CN117253619A - Noninvasive model for predicting prognosis of advanced colorectal cancer patient in immune combination therapy and application - Google Patents
Noninvasive model for predicting prognosis of advanced colorectal cancer patient in immune combination therapy and application Download PDFInfo
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Abstract
The invention discloses a noninvasive model for predicting the prognosis of advanced colorectal cancer patients in immune combination treatment and application thereof. The invention combines the age, the number of treatment lines, a baseline ctDNA (T1), a ctDNA change level delta ctDNA (T2-T1) after 1 month of treatment, a ctDNA change level delta ctDNA (T3-T1) after 2 months of treatment and a baseline proteomic score to construct a multifactor Cox regression model, wherein the model is used for predicting the prognosis of the immune combined treatment of the advanced colorectal cancer patient, the ctDNA and the immune proteomic required specimen of the model are peripheral blood, and the tumor load and the body immune system condition of the patient can be accurately analyzed only by 5ml of peripheral blood each time; the AUC of the model is as high as 0.96, which is far superior to the existing predictive factor; the index included in the model is mostly a baseline index, and only one item of data is included at the time of 2 months of treatment, so that early prediction can be realized.
Description
Technical Field
The invention relates to a noninvasive model for predicting the prognosis of advanced colorectal cancer patient immune combination therapy and application thereof, belonging to the technical field of biomedical detection.
Background
The incidence rate and the death rate of colorectal cancer in China are high, and the national health is seriously endangered. Standard treatment of advanced patients is dominated by targeting and chemotherapy. Immune treatment mainly with immune checkpoint inhibitors is excellent in various tumors, innovating existing tumor treatment, but is hardly effective in microsatellite stabilized (MSS, about 95% of advanced colorectal cancers) colorectal cancers. Logically, to promote therapeutic effects, immunotherapy should be combined with targeting or chemotherapy. In view of this, applicants initiated a phase II clinical study: the combination regimen of rituximab (one PD-1 mAb) +cetuximab+irinotecan (tisliclizumab+egfr-mab+cpt-11, tec study, NCT 05143099) was taken into 33 post-line MSS mCRC patients. The results show that ORR is raised to 33.3%, median PFS is up to 7.3 months (95% CI:5.6-8.6 months), median survival is up to 17.4 months (2022 ASCO Dahua wall report #3566, 2023ASCO-GI Dahua wall report # 125), far better than current standard treatment, further confirming the value of immune combination therapy in mCRC patient treatment.
Although the above immunization combination regimen ORR is as high as 33.3%, there are still 2/3 patients who cannot benefit or benefit little from it. How to predict and evaluate the treatment benefit of patients in early stage, so that patients who can benefit for a long time can continue to carry out the treatment of the immune combination scheme, and the early initiation of other drug treatments of patients who cannot benefit is particularly important to prolong the survival period of the patients.
Currently, there is a clinical lack of biomarkers for predicting the efficacy of immunotherapy, especially for patients with MSS colorectal cancer. Indicators like Tumor Mutational Burden (TMB), expression of PD-L1, which may have predictive significance in other cancer species, are essentially ineffective in colorectal cancer and detection of these indicators requires tissue biopsy. The evaluation means is based on imaging. But 1) imaging assessment typically does not predict early whether a patient will benefit from a treatment regimen after 2 months of treatment. 2) In the immunotherapeutic era, there may be false progression (i.e. imaging suggests tumor growth, which is conventionally thought to be the case. But may be caused by massive immune cell infiltration, the patient is actually therapeutically effective), CT does not reflect the true therapeutic situation. 3) Cancer cell cloning is in a dynamic evolutionary process, with many different genetic mutations at different times. For example, RAS/RAF status (RAS/RAF wild type patients benefit from cetuximab, RAS/RAF mutant patients should select bevacizumab) that is critical in intestinal cancer and clearly related to targeted drug selection, and the wild type patients become mutant during treatment. However, CT cannot detect these mutations. In clinical practice, histological biopsies are currently mainly relied on to determine the current genetic status of patients. However, histological biopsies suffer from the following drawbacks: 1) The wound, such as the conventional liver metastasis puncture and lung (metastasis) puncture, may have the risks of bleeding, pneumothorax and the like, so that the clinical application of tissue biopsy is greatly limited; 2) The tissue obtained by single-point puncture is very limited, and the tumor has larger heterogeneity and cannot reflect the overall appearance of the tumor; 3) The puncture basically needs hospitalization, and the medical cost is high.
