JP7555124B2 - Method for analyzing biological samples using neuroblastoma minimal residual disease markers - Google Patents

Description

The present invention relates to a method for analyzing a biological sample using a minimal residual disease marker for neuroblastoma. More specifically, the present invention relates to a method for predicting minimal residual disease for neuroblastoma.

Neuroblastoma is an intractable childhood cancer derived from neural crest cells, accounting for approximately 10% of all childhood cancers, the second most frequent after brain tumors, and is responsible for approximately 15% of childhood cancer deaths.
Neuroblastoma is classified into low, intermediate, and high risk groups using five prognostic factors (stage, pathology, age, MYCN amplification, and DNA ploidy), with more than 50% of patients classified into the high risk group. More than 50% of high-risk patients experience recurrence.

Therefore, to improve the prognosis of neuroblastoma, especially in high-risk patients, it is essential to properly evaluate minimal residual disease (MRD), which is thought to be the origin of recurrence. In neuroblastoma, where genes that are detected only in tumor cells have not been identified, attempts have been made to detect MRD by using several genes that are highly expressed in tumor cells compared to normal cells as markers.

As a genetic marker for MRD in neuroblastoma, TH was first reported in Non-Patent Document 1, followed by PHOX2B in Non-Patent Document 2. Furthermore, five genetic markers, CHGA, DCX, DDC, PHOX2B, and TH, are reported in Non-Patent Document 3. Four genetic markers, B4GALNT (GD2 synthase), CCND1, ISL1, and PHOX2B, are reported in Non-Patent Document 4. Five genetic markers, CHRNA3, DDC, GAP43, PHOX2B, and TH, are reported in Non-Patent Document 5.

On the other hand, in Non-Patent Document 6, instead of parental neuroblastoma cells cultured in adhesion, which have been commonly used in previous gene marker searches, sphere neuroblastoma cells cultured in suspension to concentrate cancer stem cells that constitute MRD in vivo are examined, and an MRD detection protocol is proposed in which if any of 11 gene markers, CHRNA3, CRMP1, DBH, DCX, DDC, GABRB3, GAP43, ISL1, KIF1A, PHOX2B, and TH, is expressed above the normal range, it is scored as MRD positive. In Non-Patent Document 7, it is reported that in two cases, the 11 gene markers were used to evaluate MRD positive earlier than the clinical diagnosis of recurrence/regrowth. In the above-mentioned Non-Patent Documents 6 and 7, real-time PCR is used to measure the gene markers.

Furthermore, Patent Document 1 has found CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH as seven specific gene markers that are particularly highly capable of detecting MRD of neuroblastoma. Although these seven gene markers are a combination of only seven, each gene marker has excellent detection ability in MRD of neuroblastoma. Furthermore, since the annealing temperatures of all primer pairs corresponding to each gene are easily aligned, any of the seven gene markers can be amplified equally efficiently. Therefore, Patent Document 1 discloses a method for determining that MRD of neuroblastoma is positive if the expression level of one or more of these seven gene markers that are amplified equally efficiently is equal to or above a threshold value. Furthermore, Patent Document 1 discloses that it is desirable to use digital PCR, which is a means for detecting the expression of extremely small amounts of gene markers, for accurate evaluation of MRD of neuroblastoma.

In addition, Non-Patent Document 8 discloses that five neuroblastoma-related genes, CHGA, DCX, DDC, PHOX2B, and TH, are used, and that the geometric mean values of the amounts of mRNA transcribed from these five genes correlate with progression-free survival in recurrent/refractory neuroblastoma. It also reports that the AUC of prognostic prediction using the geometric mean values was 0.622.

International Publication No. 2018/123764

International Journal of Cancer (1994) 57: 671-675. Journal of Clinical Oncology (2008) 26: 5443-5449. Lancet Oncology (2013) 14: 999-1008. Journal of Clinical Oncology (2015) 33:755-763. European Journal of Cancer (2016) 54: 149-158. Oncology Reports (2013) 29: 1629-1636. Oncology Letters (2016) 12: 1119-1123. Clinical Cancer Research; (2017) 23 5374-5383.

The inventors examined healthy control samples and archived samples from high-risk neuroblastoma patients by digital PCR using seven MRD markers for neuroblastoma (hereinafter also referred to as "seven gene markers") consisting of CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH.

Usually, in analyses using markers, many disease samples are analyzed, but not many control samples. However, the inventors examined many healthy control samples using digital PCR and encountered the problem of large variations (dispersion) in the expression levels of the seven gene markers in the healthy control samples. Concomitantly, it was revealed that there was an overlap in the expression levels of the seven gene markers even between samples at the time of neuroblastoma diagnosis and healthy control samples.

These problems are due to the low expression of the seven gene markers and the fact that the quantification method is not the commonly used qPCR but the extremely sensitive digital PCR. In other words, the above problems were found to be specific to the method of using digital PCR measurement to quantify the seven gene markers. Therefore, in the MRD analysis of neuroblastoma using the seven gene markers, the usual method of setting cutoff values cannot be used to perform an analysis with high sensitivity and high specificity.

In view of the above, the present invention aims to provide a new method for analyzing MRD of neuroblastoma that uses digital PCR measurement to quantify seven gene markers consisting of CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH, and that can comprehensively improve the sensitivity and specificity of the analysis.

The present inventors statistically examined the seven gene markers in healthy control samples and found that the expression patterns of each of the seven gene markers are different. For example, it was found that some markers, such as CRMP1, are expressed relatively highly from low percentiles, while others, such as DBH, remain relatively low in expression until reaching high percentiles. The seven gene markers are elite markers carefully selected for their excellent detection ability in the MRD of neuroblastoma, which is highly diverse. However, it was also found that there are differences in importance among these seven gene markers, such as those with low importance (e.g., those that are relatively easy to detect in healthy control samples, such as CRMP1) and those with high importance (e.g., those that are relatively difficult to detect in healthy control samples, such as DBH).

Therefore, the inventors came up with the idea of using the statistical values of the expression levels of each of the seven gene markers by digital PCR in a control (e.g., expression levels at a specific percentile) as an amount indicating the non-importance of the marker, and came up with a novel method of correcting the expression levels of each of the seven gene markers by digital PCR in the sample to be analyzed by weighting them with the statistical values (non-importance) in the control. Furthermore, they found that the sensitivity and specificity of the analysis can be improved overall by using the geometric mean or total value of the expression levels of the seven gene markers corrected by weighting as an evaluation value and comparing the evaluation value with a cutoff value for the presence or absence of MRD. The present invention was completed based on this knowledge and through further investigation.

That is, the present invention provides the following aspects.
<Item 1>
A step 1 of measuring the expression levels A1 to A7 of seven gene markers for minimal residual disease of neuroblastoma consisting of CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH from a biological sample in which the disease progression, therapeutic effect, or recurrence of minimal residual disease of neuroblastoma is to be evaluated by digital PCR;
A step 2 of weighting each of the expression levels A1 to A7 by taking into account statistical values B1 to B7 of the expression levels of each of the seven gene markers in a control by digital PCR to obtain weighted expression levels AB1 to AB7, and obtaining the geometric mean or sum of the weighted expression levels AB1 to AB7 as an evaluation value;
Step 3: comparing the evaluation value with a cutoff value for the presence or absence of minimal residual disease of neuroblastoma;
A method for analyzing a biological sample using a minimal residual disease marker for neuroblastoma, comprising:
<Item 2>
[Geometric mean of weighted expression levels by subtraction] (item 2-3-1)
The weighted expression amounts AB1 to AB7 are obtained by subtracting the statistical values B1 to B7 from the expression amounts A1 to A7, respectively (however, when the subtraction result is a value less than 1, the weighted expression amount is set to 1), and the geometric mean of the weighted expression amounts AB to AB7 is obtained as the evaluation value. The method for analyzing a biological sample according to item 1.
<Item 3>
[Geometric mean of weighted expression levels by subtraction - using fixed percentiles] (item 2-3-1)
The statistical values B1 to B7 are all expression levels at a common specific percentile of 1 percentile or more for each of the seven gene markers in a control;
Item 3. The method for analyzing a biological sample according to Item 2, wherein at least any one of the seven gene markers is detected in the specific percentile.
<Item 4>
[Geometric mean of weighted expression levels by subtraction - using fixed percentiles] (item 2-3-1)
the biological sample is a bone marrow sample;
Item 4. The method for analyzing a biological sample according to Item 3, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among 6th to 92nd percentiles of each of the seven gene markers in a control.
<Item 5>
[Geometric mean of weighted expression levels by subtraction - using variable percentiles] (item 2-3-1)
the biological sample is a bone marrow sample;
The statistical values B1 to B7 are respectively in the control,
CRMP1 38-44th percentile, DBH 53-66th percentile, DDC 55-63rd percentile, GAP43 49-65th percentile, ISL1 40-56th percentile, PHOX2B 51-71st percentile, TH 51-71st percentile;
CRMP1 38-44th percentile, DBH 1-52nd percentile, DDC 1-54th percentile, GAP43 85-86th percentile, ISL1 92-94th percentile, PHOX2B 1-50th percentile, TH 51-71th percentile: or
Item 3. The method for analyzing a biological sample according to Item 2, wherein the expression levels are the 83rd percentile of CRMP1, the 1st to 52nd percentile of DBH, the 1st to 54th percentile of DDC, the 85th to 86th percentile of GAP43, the 92nd to 94th percentile of ISL1, the 1st to 50th percentile of PHOX2B, and the 1st to 50th percentile of TH.
<Item 6>
[Geometric mean of weighted expression levels by subtraction - using fixed percentiles] (item 2-3-1)
the biological sample is a peripheral blood sample,
Item 3. The method for analyzing a biological sample according to Item 2, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among 65th to 99th percentiles of each of the seven gene markers in a control.
<Item 7>
[Geometric mean of weighted expression levels by subtraction - using fixed percentiles] (item 2-3-1)
the biological sample is a peripheral blood stem cell sample,
Item 4. The method for analyzing a biological sample according to Item 3, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among 21st to 99th percentiles of each of the seven gene markers in a control.
<Item 8>
[Geometric mean of weighted expression levels by subtraction - using variable percentiles] (item 2-3-1)
the biological sample is a peripheral blood stem cell sample,
The statistical values B1 to B7 are respectively the peripheral blood sample control,
Item 3. The method for analyzing a biological sample according to Item 2, wherein the expression levels are in the 91st to 99th percentiles of CRMP1, the 91st to 92nd percentiles of DBH, the 92nd to 95th percentiles of DDC, the 91st to 92nd percentiles of GAP43, the 98th to 99th percentiles of ISL1, the 87th to 92nd percentiles of PHOX2B, and the 82nd to 93rd percentiles of TH.
<Item 9>
[Sum of weighted expression levels by subtraction] (item 2-3-2-1)
The weighted expression amounts AB1 to AB7 are obtained by subtracting statistical values B1 to B7 from the expression amounts A1 to A7, respectively (however, when the subtraction results in a negative value, the weighted expression amount is set to 0), and the sum of the weighted expression amounts AB to AB7 is obtained as the evaluation value. The method for analyzing a biological sample according to item 1.
<Item 10>
[Sum of weighted expression levels by subtraction - using fixed percentile] (item 2-3-2-1)
The statistical values B1 to B7 are all expression levels at a common specific percentile of 1 percentile or more for each of the seven gene markers in a control;
Item 10. The method for analyzing a biological sample according to Item 9, wherein at least any one of the seven gene markers is detected in the specific percentile.
<Item 11>
[Sum of weighted expression levels by subtraction - using fixed percentile] (item 2-3-2-1)
the biological sample is a bone marrow sample;
Item 11. The method for analyzing a biological sample according to Item 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among 6th to 93rd percentiles of each of the seven gene markers in a control.
<Item 12>
[Sum of weighted expression levels by subtraction - using fixed percentile] (item 2-3-2-1)
the biological sample is a peripheral blood sample,
Item 11. The method for analyzing a biological sample according to Item 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 21st to 99th percentiles of each of the seven gene markers in a control.
<Item 13>
[Sum of weighted expression levels by subtraction - using fixed percentile] (item 2-3-2-1)
the biological sample is a peripheral blood stem cell sample,
Item 11. The method for analyzing a biological sample according to Item 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 21st to 99th percentiles of each of the seven gene markers in a control.
<Item 14>
[Sum of weighted expression levels by subtraction - variable percentile use] (item 2-3-2-1)
the biological sample is a bone marrow sample, a peripheral blood sample, or a peripheral blood stem cell sample;
Item 10. The method for analyzing a biological sample according to Item 9, wherein the statistical values B1 to B7 are expression amounts closest to cutoff values for the presence or absence of minimal residual disease for each of the seven gene markers among expression amounts at each percentile of the seven gene markers in a control.
<Item 15>
[Sum of weighted expression levels by division] (item 2-3-2-2)
Item 2. The method for analyzing a biological sample according to Item 1, wherein the weighted expression amounts AB1 to AB7 are obtained by dividing the expression amounts A1 to A7 by statistical values B1 to B7 (wherein none of B1 to B7 is 0), and the sum of the weighted expression amounts AB to AB7 is obtained as the evaluation value.
<Item 16>
[Sum of weighted expression levels by division - using fixed percentile] (item 2-3-2-2)
the biological sample is a bone marrow sample;
Item 16. The method for analyzing a biological sample according to Item 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among 54 percentiles or higher of each of the seven gene markers in a control.
<Item 17>
[Sum of weighted expression levels by division - using fixed percentile] (item 2-3-2-2)
the biological sample is a peripheral blood sample,
Item 16. The method for analyzing a biological sample according to Item 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 90th percentile or higher of each of the seven gene markers in a control.
<Item 18>
[Sum of weighted expression levels by division - using fixed percentile] (item 2-3-2-2)
the biological sample is a peripheral blood stem cell sample,
Item 16. The method for analyzing a biological sample according to Item 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 90th percentile or higher of each of the seven gene markers in a control.
<Item 19>
[Sum of weighted expression levels by division - variable percentile used] (item 2-3-2-2)
the biological sample is a bone marrow sample;
The statistical values B1 to B7 are respectively in the control,
CRMP1 38-44th percentile, DBH 77-83rd percentile, DDC 74-79th percentile, GAP43 38-48th percentile, ISL1 57-68th percentile, PHOX2B 72-77th percentile, TH 78-83rd percentile; or
Item 16. The method for analyzing a biological sample according to Item 15, wherein the expression levels are the 92nd percentile of CRMP1, the 91st to 92nd percentile of DBH, the 99th percentile of DDC, the 93rd percentile of GAP43, the 96th percentile of ISL1, the 92nd to 93rd percentile of PHOX2B, and the 84th to 87th percentile of TH.
<Item 20>
[Sum of weighted expression levels by division - variable percentile used] (item 2-3-2-2)
the biological sample is a peripheral blood stem cell sample,
The statistical values B1 to B7 are respectively the peripheral blood sample control,
Item 16. The method for analyzing a biological sample according to Item 15, wherein the expression levels are the 98th to 99th percentile of CRMP1, the 90th percentile of DBH, the 98th to 99th percentile of DDC, the 92nd to 93rd percentile of GAP43, the 99th percentile of ISL1, the 90th percentile of PHOX2B, and the 90th to 92nd percentile of TH.
<Item 21>
21. The method for analyzing a biological sample according to any one of items 1 to 20, wherein the expression levels A1 to A7 of the seven gene markers in the biological sample and the expression levels of the seven gene markers in the control by digital PCR are all values obtained by dividing at least the expression level of a reference gene, and the reference gene is selected from the group consisting of HPRT1, HMBS, GUSB, and TBP.

