Method for determining a prognostic score in patients with metastatic renal cancer

Abstract

The present invention relates to a method for rapidly and accurately determining a prognostic score and subsequent risk class classification in patients with metastatic renal cancer by obtaining and processing immunological analysis values for the patient and combining these immunological analysis values with the currently used IMDC classification.

Description

Technical Field

[0001] The present invention relates to a method for determining a prognostic score in patients with metastatic renal cancer.

Background Art

[0002] Renal tumors (mainly due to uncontrolled proliferation of cells that make up the tubes where blood filtration occurs) comprise a wide range of histological variants. The frequently occurring histological variants are clear cell carcinoma (70 - 80% of cases), papillary renal cell carcinoma (10 - 15% of cases), and chromophobe carcinoma (5% of cases). Renal cancer is a vascular-rich type of cancer with significant angiogenesis. For this reason, one of the main treatment strategies currently used, especially in the case of patients with metastatic renal cell carcinoma (mRCC), is the use of molecular target drugs, particularly angiogenesis inhibitors that act by blocking the formation of new blood vessels that supply oxygen and nutrients to the tumor, such as tyrosine kinase inhibitors (TKIs).

[0003] Another important treatment strategy in the treatment of metastatic renal cancer is the use of immunotherapeutic drugs, such as immune checkpoint inhibitors that act by removing the "blocking" signals that tumors confer on the patient's immune system, thus preventing the recognition by the tumor and excluding cancer cells. This second approach has been found to be particularly effective for some patients more than others, depending on the risk class.

[0004] In the scientific community, there is a consensus that an accurate prognosis for mRCC patients, that is, an accurate prediction of the course and outcome of a given clinical profile, is an essential step in the selection of the type of treatment for such patients.

[0005] In fact, the use of prognostic factors enables stratification of the patients themselves according to the risk of disease-related death, provides important information about the progression of the disease, enables more accurate comparison between clinical trials, facilitates the equal division of patients to prevent bias related to patient selection, and as a result, enables the identification of the group for which a given treatment has the greatest efficacy.

[0006] Currently, the prognostic classes of mRCC patients are based on an integrated model aimed at comprehensively analyzing clinical factors, pathological factors, and laboratory values to predict survival rates and identify patients at high risk of recurrence. The two most widely used models in clinical practice are the Memorial Sloan Kettering Cancer Center (MSKCC) prognostic system and, more recently, the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) prognostic system (or Heng prognostic system). These two systems are used to classify patients into risk classes for the purpose of defining accurate treatment indicators for each group and are still in use today.

[0007] To date, the most widely used classification has been the IMDC classification that considers six prognostic factors (Karnofsky performance status: <80%; hemoglobin: below the normal range; corrected calcium: >10 mg / dl; interval between diagnosis and treatment: less than 1 year; absolute neutrophil count: above the normal range; platelet count: above the normal range).

[0008] Using these prognostic factors, mRCC patients are stratified into three "risk classes" with different prognoses defined as "favorable", "intermediate", and "poor" (and denoted as "c-favorable", "c-intermediate", "c-poor" in the present invention), corresponding to patients with favorable, intermediate, or poor predictions regarding the response to treatment, respectively).

[0009] Specifically, No prognostic factors present: "c-favorable" class; One or two prognostic factors present: "c-intermediate" class; Three or more prognostic factors present: "c-poor" class is.

[0010] As described above, the IMDC criteria are still commonly adopted in clinical practice, but there is clear evidence that the predictive / prognostic role of this model is impaired by the significant heterogeneity present within each risk class. This observation is particularly valid for mRCC patients classified into the "c-intermediate" class (including less than 60% of patients). In fact, this group includes patients with significant heterogeneity in IMDC parameters, who are assigned to the same risk class but can actually have very different prognoses. Furthermore, patients showing only one prognostic factor often have a significantly better prognosis than those showing two negative prognostic factors.

