CN114300089A - Decision algorithm for treatment scheme of colorectal cancer at middle and late stages - Google Patents

Abstract

The invention discloses a decision algorithm for a treatment scheme of colorectal cancer at middle and late stages, which is used for carrying out individualized treatment prediction on a colorectal cancer patient and comprises the following steps: based on the gene expression profile of a patient, combined with clinical pathological staging and other biological characteristics of colorectal cancer, the colorectal cancer disease condition is sequentially evaluated by REO markers step by step, and a decision algorithm model of a treatment scheme of the colorectal cancer at middle and late stages is constructed by utilizing a REO marker set, so that the model is used for predicting the recurrence/metastasis risk of the patient at the middle and late stages, assisting chemotherapy benefit conditions (middle-stage patients) and drug resistance of immunotherapy and targeted therapy (late-stage patients), and an optimal individualized treatment suggestion is given, so that more accurate and effective individualized treatment guidance is provided for the colorectal cancer patient, and the model has good application prospect and practical significance.

Description

Decision algorithm for treatment scheme of colorectal cancer at middle and late stages

Technical Field

The invention relates to the field of cancer treatment, in particular to a decision algorithm for a treatment scheme of colorectal cancer at middle and late stages.

Background

According to data of recent cancer burden in 2020 world released by WHO international cancer institute, more than 193 million new cases of CRC (colorectal cancer) in 2020 world account for 9.7% of newly diagnosed cancers. In the same year, about 55 million new people in China suffer from colorectal cancer, accounting for 12.2 percent of the newly diagnosed cancer in the world, wherein the number of deaths of female colorectal cancer is second to that of lung cancer, and the colorectal cancer is the second leading cause of cancer deaths of women in China. In the past 30 years, the dietary structure and living habits of residents in China change, the incidence rate of colorectal cancer continuously rises and the colorectal cancer tends to be younger, the number of patients increases by 700 percent, and the number of the patients exceeds that of the countries in the United states, which are the most frequently encountered CRC in the world.

The process of occurrence and development of colorectal cancer is complex, and the mechanism is not clear, and the change of intestinal microbiota and genetic material in vivo is involved. The tumor patients have high heterogeneity, present different clinical courses and manifestations, have great difference in prognosis and medication benefits of the patients, and the full knowledge of the tumor heterogeneity and the cancer typing have great significance for the treatment of the patients. The CMS (consensus molecular subtype) typing method proposed in 2015 classified CRC into 4 subtypes, CMS1 type (MSI immunotype), CMS2 type (canonical), CMS3 type (metabolic type) and CMS4 type (mesenchymal type), respectively, based on gene expression information. The genetic molecular change and the phenotypic characteristics among the subtypes are obviously different, and the potential drug effect and the prognosis prediction value are displayed, but the genetic molecular change and the phenotypic characteristics are difficult to convert and apply to clinic so far, and personalized guidance is provided for the treatment of patients.

The most commonly used TNM staging system in clinical use today is based on primary tumor range, regional lymph node metastatic status and distant metastatic status, including stages I-IV. CRC patients can be roughly divided into early stage (I), middle stage (II-III) and late stage (IV) according to clinical pathology stages, and different treatment schemes are adopted respectively, for example, the middle stage patient recommends surgery to remove tumors, and the high risk patient needs auxiliary radiotherapy and chemotherapy; while patients with advanced stages, especially those with liver metastases, are not advised to undergo surgery. However, the heterogeneity of the tumor is not fully considered in the staging process, and the staging process has certain limitations.

Molecular markers also have important guidance for CRC treatment. As the value of microsatellite status detection is acknowledged, the MSI-H/dMMR (microsatellite high instability/mismatch repair function deficiency) type patients can be better than MSS/pMMR (microsatellite stability/mismatch repair function integrity) type patients in prognosis and efficacy as a prognostic and pharmacodynamic marker of intermediate CRC, and the MSI-H/dMMR type patients are difficult to benefit from adjuvant chemotherapy. With the development of immunotherapy, research shows that the microsatellite status has prediction efficacy on the curative effect of the advanced CRC immunotherapy, and the MSI-H/dMMR type patient has significantly higher immunotherapy response rate than the MSS/pMMR type patient, and can benefit from the MSS-H/dMMR type patient. In addition, a large number of gene/pathway changes are proved to be involved in CRC occurrence and progression, such as KRAS and BRAF V600E gene mutations, which result in higher tumor malignancy, patient prognosis is obviously worse than wild type, and the patient is resistant to EGFR inhibitors such as cetuximab and bevacizumab. However, epidemiological studies have shown that about 50% of patients with wild-type KRAS and BRAF genes still do not benefit from EGFR inhibitors. In addition, the detection sensitivity of the biomarkers is not high enough, misdiagnosis and missed diagnosis often occur, and the biomarkers are easily influenced by the quality of samples and batch effects. When the same sample is detected, the PCR low-flux detection has higher variation coefficient, and the detection result is difficult to repeat; the immunohistochemical detection has subjectivity, is greatly influenced by factors such as sample quality, a detection process and the like, and has a certain error rate in a detection result.