The present invention provides a new method for analyzing MRD of neuroblastoma that uses digital PCR measurement to quantify seven gene markers consisting of CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH, and can comprehensively improve the sensitivity and specificity of the analysis.

This shows the expression levels (relative copy numbers) of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) in bone marrow specimens (BM) from control and neuroblastoma patients at the time of diagnosis. This shows the expression levels (relative copy numbers) of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) in peripheral blood samples (PB) from control and neuroblastoma patients at the time of diagnosis. The expression levels of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) in 103 bone marrow samples from adult controls determined by digital PCR are sorted from lowest to highest and divided into 100 equal parts (1st to 50th percentile values). The expression levels of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) in 103 bone marrow samples from adult controls determined by digital PCR are sorted from lowest to highest and divided into 100 equal parts (51st to 100th percentile values). The expression levels of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) measured by digital PCR in 107 peripheral blood samples from adult controls are sorted from lowest to highest and divided into 100 equal parts (1st to 50th percentile values). The expression levels of seven gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH) in 107 peripheral blood samples from adult controls measured by digital PCR are sorted from lowest to highest and divided into 100 equal parts (51st to 100th percentile values). The expression levels of the seven gene markers in bone marrow samples (BM) of neuroblastoma patients at each disease stage were weighted using the 90th percentile expression levels in Figure 3B, and the sum (7NB-mRNAs) was calculated. The distribution of the evaluation value at each disease stage is shown. The expression levels of the seven gene markers in peripheral blood samples (PB) of neuroblastoma patients at each disease stage were weighted using the 90th percentile expression levels in Figure 4B, and the sum (7NB-mRNAs) was calculated. The distribution of the evaluation values at each disease stage is shown. The expression levels of the seven gene markers in bone marrow samples (BM) from neuroblastoma patients at each collection time point were weighted using the 90th percentile expression level in Figure 3B, and the sum (7NB-mRNAs) was calculated. The distribution of the evaluation value at each collection time point is shown. The expression levels of the seven gene markers in peripheral blood samples (PB) from neuroblastoma patients at each collection time point were weighted using the 90th percentile expression levels in Figure 4B, and the sum (7NB-mRNAs) was calculated. The distribution of the evaluation values at each collection time point is shown. The expression levels of seven gene markers in bone marrow samples taken from high-risk neuroblastoma patients at the time of diagnosis (Diagnosis) and during follow-up after treatment (Post-treatment) were weighted using the 90th percentile expression levels in Figure 3B, and the sum (7NB-mRNAs) was calculated. The results show a comparison of evaluation values between relapsed and non-relapsed patients (Relapse and Non-relapse). The results of ROC analysis for predicting recurrence are shown in Fig. 3B. The sum (non-weighted sum) of the expression levels of seven gene markers in 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, and the evaluation value derived by weighting the expression levels using the 90th percentile expression levels in Fig. 3B and then summing them (90th percentile weighted sum). For 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, the geometric mean (ddPCR_geometric mean (subtraction-weighted)) of the expression levels of 7 gene markers weighted by subtraction was used as the evaluation value using the expression levels at each percentile equal to or higher than the 6th percentile shown in Figures 3A and 3B. The results are shown in Table 1. For 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, the sum of the expression levels of 7 gene markers weighted by subtraction (ddPCR_sum (subtraction-weighted)) using the expression levels at each percentile equal to or higher than the 6th percentile shown in Figures 3A and 3B was used as the evaluation value, and the fixed percentile that maximizes the AUC for prognosis prediction (recurrence prediction) was investigated. For 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, the sum of the expression levels of 7 gene markers (ddPCR_sub (division-weighted)) weighted by division using the expression levels at each percentile above the 54th percentile shown in Figures 3A and 3B was used as the evaluation value, and the fixed percentile that maximizes the AUC for prognosis prediction (recurrence prediction) was investigated. For 21 peripheral blood samples (4 recurrent, 17 non-recurrent) collected during follow-up after treatment, the geometric mean (ddPCR_geometric mean (subtraction-weighted)) of the expression levels of 7 gene markers weighted by subtraction was used as the evaluation value using the expression levels at each percentile equal to or higher than the 52nd percentile shown in Figures 4A and 4B. The results are shown below, which are fixed percentiles that maximize the AUC for prognosis prediction (recurrence prediction). For 21 peripheral blood samples (4 recurrent, 17 non-recurrent) collected during follow-up after treatment, the sum of the expression levels of 7 gene markers weighted by subtraction (ddPCR_sum (subtraction-weighted)) was used as the evaluation value using the expression levels at each percentile equal to or higher than the 21st percentile shown in Figures 4A and 4B. The results are shown below for a fixed percentile that maximizes the AUC for prognosis prediction (recurrence prediction). For 21 peripheral blood samples (4 recurrent, 17 non-recurrent) collected during follow-up after treatment, the sum of the expression levels of 7 gene markers (ddPCR_sub (division-weighted)) weighted by division using the expression levels at each percentile equal to or higher than the 91st percentile shown in Figures 4A and 4B was used as the evaluation value, and the fixed percentile that maximizes the AUC for prognosis prediction (recurrence prediction) was investigated. For 20 samples of peripheral blood stem cells (11 relapses, 9 non-relapses) collected during treatment, the geometric mean (ddPCR_geometric mean (subtraction-weighted)) of the expression levels of 7 gene markers weighted by subtraction was used as the evaluation value using the expression levels at each percentile above the 21st percentile shown in Figures 4A and 4B. The results are shown below for a fixed percentile that maximizes the AUC for prognosis prediction (relapse prediction). For 20 samples of peripheral blood stem cells (11 relapses, 9 non-relapses) collected during treatment, the sum of the expression levels of 7 gene markers weighted by subtraction (ddPCR_sum (subtraction-weighted)) was used as the evaluation value using the expression levels at each percentile above the 21st percentile shown in Figures 4A and 4B. The results are shown below for a fixed percentile that maximizes the AUC for prognosis prediction (relapse prediction). For 20 samples of peripheral blood stem cells (11 relapses, 9 non-relapses) collected during treatment, the sum of the expression levels of 7 gene markers (ddPCR_sub (division-weighted)) weighted by division using the expression levels at each percentile above the 90th percentile shown in Figure 4B was used as the evaluation value, and the fixed percentile that maximizes the AUC for prognosis prediction (relapse prediction) was investigated.

The method for analyzing a biological sample using the minimal residual disease marker of neuroblastoma of the present invention includes the steps of: step 1: measuring the expression levels A1 to A7 of each of seven specific gene markers by digital PCR from a biological sample in which the disease progression, therapeutic effect, or recurrence of minimal residual disease of neuroblastoma is to be evaluated; step 2: weighting each of the expression levels A1 to A7 by taking into account the statistical values B1 to B7 of the expression levels of each of the seven gene markers in a control by digital PCR to obtain weighted expression levels AB1 to AB7, and obtaining the geometric mean or sum of the weighted expression levels AB1 to AB7 as an evaluation value; and step 3: comparing the evaluation value with a cutoff value for the presence or absence of minimal residual disease of neuroblastoma. The method for analyzing a biological sample using the minimal residual disease marker of neuroblastoma of the present invention will be described in detail below.

[1. Step 1]
In step 1, the expression levels A1 to A7 of seven specific gene markers are measured by digital PCR from a biological sample in which recurrence of minimal residual disease of neuroblastoma is to be evaluated.

[1-1. Biological samples]
The biological sample for evaluating the disease progression, therapeutic effect, or recurrence of minimal residual lesions of neuroblastoma is a sample derived from a neuroblastoma patient who is a subject. The neuroblastoma patient is not particularly limited as long as the disease progression, therapeutic effect, or recurrence is evaluated. Specifically, the patient for evaluating the disease progression (specifically, the progression of the disease is monitored, or the disease progression at a level that requires the first therapeutic intervention is diagnosed) includes a patient under follow-up observation from the first visit to the therapeutic intervention, the patient for evaluating the therapeutic effect includes a patient under treatment, and the patient for evaluating the recurrence (specifically, the re-growth is monitored, or the recurrence at a level that requires the second therapeutic intervention is diagnosed) includes a patient after treatment (at the time of follow-up observation and at the time of recurrence diagnosis). The treatment is not particularly limited, and includes chemotherapy, surgery, and radiation therapy. In addition, the chemotherapy includes remission induction therapy (chemotherapy before high-dose chemotherapy), high-dose chemotherapy combined with autologous hematopoietic stem cell transplantation, high-dose MIBG treatment, and differentiation induction therapy and immunotherapy (anti-GD2 antibody) performed after high-dose chemotherapy. Among these neuroblastoma patients, preferably, patients in which the therapeutic effect should be evaluated and patients in which recurrence should be evaluated are included, more preferably, patients in which recurrence should be evaluated are included. Among the patients in which recurrence should be evaluated, particularly preferred are high-risk patients according to the International Neuroblastoma Risk Classification (INRG risk classification).

The biological sample is not particularly limited as long as it contains the subject's nucleic acid. Examples of the nucleic acid include DNA or RNA, preferably RNA. In addition, the biological sample preferably includes a bone marrow sample (hereinafter also referred to as a bone marrow specimen) prepared from bone marrow fluid collected from the subject, a peripheral blood sample (hereinafter also referred to as a peripheral blood specimen) prepared from peripheral blood collected from the subject, and a peripheral blood stem cell sample (hereinafter also referred to as a peripheral blood stem cell specimen) prepared from peripheral blood stem cells collected from the subject. The peripheral blood stem cell sample can be prepared from a specimen collected for autologous transplantation (a procedure in which the patient's own peripheral blood stem cells are collected in advance and returned to the body after high-dose chemotherapy). The bone marrow sample, peripheral blood sample, and peripheral blood stem cell sample may be prepared according to a conventional method that allows the acquisition of nucleic acid, such as total RNA or mRNA.

[1-2. Seven gene markers for minimal residual disease in neuroblastoma]
The genetic markers used in the present invention are composed of seven types of genes: a gene described as CRMP1, a gene described as DBH, a gene described as DDC, a gene described as GAP43, a gene described as ISL1, a gene described as PHOX2B, and a gene described as TH (hereinafter, simply described as CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH, respectively). The expression level of each of these seven genetic markers increases when minimal residual lesions of neuroblastoma are present, and the expression level of the genetic markers correlates (positively correlates) with the occurrence level of minimal residual lesions of neuroblastoma.

The combination of these seven gene markers has high sensitivity for detecting minimal residual disease of neuroblastoma and has few false negatives for minimal residual disease of neuroblastoma. Therefore, it is useful as an indicator of minimal residual disease of neuroblastoma. Therefore, by measuring the expression levels of these seven gene markers by digital PCR, minimal residual disease of neuroblastoma can be detected with high sensitivity.

Each of these seven gene markers has the ability to detect minimal residual disease of neuroblastoma even as a single marker, and even though it is a combination of only seven markers, it has the screening sensitivity to deal with the remarkable heterogeneity that is characteristic of neuroblastoma. This number of seven gene markers, including the number of reference genes, is a number that can be simultaneously amplified by many nucleic acid amplification devices, making it easy to apply in clinical practice.

The combination of seven gene markers, CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B and TH, is useful whether the biological sample to be detected is a peripheral blood sample or a bone marrow sample.

Detailed sequence information of each of the genetic markers in the present invention can be obtained from publicly known databases. For example:
- CRMP1 (collapsin response mediator protein 1), which corresponds to accession number NM_001014809 in the RefSeq database of the National Center for Biotechnology Information (NCBI);
- DBH (dopamine β-hydroxylase) corresponds to the same accession number NM_000787,
- DDC (dopa decarboxylase) corresponding to the accession number NM_000790;
- GAP43 (growth-associated protein 43), which corresponds to the accession number NM_001130064;
- ISL1 (ISL LIM homeobox 1), which corresponds to the same accession number NM_002202,
- PHOX2B (paired-like homeobox 2b), which corresponds to the same accession number NM_003924,
- TH (tyrosine hydroxylase) includes that corresponding to the accession number NM_199292.

Specific nucleotide sequences encoding the genetic markers of the present invention include the following sequences:
CRMP1 includes that represented by SEQ ID NO: 1,
DBH includes those represented by SEQ ID NO: 2,
DDC includes those represented by SEQ ID NO: 3,
GAP43 includes that represented by SEQ ID NO: 4,
ISL1 includes that represented by SEQ ID NO:5,
PHOX2B includes the one represented by SEQ ID NO:6,
An example of TH is that shown in SEQ ID NO:7.

The nucleotide sequence encoding the gene marker in the present invention may have homology with each of the above sequences as long as it is an indicator of minimal residual disease of neuroblastoma. Preferably, it may be composed of a polynucleotide (polynucleotide is used to include DNA, RNA, and PNA (peptide nucleic acid). Polynucleotide is preferably DNA or RNA. The same applies below) that has at least 70% homology with the nucleotide sequences represented by SEQ ID NO: 1 to SEQ ID NO: 7 and is functionally equivalent. The sequence homology of such a polynucleotide is more preferably 75% or more, even more preferably 80% or more, even more preferably 85% or more, even more preferably 90% or more, and even more preferably 95% or more.

