CN118406766B

CN118406766B - Biomarker, primer set and detection kit for predicting prognosis clinical results of colorectal cancer in stage II and stage III

Info

Publication number: CN118406766B
Application number: CN202410873864.8A
Authority: CN
Inventors: 陈利民
Original assignee: Tianjin Yunjian Medical Instrument Co ltd; Tianjin Yunjian Medical Lab Co ltd
Current assignee: Tianjin Yunjian Medical Instrument Co ltd; Tianjin Yunjian Medical Lab Co ltd
Priority date: 2024-07-02
Filing date: 2024-07-02
Publication date: 2024-11-01
Anticipated expiration: 2044-07-02
Also published as: CN118406766A

Abstract

The embodiment of the invention discloses a biomarker, a primer group and a detection kit for predicting the clinical outcome of the prognosis of the colorectal cancer of stage II and stage III; the biomarkers are IRS2, TRIM25, MYBL2, KRT6B, FMNL2, NAT1, FAP, MYC, INHBA. The colorectal cancer RS score can be obtained by detecting the colorectal cancer biomarker level, so that the colorectal cancer recurrence risk can be effectively distinguished; the formula of the colorectal cancer of stage II is (IRS 2-TRIM 25) + (IRS 2-MYBL 2) + (KRT 6B-FMNL 2) + (INHBA-MYC); stage III colorectal cancer is formulated as (IRS 2-TRIM 25) + (KRT 6B-NAT 1) + (FAP-MYC) + (INHBA-MYC), providing patients with more personalized and effective treatment regimen based on the scoring results.

Description

Biomarker, primer set and detection kit for predicting prognosis clinical results of colorectal cancer in stage II and stage III

Technical Field

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker, a primer group and a detection kit for predicting the clinical outcome of colorectal cancer in stage II and stage III.

Background

The ability to predict cancer treatment outcome under standard of care may help doctors decide on potential adjuvant therapy. For example, if the patient's prognosis is poor, a more aggressive adjuvant therapy may be given. Currently, most diagnostic tests or clinical indications used in practice are not quantitative, such as Immunohistochemistry (ICH), which usually produce different results in different laboratories, possibly due to reagent non-standardization and subjective interpretation of the results.

Patients with colorectal cancer (CRC) stage II and III can be stratified according to risk of recurrence and clinical outcome after initial treatment, and a clinician can formulate a chemotherapy regimen that is best suited for the particular patient. Adjuvant chemotherapy is currently the standard of care for stage III colon patients, but lacks clinical data identifying low risk stage III colorectal cancer patients who may not require oxaliplatin-based chemotherapy. For patients with stage II colorectal cancer, only high-risk patients are recommended for adjuvant chemotherapy.

However, the current criteria defining the characteristics of high-risk patients are not adequate. For known stage II colorectal cancers, prognostic factors that lead to poor prognosis include tumor depth of infiltration (T stage), lymph node number, mismatch repair (MMR) status, tumor grade, lymph and Vascular Infiltration (LVI) score, there may be a large amount of clinical evidence supporting limited use of T stage, lymph node number and MMR status alone.

Company (Exact Sciences) developed a recurrence score test of 12 genes, namely: oncotype DX colon recurrence scoring test, which is based on a scoring system for quantitative polymerase chain reaction qPCR results, can quantitatively show the likelihood of recurrence risk. However, the risk ratio HR of the recurrence rate score IQR per quartile range was only 1.38 using single covariate Cox regression, whereas the risk ratio HR was 1.43 using multiple covariate Cox regression. The recurrence rate of Oncotype Dx for colorectal cancer is worse than the current clinical predictor T-staging and MMR status, approaching the number of lymph nodes examined. Thus, the clinical application of 12 gene recurrence score detection in colorectal cancer is low and not commonly used in clinic.

Disclosure of Invention

In view of this, in one aspect, some embodiments disclose biomarkers that predict the prognostic clinical outcome of stage II and stage III colorectal cancers, the biomarkers being IRS2, TRIM25, MYBL2, KRT6B, FMNL2, NAT1, FAP, MYC, INHBA.

Further, some embodiments disclose biomarkers for predicting the prognostic clinical outcome of stage II and stage III colorectal cancers, the biomarkers for recurrence risk assessment in stage II colorectal cancer patients being a combination of IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, and INHBA-MYC.

Some embodiments disclose biomarkers for predicting the prognostic clinical outcome of stage II and stage III colorectal cancers, characterized in that the biomarkers for recurrence risk assessment in stage III colorectal cancer patients are a combination of IRS2-TRIM25, KRT6B-NAT1, FAP-MYC, and INHBA-MYC.

