are monitored It has become an important way to detect cancer early. Metabolomics and cancer have a close connection, because cancer cells undergo major metabolic rearrangements in many physiological processes such as tissue remodeling, tumor growth, and cancer metastasis. Therefore, metabolomics can enable us to understand the progress of disease. Abnormal metabolic pathways, through our understanding of breast cancer-related metabolites, provide new metabolite biomarkers for monitoring the occurrence of breast cancer, early diagnosis of breast cancer, formulating breast cancer treatment strategies, or predicting the prognosis of breast cancer, using body fluid metabolites Biomarkers to develop a non-invasive, simple, low-cost, highly sensitive and highly specific monitoring method.

A method for predicting the risk of recurrence of breast cancer, comprising: (a) providing a sample; (b) measuring the detection values of at least six metabolites of the sample, wherein the at least six metabolites are selected from sugar beet Alkali (Betaine), Creatine (Creatine), Methionine (Methionine), 2-Methylbutyryl-L-carnitine (2-Methylbutyryl-L-carnitine), Linoleyl-L-carnitine (Linoleyl- L-carnitine), D-mannose (D-mannose), L-valine (L-valine), Tyrosine (Tyrosine), Norvaline (Norvaline), Tagatose (Tagatose) and linseed oil The group consisting of Linoleic acid; (c) Convert the concentration of metabolites into detection values, and compare the non-dairy products in step (b) The detection value of the corresponding metabolite in patients with cancer recurrence; and (d) assessing the risk of recurrence of breast cancer.

In one embodiment of the present invention, it comprises liquid chromatography-mass spectrometry (LC-MS) and/or gas chromatography-mass spectrometry (GC-MS).

In one embodiment of the present invention, the sensitivity, specificity, and AUC of the screening conditions for all metabolites must be more than 70% before being included in the most suitable combination analysis.

In a specific embodiment, the metabolites are betaine (Betaine), creatine (Creatine), methionine (Methionine), 2-Methylbutyryl-L-carnitine (2-Methylbutyryl-L-carnitine) ), Linoleyl-L-carnitine (Linoleyl-L-carnitine), D-mannose (D-mannose), L-valine (L-valine), Tyrosine (Tyrosine), Orvaline (Norvaline), Tagatose (Tagatose) and Linoleic acid (Linoleic acid) group consisting of.

In an embodiment of the present invention, the six metabolites Creatine, Linoleyl-L-carnitine, D-mannose, and L-valer are shown. The sensitivity, specificity, AUC and accuracy of the combination of L-valine, Norvaline and Tagatose are 90.00%, 96.43%, 98.21% and 94.74%, respectively. It can effectively distinguish the metabolites that are significantly different between recurrent breast cancer patients and non-recurrent patients. That is, high sensitivity, high specificity and high AUC detection methods can be used to monitor the prognosis of breast cancer recurrence.

The present invention proposes six metabolites that can distinguish the risk of breast cancer recurrence from non-recurrence, creatine (Creatine), linoleyl-L-carnitine (Linoleyl-L-carnitine), and D-mannose (D-mannose) , L-valine, Norvaline, and Tagatose are metabolite biomarkers used to detect the prognostic susceptibility of breast cancer. There are currently tens of thousands of metabolites. If the time and cost of each detection are relatively increased, the present invention first uses LC-MS and GC-MS to test plasma samples to perform metabolome analysis (metabolomics) to obtain their metabolic species Class, find out the metabolites that have a significant difference between recurrence and non-recurrence of breast cancer, and then find the most suitable metabolite combination to reduce the number of metabolites needed to be detected, which can also greatly reduce the time and cost of analyzing samples. It can provide a new detection method with low cost and accurate prediction results.