Functionally equivalent to each of the seven gene markers means that the expression level increases in the presence of minimal residual lesions of neuroblastoma equivalent to that of a specific polynucleotide having a nucleotide sequence represented by each of SEQ ID NOs: 1 to 7 above. Specifically, functional equivalence means that there is a significant difference in the expression level of the polynucleotide between the presence and absence of minimal residual lesions of neuroblastoma, and the expression level is 80-120%, preferably 90-110%, of the expression level of the specific polynucleotide in both cases.

The homology of the above-mentioned nucleotide sequences is determined by a known method (hereinafter the same). Specific examples of such methods include the BLAST algorithm by Karlin and Altschul [Proc. Natl. Acad. Sci. USA, 90, 5873-5877 (1993)]. Based on this algorithm, a program called BLASTN or BLASTX has been developed [Altschul et al. J. Mol. Biol. , 215, 403-410 (1990)], which can also be used in the present invention. Another preferred method is a method using genetic information processing software GENETYX (manufactured by Genetics). When GENETYX is used, in addition to BLAST analysis, homology analysis by the Lipman-Pearson method can be performed, and this can be advantageously used in determining homology in the present invention.

The seven gene markers mentioned above have high sensitivity for detecting minimal residual disease of neuroblastoma, and their expression levels increase when the level of minimal residual disease of neuroblastoma increases. In other words, the expression levels correlate with the occurrence level of minimal residual disease of neuroblastoma. Therefore, by measuring the expression levels of all seven gene markers, it is possible to evaluate minimal residual disease of neuroblastoma from the obtained expression levels.

[1-3. Reference genes]
Examples of reference genes for the seven gene markers include a gene described as HPRT1, a gene described as HMBS, a gene described as GUSB, and a gene described as TBP (hereinafter, simply referred to as HPRT1, HMBS, GUSB, and TBP, respectively). At least one reference gene can be selected from these.

These reference genes have a moderately low expression level, which allows for more sensitive detection of MRD even in samples with low gene marker expression levels. Furthermore, among these, the most preferred reference gene is the HPRT1 gene, which has a small variation (dispersion) in expression levels in bone marrow samples and peripheral blood samples, making it highly useful for both bone marrow-derived biological samples and peripheral blood-derived biological samples.

Detailed sequence information of the reference genes can be obtained from publicly known databases. For example:
- HPRT1 (hypoxanthine phosphoribosyltransferase 1), which corresponds to accession number NM_000194 in the RefSeq database of the National Center for Biotechnology Information (NCBI),
- HMBS (Hydroxymethylbilane synthase) corresponds to the accession number NM_000190 in the same database,
- GUSB (Glucuronidase Beta), which corresponds to the accession number NM_000181 in the same database;
- TBP (TATA-binding protein) is, for example, the one corresponding to accession number NM_003194 in the same database.

Specific nucleotide sequences encoding the reference gene include the following sequences:
HPRT1 includes that represented by SEQ ID NO:8,
HMBS includes that represented by SEQ ID NO:9,
GUSB includes the one represented by SEQ ID NO: 10,
An example of TBP is that shown in SEQ ID NO:11.

The nucleotide sequence encoding the reference gene may have homology to the above sequence as long as it serves as an endogenous control. Preferably, it may be composed of a polynucleotide having at least 70% homology to the above nucleotide sequence and functionally equivalent to each of the above genes. The sequence homology of such a polynucleotide is more preferably 75% or more, even more preferably 80% or more, even more preferably 85% or more, even more preferably 90% or more, even more preferably 95% or more.

Functionally equivalent with respect to each reference gene means that the variation in measured values between samples is small and the expression level is moderately low, equivalent to the specific polynucleotide having the nucleotide sequence represented by SEQ ID NO:8 to SEQ ID NO:11 described above, and in the case of HPRT1, the variation in measured values between bone marrow samples and peripheral blood samples is even smaller. Specifically, functional equivalence means that the standard deviation in a population of the same number of genes is 80-120%, preferably 90-110%, of the standard deviation of the specific polynucleotide, and the expression level of the polynucleotide is 80-120%, preferably 90-110%, of the expression level of the specific polynucleotide.

[1-4. Digital PCR]
In step 1, the expression levels A1 to A7 of the seven gene markers are obtained by digital PCR (polymerase chain reaction). The expression levels A1 to A7 of the seven gene markers may be absolute levels or may be relative levels to the expression level of a reference gene (i.e., values obtained by dividing the expression levels of the seven gene markers by at least the expression level of the reference gene). From the viewpoint of performing a more accurate analysis, it is preferable that the expression levels A1 to A7 of the markers are relative levels to the expression level of the reference gene.

Digital PCR is particularly suitable for detecting minimal residual lesions of neuroblastoma because it can detect samples with low expression levels of gene markers with high sensitivity. In addition, digital PCR is also effective in that it can accurately detect minimal residual lesions of neuroblastoma with little variation in measurement results even if the analysis subject, analysis time, analysis equipment, protocol (reaction time, reaction temperature, etc.) are different. Therefore, digital PCR is an excellent method in terms of high versatility and high reliability in evaluating minimal residual lesions of neuroblastoma. In digital PCR, the expression levels A1 to A7 of seven gene markers can be obtained by using the following primer pairs or by further using the following probes.

[1-4-1. Primer pair]
The primer pairs used in the present invention are a pair of polynucleotide molecules capable of amplifying nucleotide sequences encoding the above-mentioned genetic markers. In other words, a total of seven primer pairs for each of the seven genetic markers CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH are used in the present invention.

Such a primer pair includes a sense primer and an antisense primer, each of which is designed to amplify a nucleotide sequence encoding the above-mentioned genetic marker. The antisense primer is a polynucleotide molecule that hybridizes under stringent conditions to a polynucleotide to be amplified, and the sense primer is a polynucleotide molecule that hybridizes under stringent conditions to a complementary strand of the polynucleotide to be amplified. A specific primer pair can be appropriately designed by a person skilled in the art based on the nucleotide sequence of the region to be amplified. For example, one primer of the primer pair can have the sequence of the 5'-end portion in the nucleotide sequence of the region to be amplified, and the other primer can have the sequence of the 5'-end portion in the nucleotide sequence of the complementary strand of the region to be amplified.

In the present invention, by selecting the above-mentioned seven gene markers, the annealing temperatures of all primer pairs corresponding to each of the seven gene markers can be well aligned. Therefore, when the seven gene markers are simultaneously measured using the same heat source, the amplification efficiency of the region to be amplified can be well aligned. By efficiently amplifying any of the seven gene markers, the sensitivity of the minimal residual disease measurement can be improved even for samples with low gene expression levels.

The length and specific sequence of the primers are not particularly limited and can be determined appropriately by a person skilled in the art. For example, in order to simultaneously measure all seven gene markers, the primers can be designed to have Tm values that provide consistent annealing temperature conditions.

The length of the primer may be, for example, 18 to 30 nucleotides, preferably 18 to 26 nucleotides.

Specific sequences of the primers include, for example, the following.
Primer for CRMP1
5'-ccaatccctttatgctgacg-3' (sense) (SEQ ID NO: 13)
5'-ggaacgattaagttctctcctatttg-3' (antisense) (SEQ ID NO: 14)
Primers for DBH
5'-tggggacactgcctattttg-3' (sense) (SEQ ID NO: 15)
5'-ttctggggtcctctgcac-3' (antisense) (SEQ ID NO: 16)
Primer for DDC
5'-ctggagaagggggaggagt-3' (sense) (SEQ ID NO: 17)
5'-gccgatggatcactttggt-3' (antisense) (SEQ ID NO: 18)
Primers for GAP43
5'-gaggatgctgctgccaag-3' (sense) (SEQ ID NO: 19)
5'-ggcactttccttaggtttggt-3' (antisense) (SEQ ID NO: 20)
Primer for ISL1
5'-aaggacaagaagcgaagcat-3' (sense) (SEQ ID NO:21)
5'-ttcctgtcatcccctggata-3' (antisense) (SEQ ID NO:22)
Primers for PHOX2B
5'-ctaccccgacatctacactcg-3' (sense) (SEQ ID NO:23)
5'-ctcctgcttgcgaaacttg-3' (antisense) (SEQ ID NO:24)
Primer for TH
5'-tcagtgacgccaaggaca-3' (sense) (SEQ ID NO:25)
5'-gtacgggtcgaacttcacg-3' (antisense) (SEQ ID NO: 26)

In the present invention, in addition to the primer pair capable of amplifying the above-mentioned seven gene markers, a primer pair capable of amplifying the above-mentioned reference gene may be included.

The length and specific sequence of the primer capable of amplifying the reference gene are not particularly limited and can be appropriately determined by a person skilled in the art. For example, in order to simultaneously measure multiple, preferably all, of the seven types of gene markers, the primer may be designed to have a Tm value that is consistent with the annealing temperature conditions of the seven types of gene markers.

The length of the primer may be, for example, 18 to 30 nucleotides, preferably 18 to 26 nucleotides.

Specific sequences of primers for reference genes are, for example, as follows:
Primers for HPRT1
5'-tgaccttgatttattttgcatacc-3' (sense) (SEQ ID NO:27)
5'-cgagcaagacgttcagtcct-3' (antisense) (SEQ ID NO:28)
Primer for HMBS
5'-ctgaaagggccttcctgag-3' (sense) (SEQ ID NO:29)
5'-cagactcctccagtcaggtaca-3' (antisense) (SEQ ID NO: 30)
Primer for GUSB
5'-cgccctgcctatctgtattc-3' (sense) (SEQ ID NO:31)
5'-tccccacagggagtgtgtag-3' (antisense) (SEQ ID NO: 32)
Primers for TBP
5'-gaacatcatggatcagaacaaca-3' (sense) (SEQ ID NO: 33)
5'-atagggattccgggagtcat-3' (antisense) (SEQ ID NO: 34)

The primer may further comprise an additional sequence suitable for detection of the amplified target (specifically a sequence that is not complementary to genomic DNA), such as a linker sequence.
The primers may be labeled with a suitable labeling agent, for example, a radioisotope (e.g., ¹²⁵ I, ¹³¹ I, ³ H, ¹⁴ C, etc.), an enzyme (e.g., β-galactosidase, β-glucosidase, alkaline phosphatase, peroxidase, malate dehydrogenase, etc.), a fluorescent substance (e.g., fluorescamine, fluorescein isothiocyanate, Cy3, Cy5, etc.), a luminescent substance (e.g., luminol, luminol derivatives, luciferin, lucigenin, etc.), etc. Furthermore, the primers may be dissolved in water or a suitable buffer solution (e.g., TE buffer, Tris-HCl buffer, etc.) to a suitable concentration (e.g., 1 μM to 50 μM at a concentration of 2× to 20×) either separately or in a mixed state as long as the functions of each primer are not impaired, and stored at about −20° C.

[1-4-2. Probe]
The probe is a polynucleotide molecule capable of detecting a sequence encoding the above-mentioned gene marker or a complementary sequence thereof for detecting minimal residual disease of neuroblastoma using the gene marker used in the present invention as an index. Specifically, the probe may be a polynucleotide molecule capable of hybridizing under stringent conditions with a polynucleotide constituting the above-mentioned gene marker or a complementary polynucleotide thereof.

Further examples of the probe include polynucleotide molecules capable of detecting a sequence encoding a reference gene or a complementary sequence thereof. Specifically, examples of the probe include polynucleotide molecules capable of hybridizing under stringent conditions with a polynucleotide constituting the above-mentioned reference gene or a complementary polynucleotide thereof.

Complementary means that two nucleotides are capable of pairing under hybridization conditions, for example, the relationship between adenine (A) and thymine (T) or uracil (U), and the relationship between cytosine (C) and guanine (G).

Hybridization means that a polynucleotide molecule hybridizes to a genetic marker or reference gene under normal hybridization conditions (i.e., under normal annealing conditions in PCR), preferably under stringent hybridization conditions, and does not hybridize to polynucleotide molecules other than the genetic marker or reference gene.

Stringent conditions include, for example, those described in Current Protocols in Molecular Biology, John Wiley & Sons, 6.3.1-6.3.6, 1999, such as hybridization in 6xSSC (sodium chloride/sodium citrate) at 45°C, followed by one or more washes at 0.2xSSC/0.1% SDS at 50-65°C. However, a person skilled in the art can appropriately select hybridization conditions that provide equivalent stringency.

In addition, the polynucleotide molecule that hybridizes to the genetic marker or reference gene used in the present invention does not need to be completely complementary to the genetic marker or its reference gene as long as gene-specific hybridization is possible, but preferably contains all or part of the sequence of the polynucleotide molecule complementary to the genetic marker or its reference gene.

The probe may be prepared as a synthetic oligonucleotide using a commercially available oligonucleotide synthesizer, or may be prepared as a double-stranded DNA fragment obtained by restriction enzyme treatment, etc.

The length of the probe may be, for example, 18 to 30 nucleotides, preferably 18 to 26 nucleotides.

The probe may be labeled with a suitable labeling agent, for example, a radioisotope (e.g., ¹²⁵ I, ¹³¹ I, ³ H, ¹⁴ C, etc.), an enzyme (e.g., β-galactosidase, β-glucosidase, alkaline phosphatase, peroxidase, malate dehydrogenase, etc.), a fluorescent substance (e.g., fluorescamine, fluorescein isothiocyanate, Cy3, Cy5, etc.), a luminescent substance (e.g., luminol, luminol derivatives, luciferin, lucigenin, etc.), etc. Alternatively, the probe may further include a quencher (e.g., MGB, TAMRA, etc.) that absorbs the fluorescent energy emitted by a fluorescent substance (e.g., FAM, VIC, etc.) bound to the vicinity of the fluorescent substance. More specifically, the probe may be configured as a probe (TaqMan probe) in which a fluorescent substance is attached to the 5' end, a quencher is attached to the 3' end, and the 3' end is phosphorylated. In this case, the fluorescent substance and the quencher are separated during the detection reaction, and the fluorescence is detected.