On the other hand, some embodiments disclose primer sets for PCR detection of biomarkers for predicting the clinical outcome of stage II and stage III colorectal cancer prognosis, comprising IRS2 primer set, TRIM25 primer set, KRT6B primer set, MYBL2 primer set, FMNL primer set, NAT1 primer set, FAP primer set, MYC primer set, INHBA primer set, the sequences of the primer sets are respectively listed as SEQ ID No. 1-18.

In yet another aspect, some embodiments disclose a detection kit for predicting a prognostic clinical outcome of stage II and stage III colorectal cancer, comprising a primer set for PCR detection of a biomarker that predicts a prognostic clinical outcome of stage II and stage III colorectal cancer.

Further, some embodiments disclose a detection kit for predicting the prognostic clinical outcome of stage II and stage III colorectal cancers, the biomarker for the recurrence risk assessment of stage II colorectal cancer patients is a combination of IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, and INHBA-MYC.

The biomarkers for recurrence risk assessment of patients with stage III colorectal cancer are a combination of IRS2-TRIM25, KRT6B-NAT1, FAP-MYC, and INHBA-MYC.

Further, some embodiments disclose a detection kit for predicting a prognostic clinical outcome of stage II and stage III colorectal cancer for measuring the circulation threshold of a biomarker in a tissue sample of a colorectal cancer patient.

Some embodiments disclose a detection kit for predicting the prognostic clinical outcome of stage II and stage III colorectal cancers, the cycle threshold of the biomarker is converted to colorectal cancer RS score.

The embodiments disclose a test kit for predicting the prognostic clinical outcome of stage II and stage III colorectal cancers, the stage II colorectal cancer being formulated as (IRS 2-TRIM 25) + (IRS 2-MYBL 2) + (KRT 6B-FMNL 2) + (INHBA-MYC), if the colorectal cancer RS score is above the threshold 2.589, indicating that the stage II colorectal cancer patient is at high risk of poor prognosis after initial treatment; the formula of colorectal cancer stage III is (IRS 2-TRIM 25) + (KRT 6B-NAT 1) + (FAP-MYC) + (INHBA-MYC), and if the score of colorectal cancer RS is higher than a critical value of-0.1355, the colorectal cancer recurrence risk of colorectal cancer patients in stage III is higher.

The biomarker group, the primer group and the detection kit for predicting the clinical outcome of the colorectal cancer prognosis of the II stage and the III stage disclosed by the embodiment of the invention can obtain the colorectal cancer RS score by detecting the colorectal cancer biomarker level, and can effectively distinguish the recurrence risk of colorectal cancer; the risk and prognosis of treatment for colorectal cancer patients can be better assessed, providing patients with more personalized and effective treatment regimens.

Drawings

FIG. 1, kaplan-Meier, estimates three-year relapse rate for individual surgical patients (full cohort patients);

FIG. 2, kaplan-Meier estimates three-year relapse rate for individual surgical patients (phase II patients);

FIG. 3, kaplan-Meier estimates three-year relapse rate for individual surgical patients (stage III patients);

fig. 4, mProbe recurrence intervals (all patients) of the recurrence-free interval (RS) group;

Fig. 5, mProbe recurrence interval (phase II patients) of recurrence free score (RS) group;

FIG. 6, mProbe recurrence interval (IIIA/B phase patients) of Recurrence Score (RS) group;

FIG. 7, kaplan-Meier estimates three-year relapse rate for a patient-alone risk group (full-cohort patients) by using Oncotype Dx and a scoring system;

FIG. 8, kaplan-Meier estimates three-year relapse rate (phase II patients) in the individual surgical patient risk group by using Oncotype Dx and scoring system;

FIG. 9, kaplan-Meier estimates three-year recurrence rate for a patient-alone patient risk group (stage III colon cancer patient) by using Oncotype Dx and a scoring system;

fig. 10, no recurrence intervals (all patients) according to the Oncotype Dx Recurrence Score (RS) group;

FIG. 11, recurrence free interval (phase II patients) according to Oncotype Dx Recurrence Score (RS) group;

FIG. 12, recurrence free interval (IIIA/B phase patients) according to Oncotype Dx Recurrence Score (RS) group.

Detailed Description

The word "embodiment" as used herein does not necessarily mean that any embodiment described as "exemplary" is preferred or advantageous over other embodiments. Performance index testing in the examples of the present invention, unless otherwise specified, was performed using conventional testing methods in the art. It should be understood that the terminology used in the description of the embodiments of the invention presented is for the purpose of describing particular embodiments only, and is not intended to be limiting of the disclosure of the embodiments of the invention.