The present invention aims to collect samples of breast cancer patients by non-invasive methods, and the plasma samples of breast cancer patients are tested by LC-MS and GC-MS to obtain the signal strength of the metabolites, which can be regarded as proportional to the concentration. These detection values will be transformed into a normal state by z-transformation or logarithmic transformation, and then perform binary logistic regression to obtain the regression model and β coefficient of each metabolite and calculate its relative OR value (OR Value = exp ^{β coefficient * (Z conversion value of each metabolite detection value} ), sensitivity, specificity, and the area under the ROC curve (Area Under ROC Curve, AUC) of the Receiver Operator Characteristic curve (ROC curve) ) And used to evaluate the ability of various predictive models (Zou KH, O'Malley AJ, Mauri L. Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 2007, 6; 115(5): 654-7) .

The term "a" or "a" in this text is used to describe the elements and components of the present invention. This term is only for convenience of description and to give the basic idea of the present invention. This description should be understood to include one or at least one, and unless clearly indicated otherwise, the singular also includes the plural. When used with the word "including" in the scope of the patent application, the term "a" can mean one or more than one.

The term "or" in this article means the same as "and/or".

The term "breast cancer" refers to any proliferative lesions of the breast, including premalignant and malignant lesions, solid tumors, and metastatic diseases (local metastasis, such as stage III; and more extensive metastasis, such as stage IV). Breast cancer includes (but is not limited to): adenocarcinoma, lobular (small cell) carcinoma, intraductal carcinoma, medullary breast (Such as Phase IV). Breast cancer includes (but is not limited to): adenocarcinoma, lobular (small cell) carcinoma, intraductal carcinoma, medullary breast cancer, mucinous breast cancer, tubular breast cancer, papillary breast cancer, Paget's disease and inflammatory Breast cancer. Metastatic recurrent breast cancer also refers to diseases in other organs such as lung, liver, and bone that originate from metastatic lesions of the breast. Breast cancer also covers hormone-responsive cancers and hormone-independent cancers.

The term "specimen" as used herein refers to a biological sample, such as a tissue or fluid isolated from an individual (including but not limited to plasma, serum, cerebrospinal fluid, lymph, tears, saliva or tissue sections) or cell culture in vitro Composition. In a preferred embodiment, the sample is blood. The term "blood" can mean a whole blood, a plasma or a serum. In a more preferred embodiment, the specimen is plasma.

Figure 1 illustrates the workflow of scanning metabolites for finding metabolites from plasma samples of breast cancer patients by LC-MS and GC-MS.

The present invention may be implemented in different forms and is not limited to the examples mentioned in the following text. The following examples are only representative of the different aspects and characteristics of this creation. The embodiments do not limit the scope of the present invention described in the scope of application rights.

(1) Examination of specimens for breast cancer patients

In order to test the types of metabolites in breast cancer patients, plasma samples of recurrent breast cancer patients and non-recurrent patients were collected and stored at -80°C. Perform LC-MS and GC-MS scans of recurrent breast cancer patients and non-recurring patients with metabolomes analysis (metabolomics) to obtain their metabolite types. LC-MS and GC-MS test the plasma samples of breast cancer patients, and the metabolite detection value is the signal intensity, which can be regarded as There is a direct relationship with the concentration, and these detected values will be transformed into a normal state through z conversion. All samples were first extracted with 400 μl methanol, and analyzed using Agilent 1290 UHPLC system with 6500-QTOF (Agilent Technologies, Santa Clara, CA, USA). Use Acquity HSS T3 tube (100×2.1mm, 1.8μm, Waters, Milford, MA, USA) for separation. The tube must be kept at 40°C, and the mobile phase consists of 0.1% formic acid (solvent A) and 0.1% acetonitrile (Solvent B), the gradient procedure is as follows: 2% solvent B for 0-1.5 minutes, 2% to 50% linear ladder solvent B for 1.5-9 minutes, 50% to 95% for 9-14 minutes % Linear gradient solvent B and 95% solvent B for 4-15 minutes, then the column is re-equilibrated, and the flow rate is maintained at 0.3 mL/min. To ionize the specimen, an electrospray ionization source from Jet Stream was used, with 4.0 kV capillary voltage in positive and negative modes. The MS parameters are set as follows: gas temperature is 325°C, gas flow rate is 5 (l/min), sprayer is 40p.si, atomizing gas temperature is 325°C, atomizing gas flow rate is 10 (l/min), and crushing gas voltage It is 120V and the scanning range is between 50-1700m/z.