[1-4-3. Digital PCR operation]
In digital PCR, a large volume of starting sample is first divided into a number of smaller partial volume samples (split samples). The split samples are prepared to contain a single copy of the target on average. Digitality is achieved by having zero (negative) or one (positive) polynucleotide molecule present in the split sample. By counting the positives in the split samples, the starting copy number of the target in the starting sample can be estimated. Therefore, digital PCR allows the target to be quantified even if the target is at a low concentration in the biological sample. In order to make the split samples at an appropriate concentration for achieving digitality, a multiple serial dilution method of the starting sample can be used, and the volume can be determined according to the PCR device used. In this way, the expression levels A1 to A7 of the gene markers to be amplified (targets) can be quantified.

Digital PCR is preferably a droplet-based digital droplet PCR (ddPCR). The ddPCR method specifically includes a droplet generation step, a PCR amplification step, a detection step, and an analysis step. In the droplet generation step, a plurality of droplets containing reagents required for nucleic acid amplification are generated. In the PCR amplification step, the droplets (or a sample having a larger reaction volume in which the droplets are contained) are subjected to thermal cycling conditions suitable for amplifying the target. In the detection step, droplets containing PCR products (or a sample having a larger reaction volume in which the droplets are contained) and droplets not containing PCR products (or a sample having a larger reaction volume in which the droplets are contained) are identified. In the analysis step, the expression levels A1 to A7 of the target are derived.

Furthermore, digital PCR may include a step of hybridizing the above-mentioned probe to nucleic acid in a biological sample. For example, when detection of an amplified product is performed based on a label, the above-mentioned labeled probe may be used. As an example, when a TaqMan probe is used, the TaqMan probe hybridizes to a genetic marker, and when an extension reaction from the primer reaches the hybridization region, a fluorescent label is released by the action of Taq DNA polymerase. The released fluorescent label emits fluorescence when it is released from the quenching action of the quencher.

The expression levels A1 to A7 of the seven gene markers can be preferably quantified as the amount of transcription to mRNA (i.e., copy number). The expression levels A1 to A7 of the seven gene markers can be absolute copy numbers or relative amounts to the copy number of the reference gene (i.e., the value obtained by dividing the copy number of each of the seven gene markers by the copy number of the reference gene). From the viewpoint of performing a more accurate analysis, it is preferable that the expression levels A1 to A7 of the markers are relative amounts to the copy number of the reference gene. Furthermore, from the viewpoint of facilitating the handling of the analytical values, it is preferable that the relative amounts are values obtained by dividing the copy number of each of the seven gene markers by the copy number of the reference gene and multiplying by 10,000.

[2. Step 2]
In step 2, the expression levels A1 to A7 obtained in step 1 are weighted by taking into account the statistical values B1 to B7 of the expression levels by digital PCR of each of the seven gene markers in the control to obtain weighted expression levels AB1 to AB7, and the geometric mean or sum of the weighted expression levels AB1 to AB7 is obtained as an evaluation value.

[2-1. Statistical values B1 to B7 of the expression levels of each of the seven gene markers in the control by digital PCR]
The seven gene markers have different expression patterns in healthy controls. Specifically, in the statistics of the expression levels of the seven gene markers in healthy controls (n>100) by digital PCR, when the expression levels are arranged from the smallest to the largest and divided into 100 equal parts, the percentile at which the gene marker is first detected and the expression level of the gene marker vary for each marker. Examples of the statistics are shown in Figures 3A and 3B, and Figures 4A and 4B.

Figures 3A and 3B show the statistical results of expression levels by digital PCR in 103 bone marrow samples from adult control subjects. The digital PCR method is the same as the method for obtaining the expression levels A1 to A7 of the seven gene markers from the biological samples described above. Each expression level is shown as a value obtained by dividing the expression level of each of the seven gene markers by the expression level of the reference gene and multiplying the result by 10,000. As shown in Figures 3A and 3B, for example, taking CRMP1 and DBH as examples of the seven gene markers, CRMP1 is detected at the relatively low 6th percentile, while DBH is detected at the 52nd percentile, and when comparing the expression levels at the 90th percentile, CRMP1 has a much higher expression level than DBH.

In other words, in light of the statistics of the expression levels by digital PCR of each of the seven gene markers in the above control, it can be said that the seven gene markers each have a different weight, or importance (or, in other words, different degrees of unimportance). For example, when comparing CRMP1 and DBH, it can be said that CRMP1, which is relatively easy to detect in the control and has a relatively large amount of detection, is less important (more unimportant). Such differences in importance (differences in unimportance) exist between each of the seven gene markers.

Figures 4A and 4B show the statistical results of expression levels by digital PCR in 107 peripheral blood samples from adult control subjects. Similarly, in Figures 4A and 4B, there are differences in importance (or non-importance) between each of the seven gene markers.

In the present invention, statistical values B1 to B7 of the expression levels of each of the seven gene markers in the control by digital PCR are used as values indicating the degree of non-importance of each of the seven gene markers.

Note that, when the expression amounts A1 to A7 are absolute amounts, it is preferable that the expression amounts by digital PCR of the seven gene markers in the control are also absolute amounts, and when the expression amounts A1 to A7 are relative amounts to the expression amount of a specific reference gene, it is preferable that the expression amounts by digital PCR of the seven gene markers in the control are also relative amounts to the expression amount of the specific reference gene. The expression amounts shown in Figures 3A and 3B and Figures 4A and 4B are relative amounts to the expression amount of the reference gene.

The statistical values B1 to B7 in the controls used in the present invention include expression levels at fixed percentiles and expression levels at variable percentiles, as described below.

[2-1-1. Statistical values B1 to B7 of expression levels at fixed percentiles]
When a fixed percentile is used, if at least any of the seven gene markers in the control (preferably two or more markers, more preferably three or more markers, even more preferably four or more markers, even more preferably five or more markers, particularly preferably six or more markers, and most preferably all seven markers) are detected at a specific percentile, the expression amount at a common specific percentile of 1 percentile or more for each of the seven gene markers in the control can be used as the statistical values B1 to B7 in the control representing the non-importance of the seven gene markers.

3A and 3B, in the case of bone marrow samples, at least one of the seven gene markers is detected at the 6th percentile or higher of the control, two or more markers are detected at the 37th percentile or higher, three or more markers are detected at the 39th percentile or higher, five or more markers are detected at the 51st percentile or higher, six or more markers are detected at the 52nd percentile or higher, and all seven gene markers are detected at the 54th percentile or higher. Therefore, in the case of bone marrow samples, the expression levels of each of the seven gene markers in the control at a common specific percentile of the 6th percentile or higher, preferably the 37th percentile or higher, more preferably the 39th percentile or higher, even more preferably the 51st percentile or higher, particularly preferably the 52nd percentile or higher, and most preferably the 54th percentile or higher can be used as statistical values B1 to B7 in the control that indicate the non-importance of the seven gene markers.

Also, as shown in the statistics of Figures 4A and 4B, in the case of peripheral blood samples, at least one of the seven gene markers is detected at the 21st percentile or higher of the control, two or more markers are detected at the 33rd percentile or higher, three or more markers are detected at the 66th percentile or higher, four or more markers are detected at the 76th percentile or higher, five or more markers are detected at the 79th percentile or higher, six or more markers are detected at the 83rd percentile or higher, and all seven gene markers are detected at the 90th percentile or higher. Therefore, in the case of peripheral blood samples, the expression levels of each of the seven gene markers in the control at a common specific percentile of the 21st percentile or higher, preferably the 33rd percentile or higher, more preferably the 66th percentile or higher, even more preferably the 76th percentile or higher, even more preferably the 79th percentile or higher, particularly preferably the 83rd percentile or higher, and most preferably the 90th percentile or higher can be used as statistical values B1 to B7 in the control that indicate the non-importance of the seven gene markers.

In the case of peripheral blood stem cell specimens, as with bone marrow and peripheral blood specimens, statistics of the expression levels of each of the seven gene markers in healthy controls by digital PCR may be taken, and the expression levels in the peripheral blood stem cell specimen control may be used as statistical values B1 to B7. On the other hand, since it is ethically difficult to obtain healthy controls for peripheral blood stem cell specimens, the expression levels in the peripheral blood specimen control may also be used as statistical values B1 to B7. Since the method of the present invention has excellent sensitivity and specificity for MRD analysis, when a peripheral blood stem cell specimen is used as a biological sample, MRD analysis can be performed with excellent sensitivity and specificity even if the expression levels in the peripheral blood specimen control are used as statistical values B1 to B7. Therefore, in the case of a peripheral blood stem cell sample, the expression levels at a common specific percentile of each of the seven gene markers in the control that is 21 percentile or higher, preferably 33 percentile or higher, more preferably 66 percentile or higher, even more preferably 76 percentile or higher, even more preferably 79 percentile or higher, particularly preferably 83 percentile or higher, and most preferably 90 percentile or higher can be used as statistical values B1 to B7 in the control that indicate the non-importance of the seven gene markers.

In these cases, since the percentile selected is common among the seven genetic markers, the selected percentile is also described as a "fixed percentile."

As an example, in Figure 3B, when the expression levels at the 90th percentile (an example of a common specific percentile) are used as statistical values B1 to B7 for the controls, 66.2 is used as statistical value B1 for CRMP1, 7.1 as statistical value B2 for DBH, 10.0 as statistical value B3 for DDC, 8.6 as statistical value B4 for GAP43, 7.9 as statistical value B5 for ISL1, 7.4 as statistical value B6 for PHOX2B, and 6.1 as statistical value B7 for TH.

The preferred fixed percentile from the standpoint of improving the accuracy of determining the disease activity, treatment effect, or recurrence of minimal residual disease of neuroblastoma may vary depending on whether weighting by division or subtraction is used as the weighting method described below, and whether the geometric mean of the weighted expression levels or the sum of the weighted expression levels is used as the evaluation value. Specific preferred fixed percentiles will be described in detail in the column on evaluation values in item 2-3 below.

Fixed percentiles are preferable because they allow for easy and accurate determination of the presence or absence of minimal residual disease of neuroblastoma without the need to search for the optimal percentile for each of the seven gene markers individually, as is the case with the variable percentiles described below.

[2-1-2. Statistical values B1 to B7 of expression levels at variable percentiles]
Furthermore, among the expression levels at each percentile of the seven gene markers in the control, the expression level at any percentile can be used as the statistical values B1 to B7 representing the degree of non-importance. Since the percentile selected may differ between the seven gene markers, the selected percentile is also referred to as a "variable percentile."

A preferred variable percentile from the standpoint of improving the accuracy of determining the disease activity, treatment effect, or recurrence of minimal residual disease of neuroblastoma may differ depending on whether weighting by division or subtraction, as described below, is used as the weighting method, and whether the geometric mean or the sum of the weighted expression levels is used as the evaluation value. Specific preferred variable percentiles will be described in detail in the evaluation value column of item 2-3 below.

An example of a method for determining a variable percentile is, for example, a method of selecting a percentile that shows an expression level closest to the cutoff value for the presence or absence of the microresidual lesions of each of the seven gene markers among the expression levels at each percentile of the seven gene markers in the control. In the present invention, the presence or absence of microresidual lesions of neuroblastoma specifically refers to the presence or absence of microresidual lesions in the progression stage (i.e., the presence or absence of progression or regrowth of the disease of the microresidual lesions), the presence or absence of microresidual lesions of neuroblastoma at a level at which the first therapeutic intervention should be performed (i.e., the disease level (present) in the microresidual lesions of neuroblastoma at a level at which the first therapeutic intervention should be performed and the disease level (absent) in the control), the presence or absence of a therapeutic effect on the microresidual lesions of neuroblastoma, or the presence or absence of recurrence of the microresidual lesions of neuroblastoma (i.e., the disease level (present) when the microresidual lesions recur after remission by treatment and the disease level (absent) when the microresidual lesions do not recur). The cutoff value can be obtained according to the pathology to be determined for presence or absence (i.e., the purpose of diagnosis). In other words, the cutoff value can differ depending on the pathology to be determined for presence or absence. The cutoff value for each of the seven gene markers with respect to the presence or absence of minimal residual disease can be obtained by a method known to those skilled in the art. Preferably, for each of the seven gene markers, a receiver operating characteristic (ROC) analysis of the presence or absence of minimal residual disease is performed, and the expression level that maximizes the area under the curve (AUC) can be obtained as the cutoff value.

As an example, in a bone marrow sample, when the expression levels at the 83rd percentile for CRMP1, the 79th percentile for DBH, the 67th percentile for DDC, the 90th percentile for GAP43, the 51st percentile for ISL1, the 76th percentile for PHOX2B, and the 81st percentile for TH are used as statistical values B1 to B7 in the control, the following are used: statistical value B1 for CRMP1: 42.2; statistical value B2 for DBH: 3.7; statistical value B3 for DDC: 2.7; statistical value B4 for GAP43: 8.6; statistical value B5 for ISL1: 2.3; statistical value B6 for PHOX2B: 3.1; and statistical value B7 for TH: 4.2.

Another example of a method for determining a variable percentile is a method in which a fixed percentile indicating the expression level that maximizes the AUC for the presence or absence of minimal residual disease of neuroblastoma using the method of the present invention is derived, and the percentile of each genetic marker is individually changed from that fixed percentile to find an optimal combination of percentiles that exceeds the AUC determined by that fixed percentile.

Although variable percentiles require searching for the optimal percentile for each of the seven gene markers individually, they are preferable in that it may be possible to determine the presence or absence of minimal residual disease of neuroblastoma more accurately than with fixed percentiles by finding an optimal percentile combination that exceeds the AUC of the present invention when fixed percentiles are used.

[2-2. Weighted expression amounts AB1 to AB7]
In the present invention, the expression levels A1 to A7 obtained in step 1 are corrected by weighting with the statistical values B1 to B7 (non-importance) in the control, to obtain weighted expression levels AB1 to AB7. In other words, the expression levels A1 to A7 obtained in step 1 are weighted by offsetting with a correction amount based on the statistical values B1 to B7 (non-importance) in the control, to obtain weighted expression levels AB1 to AB7. For example, if the expression level A1 is the expression level of CRMP1, the expression level A1 is corrected by subtracting a larger correction amount based on non-importance, taking into account that CRMP1 is relatively easy to detect in the control and the detection amount is relatively large. Also, if the expression level A2 is the expression level of DBH, the expression level A2 is corrected by subtracting a smaller correction amount based on non-importance, taking into account that DBH is relatively difficult to detect in the control and the detection amount is relatively small.