[0011] The parameters and their possible co-existing heterogeneities deeply affect the response to treatment and the treatment effect, resulting in clinical outcomes where patients have different prognoses despite being classified into the same risk class by the IMDC classification criteria. Thus, it has not been proven that the said classification is particularly effective in either grouping patients with the same clinical characteristics or significantly increasing the available treatment options as a result.

[0012] Several studies have proposed integrating the IMDC classification with the consideration of specific biochemical and clinical parameters, such as the serum level of C-reactive protein (Kimiharu T et al., Clin Genitourin Cancer 2018), the platelet count (Guida A et al., Oncotarget 2020), or alternatively the initial site of metastasis (Di Nuzzo V et al., Clin Genitourin Cancer 2018).

[0013] In a recent clinical trial (treating mRCC patients with anti-PD1 nivolumab as monotherapy), it has been found that the simultaneous evaluation of clinical and inflammatory parameters contributes to better prognostic stratification of patients (Rebuzzi SE et al., Ther Adv Med Oncol. 2021).

[0014] Fornarini G. et al. have also hypothesized that a combination of immune-inflammatory biomarkers, such as neutrophil:lymphocyte ratio (NLR) by platelet count, PD-L1, and LDH, is a potentially useful prognostic tool for identifying patients who will benefit from immunotherapy alone or in combination with other therapies (Fornarini G. et al, ESMO Open 2021).

[0015] Other recent studies have shown how molecular profiling (based on gene profiles of transcriptional changes associated with clinical response to treatment with anti-VEGF / VEGFR alone or in combination with anti-PD-L1) can enable molecular stratification of mRCC patients by identifying novel therapeutic targets that are important for targeted drug development (Motzer RJ et al., Cancer Cell 2020). However, while analyzing important co-authentications of patients, the methods used in this study require large costs and special methodologies and have so far responded more to the research aspect than to clinical diagnosis.

[0016] Tanaka Nobuyuki et al. (Urologic Oncology: Seminars and Original Investigations, vol. 35, 2016) have proposed a modified IMDC risk model in which the neutrophil count (predicted by the standard model as already described) is replaced by the NLR parameter (neutrophil-to-lymphocyte ratio) to improve the predictive ability of the model regarding survival levels (overall survival: OS). The study demonstrated an improvement in the prediction rate of 1.7% and 6.2% in two groups of patients examined (first- and second-line targeted therapies, respectively) with a statistical value of p < 0.001 compared to the OS values.

[0017] Chrom Pawel et al. (Int J Clin Onc, vol. 24, 2019) are studying a modified IMDC prognostic model by introducing a systemic immune-inflammation index (SII) based on the total number of neutrophils, lymphocytes, and platelets, instead of only the number of neutrophils and platelets provided by the standard model. The authors generally found that, with respect to classification into three risk groups, the prognostic accuracy of the new SII-IMDC model is higher than that of the conventional model with a statistical value of p < 0.001.

[0018] Finally, recent studies have shown that + the CD137 T cell population has a predictive role in responses to TKIs and immunotherapies and is therefore shown to be used as a biomarker associated with a good prognosis (Zizzari I. et al., Cancers, vol. 12, 2020; Ugolini A. et al., Cancers, vol. 13, 2021, Cirillo A. et al. 2023). This cell subset has not been proposed as a biomarker to be used alone and / or in combination with other parameters for the improvement of the IMDC prognostic score, which is still the only criterion currently used in the classification of patients with metastatic renal cancer.

Summary of the Invention

Problems to be Solved by the Invention

[0019] None of the hypotheses proposed so far have been proven to be completely decisive in identifying a classification method that replaces the methods currently used in therapy (more reliable, easy to apply, and taking into account the immune system as the main target of new drugs used in clinical practice). Thus, the classification of patients in the "intermediate" class remains "inadequate" and incomplete, resulting in a poorly defined and inappropriately structured classification.

[0020] Thus, there remains a need to provide a simple, reproducible, and easily methodological applicable method (more accurate and reliable than current models used in clinical practice) for determining the prognostic classification of metastatic renal cancer patients in order to identify targeted and enhanced therapies for the patient.