The terms "substantially" and "about" are used herein to describe small fluctuations. For example, they may refer to less than or equal to ±5%, such as less than or equal to ±2%, such as less than or equal to ±1%, such as less than or equal to ±0.5%, such as less than or equal to ±0.2%, such as less than or equal to ±0.1%, such as less than or equal to ±0.05%. Numerical data presented or represented herein in a range format is used only for convenience and brevity and should therefore be interpreted flexibly to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range. For example, a numerical range of "1 to 5%" should be interpreted to include not only the explicitly recited values of 1% to 5%, but also include individual values and sub-ranges within the indicated range. Thus, individual values, such as 2%, 3.5% and 4%, and subranges, such as 1% to 3%, 2% to 4% and 3% to 5%, etc., are included in this numerical range. The same principle applies to ranges reciting only one numerical value. Moreover, such an interpretation applies regardless of the breadth of the range or the characteristics being described. In this document, including the claims, conjunctions such as "comprising," including, "" carrying, "" having, "" containing, "" involving, "" containing, "and the like are to be construed as open-ended, i.e., to mean" including, but not limited to. Only the conjunctions "consisting of … …" and "consisting of … …" are closed conjunctions.

Numerous specific details are set forth in the following examples in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In the examples, some methods, means, instruments, devices, etc. well known to those skilled in the art are not described in detail in order to highlight the gist of the present invention.

On the premise of no conflict, the technical features disclosed by the embodiment of the invention can be combined at will, and the obtained technical scheme belongs to the disclosure of the embodiment of the invention.

Colorectal cancer (CRC) biomarkers, CRC biomarker combinations, and methods of obtaining a CRC risk assessment biomarker level representation of a sample are disclosed. These combinations and methods play a role in assessing the risk of stage II and stage III treatment outcome and the likelihood of colorectal cancer recurrence. Also provided are systems, devices, and kits for carrying out the above methods. Details of the combinations and methods fully described below will become apparent to those skilled in the art upon review of the disclosure herein.

It is to be understood that the disclosure of the embodiments of the invention is not limited to the particular methods or combinations described, as such may vary. Also, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

Unless defined otherwise, all technical and scientific terms used herein should be interpreted according to the meanings commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some of the potential and preferred methods and materials will now be described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. In the event of a conflict, it is to be understood that the present disclosure replaces any disclosure incorporated into the publication.

It will be apparent to those skilled in the art from this disclosure that each of the specific embodiments described and illustrated herein has individual components and features that can be readily separated from or combined with the features of the other several embodiments without departing from the scope or inventive concept of the present invention. Any of the mentioned methods may be performed in the order of the mentioned events or in any other logically possible order.

Herein, "colorectal cancer" or "CRC" refers to an abnormal and uncontrolled disease of growth in the colon or rectum. It is also known as colon cancer, which is the large or large intestine. The rectum is the passage connecting the colon to the anus. So-called "risk assessment of colorectal cancer", or "determining a risk of CRC as one of the clinical signs", generally means providing a risk of CRC as an additional clinical sign, e.g. determining the risk of a patient for a recurrence of CRC after an initial treatment; risk assessment of disease progression and/or disease outcome, such as five year overall survival or progression free survival.

The following examples are put forth so as to provide those of ordinary skill in the art with a description of how to make and use the present invention, and are not intended to limit the scope of what is claimed, nor are they intended to suggest that the experiments below are all or the only experiments. The accuracy of the numbers used (e.g., amounts, temperatures, etc.) is ensured herein, but some experimental errors and deviations should be accounted for.

In some embodiments, a method of determining the expression level of a colorectal cancer biomarker in a subject, comprising:

a. Evaluating a set of colorectal cancer biomarkers in a sample to determine the expression level of each colorectal cancer biomarker in the sample, wherein the sample comprises surgically resected tumor tissue or FFPE tissue of a colorectal cancer patient; colorectal cancer biomarkers are IRS2, TRIM25, MYBL2, KRT6B, FMNL2, NAT1, FAP, MYC, INHBA; levels of RNA or DNA of the biomarker can be obtained, typically by detecting the nucleic acid sequence; the expression level of a biomarker in general can also be expressed by a cycle threshold, i.e., ct value; typically the cycle threshold can be expressed as an absolute concentration or as a median multiple (i.e., moM);

b. Colorectal cancer biomarker levels are obtained from the expression level of each colorectal cancer biomarker in the combination.

Typically, the biomarker expression level of the sample tissue/sample FFPE tissue is correlated with the risk of colorectal cancer recurrence in the tumor tissue, and the expression level of the biomarker can be accurately measured using a qPCR detection system, e.g., ABI QuantStudio 4, to derive a colorectal cancer risk score from which high risk patients and low risk patients can be distinguished.