In view of this, it is particularly critical how to noninvasively, early and accurately predict the immune combination therapy effect of patients with MSS colorectal cancer and timely track the mutation state of tumor molecules of the patients.
Liquid biopsies have great potential.
Circulating tumor cell DNA (Circulating tumor DNA, ctDNA) refers to DNA of tumor cells detected in the peripheral blood of a patient, which can reflect the systemic burden of a tumor to some extent. In advanced tumors, some studies found that ctDNA may be associated with prognosis, with much lower ctDNA than less people after treatment, with better efficacy and better prognosis. Thus, ctDNA may evaluate tumor burden more accurately and early than CT.
The Olink proteomics technology is a high-flux, high-specificity, high-sensitivity and high-dynamic-range targeted proteome quantitative technology, and can detect nearly hundred proteins from 1 mu L of peripheral blood samples. The technology utilizes DNA ortho-extension analysis technology (Proximity Extension Assay, PEA), a pair of antibodies are designed for each protein to be detected, specific DNA single chains are coupled on the antibodies, when the antibodies are combined with target proteins, two DNA single chains in ortho-position can be complementarily combined and are extended by enzyme to form a double-chain DNA template, the proteins are converted into DNA quantification ingeniously, and finally quantitative detection is carried out by utilizing microfluidic qPCR or NGS sequencing. The Olink platform can be applied to the field of autoimmune diseases comparatively more at present, but the value exploration in the tumor field is less.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: how to noninvasive, early and accurate predictive curative effect of the immune combination therapy of colorectal cancer patients.
In order to solve the technical problems, the invention adopts the following technical scheme:
in a first aspect, the invention provides a method for constructing an immune combination therapy prognosis model for a patient with advanced colorectal cancer in a noninvasive manner, which comprises the following steps:
obtaining results and clinical pathological characteristics of liquid biopsies of different treatment nodes of a sample, analyzing the correlation between the results and prognosis, and obtaining potential influence factors; the treatment node comprises before treatment, after treatment and progression; the results of the liquid biopsy comprise ctDNA indexes obtained by sequencing analysis and peripheral blood immune proteomics scores;
analyzing the correlation of each potential influencing factor and prognosis by using a single factor Cox regression analysis method;
then, the index with statistical significance (P < 0.1) in the single-factor Cox regression analysis is included in the multi-factor Cox regression model, and finally, the variables for evaluating whether the advanced colorectal cancer patient receives the immune combination treatment benefit or not are established by age, treatment line number, baseline ctDNA level (T1) (which is expressed by VAF), ctDNA change level delta ctDNA (T2-T1) after 1 month of treatment, ctDNA change level delta ctDNA (T3-T1) after 2 months of treatment and baseline proteomic score;
and constructing a multi-factor regression analysis model by utilizing a coxph function in R software according to the determined variables, namely, completing the construction of a prognosis model.
Preferably, the potential influencing factors include age, number of treatment lines and results of liquid biopsies of different treatment nodes.
Preferably, the immune combination therapy refers to a combination therapy regimen of rituximab (one PD-1 mab) +cetuximab+irinotecan;
and/or, the colorectal cancer patient refers to a microsatellite stabilized (MSS-type) metastatic colorectal cancer patient.
In a second aspect of the invention, the application of the prognosis model constructed by the construction method in preparing a product for noninvasively predicting prognosis of advanced colorectal cancer patients in immune combination therapy is provided.