Methods of weighting that subtract the correction amount based on non-importance include weighting by division and weighting by subtraction, as described below.

[2-2-1. Weighting by division]
When weighting is performed by division, specifically, the expression amounts A1 to A7 are divided by the statistical values B1 to B7 to obtain the weighted expression amounts AB1 to AB7. For example, the weighted expression amount AB1 is obtained as {A1/B1}, the weighted expression amount AB2 is obtained as {A2/B2}, ..., and the weighted expression amount AB7 is obtained as {A7/B7}. As long as the statistical values B1 to B7 are not 0, either the expression amount at the fixed percentile or the expression amount at the variable percentile can be used.

In the present invention, from the viewpoint of further improving the accuracy of determining whether or not neuroblastoma is present, it may be preferable to use division as a weighting method. For example, when the sum of weighted expression levels AB1 to AB7 is taken as the evaluation value described below, it is preferable to use division as a weighting method from the viewpoint of further improving the accuracy of determining whether or not neuroblastoma is present.

[2-2-2. Weighting by subtraction]
When weighting is performed by subtraction, specifically, the statistical values B1 to B7 are subtracted from the expression amounts A1 to A7, respectively, to obtain the weighted expression amounts AB1 to AB7. For example, the weighted expression amount AB1 is obtained as {A1-B1}, the weighted expression amount AB2 as {A2-B2}, ..., and the weighted expression amount AB7 as {A7-B7}. The statistical values B1 to B7 can be either the expression amount at the fixed percentile or the expression amount at the variable percentile.

The specific handling of subtractive weighting depends on whether the evaluation value described below is obtained as the geometric mean of the weighted expression amounts AB1 to AB7 or as the sum of the weighted expression amounts AB1 to AB7.

When obtaining the evaluation value described below as the geometric mean of the weighted expression amounts AB1 to AB7, if the result of subtracting the statistical values B1 to B7 from each of the expression amounts A1 to A7 is less than 1, the weighted expression amount will be set to 1. For example, if {A1-B1}>1, {A2-B2}<1, ..., {A7-B7}>1, then the weighted expression amount AB1 will be {A1-B1}, the weighted expression amount AB2 will be 1, ... and the weighted expression amount AB7 will be {A7-B7}.

When obtaining the evaluation value described below as the sum of weighted expression amounts AB1 to AB7, if the result of subtracting statistical values B1 to B7 from each of expression amounts A1 to A7 is a negative value, the weighted expression amount will be set to 0. For example, if {A1-B1}>0, {A2-B2}<0, ..., {A7-B7}>0, then weighted expression amount AB1 will be {A1-B1}, weighted expression amount AB2 will be 0, ... and weighted expression amount AB7 will be {A7-B7}.

[2-3. Evaluation value]
In the present invention, the expression levels A1 to A7 of the seven gene markers are weighted by taking into account the statistical values B1 to B7 (non-importance) in the control to correct the expression levels, and the geometric mean or sum of the weighted expression levels AB1 to AB7 is obtained as the evaluation value.

[2-3-1. Geometric mean of weighted expression levels]
When weighting is performed by subtraction, the evaluation value, which is the geometric mean of expression levels AB1 to AB7, is expressed as {Π[i=1→7](Ai-Bi)} ^1/7 (however, when Ai-Bi<1, Ai-Bi=1.) Below, we will describe examples of percentiles that give the statistical values B1 to B7 used for weighting when the geometric mean of expression levels weighted by subtraction is used as the evaluation value.

In bone marrow samples, when expression levels at fixed percentiles (common percentiles at any of the 1st percentile or higher) based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 6th to 92nd percentiles, more preferably the 6th to 55th percentiles, and even more preferably the 30th to 55th percentiles (see Figure 9).

In addition, in bone marrow samples, when the expression levels in the variable percentiles based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, examples of preferred percentile combinations from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma are 38 to 44 percentiles for CRMP1, 53 to 66 percentiles for DBH, 55 to 63 percentiles for DDC, 49 to 65 percentiles for GAP43, and 58 to 69 percentiles for ISL1. or 38-44 percentile for CRMP1, 1-52 percentile for DBH, 1-54 percentile for DDC, 85-86 percentile for GAP43, 92-94 percentile for ISL1, 1-50 percentile for PHOX2B, and 51-71 percentile for TH. Examples of more preferred variable percentile combinations include 83 percentile for CRMP1, 1-52 percentile for DBH, 1-54 percentile for DDC, 85-86 percentile for GAP43, 92-94 percentile for ISL1, 1-50 percentile for PHOX2B, and 51-71 percentile for TH (see Table 9(d12) in the Examples).

In peripheral blood samples, when expression levels at fixed percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 52 to 99 percentiles, and more preferably the 68 to 99 percentiles (see Figure 12).

In peripheral blood stem cell samples, when expression levels at fixed percentiles (common percentiles at or above the 21st percentile) based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 21st to 99th percentiles, and more preferably the 79th to 95th percentiles (see Figure 15).

In addition, in peripheral blood stem cell samples, when expression levels at variable percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, examples of preferred percentile combinations from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma include 91-99 percentiles for CRMP1, 91-92 percentiles for DBH, 92-95 percentiles for DDC, 91-92 percentiles for GAP43, 98-99 percentiles for ISL1, 87-92 percentiles for PHOX2B, and 82-93 percentiles for TH (see Table 12 (d12) in the Examples).

[2-3-2. Sum of weighted expression levels]
[2-3-2-1. When weighting is performed by subtraction]
The evaluation value, which is the sum of expression amounts AB1 to AB7, is expressed as Σ[k=1→7](Ak-Bk) (however, if Ak-Bk<0, Ak-Bk=0). Below, we will describe an example of percentiles that give statistical values B1 to B7 used for weighting when the sum of expression amounts weighted by subtraction is used as the evaluation value.

In bone marrow samples, when expression levels at fixed percentiles (common percentiles at any of the 6th percentile or higher) based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 6th to 93rd percentiles, more preferably the 66th to 80th percentiles, and even more preferably the 75th percentile (see Figure 10).

Furthermore, in bone marrow samples, when expression levels at variable percentiles based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred variable percentiles include the variable percentiles in the control that show the expression level closest to the cutoff value as described in section 2-1-2 above (see Table 9 (d22) in the Examples).

In peripheral blood samples, when expression levels at fixed percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 21st to 99th percentiles, and more preferably the 93rd to 99th percentiles (see Figure 13).

Furthermore, in peripheral blood samples, when expression levels at fixed percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual neuroblastoma disease, preferred variable percentiles include the variable percentiles in the control that show the expression level closest to the cutoff value, as described in section 2-1-2 above.

In peripheral blood stem cell samples, when expression levels at fixed percentiles (common percentiles at or above the 21st percentile) based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 21st to 99th percentiles, more preferably the 69th to 93rd percentiles, and even more preferably the 92nd percentile (see Figure 16).

Furthermore, in peripheral blood stem cell samples, when expression levels in variable percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred combinations of variable percentiles include the 91st to 99th percentiles for CRMP1, the 91st percentile for DBH, the 95th percentile for DDC, the 88th to 99th percentiles for GAP43, the 98th to 99th percentiles for ISL1, the 79th percentile for PHOX2B, and the 89th to 93rd percentiles for TH (see Table 12 (d22) in the Examples).

[2-3-2-2. When weighting is performed by division]
The evaluation value, which is the sum of the expression amounts AB1 to AB7, is expressed as Σ[k=1→7]Ak/Bk when weighting is performed by division. Below, we will describe an example of percentiles that give the statistical values B1 to B7 used for weighting when the sum of the expression amounts weighted by division is used as the evaluation value.

In bone marrow samples, when expression levels at fixed percentiles based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, expression levels at fixed percentiles equal to or higher than the 54th percentile, where the expression levels of all 7 gene markers in the control are not 0, are used. From the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include 54 to 99 percentiles, and more preferably 55 to 99 percentiles (see Figure 11).

Furthermore, in bone marrow samples, when expression levels in variable percentiles based on the statistics of Figures 3A and 3B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred combinations of variable percentiles include 38 to 44 percentiles for CRMP1, 77 to 83 percentiles for DBH, 974 to 79 percentiles for DDC, 38 to 48 percentiles for GAP43, 57 to 68 percentiles for ISL1, 72 to 77 percentiles for PHOX2B, and 78 to 83 percentiles for TH. More preferred variable percentile combinations include the 92nd percentile for CRMP1, the 91st to 92nd percentile for DBH, the 99th percentile for DDC, the 93rd percentile for GAP43, the 96th percentile for ISL1, the 92nd to 93rd percentile for PHOX2B, and the 84th to 87th percentile for TH (see Table 9(d32) in the Examples).

In peripheral blood samples, when expression levels at fixed percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, expression levels at fixed percentiles equal to or higher than the 90th percentile, where the expression levels of all 7 gene markers in the control are not 0, are used. From the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 91st to 99th percentiles, and more preferably the 94th to 97th percentiles (see Figure 14).

In peripheral blood stem cell samples, when expression levels at fixed percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, expression levels at fixed percentiles equal to or higher than the 90th percentile, where the expression levels of all seven gene markers in the control are not 0, are used. From the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred fixed percentiles include the 90th to 99th percentiles (see Figure 17).

In addition, in peripheral blood stem cell samples, when expression levels in variable percentiles based on the statistics of Figures 4A and 4B are used as statistical values B1 to B7, from the viewpoint of improving the accuracy of determining the presence or absence of minimal residual disease of neuroblastoma, preferred combinations of variable percentiles include the 98th to 99th percentile for CRMP1, the 90th percentile for DBH, the 98th to 99th percentile for DDC, the 92nd to 93rd percentile for GAP43, the 99th percentile for ISL1, the 90th percentile for PHOX2B, and the 90th to 92nd percentile for TH (see Table 12 (d32) in the Examples).

[2-3-3. Relationship between evaluation value and symptoms]
The evaluation value can significantly distinguish the disease stage of a neuroblastoma patient, i.e., remission, stable, and progression. The evaluation value also significantly reflects the difference in tumor volume due to the time of sample collection of a neuroblastoma patient, i.e., diagnosis, treatment, post-treatment follow-up, and relapse. Therefore, the evaluation value can be effectively used to evaluate the disease progression of neuroblastoma, determine the effectiveness of treatment, or determine the presence or absence of recurrence.

[3. Step 3]
The evaluation of the minimal residual lesion of neuroblastoma is performed by comparing the evaluation value with the cutoff value for the presence or absence of the minimal residual lesion of neuroblastoma. The presence or absence of the minimal residual lesion of neuroblastoma refers to the presence or absence of the minimal residual lesion of progression stage, the presence or absence of the therapeutic effect on the minimal residual lesion of neuroblastoma, or the presence or absence of the recurrence of the minimal residual lesion of neuroblastoma, as described above. When the evaluation value is equal to or greater than the cutoff value, it can be determined that the disease is in the advanced stage, that the therapeutic effect is ineffective, or that there is a recurrence. Therefore, when the evaluation value is equal to or greater than the cutoff value, it can be determined that the patient from whom the biological sample is derived has a minimal residual lesion of neuroblastoma in the advanced stage, that the therapeutic effect is ineffective, or that there is a recurrence.

The cutoff value can be set by a known statistical method, referring to the evaluation value empirically collected in advance by the above-mentioned method. Specific examples of the cutoff value setting method include the ROC (Receiver Operating Characteristic) analysis method.

In the present invention, in consideration of the problems specific to highly sensitive digital PCR, a unique correction is performed in which the expression levels A1 to A7 of seven gene markers are weighted by taking into account the statistical values B1 to B7 (non-significant) in the control to obtain the geometric mean or sum, thereby making it possible to more accurately determine the presence or absence of minimal residual disease of neuroblastoma compared to a method in which minimal residual disease of neuroblastoma is determined to be positive when any one marker is positive, a method in which minimal residual disease of neuroblastoma is determined to be positive when the geometric mean of the expression levels of each marker is equal to or above a cutoff value, or a method in which minimal residual disease of neuroblastoma is determined to be positive when the sum of the expression levels of each marker is equal to or above a cutoff value.

The present invention can comprehensively improve the accuracy of detecting minimal residual lesions of neuroblastoma (AUC for the presence or absence of minimal residual lesions of neuroblastoma), thereby improving the reliability of monitoring disease progression and treatment effect or predicting recurrence. For example, in monitoring disease progression and treatment effect, by accurately determining whether disease progression is worsening/treatment is ineffective, or disease progression is improving/treatment is effective, more accurate clinical judgment can be made regarding treatment methods and prognosis can be improved. In addition, in predicting recurrence, by accurately determining whether recurrence is present (i.e., treatment intervention is necessary) or not (continued follow-up observation can be performed), more accurate clinical judgment can be made regarding treatment intervention and prognosis can be improved. In particular, making such accurate clinical judgments for high-risk patients with a high recurrence rate makes it possible to intervene in treatment at a stage that could not be detected before, and therefore improvement in survival rate can be expected.

The present invention will be specifically described below with reference to examples, but the present invention is not limited to the following examples. In the following examples, CRMP1 (SEQ ID NO: 1), DBH (SEQ ID NO: 2), DDC (SEQ ID NO: 3), GAP43 (SEQ ID NO: 4), ISL1 (SEQ ID NO: 5), PHOX2B (SEQ ID NO: 6) and TH (SEQ ID NO: 7) were used as MRD gene markers, and HPRT1 (SEQ ID NO: 8) was used as a reference gene.

[1] Sample Background [1-1] Control Samples As control (non-NB control) samples, 103 bone marrow samples from healthy adults were obtained from a commercial source, and 107 peripheral blood samples from healthy adults were obtained from the Japanese Red Cross Society. The use of donated blood was approved by the Kobe University Hospital and the Japanese Red Cross Society Review Committee.