In certain embodiments, the primers used for qPCR to detect the expression level of the biomarker include IRS2 primer set, TRIM25 primer set, KRT6B primer set, MYBL2 primer set, FMNL primer set, NAT1 primer set, FAP primer set, MYC primer set, INHBA primer set; for genes associated with colorectal cancer recurrence that occur in primary tumor tissue, tissue biomarker gene expression, e.g., copy number, is accurately measured using a quantitative PCR system to derive a recurrence score RS that distinguishes between high-risk and low-risk patients for stage II and III CRC. PCR systems include, for example, applied Biosystem QuantStudio, 7500 Real-Time PCR systems or Bio-Rad CFX systems. The IRS2 primer group refers to a specific primer pair of IRS2 genes, and the specific primer pair can convert mRNA of the IRS2 genes into cDNA in qPCR analysis, then amplify the cDNA, and then perform qPCR detection and analysis.

The gene names and the sequences of the corresponding primer sets are as follows:

Gene name: IRS2;

SEQ ID No.1：CCTACGCCAGCATTGACTT；

SEQ ID No.2：TGACATCCTGGTGATAAAGCC；

gene name: TRIM25;

SEQ ID No.3：GGATGAGTTCGAGTTTCTGGAG；

SEQ ID No.4：GCCTTTTATCAGCTTGTGGTTC；

Gene name: MYBL2;

SEQ ID No.5：GCCGAGATCGCCAAGATG；

SEQ ID No.6：CTTTTGATGGTAGAGTTCCAGTGATTC；

gene name: KRT6B;

SEQ ID No.7：GCCCTCACTTTTCTTCTCATCA；

SEQ ID No.8：CATGTCTGAGTGCTGATAACTGT；

Gene name: FNML2;

SEQ ID No.9：AGAATGAAGCCATGTCCAAGA；

SEQ ID No.10：GTGTGAACTTGAGTATTTGCATCT；

gene name: NAT1;

SEQ ID No.11：AACTGAAGATCAACCTACTTTCAAC；

SEQ ID No.12：GTTTCCAAGTCCAATTTGTTCCTA；

Gene name: FAP;

SEQ ID No.13：CTGACCAGAACCACGGCT；

SEQ ID No.14：GGAAGTGGGTCATGTGGG；

gene name: MYC;

SEQ ID No.15：TCCCTCCACTCGGAAGGACTA；

SEQ ID No.16：CGGTTGTTGCTGATCTGTCTCA；

gene name: INHBA;

SEQ ID No.17：GTGCCCGAGCCATATAGCA；

SEQ ID No.18：CGGTAGTGGTTGATGACTGTTGA。

In some embodiments, biomarkers are used to distinguish between the risk of recurrence in patients with stage II and stage III colorectal cancer. In stage II colorectal cancer risk assessment, biomarkers are the cycle threshold differences ΔCt of IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2 and INHBA-MYC gene expression. For stage III colorectal cancer risk assessment, the biomarkers are the cycle threshold differences ΔCt of IRS2-TRIM25, KRT6B-NAT1, FAP-MYC and INHBA-MYC.

In some embodiments, a panel of biomarker combinations is used to distinguish between the risk of recurrence in colorectal cancer patients. The biomarker combination for colorectal cancer risk assessment may comprise at least the above biomarker. In general, a biomarker combination may include any combination of all biomarkers, in general, a smaller biomarker combination is sufficient to distinguish between high and low risk patients that are less likely to be present, but more economical; however, larger combinations may provide more detailed information and may be applied to population groups in different areas.

Some embodiments disclose a method of calculating a recurrence score based on a bi-classification scoring system by RS scoring biomarker panels to distinguish between high risk patients and low risk patients. A low RS score indicates that the patient is unlikely to relapse, and a high RS score indicates that the patient is likely to relapse.

In certain instances, the clinical parameters are used in combination with the biomarkers described herein for colorectal cancer recurrence risk assessment. Methods for determining a patient's RS score and other recurrent clinical predictors are disclosed.

In some embodiments, colorectal cancer RS scores are obtained by detecting colorectal cancer biomarker levels, wherein the RS scores may be calculated from measurements of biomarkers in the blood by geometric means, multivariate linear discriminant analysis, or distributed gradient-enhanced decision tree machine learning, e.g., XGBoost. In a bigram, the subject working characteristics ROC curve may be derived from biomarker combinations or pairings. RS scores may be categorized as low risk CRC recurrence clinical scores or high risk CRC recurrence clinical scores according to the methods described herein.

In some embodiments, the method of risk assessment of a colorectal cancer patient following initial remission of the subject is represented by the expression level of colorectal cancer biomarker in a tissue sample of the subject; for colorectal cancer risk assessment, colorectal cancer RS scores are assessed as high or low in two ranges to determine the risk of recurrence after initial treatment; if the colorectal cancer RS score is higher than the RS threshold 2.589, the surface is exemplified, indicating that the stage II colorectal cancer patient is at a high risk of poor colorectal cancer prognosis after initial treatment; the threshold value can be adjusted according to people; if the colorectal cancer RS score is higher than the critical value of-0.1355, the colorectal cancer recurrence risk of the colorectal cancer patient in stage III is higher; the threshold can be adjusted according to the population.