Various laboratories have developed Panel based on polygene expression to predict prognosis and efficacy in patients with CRC. For example, in the NCCN and CSCO clinical practice guidelines, post-operative risk assessment of recurrence is recommended for mid-term CRC patients using markers such as 7-recurrence gene markers, ColoPrint, ColDx, and the like, to guide post-operative adjuvant chemotherapy, the effectiveness of which has been demonstrated in multiple independent data sets. The markers all use quantitative gene expression information, and the accurate quantitative information is influenced by various abiotic factors and has no stability and repeatability. In clinical sampling, different clinicians have different cancer sampling positions, the proportion of cancer cells in different sampling positions is greatly different, and poor quality biopsy may not even obtain cancer tissues. During the transportation, storage and treatment of the sample, RNA in the sample is easily degraded, so that the content is changed. In the high-throughput detection of trace samples, such as biopsy samples, CTC and single cell samples, amplification is needed to enable the RNA in the samples to reach a sufficient amount, and bias is easily generated when nucleic acid substances are integrally amplified, so that the accurate detection value is unreliable.

Disclosure of Invention

In view of the above drawbacks in the background art, the present invention provides a decision algorithm for a colorectal cancer treatment plan in a middle and late stage, which is used for evaluating prognosis and drug effect of a colorectal cancer patient and improving efficiency of colorectal cancer treatment.

In order to achieve the purpose, the invention adopts the following technical scheme:

a decision-making algorithm for a treatment regimen for mid-late colorectal cancer, the method comprising: based on the gene expression profile of a patient, combined with the pathological stage and molecular characteristics of colorectal cancer, the colorectal cancer disease is sequentially evaluated step by step, and a decision algorithm model of a colorectal cancer treatment scheme at middle and late stages is constructed by utilizing a REO marker set, wherein the algorithm specifically comprises the following treatment decisions:

1. a decision-making algorithm for a treatment regimen for mid-late colorectal cancer, the method comprising: based on a gene expression profile of a patient, combined with different characteristics of colorectal cancer clinical pathology stages and stages, sequentially performing stepwise REO marker evaluation on colorectal cancer diseases, and constructing a decision algorithm model of a colorectal cancer treatment scheme at middle and late stages by utilizing a REO marker set, wherein the algorithm specifically comprises the following treatment decisions:

decision 1) assessing whether the mid-term patient is at risk of postoperative recurrence;

decision 2) whether a patient at high risk of relapse of said decision 1) would benefit from postoperative adjuvant therapy in combination with primary focus location;

decision 3) combines the decision 1) and the decision 2) to comprehensively analyze the illness state of the middle-stage patient and give the illness state, treatment suggestion and prognosis result of the middle-stage patient;

decision 4) detecting the activation state of the RAS signal channel by combining the REO mark according to the KRAS and BRAF site mutation conditions of the late-stage patient, namely evaluating whether the target treatment medicament cetuximab can be benefited or not;

decision 5) use the REO marker to test whether the late-stage patient would benefit from the targeted therapeutic bevacizumab;

decision 6) detecting the microsatellite instability state of the patient in combination with the primary focus position;

decision 7) in combination with decision 4), decision 5), and decision 6) to comprehensively analyze the disease state of the late-stage patient, and obtain treatment decision and prognosis results.

Wherein, each REO mark is composed of a plurality of gene pairs, and the evaluation is respectively carried out on the primary focus position of the colorectal cancer, the postoperative recurrence risk, the postoperative auxiliary treatment benefit, the curative effect of targeted treatment medicines of cetuximab and bevacizumab and the MSI state detection.