(2) Analyze significant differences in metabolites and relative OR values

The two-sample t t-test analysis showed that 41 metabolites are significantly different between recurrent breast cancer patients and non-recurring patients. Perform binary logistic regression to analyze the different metabolites of recurrent breast cancer patients compared to non-recurring patients, and the OR value must be Significant metabolites greater than 2 and less than 0.5 with p-value less than 0.06 are then calculated for sensitivity, specificity, and Area Under ROC curve (AUC) under the Receiver Operator Characteristic curve (ROC curve), To assess the accuracy of prediction, sensitivity, specificity, and AUC must all reach 70% or more before being included in the most suitable combination analysis. Finally, 11 metabolites with significant differences between recurrence and non-recurrence of breast cancer were selected, including Betaine, Creatine, Methionine, and 2-Methylbutyryl-L-Carnitine (2-Methylbutyryl-L-carnitine), linseed oil -L-carnitine (Linoleyl-L-carnitine), D-mannose (D-mannose), L-valine (L-valine), tyrosine (Tyrosine), norvaline (Norvaline), Tagatose and Linoleic acid (see Table 1).

之Z轉換值)，

，

-1.27+1.34*(甲硫胺酸檢測值之Z轉換值)，

基-L-肉鹼檢測值之Z轉換值)，

值之Z轉換值)，

。 The regression patterns of the 6 metabolites and the 6 metabolites for breast cancer recurrence in the plasma samples of the LC-MS test subjects are as follows:

Z conversion value),

,

-1.27+1.34*(Z conversion value of methionine detection value),

Z conversion value of the detection value of base-L-carnitine),

Z conversion value of the value),

.

If the Z conversion value of the betaine test value increases by one (X) unit, the risk of breast cancer recurrence increases by 3.45 times (OR value=exp ^1.24*X =exp ^1.24*1 =3.45), and the Z conversion value of the creatine test value Each increase of one (X) unit also represents a 5.34 times increase in the risk of breast cancer recurrence (OR value=exp ^1.68*X =exp ^1.68*1 =5.34), and each increase of one (X) unit of the Z conversion value of the methionine test value , Showing that the risk of breast cancer recurrence increased by 3.82 times (OR value=exp ^1.34*X =exp ^1.34*1 =3.82), and the Z conversion value of the detection value of 2-methylbutyryl-L-carnitine increased by one (X) unit. It means that the risk of breast cancer recurrence increased by 3.78 times (OR value=exp ^1.33*X =exp ^1.33*1 =3.78), but every increase of one (X) unit in the Z conversion value of the linolein-L-carnitine detection value represents breast cancer recurrence The risk is reduced by 0.21 times (OR value=exp ^(-1.55)*X =exp ^(-1.55)*1 =0.21). Finally, if the Z conversion value of the detection value of D-mannose increases by one (X) unit, it means The risk of breast cancer recurrence increased by 4.40 times (OR value=exp ^1.48*X =exp ^1.48*1 =4.40).