Some embodiments disclose a method of assessing prognosis/recurrence of a colorectal cancer patient, comprising the steps of:

obtaining a tumor sample of a patient, wherein the tumor sample comprises formaldehyde fixed paraffin embedded tissue, namely FFPE tissue;

Measuring the level, e.g., RNA/DNA copy number or concentration, of each biomarker in a tumor sample;

The level of each biomarker is compared to the value of the corresponding gene pair of the biomarker. In general, the range of paired values can represent biomarker levels for one or more tumor samples from the same subject, or biomarker levels for one or more tumor samples from one or more cancer patients, wherein a differential level of biomarker combinations in the biomarker samples, as compared to a reference value for biomarker for a good prognosis subject, indicates whether the patient is at high risk of relapse.

In some embodiments, the method of assessing prognosis/recurrence of a colorectal cancer patient further comprises calculating an RS score to distinguish between high-risk and low-risk colorectal cancer patients.

In general, colorectal cancer RS scores can be obtained from colorectal cancer biomarker levels, wherein the method of obtaining the RS score comprises:

a. Can be derived from the difference in circulation threshold C (t) between two different genes, for example, the biomarker panel with Ct difference values is IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, KRT6B-NAT1, FAP-MYC and INHBA-MYC.

The value of the measured blood biomarker can be obtained through geometric mean, multi-element linear discriminant analysis or distributed gradient enhancement decision tree machine learning; or alternatively, the first and second heat exchangers may be,

C. The fold difference, such as Δmom for Δc (t), concentration difference, or Ct value, for each pair of biomarkers can be normalized to fit a range of magnitudes with appropriate thresholds to distinguish between high and low risk patients. For example, it can be deduced from the stage II colorectal cancer formula (IRS 2-TRIM 25) + (IRS 2-MYBL 2) + (KRT 6B-FMNL 2) + (INHBA-MYC) and the stage III colorectal cancer formula (IRS 2-TRIM 25) + (KRT 6B-NAT 1) + (FAP-MYC) + (INHBA-MYC).

In general, biomarkers can be measured using specific primers and reporting systems to determine the gene expression, e.g., gene copy number, of the biomarker and biomarker. In general, methods for determining the copy number of a biomarker include, but are not limited to, quantitative PCR detection, reverse transcription quantitative PCR, gene microarray, RNA or DNA sequencing, sandwich analysis, magnetic capture, microsphere capture, electrophoresis blotting, surface enhanced raman spectroscopy, flow cytometry, or mass spectrometry.

In some embodiments, the copy number of the biomarker is determined by binding of a specific DNA primer to the biomarker RNA, followed by reverse transcription and polymerase chain reaction using a detectable reporter; reporter molecules such as SYBR green dyes and the like.

Some embodiments disclose a kit for a quantitative qPCR system for measuring RNA/DNA biomarkers in a tumor sample. The kit may comprise a container for storing biological samples collected and isolated from human patients suffering from stage II and stage III colorectal cancer. The kit comprises at least one reagent for measuring a colorectal cancer risk biomarker, for reacting the reagent with the biological sample or a portion of the biological sample to measure the at least one colorectal cancer biomarker in the biological sample. The reagents may be packaged in separate containers.

In some embodiments, the kit for a quantitative qPCR system further comprises one or more control reference samples and reagents for performing qPCR to detect biomarker copy numbers as described herein.

In some embodiments, a quantitative qPCR system for detecting biomarkers using a kit, comprising:

a platform system for measuring colorectal cancer biomarkers, such as QuantStudio system 4;

a calculation table for calculating colorectal cancer risk scores;

Determining an indicator of risk of patient relapse and poor prognosis.

In some embodiments, the copy number of the biomarker expression level is expressed as a cycle threshold C (t) for a kit for use in a quantitative qPCR system.

In some embodiments, a method of assessing risk of recurrence in a colorectal cancer patient for detecting a biomarker in tumor tissue comprises:

Collecting a primary tumor or FFPE tumor tissue sample from a colorectal cancer patient, measuring the copy number of each biomarker in the tumor tissue;

Comparing the measured values of a set of biomarkers in primary tumor or FFPE tumor tissue of colorectal cancer patient with reference values of corresponding counterpart genes, the differences in expression of these biomarkers in the tissue can be assessed;

Based on these differentially expressed data, a colorectal cancer recurrence risk score, i.e., RS score, for the patient can be calculated, and the patient's recurrence risk can be further assessed based on the RS score.

Typically, in order to measure at least one biomarker, it is necessary to bind to the biomarker or fragment thereof using specific primers and determine the copy number of the biomarker by PCR techniques. These primers are designed based on the coding region (exon) sequence of the biomarker.