Wherein, when the late-stage patient clinically detects that neither the KRAS nor the BRAF gene has mutation in decision 4), the patient target therapeutic drug cetuximab is detected according to the REO marker to benefit, otherwise, the patient is directly judged not to benefit from the target therapeutic drug cetuximab; and if the clinical detection reports that the KRAS or BRAF gene is mutated, evaluating the drug resistance of the target treatment drug cetuximab of the patient.

Further, the algorithm specifically comprises the following steps:

step 1) combining pathological stages and molecular characteristics of colorectal cancer, and storing events and treatment schemes which may occur under different clinical pathological stages;

step 2) combining events and countermeasures to construct a disease branch decision tree;

step 3) combining the disease branch decision tree in the step 2) to construct an REO mark set;

and 4) applying the colorectal cancer REO mark in the step 3) to each decision point of the decision tree to construct a decision algorithm model of a colorectal cancer treatment scheme in middle and late stages.

Compared with the prior art, the invention has the following beneficial effects:

the REO marker in the invention uses the relative magnitude relation of two gene expression values in the same patient gene expression profile, is a qualitative marker, is insensitive to a data standardization mode, can avoid the limitation of the quantitative marker on sample quality and a detection platform, and has stability in clinical application. The REO marker of the invention has stable prediction efficiency in data generated by gene chip or RNA-seq technology and different detection platforms, and multiple independent data sets have verification results in line with expectations. Different types of patients are adopted according to different evaluation indexes during the construction of the REO marker, so that the medicine is prevented from being mixed, for example, the prognosis state of a patient is evaluated, and only the patient who does not receive postoperative adjuvant therapy is used for marker training; when the postoperative adjuvant chemotherapy drug effect of the middle-stage patient is evaluated, only the patient with high recurrence risk is used for training, and the evaluation result has more clinical value.

In clinical practice, the method can realize one-time sampling detection, evaluate a plurality of indexes related to the colorectal cancer, organically combine REO marks related to key decision points, construct a treatment branch decision tree by taking an evaluation result as a node, and provide an optimal personalized treatment scheme for a patient.

The invention provides a decision algorithm for a treatment scheme of colorectal cancer at a middle and late stage, which has the advantages of good algorithm performance, strong evaluation capability and wide range of relation, and can be used for assisting the individualized evaluation of the prognosis and the medicine benefit condition of a colorectal cancer patient.

Drawings

The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.

FIG. 1 is a flow chart of a decision-making algorithm for a treatment regimen for mid-late colorectal cancer in accordance with the present invention;

FIG. 2 is a schematic diagram of a decision-making algorithm for a treatment regimen for mid-late colorectal cancer in accordance with the present invention.

Detailed Description

In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.

In the description of the present application, it is to be noted that the REO marker combination is a single-use marker, and the REO marker combination is the whole of REO markers for late stage treatment decision in colorectal cancer of the present patent.

The invention adopts the following technical scheme, which specifically comprises the following 3 aspects:

in a first aspect, the present invention provides a decision-making algorithm for a treatment plan for colorectal cancer in middle and advanced stages, wherein the method comprises: based on a gene expression profile of a patient, combined with different characteristics of colorectal cancer clinical pathology stages and stages, sequentially performing stepwise REO marker evaluation on colorectal cancer diseases, and constructing a decision algorithm model of a colorectal cancer treatment scheme at middle and late stages by utilizing a REO marker set, wherein the algorithm specifically comprises the following treatment decisions:

decision 1) assessing whether the mid-term patient is at risk of postoperative recurrence;

decision 2) whether a patient at high risk of relapse of said decision 1) would benefit from postoperative adjuvant therapy in combination with primary focus location;

decision 3) combines the decision 1) and the decision 2) to comprehensively analyze the illness state of the middle-stage patient and give the illness state, treatment suggestion and prognosis result of the middle-stage patient;

decision 4) detecting the activation state of the RAS signal channel by combining the REO mark according to the KRAS and BRAF site mutation conditions of the late-stage patient, namely evaluating whether the target treatment medicament cetuximab can be benefited or not;

decision 5) use the REO marker to test whether the late-stage patient would benefit from the targeted therapeutic bevacizumab;

decision 6) detecting the microsatellite instability state of the patient in combination with the primary focus position;

decision 7) in combination with decision 4), decision 5), and decision 6) to comprehensively analyze the disease state of the late-stage patient, and obtain treatment decision and prognosis results.

Wherein, each REO mark is composed of a plurality of gene pairs, and the evaluation is respectively carried out on the primary focus position of the colorectal cancer, the postoperative recurrence risk, the postoperative auxiliary treatment benefit, the curative effect of targeted treatment medicines of cetuximab and bevacizumab and the MSI state detection.