Preferably, the product is an in vitro diagnostic product.
Preferably, the in vitro diagnostic product comprises a diagnostic kit, a diagnostic system or a device.
In a third aspect of the invention, there is provided a kit for noninvasively predicting prognosis of an immune combination therapy of a patient with advanced colorectal cancer, the kit comprising reagents for detecting ctDNA levels and baseline proteomic levels in a sample, the kit further comprising a carrier describing a prognostic model; the prognosis model is constructed by the construction method of the first aspect of the invention.
Preferably, the test sample is peripheral blood of a patient.
Preferably, the reagents include total RNA extraction reagents, reverse transcription reagents, and/or second generation sequencing reagents.
In a fourth aspect of the invention, there is provided a diagnostic system or apparatus for noninvasively predicting prognosis of immune-coupled therapy in patients with advanced colorectal cancer, the system or apparatus comprising:
the data acquisition module is used for acquiring the joint index data of the to-be-tested subject and comprises the following components: age, number of treatment lines, baseline ctDNA (T1), level of change in ctDNA Δctdna after 1 month of treatment (T2-T1), level of change in ctDNA Δctdna after 2 months of treatment (T3-T1), and baseline proteomic score;
the scoring module is used for inputting the combined index data obtained by the data obtaining module into a pre-constructed prognosis model and calculating a risk score riskscore of a subject to be tested, which does not benefit from an immune combined treatment scheme; the prognosis model is constructed by the construction method of the first aspect of the invention;
and the judging module is used for comparing the risk score riskscore obtained by the scoring module with the median value of the risk score of 0.39 so as to judge whether the to-be-tested subject receives the immune combination treatment scheme to benefit or not.
Preferably, the scoring module calculates a risk score riskscore for the subject under test that does not benefit from the immune combination therapy regimen using a prediction function in the R software.
Preferably, the system or apparatus further comprises a model building module for:
obtaining results and clinical pathological characteristics of liquid biopsies of different treatment nodes of a sample, analyzing the correlation between the results and prognosis, and obtaining potential influence factors; the treatment node comprises before treatment, after treatment and progression; the results of the liquid biopsy comprise ctDNA indexes obtained by sequencing analysis and peripheral blood immune proteomics scores;
analyzing the correlation of ctDNA index, peripheral blood immune proteomics score and key clinical index with prognosis by using a single factor Cox regression analysis method;
then, the index with statistical significance (P < 0.1) in the single-factor Cox regression analysis is included in the multi-factor Cox regression model, and finally, the variables for evaluating whether the advanced colorectal cancer patient receives the immune combination treatment benefit or not are established by age, treatment line number, baseline ctDNA level (T1) (which is expressed by VAF), ctDNA change level delta ctDNA (T2-T1) after 1 month of treatment, ctDNA change level delta ctDNA (T3-T1) after 2 months of treatment and baseline proteomic score;
and constructing a multi-factor regression analysis model by utilizing a coxph function in R software according to the determined variables, namely, completing the construction of a prognosis model.
In a fifth aspect of the present invention, there is provided a computer device including a memory and a processor, the memory storing a program, the processor implementing the following method when executing the program:
acquiring joint index data of a subject to be tested, comprising: age, number of treatment lines, baseline ctDNA (T1), level of change in ctDNA Δctdna after 1 month of treatment (T2-T1), level of change in ctDNA Δctdna after 2 months of treatment (T3-T1), and baseline proteomic score;
inputting the combined index data as input data into a pre-constructed prognosis model, and calculating a risk score riskscore of a subject to be tested, which does not benefit from an immune combined treatment scheme; the prognosis model is constructed by the construction method of the first aspect of the invention;
and comparing the calculated risk score riskscore with the median value of the risk score of 0.39, so as to judge whether the tested subject receives the immune combination treatment scheme to benefit or not.