In some embodiments, specific primer pairs are selected that are capable of specifically binding to a range of specific biomarkers, such as IRS2, TRIM25, MYBL2, KRT6B, FMNL, INHBA, MYC, NAT1, FAP, and the like, for accurate measurement and analysis.

In some embodiments, at least one pair of CRC biomarkers is measured, and the RS score is calculated by detecting for the biomarker by an oligonucleotide primer, wherein the primer specifically binds to the biomarker or a biomarker fragment comprising the RNA/DNA sequence of the biomarker;

a primer is selected from a partially complementary DNA sequence of a biomarker target;

The dye or dye/quencher report label can accurately interpret the circulation threshold of the target biomarker, such as the amount of RNA or DNA in the sample;

qPCR instruments, such as qustudio4, determine the expression level of each biomarker by deriving Ct values for each biomarker based on the amplification results of the biomarker templates.

Example 1

1 Experimental method

1.1 Sample information

Patient cohorts, demographics, and clinical criteria of example 1

All study protocols for colorectal cancer (CRC) patient validation cohort, demographic information, and clinical criteria have been approved by the complex university cancer hospital Institutional Review Board (IRB). Formaldehyde Fixed Paraffin Embedded (FFPE) tissue samples were obtained from colorectal cancer patients with outcome information, and these samples were classified as high risk and low risk populations. In addition, colorectal cancer patients with normal mismatch repair (MMR) function were also enrolled for study. FFPE primary cancer tissue was selected and characterized in the complex denier university cancer hospital for analysis. For each patient, 5 unstained 5 micron sections were taken. Patient cohorts, demographics, and clinical information are shown in table 1.

Table 1 patient cohort, demographic and clinical information list

1.2 Candidate marker selection

This example 1 uses meta-analysis of microarray data sets (GEO) to analyze differentially expressed genes related to colorectal cancer outcome and risk using training and validation machine learning algorithms. A total of six data sets were used, as listed in table 2 below.

Table 2 data set list

First, three datasets GSE18105, GSE26906, and GSE4526 were used to reduce the number of gene signatures from 19320 to 4548;

Using Cox regression and LASSO regularization methods, layering 4548 genes in combination with datasets GSE17536 and GSE 17537; LASSO regression was improved using bagging (b=100);

The stratified genes were then retrained using GSE17536, GSE17537, and GSE17538, identifying twenty-nine genes closely related to colorectal cancer recurrence risk and outcome, and validating twenty-two genes to determine their ability to be amplified by q-PCR in formaldehyde-fixed paraffin-embedded tissue (CRC FFPE), including six intestinal stem cell genes related to recurrence risk; then, twelve Oncotype Dx genes were also included as controls for the discovery panel, with a total of forty genes included in the qPCR validation panel. Forty genes were IRS2、TRIM25、MET、GSTM3、KRT6B、PKIB、VLDLR、ADAMTS5、ANAPC5、DPT、FAM111A、NADKD1、NAT1、WNT5A、FOXN3、CCL20、KLK6、DCBLD2、PKIG、DCTN5、SAMHD1、C5orf4、BGN、FAP、INHBA、Mki67、MYC、MYBL2、GADD45B、ATP5E、GPX1、PGK1、VDAC2、UBB、BCL2、FNML2、IGFBP4、SLCO3A1、CXCR4 and VEGFA.

Sample RNA extraction and qPCR detection

MRNA in formaldehyde-fixed paraffin embedded tissues is extracted, real-time fluorescent quantitative PCR detection is carried out, and colorectal cancer FFPE tissue sections with mismatch repair function are treated by dewaxing solution. Formaldehyde-fixed paraffin-embedded tissue was treated using Deparaffinization Solution (Qiagen, 19093) and rneasy_ffpe Kit (Qiagen, 73504) to retain mRNA useful for subsequent qPCR analysis. The mRNA was converted to cDNA using 40 gene specific primers. The cDNA was then pre-amplified for 15 cycles and the product diluted 1:5 for qPCR detection, analysis.

In some embodiments, the qPCR system is as follows table 3.

Table 3 qPCR list of System composition

QPCR amplification reaction procedure is shown in Table 4 below.

TABLE 4 qPCR amplification reaction procedure

1.4 Statistical analysis

All statistical analyses were performed using R software (version 4.1.2), including the Bioconductor package (version 3.5) and the R-Studio package (version 2022.02.0). All tests were double-sided log rank test; p value <0.05 and univariate risk ratio >2 are considered statistically significant.