Wherein, when the late-stage patient clinically detects that neither the KRAS nor the BRAF gene has mutation in decision 4), the patient target therapeutic drug cetuximab is detected according to the REO marker to benefit, otherwise, the patient is directly judged not to benefit from the target therapeutic drug cetuximab; and if the clinical detection reports that the KRAS or BRAF gene is mutated, evaluating the drug resistance of the target treatment drug cetuximab of the patient.

In a second aspect, the present invention provides a set of colorectal cancer REO markers according to the first aspect, comprising the following 9 colorectal cancer REO marker combinations:

marker 1) a postoperative recurrence risk prediction marker for assessing recurrence risk of a patient in the mid-stage of colorectal cancer;

marker 2) postoperative adjuvant chemotherapy efficacy marker for assessing whether patients in mid-stage colorectal cancer would benefit from postoperative adjuvant chemotherapy;

marker 3) RAS pathway-KRAS mutation status marker for evaluating whether patients with advanced colorectal cancer can benefit from target treatment drug cetuximab;

marker 4) RAS pathway-NRAS mutation status marker for assessing whether patients with advanced colorectal cancer would benefit from the targeted therapeutic agent cetuximab;

marker 5) RAS pathway-BRAF mutation status marker for assessing whether patients with advanced colorectal cancer would benefit from the targeted therapeutic agent cetuximab;

marker 6) RAS pathway-PIK 3CA mutation status marker for assessing whether patients with advanced colorectal cancer would benefit from the targeted therapeutic agent cetuximab;

marker 7) bevacizumab pharmacodynamic marker for assessing whether patients with advanced colorectal cancer can benefit from the targeted therapeutic drug bevacizumab;

marker 8) microsatellite status markers for assessing whether a colorectal cancer patient will benefit from immunotherapy;

marker 9) microsatellite status markers for assessing whether a colorectal cancer patient will benefit from immunotherapy.

The REO marker is a significant result when the number of gene pairs of gene1> gene2 is greater than or equal to a specific threshold value by comparing the magnitude relationship of gene expression values in a sample in a patient gene expression profile.

Wherein, marker 3), marker 4), marker 5), marker 6) are combination markers, and when KRAS, NRAS, BRAF, PIK3CA are all predicted to be wild-type, the patient is assessed to benefit from the targeted drug cetuximab.

It is worth noting that the REO algorithm uses a stable qualitative value extracted from an accurate quantitative value instead of an accurate measurement value, and has high consistency in different detections, so that detection technology differences and experimental batches can be effectively avoided, and cross-platform application is realized; the REO algorithm has lower requirements on clinical cancer sampling quality than an accurate quantitative algorithm, can be applied to different types of samples, extracts reliable qualitative information, is insensitive to cancer cell proportion and RNA partial degradation, and is suitable for micro samples; the REO algorithm is insensitive to a data standardization mode, can avoid the limitation of a quantitative marker by sample quality and a detection platform, and has stability in clinical application.

Furthermore, the REO mark constructed based on the REO algorithm can realize decision guidance for one-time detection and multi-aspect detection, and can provide an individualized scheme for patients.

In a third aspect, the algorithm of the first aspect comprises:

step 1) combining pathological stages and molecular characteristics of colorectal cancer, and storing events and treatment schemes which may occur under different clinical pathological stages;

step 2) combining events and countermeasures to construct a disease branch decision tree;

step 3) combining the disease branch decision tree in the step 2) to construct an REO mark set;

and 4) mapping the REO markers of the colorectal cancer in the step 3) to each branch decision point of the decision tree in sequence to obtain a REO marker-based decision algorithm for the colorectal cancer treatment scheme of middle and late stages.

Further, the specific steps of the step 1) are as follows:

firstly, the Chinese specialist consensus on colorectal cancer REO marker clinical detection in 2021 is consulted in detail, events and countermeasures which may occur under different pathological stages are collected by combining colorectal cancer related clinical data and colorectal cancer related field documents, common problems of the colorectal cancer at present are analyzed, and a middle and late stage treatment branch decision tree is constructed according to pathological typing, primary focus positions, molecular typing, recurrence risks and treatment schemes (fig. 1).