[1-2] Neuroblastoma patient samples As neuroblastoma patient (NB patient) samples, 208 bone marrow (BM) samples, 67 peripheral blood (PB) samples, and 20 peripheral blood stem cell (PBSC) samples were obtained from 20 high-risk neuroblastoma patients according to the International Neuroblastoma Risk Classification (INRG risk classification) as residual samples collected for diagnosis and/or treatment at the Department of Pediatrics, Kobe University Hospital and the Department of Hematology and Oncology, Hyogo Children's Hospital. These samples were collected as frequently as possible throughout the entire course of neuroblastoma treatment (at diagnosis, treatment, post-treatment, and relapse) for each high-risk neuroblastoma patient. Written informed consent was obtained to collect the samples. This study was approved by the ethics committees of the Kobe University Graduate School of Medicine and the Hyogo Children's Hospital and was conducted in accordance with the Kobe University Graduate School of Medicine Clinical Research Guidelines.

All bone marrow, peripheral blood and peripheral blood stem cell specimens were assessed for disease status at the time of collection according to the International Neuroblastoma Response Criteria (INRC Response Criteria) in accordance with J Clin Oncol 1993, 11:1466-1477. and Cancer 2017, 123:1095-1105. The disease status was evaluated as corresponding to either complete remission (CR) or very good partial remission (VGPR), stable partial remission (PR), mixed response (MR) or no response (NR), or progressive disease (PD). The background of all specimens (disease status and collection time point) is shown in the table below. The numbers in the table below represent the number of specimens/number of patients corresponding to the disease status or collection time.

[2] Measurement of 7 Gene Markers Gene markers (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B and TH) were measured by the following method.
1) Nucleated cells were centrifuged from 2 to 5 ml of a sample containing an anticoagulant (EDTA or heparin) using Mono-Poly Separation Solution (DS Pharma Biomedical).
2) Total RNA was extracted from the obtained nucleated cells using a TRIZOL PLUS RNA Purification kit (Life Technologies) and stored at −80° C. until use.
3) RNA concentration was measured using a Nanodrop 2000 spectrophotometer (ThermoFisher Scientific), and the quality and integrity of total RNA was evaluated using a 2100 Bioanalyzer (Agilent Technologies).
4) cDNA was synthesized from 1.0 μg of total RNA whose quality had been evaluated using a QuantiTect Reverse Transcription kit (Qiagen), and diluted with TE buffer to a total volume of 80 μl.

5) Using the QX200 Droplet Digital PCR System (Bio-Rad), all seven gene markers were simultaneously subjected to a ddPCR (digital PCR) reaction in a single run, and the expression level of each gene marker was analyzed.
Specifically, the primers used for each gene marker (CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B and TH) and the reference gene (HPRT1) had the sequences shown in SEQ ID NO: 13 to SEQ ID NO: 26 and SEQ ID NO: 27 to 28, respectively, and the probes used were Universal Probe Library (Roche) #65 (for CRMP1 marker), #3 (for DBH marker), #49 (for DDC marker), #26 (for GAP43 marker), #66 (for ISL1 marker), #17 (for PHOX2B marker), #42 (for TH marker), and #73 (for HPRT1 marker).
A reaction solution in a total volume of 20 μl was prepared containing 1.0 μl of template cDNA (corresponding to 12.5 ng of total RNA), 10 μl of 2 x ddPCR Supermix for Probes (Bio-Rad), sense primers (final 500 nM each), antisense primers (final 500 nM each), and Universal Probe Library (Roche) (final 250 nM each).
Droplets of the reaction solution were generated using a QX200 Droplet Generator (Bio-Rad Laboratories), and each cDNA was amplified using a C1000 Touch Thermal Cycler (Bio-Rad Laboratories). The thermal cycling conditions were a pre-cycle of 95°C for 10 minutes, 40 cycles of 94°C for 30 seconds and 56°C for 1 minute 30 seconds, and a post-cycling of 98°C for 10 minutes. The temperature increase rate was 2°C/second.
After ddPCR amplification, droplets were measured with a QX200 Droplet Reader (Bio-Rad) and target copy numbers were analyzed by QuantaSoft (version 1.6.6, Bio-Rad). Thresholds for positive and negative droplet populations were set manually using non-template control (NTC) reactions. To correct for differences in the amount of total RNA and cDNA synthesis efficiency, target copy numbers were normalized using the reference gene HPRT1.
To ensure reproducible ddPCR reactions, only cDNAs that yielded a minimum of 0.045 copies of HPRT1 mRNA per drop were selected, and the selected cDNAs were further diluted 2-8 times to obtain a maximum of 0.45 copies per drop. As a result, ddPCR reactions were repeated 2-3 times for all samples from neuroblastoma patients and controls. This study was performed in accordance with the digital MIQE guidelines.

6) The expression level of each MRD gene marker in the sample was calculated as the copy number (copies per sample) from the copy number (copies per well) of the MRD gene marker and the copy number (copies per well) of the reference gene using the following formula. In other words, the expression level of each MRD gene marker in the sample was obtained as the relative expression level (relative copy number) to the reference gene.

[3] Expression levels of 7 gene markers in controls [3-1] Expression levels of 7 gene markers in control and neuroblastoma patient specimens at diagnosis Figure 1 shows the expression levels (relative copy numbers) of 7 gene markers in bone marrow samples (BM) of control and neuroblastoma patients at diagnosis, and Figure 2 shows the expression levels (relative copy numbers) of 7 gene markers in peripheral blood samples (PB). As shown in Figures 1 and 2, it was confirmed that there was an overlap in the expression levels of 7 gene markers by digital PCR between the neuroblastoma patient specimens at diagnosis and the control specimens. Such an overlap was not observed when measured by qPCR, which has a lower sensitivity than digital PCR. In other words, it was found that the overlap in expression levels confirmed between the neuroblastoma patient specimens at diagnosis and the control specimens is a phenomenon specific to the use of digital PCR, which has excellent sensitivity.

[3-2] Detection rate of 7 gene markers in control samples The number of samples (detectable) in which each of the 7 gene markers was detected by digital PCR among all control samples and the probability (%) are shown in the table below.

As shown in the table above, when the seven gene markers analyzed by digital PCR were statistically analyzed using a large number of control samples, the seven genes were detected with a high probability, which was significantly different from when they were analyzed by qPCR.

[3-3] Percentile statistics of expression of 7 gene markers in control samples Figures 3A and 3B show the statistical results of the expression of 7 gene markers by digital PCR in 103 control bone marrow samples. As shown in Figures 3A and 3B, when the expression levels of the 7 gene markers by digital PCR are arranged from the smallest to the largest and divided into 100 equal parts, the percentile at which the gene markers are first detected and the expression levels of the gene markers vary for each marker, and even when compared at the same percentile, the expression levels of the 7 gene markers vary. In other words, it was found that the importance of each of the 7 gene markers varies (there is a variation in the degree of non-importance).

The statistical results of the expression levels of the seven gene markers by digital PCR in 107 control peripheral samples are also shown in Figures 4A and 4B. As with Figures 3A and 3B, Figures 4A and 4B also show that the seven gene markers vary in importance (and vary in non-importance).

[Test Example 1: Comparison of diagnostic ability based on the sum of 7 marker weighted expression levels by division method and diagnostic ability based on single marker positivity]
The expression levels of each of the seven gene markers were measured by the method of [2] above for the control bone marrow (BM) sample group, the bone marrow (BM) sample group of neuroblastoma patients collected at the time of diagnosis, and the control peripheral blood (PB) sample group and the peripheral blood (PB) sample group of neuroblastoma patients collected at the time of diagnosis. The expression levels of each of the seven gene markers (Each NB-mRNA) and the expression levels at the 90th percentile in FIG. 3B (for BM) or the expression levels at the 90th percentile in FIG. 4B (for PB) were used as statistical values (non-importance), and the expression levels of each of the seven gene markers were corrected to weighted expression levels by dividing the expression levels by the statistical values, and the sum of the weighted expression levels (7NB-mRNAs) was used as the evaluation value. The cutoff value (threshold: TV), sensitivity, specificity, and area under the ROC curve (AUC) between the control bone marrow sample group and the bone marrow sample group of neuroblastoma patients collected at the time of diagnosis were analyzed. The results are shown in the table below.

As shown in Tables 3 and 4 above, in a method in which a cutoff is set for each of the seven gene markers and a positive result is determined when at least one marker exceeds the cutoff (diagnosis based on single marker positivity), the AUC values of each marker vary widely, and some markers have low AUC values. This tendency is particularly noticeable in peripheral blood. For this reason, it has been shown that a method that uses an evaluation value (7NB-mRNAs) that is weighted by taking into account the expression levels of the seven gene markers and the expression levels in the control (statistical values) always has a larger AUC value regardless of whether it is bone marrow or peripheral blood (superior overall sensitivity and specificity) than a diagnosis based on a single marker positivity, and therefore enables a more accurate diagnosis.

[Test Example 2: Relationship between the sum of 7 marker weighted expression levels by division and disease progression]
For bone marrow samples (BM) and peripheral blood samples (PB) of neuroblastoma patients at each disease stage, the expression levels of 7 gene markers (relative copy numbers) measured by the method of [2] above were corrected to weighted expression levels by division using the expression levels at the 90th percentile in Figures 3B and 4B as statistical values (non-significance), and the sum of the weighted expression levels (7NB-mRNAs) was derived as an evaluation value. The distribution of the evaluation values at each disease stage is shown in Figures 5A and 5B, respectively. The results of analyzing the significant difference in evaluation values at disease stages are shown in the table below.

As shown in the above table, the evaluation value obtained as the sum of the weighted expression levels of the seven gene markers (7NB-mRNAs) showed significant differences in each disease stage of neuroblastoma patients. In other words, it was found that the evaluation value reflects the differences in the disease stages of neuroblastoma patients, namely remission, stable, and progression, and particularly well reflects the differences between progression and other disease stages. In other words, it was shown that the sum of the weighted expression levels of the seven gene markers (7NB-mRNAs) makes it possible to diagnose disease progression, particularly pathological conditions requiring therapeutic intervention, and remission during treatment.

[Test Example 3: Relationship between the sum of 7 marker weighted expression levels by division method and sample collection time (diagnosis, treatment, follow-up, recurrence)]
For bone marrow samples (BM) from neuroblastoma patients collected at each collection time point, the expression levels of the seven gene markers measured by the method in [2] above were corrected to weighted expression levels by division using the 90th percentile expression level in FIG. 3B as a statistical value (non-importance), and the sum of the weighted expression levels (7NB-mRNAs) was derived as an evaluation value. The distribution of the evaluation values at each collection time point is shown in FIG. 6A. The results of analyzing the significant difference in the evaluation values at the collection time point are shown in the table below.

For peripheral blood samples (PB) from neuroblastoma patients collected at each collection time, the expression levels of the seven gene markers measured by the method in [2] above were adjusted to weighted expression levels by division using the 90th percentile expression level in Figure 4B as a statistical value (non-significance), and the sum of the weighted expression levels (7NB-mRNAs) was derived as the evaluation value. The distribution of the evaluation values at each collection time is shown in Figure 6B. The results of an analysis of the significant differences in evaluation values at the collection time point are shown in the table below.

As shown in Table 6 and Figure 6A, as well as Table 7 and Figure 6B, the evaluation value obtained as the sum of the weighted expression levels of the seven gene markers (7NB-mRNAs) showed significant differences between the time points of specimen collection for neuroblastoma patients. In other words, the evaluation value faithfully reflected the difference in tumor mass due to the time points of specimen collection for neuroblastoma patients, that is, diagnosis, treatment, post-treatment follow-up, and relapse. In particular, the difference in tumor mass between relapse and other periods was favorably reflected, demonstrating that the prognosis of relapse can be predicted by the sum of the weighted expression levels of the seven gene markers (7NB-mRNAs).

[Test Example 4: Prognosis based on the sum of 7 marker weighted expression levels by division]
For bone marrow samples taken at the time of diagnosis (Diagnosis) and during follow-up after treatment (Post-treatment) of high-risk neuroblastoma patients, the expression levels of the seven gene markers measured by the method of [2] above were corrected to weighted expression levels by division using the 90th percentile expression level in Figure 3B as a statistical value (non-importance), and the sum of the weighted expression levels (90 percentile weighted sum) was derived as an evaluation value. The evaluation values were compared between relapse patients (Relapse) and non-relapse patients (Non-relapse). The results are shown in Figure 7. The background of the samples used in this test example is shown in the table below.

As shown in Figure 7 and Table 8, for 24 bone marrow samples (17 recurrent, 7 non-recurrent) collected at the time of diagnosis, no significant difference was observed in the evaluation value, the sum of the weighted expression levels of 7 gene markers (7NB-mRNAs), between recurrent and non-recurrent patients. On the other hand, for 89 bone marrow samples (55 recurrent, 34 non-recurrent) collected during treatment and 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, a significant difference was observed in the weighted sum of the 7 markers (7NB-mRNAs) between recurrent and non-recurrent patients. In other words, it was shown that the prognosis of recurrence can be predicted by the sum of the weighted expression levels of 7 gene markers (7NB-mRNAs).

[Test Example 5: Comparison of diagnostic ability based on sum of expression levels weighted by division method of 7 markers and diagnostic ability based on simple sum of 7 markers]
For 73 bone marrow samples (17 recurrent, 56 non-recurrent) collected during follow-up after treatment, the expression levels of the 7 gene markers measured by the method of [2] above were corrected to weighted expression levels by division using the 90th percentile expression levels of the 7 gene markers in FIG. 3B as statistical values (non-importance), and the sum of the weighted expression levels (7NB-mRNAs (90 percentile weighted sum)) was derived as an evaluation value. In addition, a simple sum (non-weighted sum) of the expression levels of the 7 gene markers was derived without weighting. ROC analysis was performed on the weighted evaluation value (7NB-mRNAs (90 percentile weighted sum)) and the unweighted evaluation value (7NB-mRNAs (non-weighted sum)) for predicting the prognosis of neuroblastoma patients, i.e., predicting recurrence. The results are shown in FIG. 8.

As shown in Figure 8, when using the simple sum of the expression levels of the seven gene markers (7NB-mRNAs (non-weighted sum)), no weighting is performed and each gene marker is treated equally. Therefore, when using an evaluation value (7NB-mRNAs (90 percentile weighted sum)) weighted by taking into account the expression levels of the seven gene markers and the expression levels in the control (statistical values), the AUC value is larger, indicating that more accurate prognosis prediction (recurrence prediction) is possible.