Forty genes were paired with a reference gene and normalized using the Ct value differences between each up-regulated gene and down-regulated gene. The sum combination of all gene pairs is searched in detail to determine the best gene pair panel by test hypothesis: for phase II and phase III patient cohorts, the proportion of relapse free patients will be significantly higher in the low-expression differential group than in the high-expression differential group. The optimal cut-off point on the ROC curve was determined by comparing panel RS scores for relapsing and non-relapsing patients in phase II and phase III patient cohorts using the Youden index. Three years of recurrence in surgical-only patients were assessed by Kaplan-Meier estimation based on the high and low risk groups defined by the best cut-off panel score.

Results

2.1 Patient demographics and characteristics:

This example 1 focuses on 95 patients with colorectal cancer stage II and III with mismatch repair. Of these, 45 relapsed and 50 non-relapsed colorectal cancer patients. For stage II colorectal cancer, there were 20 relapsed and 25 non-relapsed patients. For stage III colorectal cancer, there were 25 relapsed and 25 non-relapsed patients. Demographic information and clinical characteristics of the patients are described in table 1 with no significant differences.

Differentially expressed genes associated with CRC risk assessment

Differentially expressed genes associated with CRC risk assessment were identified from microarray dataset analysis and formaldehyde fixed paraffin embedding validation panels, for a total of 29 genes identified from microarray dataset metadata analysis, which were ADAMTS5、ANAPC5、C5orf4、CCL20、CRNDE、DCBLD2、DNER、DPT、FAM111A、GSTM3、IRS2、KLK6、KRT6B、MET、NADKD1、NAT1、NMU、NOB1、PKIB、PKIG、PTPLA、TPBG、TRIM25、VLDLR、WNT5A、ZFYVE27、DCTN5、FOXN3 and SAMHD1.

These 29 genes were tested for their ability to be detected in colorectal cancer formaldehyde fixed paraffin embedded tissues, and 22 of these genes were found to have detectable levels and were included in the panel. In addition, 6 intestinal stem cell genes associated with colorectal cancer recurrence, and the Oncotype Dx gene included as a control in the panel, the final selected genome included IRS2、TRIM25、MET、GSTM3、KRT6B、PKIB、VLDLR、ADAMTS5、ANAPC5、DPT、FAM111A、NADKD1、NAT1、WNT5A、FOXN3、CCL20、KLK6、DCBLD2、PKIG、DCTN5、SAMHD1、C5orf4、BGN、FAP、INHBA、Mki67、MYC、MYBL2、GADD45B、ATP5E、GPX1、PGK1、VDAC2、UBB、BCL2、FNML2、IGFBP4、SLCO3A1、CXCR4 and VEGFA. Expression of these genes in each FFPE sample was evaluated individually, and all expression data are expressed as C (t) values expressed in FFPE tissue.

Further, biomarkers, such as IRS2, TRIM25, MYBL2, KRT6B, FMNL2, INHBA, MYC, NAT1, FAP, and the like, that are capable of predicting the prognostic clinical outcome of colorectal cancer are selected for accurate measurement and analysis.

And performing univariate analysis by adopting the relation between 2 internal reference genes and genes, calculating p value, AUC (area under curve) and risk ratio (HR) of delta Ct values of paired genes, and screening out gene pairs capable of distinguishing recurrence from non-recurrence conditions. The selection criteria are shown in table 5 below.

Table 5 single variable analysis selection criteria

Finally, 15 gene pairs were determined to meet the above criteria, see Table 6 below.

Table 6 list of results of analysis and selection of single variables

Based on the above 15 gene pairs, all possible combinations were calculated and the biomarker combinations with highest risk ratios were finally selected in the phase II and phase III patient cohorts, see table 7.

Table 7 combinations of biomarkers with highest risk ratio in stage II and stage III queues

2.3 Construction and validation of stage II and III colorectal cancer Risk assessment/recurrence combinations

Markers were paired according to gene expression differences, as biomarkers for predicting colorectal cancer risk, and an exhaustive search was performed to maximize risk prediction.

For stage II colorectal cancer, gene expression differential combinations based on 2.589 of the Youden index as reference value:

（IRS2-TRIM25）+（IRS2-MYBL2）+（KRT6B-FNML2）+（INHBA-MYC）

The CRC risk assessment formula described above can make the most effective predictions of the risk of recurrence of stage II colorectal cancer, as listed in table 8, with a predicted risk ratio (HR) of 2.5 for stage II colorectal cancer.

For stage III colorectal cancer, gene expression differential combinations based on Youden index, -0.1356 as reference value:

（IRS2-TRIM25）+（KRT6B-NAT1）+（FAP-MYC）+（INHBA-MYC）

the CRC risk assessment formula described above can make the most effective prediction of colorectal cancer recurrence risk, as listed in table 8, with a predictive risk ratio of 4.2 for stage III colorectal cancer recurrence.

If the Oncotype Dx combination of the prior art is used, the risk ratio (HR) of the two stages of stage II and stage III colorectal cancer, derived from qPCR data of example 1 of the present invention, is 1.6.