Further, the specific steps of step 2) are as follows:

as shown in fig. 1, a branch decision tree for treating colorectal cancer at middle and late stages is constructed, which specifically comprises the following steps:

step 2.1) setting input information of the molecular decision tree, wherein the input information comprises clinical pathology grades (middle stage and late stage) of a sample to be detected, the position of a primary tumor primary focus and the like;

step 2.2) inquiring the colorectal cancer related literature data, and combining the input information in the step 2.1) to construct a decision condition of a branch decision tree;

step 2.3) since patients with mid-stage colorectal cancer often have recurrence risk after surgical treatment, adjuvant chemotherapy may be beneficial for prognosis, so the decision conditions "whether there is recurrence risk" and "whether there can be benefit from adjuvant chemotherapy" are set according to the decision conditions of step 2.2); patients with advanced colorectal cancer usually use a targeted therapy or immunotherapy regimen, and then the decision conditions "whether to benefit from targeted therapy", "whether to benefit from immunotherapy" are set according to the decision conditions of step 2.2), and the decision result (treatment recommendation) of the termination node is obtained by combining colorectal cancer related knowledge;

step 2.4) integrating the branch decision tree for treating the middle and late colorectal cancer by using mapping software.

Further, the specific content of the step 3) is as follows:

based on the research in recent years in the laboratory, REO marker sets at different stages of colorectal cancer are extracted and arranged as shown in tables 1-9; where the rows represent a set of paired gene pairs, each table being the contents of one REO marker. The REO flag is used as explained below:

as shown in table 1, 44-GPS is used to predict recurrence risk after mid-stage colorectal cancer surgery, and when the number of gene expression values "gene 1> gene 2" in the sample to be tested is greater than or equal to 24, the patient is judged to be at high recurrence risk, otherwise, the patient is at low recurrence risk;

as shown in table 2, 4-GPS is used to evaluate the post-operative adjuvant chemotherapy benefit condition of mid-term colorectal cancer patients, when the sample to be tested is evaluated as high recurrence risk by 44-GPS and the number of gene expression values "gene 1> gene 2" is greater than or equal to 2, the sample is judged as being prone to benefit from adjuvant chemotherapy, otherwise, the sample is resistant to adjuvant chemotherapy;

as shown in tables 3 and 4, the 6-GPS and the 10-GPS are respectively used for predicting the left-side (far-end) CRC and right-side (near-end) CRC microsatellite status, and when the number of gene expression values "gene 1> gene 2" in the sample to be tested is respectively greater than or equal to 4 (left side) or greater than or equal to 7 (right side), the patient is judged to be the MSI type, which can benefit from immunochemistry, otherwise, the patient is the MSS type, which is difficult to benefit from;

as shown in tables 5, 6, 7 and 8, the REO marker combination is used to evaluate RAS pathway activation status in patients with advanced colorectal cancer, and comprises 4 sub-classifiers for evaluating KRAS, NRAS, BRAF and PIK3CA gene mutation status in pathways respectively. When the number of the genes 1 and 2 in the 48-GPS in the detection sample is more than or equal to 25, judging that the patient is KRAS mutant, or else, judging that the patient is KRAS wild; the number of the gene1 gene2 in the 121-GPS is more than or equal to 61, the NRAS mutant type is judged, and otherwise, the NRAS wild type is judged; 79-the number of the gene1 and the gene2 in the GPS is more than or equal to 40, the BRAF mutant type is judged, otherwise, the BRAF wild type is judged; the number of the gene1 and the gene2 in 285-GPS is more than or equal to 143, and the PIK3CA mutant is judged, otherwise, the wild type is judged. If any one of the sub-markers is evaluated as a mutant type, the patient is evaluated to be an RAS pathway activated type and resistant to a target drug cetuximab; if the sub-markers are all evaluated as wild type, the patient is RAS pathway normal type, and cetuximab is recommended;

as shown in table 9, 64-GPS is used to evaluate the metastasis risk of patients with advanced colorectal cancer and the efficacy of bevacizumab as a target drug, and when the number of gene expression values "gene 1> gene 2" in a sample to be tested is 43 or more, the patients are evaluated to be at high metastasis risk and can benefit from bevacizumab, otherwise, the patients are at low recurrence risk and are difficult to benefit from bevacizumab.