In a sixth aspect of the present invention, there is provided a computer-readable storage medium including a stored computer program;
wherein the computer readable storage medium is controlled to implement the method according to the fourth aspect of the present invention when the computer program is run.
Compared with the prior art, the invention has the following beneficial effects:
(1) Noninvasive: the samples required by ctDNA and immune proteomics in the model are peripheral blood, and only 5ml of peripheral blood can accurately analyze the tumor load and the body immune system condition of a patient each time; at present, clinical dependent histological biopsies such as liver (metastasis) puncture, lung (metastasis) puncture and the like have the risks of bleeding, pneumothorax and the like, have high operation requirements, have poor patient acceptance, need hospitalization and have high medical cost;
(2) The accuracy is that: AUC of the model is as high as 0.96, and the current conventional prediction indexes such as TMB, PD-L1 and the like are basically between 0.6 and 0.7; the model is far superior to the existing prediction factors;
(3) Early stage: the index included in the model is mostly a baseline index, and only one item of data is included in the treatment for 2 months, namely the final result of the immune combination treatment of colorectal cancer patients can be well predicted in the treatment for 2 months.
Drawings
Fig. 1: (a) The VAF of treatment-nonresponsive (PD) patients was significantly higher than the other patients (SD/PR) at the different assessment nodes for the patients, with significantly lower VAF after treatment and higher VAF at progression; (b) Overall prognosis for high VAF patients is significantly worse than for low VAF patients;
fig. 2: (a) A change in VAF at the first evaluation node after treatment of the patient is positively correlated with a change in the size of the tumor in the patient, (b) a patient with more than 50% reduction in VAF at the first evaluation node after treatment of the patient has better overall survival;
fig. 3: olink evaluates the overall survival curve of patients with high and low peripheral immunohistochemical scores;
fig. 4: forest plots of a multifactor Cox regression analysis model incorporating clinical indicators, ctDNA and peripheral immune protein scores;
fig. 5: (a) providing model evaluation results with different patient OS total survival curves; (b) A model evaluation result is a PFS progression-free survival curve of different patients;
fig. 6: the model and a single index evaluate the area under the ROC curve at 12 months of the queue;
fig. 7: bootstrap method (a) and Leave-one-out (b) results of cross-validation of the model.
Detailed Description
In order to make the invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
The test methods used in the following examples are conventional methods unless otherwise specified; the materials, reagents and the like used, unless otherwise specified, are those commercially available.
Example 1: construction of a model
The correlation with prognosis was analyzed by analyzing the results of liquid biopsies (ctDNA results from second generation sequencing analysis and immune tumor proteomics in plasma from Olink platform analysis) of 33 total cases of advanced colorectal cancer patients of MSS type in a total of 33 different treatment nodes (pre-treatment, in-treatment, post-progression) from one IIT study (TEC study, NCT05143099, treatment regimen PD-1 mab + cetuximab + irinotecan) initiated earlier by the applicant. And combining clinical pathological characteristics of the patient, constructing a multi-factor Cox regression model, and predicting the prognosis of the patient immune combination therapy noninvasively, early and accurately.
(1) Analysis of the frequency of the allelic variation gene (Variant Allele Frequency, VAF) in ctDNA liquid biopsies at different nodes of patients (where VAF represents ctDNA high or low levels, VAF = number of reads corresponding to mutated allele/number of all reads at the site):
(1) patient prognosis with high VAF at patient baseline is significantly worse than patient with low VAF;
(2) the more significant the VAF reduction at month 1 after patient treatment, the better the patient treatment oncolysis effect, the more significant positive correlation the two; and patient prognosis with more than 50% reduction in patient VAF is significantly better;
(3) VAF increases significantly after tumor progression in patients.