檢測值之Z轉換值)，

，ln

，

-1.26+1.40*(塔格糖檢測值之Z轉換值)，

值之Z轉換值)。L-纈胺酸檢測值之Z轉換值每增加一(X)單位，則乳癌復發風險增加6.64倍(OR值=exp^1.89*X=exp^1.89*1=6.64)，酪胺酸檢測值之Z轉換值每增加一(X)單位，代表乳癌復發風險增加4.56倍(OR值=exp^1.52*X=exp^1.52*1=4.56)，正纈胺酸檢測值之Z轉換值每增加一(X)單位，顯示乳癌復發風險增加4.37倍(OR值=exp^1.47*X=exp^1.47*1=4.37)，塔格糖檢測值之Z轉換值每增加一(X)單位，也顯示乳癌復發風險增加4.07倍(OR值=exp^1.40*X=exp^1.40*1=4.07)，但若亞麻油酸檢測值之Z轉換值每增加一(X)單位，則表示乳癌復發風險減少0.27倍(OR值=exp^(-1.31)*X=exp^(-1.31)*1=0.27)。 In addition, in the GC-MS test subjects' plasma samples, the regression patterns of the five metabolites and the five metabolites for breast cancer recurrence are as follows:

Z conversion value of detection value),

, Ln

,

-1.26+1.40*(Z conversion value of tagatose detection value),

Z conversion value of the value). Each increase of one (X) unit in the Z conversion value of the L-valine test value increases the risk of breast cancer recurrence by 6.64 times (OR value=exp ^1.89*X =exp ^1.89*1 =6.64), the Z value of the tyrosine test value Each increase of one (X) unit in the conversion value represents an increase of 4.56 times the risk of breast cancer recurrence (OR value=exp ^1.52*X =exp ^1.52*1 =4.56), and each increase of one (X) in the Z conversion value of the norvaline test value Unit, showing that the risk of breast cancer recurrence increased by 4.37 times (OR value=exp ^1.47*X =exp ^1.47*1 =4.37). Each increase of one (X) unit in the Z conversion value of the tagatose test value also shows an increase of 4.07 times the risk of breast cancer recurrence Times (OR value=exp ^1.40*X =exp ^1.40*1 =4.07), but if the Z conversion value of the linoleic acid detection value increases by one (X) unit, it means that the risk of breast cancer recurrence is reduced by 0.27 times (OR value=exp ^(-1.31)*X =exp ^(-1.31)*1 =0.27).

(3) Select the metabolite combination that best predicts the prognosis of breast cancer

Find 11 metabolites with significant differences in sensitivity, specificity and AUC of more than 70% between patients with recurrent breast cancer and non-relapsed patients, and further analyze the sensitivity, specificity and AUC value ranking The obtained important metabolites are used to select the best predicted metabolite combination. The larger the AUC value, the higher the accuracy rate. In patients with relapse and non-relapse, all 11 or any 10, any 9, any 8 and any 7 metabolites of the 11 significantly different metabolites have sensitivity, specificity and AUC of 100% combination. There is only one combination of all 11 metabolites, and its sensitivity, specificity and AUC are all 100% (see Table 2).

There are 11 combinations of any 10 "C(11,10)", among which 7 combinations achieve 100% sensitivity, specificity and AUC (see Table 3)

Table 3: Any combination of 10 metabolites

Among the 55 combinations of any 9 "C(11,9)", 10 combinations achieve 100% sensitivity, specificity and AUC, and 6 of these 10 combinations with 100% AUC These metabolites are common, including Creatine, Linoleyl-L-carnitine, D-mannose, L-valine ), Norvaline, Tagatose, and the other 5 metabolites (11-6=5). Use C(5,2) to calculate the combination of any 2 of 10 Any 2 metabolites plus 6 common metabolites to achieve 10 combinations of any 8 metabolites, and the sensitivity, specificity and AUC of these 10 combinations are all 100% (as shown in Table 4 and table 5).

Table 4: Any combination of 9 metabolites

Choose from 11 significant metabolites. There are 165 combinations of any 8 metabolites "C(11,8)". Therefore, with the above calculation method, the calculation of the AUC of 165 any 8 metabolite combinations can be directly omitted. Directly find out the 165 combinations of any 8 metabolites, and 10 combinations have a fast calculation process with 100% sensitivity, specificity and AUC (see Table 5).