In addition, the AUC of prognosis prediction using an evaluation value (7NB-mRNAs (90 percentile weighted sum)) weighted by taking into account the expression levels of the seven gene markers and the expression levels in the control (statistical values) exceeded the AUC of 0.622 for prognosis prediction using five gene markers for neuroblastoma diagnosis reported in Non-Patent Document 8. In other words, the method of the present invention is considered to be the most accurate method in the world at present for predicting the prognosis (predicting recurrence) of neuroblastoma patients. Not only that, but the AUC in this test example was also excellent in that it exceeded 0.7, which is the standard for moderate accuracy.

[Test Example 6: Search for fixed percentiles that maximize the AUC of recurrence prediction (bone marrow)]
For 73 bone marrow samples (17 relapses, 56 non-relapses) collected during follow-up after treatment, the expression levels of 7 gene markers were measured by the ddPCR method described in [2] above. The expression levels at fixed percentiles selected from Figures 3A and 3B were selected as statistical values (non-significant), and the area under the ROC curve (AUC) for predicting relapse was analyzed based on the following methods (i), (ii), and (iii).

(i) Weighted geometric mean by subtraction of 7 gene markers The expression level at a fixed percentile selected from Figures 3A and 3B was selected as a statistical value (non-importance), and the expression level of each of the 7 gene markers measured by ddPCR was corrected to a weighted expression level by subtraction by subtracting the statistical value (non-importance) (however, if the weighted expression level is less than 1 after subtraction, the weighted expression level was set to "1"). The geometric mean of the weighted expression levels obtained was derived as an evaluation value. The derived evaluation value was subjected to ROC analysis based on the presence or absence of recurrence to set a cutoff value. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive. The relationship between the fixed percentile and AUC is shown in Figure 9.

As shown in Figure 9, when the 6th to 92nd percentiles were used as the fixed percentiles, the AUC exceeded 0.622 reported in Non-Patent Document 8, when the 6th to 55th percentiles were used as the fixed percentiles, the AUC exceeded 0.7, which is the criterion for moderate accuracy, and when the 30th to 55th percentiles were used as the fixed percentiles, the AUC was even better. In addition, when the 50th percentile was used as the fixed percentile, the AUC was the maximum (0.729).

(ii) Weighted sum of 7 gene markers by subtraction The expression level at a fixed percentile selected from Figures 3A and 3B was selected as a statistical value (non-importance), and the expression level of each of the 7 gene markers by ddPCR measurement was corrected to a weighted expression level by subtraction by subtracting the statistical value (non-importance) (however, if the weighted expression level is less than 0 after subtraction, the weighted expression level was set to "0"). The sum of the weighted expression levels obtained was derived as an evaluation value. A cutoff value was set by performing ROC analysis on the derived evaluation value based on the presence or absence of recurrence. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive. The relationship between the fixed percentile and AUC is shown in Figure 10.

As shown in Figure 10, when the 6th to 93rd percentiles were used as the fixed percentiles, the AUC exceeded the 0.622 reported in Non-Patent Document 8, and when the 66th to 80th percentiles were used as the fixed percentiles, the AUC was even better. In addition, when the 75th percentile was used as the fixed percentile, the AUC was maximized (0.688).

(iii) Weighted sum of 7 gene markers by division The expression level at a fixed percentile (54th percentile or higher) selected from Figures 3A and 3B was selected as a statistical value (non-importance), and each of the expression levels of the 7 gene markers measured by ddPCR was corrected to a weighted expression level by division by dividing it by the statistical value (non-importance), and the sum of the weighted expression levels obtained was derived as an evaluation value. A cutoff value was set for the derived evaluation value by performing ROC analysis on the presence or absence of recurrence. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive. The relationship between the fixed percentile and AUC is shown in Figure 11.

As shown in Figure 11, when the 54th to 100th percentiles were used as the fixed percentiles, the AUC exceeded 0.622 reported in Non-Patent Document 8 and the AUC of 0.7, which is the standard for moderate accuracy. In addition, the AUC was maximized (0.728) when the 99th percentile was used as the fixed percentile.

[Test Example 7: Prediction of recurrence using various weighting methods (bone marrow)]
For 73 bone marrow samples (17 relapses, 56 non-relapses) collected during follow-up after treatment, the expression levels of the seven gene markers were measured by the ddPCR measurement method described above in [2]. The expression levels of the seven gene markers were also measured by the qPCR measurement method for the same samples. The area under the ROC curve (AUC) and cutoff value (threshold: TV) for predicting relapse were analyzed based on the following methods (a), (b), (c), (d11), (d12), (d21), (d22), (d31), and (d32).

(a) Simple geometric mean of 7 gene markers by qPCR measurement The copy number of 7 gene markers was measured by qPCR measurement. In the qPCR measurement, the same primers as those used in the ddPCR measurement in [2] above were used as primers for the 7 gene markers, and B2M, GAPDH, and PGK1 were used as reference genes. The primers for B2M had the sequences shown in SEQ ID NO: 35 and 36, the primers for GAPDH had the sequences shown in SEQ ID NO: 37 (sense) and SEQ ID NO: 38 (anti-sense), and the primers for PGK1 had the sequences shown in SEQ ID NO: 39 (sense) and SEQ ID NO: 40 (anti-sense). The geometric mean of the Ct values of each of the 7 gene markers was subtracted from the geometric mean of the Ct values of each of the three reference genes to perform ROC analysis based on the presence or absence of recurrence to set a cutoff value. If the geometric mean of the expression levels of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive.

(b) Simple geometric mean of 7 gene markers by ddPCR measurement The geometric mean of the expression levels of the measured 7 gene markers was derived, and the derived geometric mean was subjected to ROC analysis based on the presence or absence of recurrence to set a cutoff value. If the geometric mean of the expression levels of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive.

(c) Simple sum of 7 gene markers by ddPCR measurement The sum of the expression levels of the measured 7 gene markers was derived, and the derived sum was subjected to ROC analysis based on the presence or absence of recurrence to set a cutoff value. If the sum of the expression levels of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive.

(d11) Weighted geometric mean by subtraction (using fixed percentile) of 7 gene markers by ddPCR measurement From FIG. 3A, the expression level at the fixed percentile shown in Table 9 (the percentile that maximizes the AUC of recurrence prediction found in Test Example 6) was selected as a statistical value (non-importance), and the expression level of each of the 7 gene markers by ddPCR measurement was corrected to a weighted expression level by subtraction by subtracting the statistical value (non-importance) (however, if the weighted expression level is less than 1 as a result of subtraction, the weighted expression level was set to "1"). The geometric mean of the weighted expression levels obtained was derived as an evaluation value. For the derived evaluation value, ROC analysis was performed depending on the presence or absence of recurrence to set a cutoff value. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive.

(d12) Weighted geometric mean by subtraction (using variable percentile) of 7 gene markers by ddPCR measurement From FIG. 3A and FIG. 3B, the expression level in the variable percentile shown in Table 9 was selected as a statistical value (non-importance), and the expression level of each of the 7 gene markers by ddPCR measurement was corrected to a weighted expression level by subtraction by subtracting the statistical value (non-importance) (however, if the weighted expression level is less than 1 after subtraction, the weighted expression level was set to "1".), and the geometric mean of the weighted expression level obtained was derived as an evaluation value. For the derived evaluation value, a cutoff value was set by performing ROC analysis on the presence or absence of recurrence. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was judged to be positive. The expression levels in the variable percentiles shown in Table 9 were found by arbitrarily changing the percentile of each gene marker from the fixed percentile used in (d11) and searching for a higher AUC than (d11).

(d21) Weighted sum of 7 gene markers by subtraction (using fixed percentile) by ddPCR measurement From FIG. 3B, the expression level at the fixed percentile shown in Table 9 (the percentile that maximizes the AUC of recurrence prediction found in Test Example 6) was selected as a statistical value (non-importance), and the expression level of each of the 7 gene markers by ddPCR measurement was corrected to a weighted expression level by subtraction by subtracting the statistical value (non-importance) (however, if the weighted expression level is less than 0 as a result of subtraction, the weighted expression level was set to "0"). The sum of the obtained weighted expression levels was derived as an evaluation value. For the derived evaluation value, ROC analysis was performed on the presence or absence of recurrence to set a cutoff value. If the evaluation value of the measured 7 gene markers exceeds the cutoff value, it was determined to be positive.

(d22) Weighted sum of 7 gene markers by subtraction (using variable percentile) by ddPCR measurement From Figures 3A and 3B, the expression levels in the variable percentiles shown in Table 9 were selected as statistical values (non-importance), and the expression levels of the 7 gene markers by ddPCR measurement were corrected to weighted expression levels by subtraction by subtracting the statistical values (non-importance) from each of them (however, if the weighted expression level is less than 0 after subtraction, the weighted expression level was set to "0"). The sum of the weighted expression levels obtained was derived as an evaluation value. A cutoff value was set by performing ROC analysis on the derived evaluation value based on the presence or absence of recurrence. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was judged to be positive. The expression levels in the variable percentiles shown in Table 9 were selected as the expression levels closest to the cutoff value for the presence or absence of minimal residual disease for each of the 7 gene markers among the expression levels in each percentile of the 7 gene markers shown in Figures 3A and 3B.

(d31) Weighted sum of 7 gene markers by division (using fixed percentile) by ddPCR measurement From FIG. 3B, the expression level at the fixed percentile shown in Table 9 (the percentile found in Test Example 6 that maximizes the AUC of recurrence prediction) was selected as a statistical value (non-importance), and each of the expression levels of the 7 gene markers by ddPCR measurement was corrected to a weighted expression level by division by dividing it by the statistical value (non-importance), and the sum of the weighted expression levels obtained (7NB-mRNAs) was derived as an evaluation value. For the derived evaluation value, ROC analysis was performed on the presence or absence of recurrence to set a cutoff value. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was determined to be positive.

(d32) Weighted sum of 7 gene markers by division (using variable percentile) by ddPCR measurement From FIG. 3A and FIG. 3B, the expression amount in the variable percentile shown in Table 9 was selected as a statistical value (non-importance), and each of the expression amounts of the 7 gene markers by ddPCR measurement was corrected to a weighted expression amount by division by dividing it by the statistical value (non-importance), and the sum of the weighted expression amounts obtained (7NB-mRNAs) was derived as an evaluation value. For the derived evaluation value, ROC analysis was performed on the presence or absence of recurrence to set a cutoff value. If the evaluation value of the measured 7 gene markers exceeded the cutoff value, it was judged to be positive. The expression amount in the variable percentile shown in Table 9 was found by arbitrarily changing the percentile of each gene marker from the fixed percentile used in (d31) and searching for a higher AUC than (d31).

The area under the ROC curve (AUC) and cutoff values (threshold values: TV) for predicting recurrence based on methods (a), (b), (c), (d11), (d12), (d21), (d22), (d31), and (d32) are shown in the table below.

As shown in the results of Table 10, compared with (a) where the expression levels by qPCR were simply geometrically averaged, (b) where the expression levels by ddPCR were simply geometrically averaged showed improved AUC, indicating that prognosis of recurrence can be predicted more accurately. Furthermore, (d11) and (d12) where the expression levels of the seven gene markers by ddPCR were taken into account and the expression levels (statistical values) in the control were taken as the geometric average showed further improved AUC, indicating that prognosis of recurrence can be predicted more accurately.

In addition, compared to (c), which was a simple sum of the expression levels of the seven gene markers measured by ddPCR, (d21), (d22), (d31) and (d32), which were calculated by adding up the expression levels of the seven gene markers measured by ddPCR and taking into account the expression levels in the control (statistical values), the AUC was further improved, indicating that the prognosis of recurrence can be predicted more accurately.

Furthermore, among (d21), (d22), (d31), and (d32), which were calculated by adding up the expression levels of the seven gene markers measured by ddPCR and taking into account the expression levels in the control (statistical values), it was found that (d31) and (d32), which were weighted by division, were able to further improve the AUC compared to (d21) and (d22), which were weighted by subtraction.

[Test Example 8: Search for fixed percentiles that maximize the AUC of recurrence prediction (peripheral blood)]
The expression levels of the seven gene markers were measured for 21 peripheral blood samples (4 recurrent, 17 non-recurrent) collected during follow-up after treatment by the ddPCR measurement method described in [2] above. The area under the ROC curve (AUC) for predicting recurrence was analyzed in the same manner as in (i) (weighted geometric mean of the seven gene markers by subtraction), (ii) (weighted sum of the seven gene markers by subtraction), and (iii) (weighted sum of the seven gene markers by division) in Test Example 6, except that the expression levels at fixed percentiles selected from Figures 4A and 4B were selected as statistical values (non-significance). The respective results are shown in Figures 12 to 14.

As shown in Figure 12 (weighted geometric mean by subtraction), the weighted geometric mean by subtraction of the seven gene markers showed that when the 52nd percentile or higher was used as the fixed percentile, the AUC exceeded 0.622 reported in Non-Patent Document 8, and when the 68th percentile or higher was used as the fixed percentile, the AUC was 0.7 or higher, which is the criterion for moderate accuracy. In addition, among the fixed percentiles excluding the 100th percentile, the AUC was maximum (0.824) when the 99th percentile was used.

As shown in Figure 13 (weighted sum by subtraction), the weighted sum by subtraction of the seven gene markers showed that when all fixed percentiles were used, the AUC exceeded 0.622 reported in Non-Patent Document 8 and 0.7, which is the criterion for moderate accuracy, and the AUC was even better when the 93rd to 99th percentiles were used as fixed percentiles. In addition, the AUC was maximized (0.838) when the 99th percentile was used.

As shown in Figure 14 (weighted sum by division), according to the weighted sum by division of the seven gene markers, when the 91st percentile or higher was used as the fixed percentile, the AUC exceeded 0.622 reported in Non-Patent Document 8, and when the 94th to 97th percentiles were used as the fixed percentile, the AUC was 0.7 or higher, which is the criterion for moderate accuracy. In addition, the AUC was maximized (0.706) when the 97th percentile was used among the fixed percentiles.