Means for Solving the Problems

[0021] An object of the present invention is to provide a method for determining a novel prognostic algorithm in metastatic renal cancer patients in a rapid, accurate, reliable, and reproducible manner so as to accurately classify the patients into various risk classes.

[0022] A further object of the present invention is the use of the method of the present invention for mRCC patients for identifying effective therapies based on the obtained classification.

[0023] These and other objects are achieved by the present invention, which aims to establish a method for determining a prognostic score in metastatic renal cancer patients.

Brief Description of the Drawings

[0024]

Figure 1

Figure 2

Modes for Carrying Out the Invention

[0025] To date, in terms of the efficacy of the treatment to be implemented, increasing the effectiveness of stratifying mRCC patients is an urgent clinical issue.

[0026] As used herein, the terms "stratify" and "stratification" are defined as the act of classifying patients into risk classes from lowest to highest risk by using parameters identified by the protocols used in treatments to date and / or parameters of the method according to the present invention.

[0027] As previously described, said stratification is achieved by the assignment of a score related to the presence or absence of identified prognostic factors that form the basis of the classification.

[0028] Since the present invention specifies a method for obtaining and processing immunological analysis values for a patient and determining a prognostic score in metastatic renal cancer patients by combining the results of said immunological analysis with IMDC stratification, it directly addresses the above-mentioned clinical need. This enables an established prognostic algorithm to be adjusted according to the immunological parameters of a patient with respect to a novel immunotherapy that precisely targets the targeting (identifying as a target) of the patient's immune system.

[0029] As previously pointed out, the stratification of patients resulting from IMDC classification is herein referred to as "c-favorable", "c-intermediate", "c-poor".

[0030] In particular, the object of the present invention is to enable the stratification of mRCC patients in order to identify the prognostic risk classes of mRCC patients and then select the optimal therapy for each patient based on the predicted prognosis, in view of the fact that the therapies used to date in the treatment of mRCC are mainly based on the activation of the immune system. Said combined approach is in fact precisely configured by the integration of the current IMDC prognostic classification and the evaluation of two immunological parameters, thus enabling a more accurate assessment of the patient's chances of response to therapy.

[0031] According to a particularly preferred embodiment of the present invention, the immunological parameters considered for use in combination with the IMDC classification for determining a patient's prognostic score are the serum value of vascular endothelial growth factor (VEGF) and circulating CD8 + CD137 + the percentage of CD137 T lymphocytes. Said parameters are used to define an "immunological classification" and an "immunological score".

[0032] In particular, the concentration of VEGF in serum is measured from an isolated sample of the patient's blood by one of the methods known to those skilled in the art, preferably by an ELISA test. Circulating CD8 + CD137 + the percentage of CD137 T lymphocytes is measured from an isolated sample of the patient's blood by collecting peripheral blood mononuclear cells (PBMCs) (preferably subjected to flow cytometry analysis to evaluate the expression of CD137 on the cells). Circulating CD8 + CD137 on the cells + The percentage value of CD137 T lymphocytes is expressed relative to the value of CD8 + CD137 + CD137 T lymphocytes. + CD137 + T lymphocytes.

[0033] Therefore, according to the present invention, in order to determine the prognosis of a patient (classifying the patient into one of the identified risk classes ("good", "intermediate", "poor") and subsequently selecting a therapy plan), the state-of-the-art IMDC prognostic classification is applied, to which are added two immunological parameters, namely the serum VEGF concentration value and circulating CD8 + CD137 + the percentage of CD137 T lymphocytes.

[0034] In particular, said parameters make it possible to reclassify the patient from an immunological point of view.

[0035] Said parameters, namely the serum VEGF concentration value and circulating CD8 + CD137+ The % of T lymphocytes was selected after a number of possible immunological screenings that have been identified and reported in the literature as having a correlation with cancer diseases. At the end of this screening, two criteria (serum VEGF and circulating CD8 + CD137 + T lymphocytes) were selected, which have been demonstrated to have the greatest statistical contribution to the improvement of the predictive ability of the combined method compared to others, and at the same time, have been found to be directly related to the action of the therapies (ICI and TKI) currently used in metastatic renal cancer. In fact, TKIs block the action of VEGF receptors and thus inhibit the immunosuppressive and angiogenesis effects of VEGF, while circulating CD8 + CD137 + T lymphocytes have been, to date, specific markers of response to ICI immunotherapy in several solid tumors (Zizzari I.G. et al. 2022; Cirillo A. et al., 2023).