Table 8 mProbe recurrence combination Properties of Oncotype Dx

In Table 8, mProbe II phase gene combinations were IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, INHBA-MYC; the mProbeIII-stage gene combination is IRS2-TRIM25, KRT6B-NAT1, FAP-MYC, INHBA-MYC; the Oncotype Dx gene combination is FAP, GADD45B, MKi67, MYBL2 and MYC, BGN, INHBA; the reference genes are GPX1, PGK1, UBB and ATP5E, VDAC2.

FIGS. 1-3 are Kaplan-Meier estimates of three years of recurrence rate for individual surgical patients, and observe recurrence of colon cancer patients according to mProbe RS score classification risk groups, wherein FIG. 1 is full-cohort patients, FIG. 2 is phase II patients, and FIG. 3 is phase III patients;

FIGS. 4-6 are relapse free intervals of mProbe Relapse Score (RS) groups, wherein FIG. 4 is for all patients, FIG. 5 is for phase II patients, and FIG. 6 is for phase IIIA/B patients;

Fig. 7-9 are Kaplan-Meier estimates three-year recurrence rate for a patient risk group for individual surgery by using Oncotype Dx and a scoring system, where fig. 7 is a full cohort of patients, fig. 8 is a phase II patient, and fig. 9 is a phase III colon cancer patient.

Fig. 10 to 12 show the recurrence-free intervals according to the Oncotype Dx Recurrence Score (RS) group, wherein fig. 10 shows all patients, fig. 11 shows phase II patients, and fig. 12 shows phase IIIA/B patients.

Colorectal cancer patients were classified according to the combination of colorectal cancer genes at stage II and stage III and the RS scoring system, and the reference values for the stages in which the corresponding colorectal cancer patients were located were set to 2.589 and-0.1356. As shown in fig. 1 and 2, qPCR analysis based on the gene expression differences of the paired genes, followed by RS scoring system, can accurately predict and distinguish between three-year recurrent high-risk and low-risk populations of only surgical patients.

Parallel analysis was performed with Oncotype Dx combinations and formulas as controls, as shown in fig. 3 and 4. The results showed that the risk ratio (HR) was about 1.38, similar to that of Oncotype Dx, and the risk ratio observed in this example 1 was 1.6. The data show that the performance of the panel and RS model of colorectal cancer risk assessment and recurrence of this example 1 is far superior to the Oncotype Dx combination and model of the prior art.

The technical solutions disclosed in the embodiments of the present invention and the technical details disclosed in the embodiments of the present invention are only exemplary to illustrate the inventive concept of the present invention, and do not constitute a limitation on the technical solutions of the embodiments of the present invention, and all conventional changes, substitutions or combinations of the technical details disclosed in the embodiments of the present invention have the same inventive concept as the present invention, and are within the scope of the claims of the present invention.

Claims

1. A biomarker for predicting the prognostic clinical outcome of stage II and stage III colorectal cancer, characterized in that,

Biomarkers for relapse risk assessment in stage II colorectal cancer patients are combinations of gene pairs IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, and INHBA-MYC;

Biomarkers for relapse risk assessment in patients with stage III colorectal cancer are combinations of gene pairs IRS2-TRIM25, KRT6B-NAT1, FAP-MYC and INHBA-MYC.

2. The application of a primer group for PCR detection of the expression level of the biomarker of claim 1 in preparing a detection kit for predicting the clinical outcome of the prognosis of the colorectal cancer in stage II and stage III, which is characterized in that the primer group consists of an IRS2 primer group, a TRIM25 primer group, a KRT6B primer group, a MYBL2 primer group, a FMNL2 primer group, a NAT1 primer group, a FAP primer group, a MYC primer group and an INHBA primer group, and the sequences of the primer groups are respectively listed as SEQ ID No. 1-18.

3. The use according to claim 2, characterized in that:

the formula for the recurrence risk assessment of stage II colorectal cancer patients is:

（IRS2-TRIM25）+（IRS2-MYBL2）+（KRT6B-FMNL2）+（INHBA-MYC）；

if the score is above threshold 2.589, it indicates that the stage II colorectal cancer patient is at high risk of poor prognosis after initial treatment;

The formula for recurrence risk assessment for patients with stage III colorectal cancer is:

（IRS2-TRIM25）+（KRT6B-NAT1）+（FAP-MYC）+（INHBA-MYC）；

If the score is higher than the critical value-0.1356, the colorectal cancer recurrence risk of the patient suffering from the III-stage colorectal cancer is higher;

wherein IRS2-TRIM25, IRS2-MYBL2, KRT6B-FMNL2, INHBA-MYC, KRT6B-NAT1 and FAP-MYC represent the cycle threshold difference delta Ct of genes.