CN114354943A - Immune therapy efficacy prediction marker and prediction model construction and application - Google Patents
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Abstract
The invention discloses an immunotherapy efficacy prediction marker, wherein after starting two cycles of immunotherapy (4-6 weeks) for patients who benefit from ICB therapy, the activation of Interleukins (ILs) in the plasma of the patients can predict the response of the patients to the immunotherapy; this time is 6-12 weeks earlier than traditional Computed Tomography (CT), and is critical for cancer patients receiving immunotherapy; the construction of the immunotherapy activation prediction marker and the prediction model is noninvasive dynamic analysis of blood detection before treatment and in early treatment, and has important significance for predicting the curative effect of immunotherapy; the technical scheme of the invention has guiding significance for predicting the long-term curative effect of the tumor immunotherapy patients. The dynamic prediction model improves the accuracy of the prediction of the curative effect of the immunotherapy and has wide applicability in clinic.
Description
Technical Field
The invention relates to construction and application of an immunotherapy curative effect prediction marker and a prediction model, and relates to the technical field of immunotherapy.
Background
Immunotherapy of tumors is a revolution in the medical community. How to accurately predict the curative effect of the medicine is a challenge of immunotherapy, however, the traditional means based on detection of tumor mutation load, PD-L1 protein expression and the like before treatment cannot effectively predict the curative effect of all patients; immune Checkpoint Blockade (ICB) therapy shows significant clinical benefit in different cancers, but the response rate of most patients is relatively low, which is closely related to the Tumor's Immune Microenvironment (TIME). The TIME includes diverse and plastic populations of immune cells, along with expression of various activating and inhibitory immune checkpoints, which are important in maintaining immune homeostasis and in regulating the type, magnitude, and duration of the immune response. The comprehensive understanding of the relationship between tumor infiltrating immune cell populations and immune checkpoints is crucial to the deep analysis of tumor immunotherapy mechanism, and is beneficial to find effective markers for predicting the treatment effect of ICB. Previous studies revealed that tumor mutation burden, neoantigen burden, microsatellite instability, etc. could be used as biomarkers to predict patient response to ICB treatment.
However, these markers are mainly used for patient assessment prior to ICB treatment, and the effect of dynamic changes during treatment on the accuracy of these biomarkers is poorly understood. Therefore, establishing an ICB efficacy prediction model based on the dynamic process of immunotherapy is of great clinical significance.
Disclosure of Invention
The invention aims to provide an immunotherapy curative effect prediction marker, a prediction model construction method and application, discloses an immunotherapy activation prediction model constructed on the basis of dynamic changes of Interleukins (ILs) in blood plasma, can well predict the curative effect and long-term outcome of immunotherapy of a patient, and provides a noninvasive, economic and time-saving method for predicting the curative effect of immunotherapy.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
the prediction marker of the curative effect of the immunotherapy is interleukin ILs in plasma and dynamic change thereof.
Further, the immunotherapeutic efficacy predictive marker may represent immune microenvironment activation.
Furthermore, the dynamic change of the interleukin ILs in the plasma constructs an immunotherapy curative effect prediction model, and the curative effect and long-term outcome of the immunotherapy of patients can be well predicted.
The model for predicting the curative effect of immunotherapy is constructed by the dynamic change of interleukin ILs in plasma.
Further, the prediction model is an ICB efficacy prediction model constructed based on the dynamic changes of ILs in the plasma of the patient before and after treatment.
The application of the dynamic change of interleukin ILs in the constructed ICB curative effect prediction.