[0036] The two selected parameters, despite what is known in the literature about their role in tumor differentiation and growth (VEGF), and the activation of specific antitumor responses (circulating CD8 + CD137 + T lymphocytes), have never been proposed to perform the well-known IMDC prognostic classification either as single markers or in combination with each other.

[0037] For the purpose of assigning scores for classification, the quartiles were identified as follows among the serum VEGF concentration and circulating CD8 + CD137 + T lymphocytes in the patients being analyzed: 25th percentile (first quartile: Q1); 75th percentile (third quartile: Q3); Values falling within the interquartile range (IQR).

[0038] In other words, the quartiles are position indices that divide an ordered data population into four groups containing approximately equal numbers of observations and identify the value below which a given percentage of the distribution falls.

[0039] The first quartile (Q1), also known as the 25th percentile, is the value that identifies 25% of the observations below Q1 and excludes the remaining 75%. Similarly, the third quartile (Q3), also known as the 75th percentile, is the value that identifies 75% of the observations below Q3 and excludes the remaining 25%. The interquartile range (IQR) is defined as the difference between the third and first quartiles (Q3 - Q1) and is a measure of dispersion that corresponds to the range within which at least 50% of the data is found.

[0040] Considering this type of data division, the circulating CD8 + CD137 + % of T lymphocytes can be used to divide patients into "T lympho - good": CD8 + CD137 + % value ≥ 75th percentile; "T lympho - intermediate": CD8 + CD137 + % value within the interquartile range (IQR) (Q1 ≤ CD8 + CD137 + % < Q3, i.e., values between Q1 and Q3); "T lympho - poor": CD8 + CD137 + % value < 25th percentile which enables patients to be considered as such.

[0041] By dividing serum VEGF concentration values into percentiles, patients can be "VEGF - good": serum VEGF < 25th percentile; "VEGF - intermediate": serum VEGF values within the interquartile range (IQR) (Q1 ≤ VEGF < Q3, i.e., values between Q1 and Q3); "VEGF - poor": VEGF value ≥ 75th percentile which enables patients to be considered as such.

[0042] As shown in Table 1, by combining the distributions of patients obtained from the percentiles of the two described immunological parameters together, the patients were classified as "i-good", "i-intermediate", and "i-poor" according to "immunological classification (i)" (i = immunological).

[0043]

Table 1

[0044] Subsequently, "immunological classification (i)" was combined with the parameters of patients in the classes "c-good" and "c-poor" resulting from IMDC classification according to the scheme in Table 2, enabling new and favorable stratification of patients in these two classes, and as a result, an improvement in the survival curve could be achieved.

[0045]

Table 2

[0046] However, the combination of these two classifications still does not allow for optimal discrimination of patients in the "c-intermediate" class.

[0047] Subsequently, for patients in this class, an "immunological score" was calculated based on the median of circulating CD8 + CD137 + T lymphocytes and the median of serum VEGF concentration.

[0048] Scores of 1 or 0 were assigned to values above or below the median as follows: CD8 + CD137 + % ≥ median: score = 1; CD8 + CD137 + % < median: score = 0; VEGF ≥ median: score = 0; VEGF < median: score = 1.

[0049] The arithmetic sum of the individual scores determines the "immunological score" (ranging from 0 to 2) for each patient. As tabulated in Table 3, patients with a score of 0 were ultimately classified as "intermediate", and patients with a score of 1 or 2 were classified as "good".