TABLE 1 prediction of recurrence risk after mid-term surgery

TABLE 2 MEDICAL-term postoperative adjuvant chemotherapy drug efficacy markers

TABLE 3 microsatellite status markers (tumor location: left side)

TABLE 4 microsatellite status markers (tumor location: right side)

Table 5 late colorectal cancer RAS pathway mutation/cetuximab potency-KRAS mutant signature;

TABLE 6 RAS pathway mutation/cetuximab pharmacodynamic-NRAS mutator signature for advanced colorectal cancer

TABLE 7 RAS pathway mutation/cetuximab pharmacodynamic-BRAF mutant signature for advanced colorectal cancer

TABLE 8 RAS pathway mutation/cetuximab pharmacodynamic-P I K3CA mutant signature for advanced colorectal cancer

TABLE 9 late colorectal cancer metastasis Risk/Bevacizumab pharmacodynamic marker

The algorithm of the invention combines the clinical pathological characteristics of colorectal cancer, uses REO marks to carry out prognosis evaluation and drug effect prediction on patients, refines disease characteristics and provides targeted treatment schemes for the patients.

In summary, the application obtains the pathological stage and molecular characteristic information of colorectal cancer from the literature, including the events and strategies which may occur in the middle and late stages of colorectal cancer, and then constructs a disease branch decision tree. And then arranging an REO mark set, mapping REO marks to each decision point of a decision tree according to the mark function and the using mode, perfecting the treatment process, and constructing a decision algorithm of a colorectal cancer treatment scheme at the middle and late stages, wherein the algorithm is used for evaluating the postoperative recurrence risk of colorectal cancer, the postoperative adjuvant therapy benefit, the curative effect of targeted therapy medicines cetuximab/bevacizumab, the microsatellite state and the like respectively to obtain an individualized accurate treatment decision.

The invention is not the best known technology.

So far, the technical solutions of the present invention have been described with reference to the preferred embodiments shown in the drawings, and it is apparent that the scope of the present invention is not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims (2)

1. A decision-making algorithm for a treatment regimen for advanced colorectal cancer, the method comprising: based on the gene expression profile of a patient, combined with clinical pathology stage and other biological characteristics of colorectal cancer, sequentially performing stepwise REO marker evaluation on the colorectal cancer disease, and constructing a decision algorithm model of a treatment scheme of the colorectal cancer at middle and late stages by utilizing a REO marker set, wherein the algorithm specifically comprises the following treatment decisions:

decision 1) predicting whether the mid-term patient has postoperative recurrence risk;

decision 2) whether a patient at high risk of relapse of said decision 1) would benefit from postoperative adjuvant therapy in combination with primary focus location;

decision 3) combines the decision 1) and the decision 2) to comprehensively analyze the illness state of the middle-stage patient and give the illness state, treatment suggestion and prognosis result of the middle-stage patient;

decision 4) detecting the activation state of the RAS signal channel by combining with the REO mark according to the KRAS and BRAF site mutation conditions of the late-stage patient, namely evaluating whether the patient can benefit from the target treatment medicament cetuximab;

decision 5) use the REO marker to assess whether the late stage patient would benefit from the targeted therapeutic bevacizumab;

decision 6) detecting the microsatellite status of the patient in combination with the position of the primary focus;

decision 7) is combined with decision 4), decision 5) and decision 6) to comprehensively analyze the disease state of the late-stage patient to obtain treatment decision and prognosis evaluation;

wherein each REO mark is composed of a plurality of gene pairs and is evaluated in the aspects of primary focus position of colorectal cancer, postoperative recurrence risk, postoperative auxiliary treatment benefit, curative effect of targeted treatment medicines of cetuximab and bevacizumab and MSI state detection;

wherein, when the late-stage patient clinically detects that neither the KRAS nor the BRAF gene has mutation in decision 4), the patient target therapeutic drug cetuximab is detected according to the REO marker to benefit, otherwise, the patient is directly judged not to benefit from the target therapeutic drug cetuximab; and if the clinical detection reports that the KRAS or BRAF gene is mutated, evaluating the drug resistance of the target treatment drug cetuximab of the patient.

2. The decision algorithm for a treatment plan for colorectal cancer in middle and advanced stages according to claim 1, wherein the algorithm comprises the following steps:

step 1) combining pathological stages and molecular characteristics of colorectal cancer, and storing events and treatment schemes which may occur under different clinical pathological stages;

step 2) combining events and countermeasures to construct a disease branch decision tree;

step 3) combining the disease branch decision tree in the step 2) to construct an REO mark set;

and 4) applying the colorectal cancer REO mark in the step 3) to each decision point of the decision tree to construct a decision algorithm model of a colorectal cancer treatment scheme in middle and late stages.

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