The invention has the beneficial effects that:
the invention discloses the change of the inhibition type or activation type immune characteristics of ICB treatment beneficiaries and non-beneficiaries before and after treatment, and simultaneously constructs an ICB curative effect prediction model based on the dynamic change of ILs in plasma of patients before and after treatment;
the immunotherapy efficacy prediction marker of the invention, after the initiation of two cycles of immunotherapy (4-6 weeks) in patients who benefit from ICB treatment, the activation of Interleukins (ILs) in the patient's plasma can predict the patient's response to immunotherapy; this time is 6-12 weeks earlier than traditional Computed Tomography (CT), and is critical for cancer patients receiving immunotherapy;
the construction of the immunotherapy curative effect prediction marker and the prediction model is noninvasive blood detection and dynamic analysis before immunotherapy and in early treatment, and has important significance in predicting the immunotherapy curative effect;
the technical scheme of the invention has guiding significance for predicting the long-term curative effect of the tumor immunotherapy patients. The dynamic prediction model improves the accuracy of the prediction of the curative effect of the immunotherapy and has wide applicability in clinic.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
FIG. 1 is a schematic view of a study flow according to an embodiment of the present invention; wherein (a) the sample is collected from a primary tumor, a metastatic tumor, and an ICB-treated patient; (B) the relationship between activation and inhibition checkpoints and the relative abundance of immune cell populations. (C) Dynamic analysis of clinical impact of treatment of ICB patients by non-invasive blood samples; (D) proof of concept for dynamic analysis in plasma to predict immunotherapy response;
FIG. 2 is a dynamic profile of immune profiles to predict immune response in accordance with embodiments of the present invention;
FIG. 3 is a schematic representation of the immune profile prediction of an immunotherapeutic response according to an embodiment of the invention;
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example 1
As shown in fig. 1:
the genomic map of Cancer (The Cancer Genomics Atlas) containing 33 cancers, metastatic tumor datasets over 1000 samples, and pairwise correlations between The expression of 14 active and 20 inhibitory immune checkpoints in multiple independent ICB treatment datasets, as well as correlations in The abundance of various types of immune cells, was systematically resolved using bioinformatics analysis tools, suggesting that these immune features are in a state of dynamic equilibrium (fig. 1A-B); wherein (a) the sample is collected from a primary tumor, a metastatic tumor, and an ICB-treated patient; (B) the relationship between activation and inhibition checkpoints and the relative abundance of immune cell populations. (C) Dynamic analysis of clinical impact of treatment of ICB patients by non-invasive blood samples; (D) proof of concept for dynamic analysis in plasma to predict immunotherapy response;
further comparison of the changes in immune characteristics in TIME before and after treatment revealed that most immune checkpoints and immune cell populations were significantly enriched in the beneficiary from immunotherapy, while no significant change was seen in the non-beneficiary (fig. 1C), and that these immune characteristics were not significantly different between the beneficiary and non-beneficiary before and after treatment, indicating that the benefit of ICB treatment was related to immune activation.
Example 2
As shown in fig. 1-2
Clinically, expression of ILs in the plasma of patients 2 cycles (4-6 weeks) after treatment initiation was examined in two separate anti-PD-1 mab lung cancer treatment cohorts, a model was established to predict immunotherapy efficacy (fig. 1D, fig. 2A-E), and model accuracy was verified on different data sets (fig. 2F-H). The method is non-invasive, has the characteristics of low cost and high efficiency, can evaluate the ICB curative effect in the early treatment period, is earlier than the traditional detection methods based on imaging and the like, and is a new paradigm for dynamically predicting the curative effect of immunotherapy.
As shown in fig. 2, dynamic profiles of immune characteristic predictive immunotherapy response (a) a sample collection schematic of lung cancer patients treated with anti-PD-1 alone or chemotherapy and anti-PD-1 in combination. (B) Differential IL expression (IL)Δ) Responders (n-11) were significantly higher than non-responders (n-11) after 2 weeks of anti-PD-1 treatment. P values were determined using a paired Mann-Whitney-Wilcoxon test. The boxes represent ± 1 quartile, the minimum or maximum value extending from the hinge into the box boundary within 1.5 × IQR; (C-H) immune activation (IS)Δ) The response of cohort 1(C-E) and cohort 2(F-H) patients to anti-PD-1 treatment can be accurately predicted. (C and F) immune-activated patient D>0) There is a greater likelihood of response to anti-PD-1 treatment after ICB. The P value was determined by Fisher's test. (D and G) immune-activated patient D>0) PFS was shown to be significantly better. P values were determined by log rank test. (E and H) significant reduction in tumor size in immunocompetent patients D>0) Based on the CT image.