Figure 1 (a) shows that the VAF of treatment non-responsive (PD) patients is significantly higher than the other patients (SD/PR) at the different evaluation nodes of the patients, the VAF is significantly reduced after treatment and increased as progress occurs; figure 1 (b) shows that the overall prognosis for high VAF patients is significantly worse than for low VAF patients;
figure 2 (a) shows that the change in VAF at the first evaluation node after patient treatment is positively correlated with the change in tumor size in that patient, and figure 2 (b) shows that patients with more than 50% reduction in VAF at the first evaluation node after patient treatment have better overall survival.
(2) By sequencing the peripheral blood immunoproteomics of patients at different time points, the analysis found that:
(1) most proteins in patients who are effective in immune combination therapy are significantly higher than those who are not, and are associated with PFS;
(2) each protein was assigned a weight (β) using the DESeq2 package, and the expression levels of each protein were added β to obtain a peripheral immunoproteomics score for each sample as follows:
patients with high immunopinomic scores were found to have a significantly better prognosis than patients with low scores. Figure 3 shows the overall survival curve of patients with high and low peripheral immunohistochemical scores for Olink evaluation.
Based on this, the correlation of ctDNA index, peripheral blood immunoproteomics score and key clinical index with OS was first analyzed using a single factor Cox regression analysis method. And then taking the index with statistical significance (P < 0.1) in the single-factor Cox regression analysis into a multi-factor Cox regression model, finally establishing a variable which is composed of age, treatment line number, baseline (T1) ctDNA (which is expressed by VAF and is used for representing ctDNA level), delta ctDNA (T2-T1), delta ctDNA (T3-T1) and baseline proteomic scoring characteristics and used for evaluating whether a later colorectal cancer patient receives immunotherapy to benefit or not, constructing a multi-factor regression analysis model by using a coxph function of R software as an evaluation model, drawing a forest map visualization regression analysis model by using a ggforest function, as shown in fig. 4, and then calculating a risk score risscore of poor prognosis (which is not beneficial to immune combined therapy) by using a pre function in the R software according to the established evaluation model, wherein risscore is less than or equal to 0.39 is low risk, indicating that the patient can benefit by an immune combined therapy scheme, and risscore is more than 0.39 is high, and the patient cannot benefit by the immune combined therapy scheme.
Example 2: application and verification of models
ctDNA liquid biopsies were performed before, 1 month, and 2 months after treatment of the patient in this example; peripheral immunoproteomics was examined using an Olink platform prior to patient treatment and a peripheral immunoproteomics score was calculated (the method was the same as in example 1). Incorporating the collected patient age, treatment line number, baseline (T1) ctDNA, Δctdna (T2-T1), Δctdna (T3-T1) and peripheral blood immunoproteomics features into a multi-factor Cox regression model, predicting the final outcome of the patient receiving immune combination treatment; the K-M survival curves of the patients with total survival (OS) and Progression Free Survival (PFS) showed that the total survival (P < 0.0001) and progression free survival (P < 0.001) were significantly shorter for the high risk group colon cancer patients than for the low risk group patients, as shown in fig. 5A and 5B, indicating that the assessment model established by the present invention is able to accurately distinguish between the high risk group and the low risk group of colorectal cancer patients;
ROC analysis showed that the AUC value of the model score as a colorectal cancer prognosis model for prognosis diagnosis of colorectal cancer was as high as 0.96, which is far higher than the area under the ROC curve at 12 months in a single index evaluation queue, as shown in fig. 6, which further indicates that the evaluation model built by the present invention has very high prognosis prediction accuracy.
Further, the models were cross-validated using the Bootstrap method and Leave-one-out method, respectively, as shown in FIGS. 7A and 7B, which demonstrate that the evaluation model established by the present invention has stable distinguishing efficacy and accuracy for distinguishing prognosis of colorectal cancer patients.