[Test Example 9: Prediction of recurrence using various weighting methods (peripheral blood)]
The expression levels of 7 gene markers were measured for 21 peripheral blood samples (4 recurrent, 17 non-recurrent) collected during follow-up after treatment using the ddPCR measurement method described above in [2]. The AUC and cutoff value (threshold value: TV) for predicting recurrence were calculated using the weighting methods (d11), (d21), and (d31) of Test Example 7, except that the percentiles that maximized the AUC derived in Test Example 8 were used, as shown in the table below.

[Test Example 10: Search for a fixed percentile that maximizes the AUC of recurrence prediction (peripheral blood stem cells)
For 20 peripheral blood stem cell samples (11 relapses, 9 non-relapses) collected during treatment, the expression levels of 7 gene markers were measured by the ddPCR measurement method described in [2] above. The area under the ROC curve (AUC) for predicting relapse was analyzed in the same manner as in (i), (ii), and (iii) of Test Example 6, except that the expression levels at percentiles selected from Figures 4A and 4B were selected as statistical values (non-significant). The relationship between fixed percentiles and AUC is shown in Figure 15 (weighted geometric mean by subtraction), Figure 16 (weighted sum by subtraction), and Figure 17 (weighted sum by division), respectively.

As shown in Figure 15 (weighted geometric mean by subtraction), when the 21st to 99th percentiles were used as the fixed percentiles, the AUC exceeded the 0.622 reported in Non-Patent Document 8 (0.626 to 0.808), when the 44th to 55th percentiles and the 69th to 95th percentiles were used as the fixed percentiles, the AUC was almost equal to or exceeded the 0.7 standard for moderate accuracy (0.697), and when the 79th to 95th percentiles were used as the fixed percentiles, the AUC was even better. The AUC was maximum (0.808) when the 92nd percentile was used as the fixed percentile.

As shown in Figure 16 (weighted sum by subtraction), when the 21st to 99th percentiles were used as the fixed percentiles, the AUC exceeded 0.622 reported in Non-Patent Document 8 (0.667 to 0.788), when the 34th to 95th percentiles and the 98th percentile were used as the fixed percentiles, the AUC exceeded 0.7, which is the criterion for moderate accuracy, and when the 69th to 93rd percentiles were used as the fixed percentiles, the AUC was even better. In addition, the AUC was maximized (0.788) when the 92nd percentile was used as the fixed percentile.

As shown in Figure 17 (weighted sum by division), when the 90th to 99th percentiles were used as fixed percentiles, the AUC exceeded 0.7 (0.707 to 0.768), which is the criterion for moderate accuracy. In addition, the AUC was maximized (0.768) when the 90th percentile was used as the fixed percentile.

[Test Example 11: Prediction of recurrence using various weighting methods (peripheral blood stem cells)]
For 20 peripheral blood stem cell specimens (11 relapses, 9 non-relapses) collected during treatment, the expression levels of 7 gene markers were measured using the ddPCR measurement method described above in [2]. The area under the ROC curve (AUC) and cutoff value (threshold: TV) for predicting relapse were analyzed using the following methods (b), (c), (d11-1), (d11-2), (d12), (d21-1), (d21-2), (d22), (d31-1), (d31-2), and (d32).

(b) Simple geometric mean of seven gene markers by ddPCR measurement Analysis was performed in the same manner as in Test Example 7(b).

(c) Simple Sum of Seven Gene Markers by ddPCR Measurement Analysis was performed in the same manner as in Test Example 7(c).

(d11-1) and (d11-2) Weighted geometric mean by subtraction (using fixed percentile) of 7 gene markers by ddPCR measurement Analysis was performed in the same manner as in (d11) of Test Example 7, except that the expression levels at the fixed percentiles shown in Table 12 (the percentiles that maximize the AUC of recurrence prediction found in Test Example 10, or the appropriately selected 50th percentile) were selected as statistical values (non-significant) from Figures 4A and 4B.

(d12) Weighted geometric mean by subtraction (using variable percentile) of 7 gene markers by ddPCR measurement From FIG. 4B, the expression levels at the variable percentiles shown in Table 12 were selected as statistical values (non-significant) and analyzed in the same manner as in (d12) of Test Example 7. The expression levels at the variable percentiles shown in Table 12 were found by arbitrarily changing the percentiles of each gene marker from the fixed percentiles that maximize the AUC of recurrence prediction used in (d11-1) and searching for AUCs higher than (d11-1).

(d21-1) and (d21-2) Weighted sum by subtraction (using fixed percentile) of 7 gene markers by ddPCR measurement From Figures 4A and 4B, the expression levels at the fixed percentiles shown in Table 12 (the percentiles that maximize the AUC of recurrence prediction found in Test Example 10, or the appropriately selected 50th percentile) were selected as statistical values (non-significant), and the analysis was performed in the same manner as in (d21) of Test Example 7, except that the expression levels at the fixed percentiles shown in Table 12 (the percentiles that maximize the AUC of recurrence prediction found in Test Example 10, or the appropriately selected 50th percentile) were selected as statistical values (non-significant).

(d22) Weighted sum by subtraction (using variable percentile) of 7 gene markers by ddPCR measurement From FIG. 4B, the expression levels at the variable percentiles shown in Table 12 were selected as statistical values (non-significant) and analyzed in the same manner as in (d22) of Test Example 7. The expression levels at the variable percentiles shown in Table 12 were found by arbitrarily changing the percentiles of each gene marker from the fixed percentiles that maximize the AUC of recurrence prediction used in (d21-1) and searching for AUCs higher than (d21-1).

(d31-1) and (d31-2) Weighted sum by division (fixed percentile use) of 7 gene markers by ddPCR measurement From FIG. 4B, the expression level at the fixed percentile shown in Table 12 (the percentile that maximizes the AUC of recurrence prediction found in Test Example 10, or the appropriately selected 50th percentile) was selected as a statistical value (non-importance), except that it was analyzed in the same manner as (d31) in Test Example 7. The expression levels at the variable percentiles shown in Table 12 were found by arbitrarily changing the percentile of each gene marker from the fixed percentile that maximizes the AUC of recurrence prediction used in (d31-1) and searching for a higher AUC than (d31-1).

The area under the ROC curve (AUC) and cutoff value (threshold: TV) for predicting recurrence based on the methods (b), (c), (d11-1), (d11-2), (d12), (d21-1), (d21-2), (d22), (d31-1), (d31-2) and (d32) are shown in the table below.

As shown in the results in Table 13, compared to (b), which was a simple geometric mean of the expression levels of the seven gene markers measured by ddPCR, (d11-1), (d11-2), and (d12), which were obtained by taking the geometric mean of the expression levels of the seven gene markers measured by ddPCR taking into account the expression levels in the control (statistical values), the AUC was further improved, indicating that the prognosis of recurrence can be predicted more accurately.

Furthermore, as shown in the results in Table 14, compared to (c), which was a simple sum of the expression levels of the seven gene markers measured by ddPCR, (d21-1), (d21-2), (d22), (d31-1), (d31-2) and (d32), which were calculated by adding up the expression levels of the seven gene markers measured by ddPCR and taking into account the expression levels in the control (statistical values), the AUC was further improved, indicating that the prognosis of recurrence can be predicted more accurately.

SEQ ID NO:13 to SEQ ID NO:40 are primers.

Claims (21)

A step 1 of measuring the expression levels A1 to A7 of seven gene markers for minimal residual disease of neuroblastoma consisting of CRMP1, DBH, DDC, GAP43, ISL1, PHOX2B, and TH from a biological sample in which the disease progression, therapeutic effect, or recurrence of minimal residual disease of neuroblastoma is to be evaluated by digital PCR;
A step 2 of weighting each of the expression levels A1 to A7 by taking into account statistical values B1 to B7 of the expression levels of each of the seven gene markers in a control by digital PCR to obtain weighted expression levels AB1 to AB7, and obtaining the geometric mean or sum of the weighted expression levels AB1 to AB7 as an evaluation value;
Step 3: comparing the evaluation value with a cutoff value for the presence or absence of minimal residual disease of neuroblastoma , and judging the biological sample to be positive for minimal residual disease of neuroblastoma if the evaluation value exceeds the cutoff value ;
A method for analyzing a biological sample using a minimal residual disease marker for neuroblastoma, comprising: The method for analyzing a biological sample according to claim 1, wherein the weighted expression amounts AB1 to AB7 are obtained by subtracting statistical values B1 to B7 from the expression amounts A1 to A7, respectively (provided that, if the subtraction result is a value less than 1, the weighted expression amount is set to 1), and the geometric mean of the weighted expression amounts AB 1 to AB7 is obtained as the evaluation value. The statistical values B1 to B7 are all expression levels at a common specific percentile of 1 percentile or more for each of the seven gene markers in a control;
The method for analyzing a biological sample according to claim 2 , wherein at least any one of the seven gene markers is detected in the particular percentile. the biological sample is a bone marrow sample;
The method for analyzing a biological sample according to claim 3, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 6th to 92nd percentiles of each of the seven gene markers in a control. the biological sample is a bone marrow sample;
The statistical values B1 to B7 are respectively in the control,
CRMP1 38-44th percentile, DBH 53-66th percentile, DDC 55-63rd percentile, GAP43 49-65th percentile, ISL1 40-56th percentile, PHOX2B 51-71st percentile, TH 51-71st percentile;
CRMP1 38-44th percentile, DBH 1-52nd percentile, DDC 1-54th percentile, GAP43 85-86th percentile, ISL1 92-94th percentile, PHOX2B 1-50th percentile, TH 51-71th percentile: or
The method for analyzing a biological sample according to claim 2, wherein the expression levels are the 83rd percentile of CRMP1, the 1st to 52nd percentile of DBH, the 1st to 54th percentile of DDC, the 85th to 86th percentile of GAP43, the 92nd to 94th percentile of ISL1, the 1st to 50th percentile of PHOX2B, and the 1st to 50th percentile of TH. the biological sample is a peripheral blood sample,
The method for analyzing a biological sample according to claim 2, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 65th to 99th percentiles of each of the seven gene markers in a control. the biological sample is a peripheral blood stem cell sample,
The method for analyzing a biological sample according to claim 3, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 21st to 99th percentiles of each of the seven gene markers in a control. the biological sample is a peripheral blood stem cell sample,
The statistical values B1 to B7 are respectively the peripheral blood sample control,
The method for analyzing a biological sample according to claim 2, wherein the expression levels are in the 91st to 99th percentiles of CRMP1, the 91st to 92nd percentiles of DBH, the 92nd to 95th percentiles of DDC, the 91st to 92nd percentiles of GAP43, the 98th to 99th percentiles of ISL1, the 87th to 92nd percentiles of PHOX2B, and the 82nd to 93rd percentiles of TH. The method for analyzing a biological sample according to claim 1, wherein the weighted expression amounts AB1 to AB7 are obtained by subtracting statistical values B1 to B7 from the expression amounts A1 to A7, respectively (provided that the weighted expression amount is set to 0 when the subtraction results in a negative value), and the sum of the weighted expression amounts AB 1 to AB7 is obtained as the evaluation value. The statistical values B1 to B7 are all expression levels at a common specific percentile of 1 percentile or more for each of the seven gene markers in a control;
The method for analyzing a biological sample according to claim 9 , wherein at least any one of the seven gene markers is detected in the particular percentile. the biological sample is a bone marrow sample;
The method for analyzing a biological sample according to claim 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 6th to 93rd percentiles of each of the seven gene markers in a control. the biological sample is a peripheral blood sample,
The method for analyzing a biological sample according to claim 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 21st to 99th percentiles of each of the seven gene markers in a control. the biological sample is a peripheral blood stem cell sample,
The method for analyzing a biological sample according to claim 10, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 21st to 99th percentiles of each of the seven gene markers in a control. the biological sample is a bone marrow sample, a peripheral blood sample, or a peripheral blood stem cell sample;
The method for analyzing a biological sample according to claim 9, wherein the statistical values B1 to B7 are, respectively, expression amounts of the seven gene markers at each percentile in a control that are closest to a cutoff value for the presence or absence of the minimal residual disease for each of the seven gene markers. The method for analyzing a biological sample according to claim 1, wherein the weighted expression amounts AB1 to AB7 are obtained by dividing the expression amounts A1 to A7 by statistical values B1 to B7 (wherein none of B1 to B7 is 0), and the sum of the weighted expression amounts AB 1 to AB7 is obtained as the evaluation value. the biological sample is a bone marrow sample;
The method for analyzing a biological sample according to claim 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 54th percentile or higher of each of the seven gene markers in a control. the biological sample is a peripheral blood sample,
The method for analyzing a biological sample according to claim 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 90th percentile or higher of each of the seven gene markers in a control. the biological sample is a peripheral blood stem cell sample,
The method for analyzing a biological sample according to claim 15, wherein the statistical values B1 to B7 are all expression levels at a common specific percentile among the 90th percentile or higher of each of the seven gene markers in a control. the biological sample is a bone marrow sample;
The statistical values B1 to B7 are respectively in the control,
CRMP1 38-44th percentile, DBH 77-83rd percentile, DDC 74-79th percentile, GAP43 38-48th percentile, ISL1 57-68th percentile, PHOX2B 72-77th percentile, TH 78-83rd percentile; or
The method for analyzing a biological sample according to claim 15, wherein the expression levels are the 92nd percentile of CRMP1, the 91st to 92nd percentile of DBH, the 99th percentile of DDC, the 93rd percentile of GAP43, the 96th percentile of ISL1, the 92nd to 93rd percentile of PHOX2B, and the 84th to 87th percentile of TH. the biological sample is a peripheral blood stem cell sample,
The statistical values B1 to B7 are respectively the peripheral blood sample control,
The method for analyzing a biological sample according to claim 15, wherein the expression levels are the 98th to 99th percentile of CRMP1, the 90th percentile of DBH, the 98th to 99th percentile of DDC, the 92nd to 93rd percentile of GAP43, the 99th percentile of ISL1, the 90th percentile of PHOX2B, and the 90th to 92nd percentile of TH. The method for analyzing a biological sample according to any one of claims 1 to 20, wherein the expression levels A1 to A7 of the seven gene markers in the biological sample and the expression levels of the seven gene markers in the control by digital PCR are all values obtained by dividing by at least the expression level of a reference gene, and the reference gene is selected from the group consisting of HPRT1, HMBS, GUSB, and TBP.

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