Plasma from these patients was collected 1 day prior to each cycle of anti-PD-1 treatment (fig. 2A). We focused on the protein expression levels of the four ILs, IL-2, IL-4, IL-6 and IL-10, since immune cells produce extensive ILs during the activation of ILs. Gene pool enrichment analysis (GSEA) showed that in four independent ICB treatment cohorts, patients with immunotherapy benefit had a significant enrichment of the IL gene pool after treatment compared to before treatment. Furthermore, ILs are highly positively correlated with checkpoints and CYTs; more importantly, the fourThe cytokine can be detected by a Th1/2/17 standard cytokine detection kit, and can be widely applied. During the course of anti-PD-1 treatment, we observed that immunocompetent patients had global activation of IL protein during cycles 1 and 2 of anti-PD-1 treatment, while there was little change in unresponsive IL. Indeed, all four IL proteins showed significant upregulation at cycle 2 of anti-PD-1 treatment (fig. 2B). Furthermore, the up-regulation of each IL protein during anti-PD-1 treatment was significantly inversely correlated with tumor size. To assess activation of the immune system, we designed an immune score IS based on these four ILsΔ>0 IS designated as immune activation and ISΔ≦ 0 is designated immune inactivity.
Of the 22 patients enrolled, 11 patients presented with immune activation during cycle 2 of anti-PD-1 treatment (IS)Δ>0) Of these 11 patients, 10 (90.9%) responded to anti-PD-1 treatment. This is significantly higher than the percentage of patients without immune activation 10/11 and 1/11; fig. 2C). Progression-free survival (PFS) of these titers (logrank test, p-1.63105, fig. 2D). Imaging results showed that plasma immune activation after 6 weeks of treatment with MYZL-116 in anti-PD-1 patients reduced tumor diameter by 95%, whereas MYZL-16 patient 8 had no immune activation and increased tumor diameter by 200% (fig. 2E). To assess whether activation score IS an independent predictor, we considered age, gender, expression of PD-L1, and ISΔAs a variable for performing multivariate Cox regression analysis, we found ISΔIs an independent predictor which is obviously related to better prognosis
Further taking different medicines and IS as variables to perform multivariate Cox regression analysis so as to avoid the influence of different medicines; it is crucial to study potential biomarkers at an earlier time point. We further examined the level of ILs for cycle 1 (2-3 weeks) and observed up-regulation of ILs, but unfortunately not statistically significant;
given the small sample size of our cohort of patients (n-22), we further collected 67 independent lung cancer patients treated with anti-PD-1 alone or in combination with chemotherapy and anti-PD-1;
among the 67 patients of the group of patients,of the 43 patients, 40 patients who appeared immunocompetent (93.0%) on cycle 2 responded to anti-PD-1 treatment significantly higher than patients without immune activation (IS)Δ0, 9/24 and 40/43, FIG. 2F). Furthermore, in the imaging analysis, PFS was significantly better in immune-activated patients (fig. 2G) and tumor size was significantly reduced (fig. 2H). We performed survival analysis on NSCLC and SCLC patients in the activated and non-activated groups, respectively, and found that immune activation of both cancer types was associated with better PFS. We further performed multivariate Cox regression analysis and demonstrated ISΔ≦ 0 by considering confounding factors including age, sex, PD-L1 expression, and different drug treatment modalities. In summary, our findings provide the first conceptual evidence that activation at an early stage of treatment can accurately predict the response to immunotherapy.