While the invention has been described with respect to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The method for constructing the noninvasive prediction advanced colorectal cancer patient immune combination therapy prognosis model is characterized by comprising the following steps of:
obtaining results and clinical pathological characteristics of liquid biopsies of different treatment nodes of a sample, analyzing the correlation between the results and prognosis, and obtaining potential influence factors; the treatment node comprises before treatment, after treatment and progression; the results of the liquid biopsy comprise ctDNA indexes obtained by sequencing analysis and peripheral blood immune proteomics scores;
analyzing the correlation of each potential influencing factor and prognosis by using a single factor Cox regression analysis method;
then, the index with statistical significance in the single-factor Cox regression analysis is incorporated into a multi-factor Cox regression model, and finally, the variables for evaluating whether the advanced colorectal cancer patient receives the immune combination treatment to benefit or not are established, wherein the variables comprise age, treatment line number, baseline ctDNA level, ctDNA change level delta ctDNA after 1 month of treatment, ctDNA change level delta ctDNA after 2 months of treatment and baseline proteomic score; the statistical significance is that P is less than 0.1;
and constructing a multi-factor regression analysis model by utilizing a coxph function in R software according to the determined variables, namely, completing the construction of a prognosis model.
2. The method of claim 1, wherein the immune combination therapy is a combination therapy regimen of rituximab + cetuximab + irinotecan;
and/or, the colorectal cancer patient refers to a microsatellite stabilized metastatic colorectal cancer patient.
3. Use of a prognostic model constructed by the construction method of claim 1 for the preparation of a product for noninvasive prediction of prognosis of an immune combination therapy in a patient with advanced colorectal cancer.
4. The use according to claim 3, wherein the product is an in vitro diagnostic product; the in vitro diagnostic product comprises a diagnostic kit, a diagnostic system or a device.
5. A kit for noninvasively predicting prognosis of an immune combination therapy of a patient with advanced colorectal cancer, comprising reagents for detecting ctDNA levels and baseline proteomic levels in a sample, and a carrier in which a prognostic model is described; the prognosis model is constructed by the construction method of claim 1.
6. The kit of claim 5, wherein the test sample is peripheral blood of the patient.
7. The kit of claim 5, wherein the reagents comprise total RNA extraction reagents, reverse transcription reagents, and/or second generation sequencing reagents.
8. A diagnostic system or apparatus for noninvasively predicting prognosis of immune combination therapy in patients with advanced colorectal cancer, the system or apparatus comprising:
the data acquisition module is used for acquiring the joint index data of the to-be-tested subject and comprises the following components: age, number of treatment lines, baseline ctDNA (T1), level of change in ctDNA Δctdna after 1 month of treatment (T2-T1), level of change in ctDNA Δctdna after 2 months of treatment (T3-T1), and baseline proteomic score;
the scoring module is used for inputting the combined index data obtained by the data obtaining module into a pre-constructed prognosis model and calculating a risk score riskscore of a subject to be tested, which does not benefit from an immune combined treatment scheme; the prognosis model is constructed by the construction method of claim 1;
and the judging module is used for comparing the risk score riskscore obtained by the scoring module with the median value of the risk score of 0.39 so as to judge whether the to-be-tested subject receives the immune combination treatment scheme to benefit or not.
9. A computer device comprising a memory and a processor, the memory storing a program, the processor implementing the following method when executing the program:
acquiring joint index data of a subject to be tested, comprising: age, number of treatment lines, baseline ctDNA (T1), level of change in ctDNA Δctdna after 1 month of treatment (T2-T1), level of change in ctDNA Δctdna after 2 months of treatment (T3-T1), and baseline proteomic score;
inputting the combined index data as input data into a pre-constructed prognosis model, and calculating a risk score riskscore of a subject to be tested, which does not benefit from an immune combined treatment scheme; the prognosis model is constructed by the construction method of claim 1;
and comparing the calculated risk score riskscore with the median value of the risk score of 0.39, so as to judge whether the tested subject receives the immune combination treatment scheme to benefit or not.
10. A computer readable storage medium, wherein the computer readable storage medium comprises a stored computer program;
wherein the computer readable storage medium is controlled to implement the method of claim 9 when the computer program is run.
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