There are a total of 330 combinations of any 7 "C(11,7)". If you use a quick calculation technique to combine the 6 common metabolites, plus any one of the 5 other metabolites "C(5,1) )” to achieve 5 combinations of any 7 metabolites, 4 of which have 100% sensitivity, specificity and AUC (see Table 6). Therefore, 4 of the 330 combinations of any 7 metabolites The sensitivity, specificity and AUC of this combination are all 100%.

There are 462 combinations of any 6 "C(11,6)", because in any 9 "C(11,9)" combinations, there are 10 combinations that achieve 100% sensitivity, specificity and AUC. In these 10 combinations of 100% AUC, 6 metabolites have been co-occurred, including Creatine, Linoleyl-L-carnitine, and D-mannose Sugar (D-mannose), L-valine (L-valine), Norvaline (Norvaline) and Tagatose (Tagatose), their sensitivity, specificity and AUC were 90.00%, 96.43% and 98.21, respectively %. Among the 462 metabolic combinations, this combination can achieve the highest AUC ranking (see Table 7).

Table 7: Any combination of 6 metabolites

In order for those with ordinary knowledge in the technical field of this invention to understand the method of making and using this technique, this invention has been described and exemplified in sufficient detail. However, various variations, modifications or improvements should be considered It is tantamount to the spirit and scope of this invention.

Those with ordinary knowledge in the technical field of the present invention can easily understand and achieve the objectives of the present invention, and obtain the results and advantages mentioned earlier. The cells, animals, and the processes and methods for producing them used in the present invention represent the best embodiments, are exemplary in nature, and are not intended to limit the scope of the present invention. Those skilled in the art and the modifications or other uses that will be produced when making or using this technique are all within the spirit of the present invention and are defined by the scope of rights.

Claims (7)

A method for predicting the risk of breast cancer recurrence, comprising: (a) providing a sample; (b) separately measuring the concentration of at least six metabolites of the sample, wherein the at least six metabolites are selected from betaine (Betaine), Creatine (Creatine), Methionine (Methionine), 2-Methylbutyryl-L-carnitine (2-Methylbutyryl-L-carnitine), Linoleyl-L-carnitine (Linoleyl-L -carnitine), D-mannose (D-mannose), L-valine (L-valine), Tyrosine (Tyrosine), Norvaline (Norvaline), Tagatose (Tagatose) and Linoleic acid (Linoleic acid); (c) Convert the concentration of the metabolite into a detection value, and compare the detection value of the corresponding metabolite in the non-breast cancer recurrence patients in step (b); and (d) To assess the risk of recurrence of a breast cancer. The method described in item 1 of the scope of patent application, wherein the detection value of the corresponding metabolite in step (b) is through liquid chromatography-mass spectrometry (LC-MS) and/or gas chromatography- Mass spectrometry combined with (GC-MS) for the detection of the sample to obtain it. The method described in item 1 of the scope of patent application, wherein the comparison in step (c) is a value obtained after the detection value of the corresponding metabolite is processed through a data processing method, and the value is used to evaluate the risk of breast cancer recurrence through a corresponding formula degree. Such as the method described in item 3 of the scope of patent application, wherein the data processing method is z-transformation or logarithmic transformation. The method described in item 1 of the scope of patent application, wherein the test system is plasma. The method described in item 1 of the scope of patent application, wherein the at least six metabolites are creatine (Creatine), Linoleyl-L-carnitine (Linoleyl-L-carnitine), D-mannose (D-mannose), L-valine (L-valine), Norvaline (Norvaline) and tower Tagatose. According to the method described in item 6 of the scope of patent application, wherein the at least six metabolites can be further combined with any number of metabolites, and the any number of metabolites are selected from Betaine, Methionine ), 2-Methylbutyryl-L-carnitine (2-Methylbutyryl-L-carnitine), Tyrosine (Tyrosine) and Linoleic acid (Linoleic acid).

Images