Importantly, patients who benefited from immunotherapy tended to have a general upregulation of immune activation and suppression profiles on blood tests during treatment compared to blood tests prior to treatment (fig. 3I-3K), and patients who benefited from immunotherapy tended to have a general upregulation of immune activation and suppression profiles on biopsy during treatment compared to biopsy prior to treatment, while patients who did not benefit from immunotherapy did not have a significant immune change after treatment (fig. 3K and 3L). Furthermore, in the pre-treatment samples, most immune checkpoints and immune cell populations did not differ significantly between the benefitting and non-immunotherapeutic patients, suggesting that it is difficult to determine which patients would benefit from treatment based solely on the pre-treatment samples. While single pre-treatment biomarkers are also useful, a recent new concept demonstrates the clinical utility of dynamic biomarkers, particularly for complex systems that may have multiple stage features. Our studies underscore the possibility of predicting the immunotherapeutic response by comparing pre-and in-treatment biological specimens of various different immunological criteria. We found that non-invasive blood samples treated as early as 7-14 days after treatment initiation can provide important information about which patients may benefit (fig. 3M). We validated a non-invasive method of predicting immunotherapy efficacy in two clinical cohorts receiving immunotherapy by testing the patient's plasma for ILs activation as early as two cycles of treatment initiation (4-6 weeks), which is low cost, time saving, efficient and provides a new paradigm for identifying markers of predicted immunotherapy efficacy (fig. 3N).
The invention discloses the change of the inhibition type or activation type immune characteristics of ICB treatment beneficiaries and non-beneficiaries before and after treatment, and simultaneously constructs an ICB curative effect prediction model based on the dynamic change of ILs in plasma of patients before and after treatment;
the immunotherapy efficacy prediction marker of the invention, after the initiation of two cycles of immunotherapy (4-6 weeks) in patients who benefit from ICB treatment, the activation of Interleukins (ILs) in the patient's plasma can predict the patient's response to immunotherapy; this time is 6-12 weeks earlier than traditional Computed Tomography (CT), and is critical for cancer patients receiving immunotherapy;
the construction of the immunotherapy curative effect prediction marker and the prediction model is noninvasive blood detection and dynamic analysis before immunotherapy and in early treatment, and has important significance in predicting the immunotherapy curative effect;
the technical scheme of the invention has guiding significance for predicting the long-term curative effect of the tumor immunotherapy patients. The dynamic prediction model improves the accuracy of the prediction of the curative effect of the immunotherapy and has wide applicability in clinic.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. The marker for predicting the curative effect of immunotherapy is characterized in that: the prediction marker is interleukin ILs in plasma and dynamic change thereof.
2. The marker for predicting the therapeutic effect of immunotherapy according to claim 1, wherein: the immunotherapy efficacy prediction marker may indicate activation of an immune microenvironment.
3. The marker for predicting the therapeutic effect of immunotherapy according to claim 1, wherein: the dynamic change of the interleukin ILs in the plasma constructs an immunotherapy activation prediction model, and the long-term outcome of the immunotherapy of a patient can be well predicted.
4. The model for predicting the curative effect of immunotherapy is characterized in that: the prediction model is constructed by the dynamic change of interleukin ILs in plasma.
5. The immunotherapy efficacy prediction model of claim 4, wherein: the prediction model is an ICB curative effect prediction model constructed on the basis of dynamic changes of ILs in plasma of patients before and after treatment.
6. The application of the dynamic change of interleukin ILs in the constructed ICB curative effect prediction.
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| CN117192116A (en) * | 2023-11-06 | 2023-12-08 | 陕西秒康医疗科技有限公司 | Ozone intelligent supply system under blood immune activation scene |
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| CN111012761A (en) * | 2019-12-24 | 2020-04-17 | 中南大学湘雅医院 | Drug-loaded microsphere, anti-tumor drug and preparation method |
| CN113358872A (en) * | 2021-06-03 | 2021-09-07 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Marker group and system for evaluating curative effect of tumor immunotherapy |
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