[0050]

Table 3

[0051] The IMDC classification described herein and the combination of two immunological parameters (the immune-IMDC combination systematized in the block diagram of Figure 2) enable a statistically more significant stratification of patients according to risk class (p < 0.0001 vs p = 0.0005, respectively) than IMDC classification alone (especially for "intermediate" patients (p = 0.0206 vs p = 0.1987, respectively)) (see Figures 1a and 1b for data from the patient population of the "experimental part").

[0052] Thus, immune-IMDC enables the generation of a new prognostic algorithm according to the scheme shown in Figure 2, which, by combining IMDC with immunological classification (i), allows for the reclassification of "c-intermediate (IMDC)" patients by combining them with immunological scores and parameters and "c-good" and "c-poor" patients, significantly improving prognostic stratification.

[0053] In other words, according to the present invention, it has been proven that the combination of two classifications, IMDC and immunological classification, can significantly distinguish patients belonging to three risk classes.

[0054] In particular, the experimental data shows that the use of IMDC classification results in a p-value = 0.0005 with respect to survival rate, whereas the combined use with the immunological parameters according to the present invention, the immune-IMDC analysis, results in a calculated value of p < 0.0001 (Figure 1). In inferential statistics, the p-value is the probability of obtaining a result equal to or less compatible than what was observed during the test of the hypothesis that is considered to be true. In other words, the p-value helps to understand whether the difference between the observed result and the assumed result is due to the randomness introduced by sampling, or instead, whether this difference is statistically significant. The closer the p-value is to 0, the more the hypothesis is proven to be true.

[0055] Furthermore, it was experimentally observed that the method according to the present invention can distinguish the survival curves of patients belonging to the "intermediate" risk class from those of patients in the "favorable" risk class (p-value = 0.0206 by using combined immune-IMDC analysis versus p-value = 0.1987 obtained by IMDC); can improve the survival curves between "favorable" and "poor" patients (p-value = 0.0001 by using combined immune-IMDC analysis versus p-value = 0.0013 obtained by IMDC); and can maintain the significant difference in survival between "favorable" and "poor" patients (p-value = 0.0210 by using combined immune-IMDC analysis versus p-value = 0.007 obtained by IMDC). These values are clearly shown in FIG. 1 (in the figure, the survival curves (calculated for overall survival) of patients by IMDC (upper a) and by immune-IMDC classification (lower b)) are shown). The stratification into different risk classes obtained through the proposed immune-IMDC classification is not only more accurate than that obtained through the standard IMDC method, as understood from the statistical p-values shown in FIG. 1, but also more accurate than the variants thereof that are known and have already been considered in the art to date. In particular, the proposed immune-IMDC stratification is excellent in significantly distinguishing all OS curves of patients belonging to the three risk classes ("intermediate" vs. "favorable": p = 0.02; "favorable" vs. "poor": p < 0.0001; "intermediate" vs. "poor": p = 0.021). Although important OS rates can be inferred from other variants known to date in the prior art (Tanaka N. et al., 2017; Pawel Chrom et al., 2019), there is no analysis of various survival curves between different risk classes. Furthermore, the overall OS rates calculated by the proposed method are clearly higher than those of the methods already considered (p < 0.0001 vs. p < 0.001), demonstrating the excellent statistical power of the immune-IMDC method described herein.

[0056] According to a preferred embodiment of the present invention, by integrating IMDC classification with a score obtained by measuring specific immunological parameters, accurate stratification of patients within three specified risk classes is achieved.

[0057] In particular, for patients classified into the "c-intermediate" category after IMDC classification, the immunological score (obtained from scores derived from VEGF and lymphocyte immunological parameters) enables further subdivision into the "intermediate" or "good" classes when patients have scores of 0 or 1-2 respectively, as organized in Table 3 and Figure 2. Thus, the "intermediate" class is more precisely redefined through immunological parameters.

[0058] Furthermore, patients classified into the "c-good" or "c-poor" categories according to IMDC classification are well-distinguished into three "good", "intermediate", and "poor" categories by integration with immunological classification (i) according to the diagrams in Figure 2 and Table 2.

[0059] Thus, the method according to the present invention can stratify patients more accurately than the current state of the art from a prognostic perspective and, in particular, by virtue of the advantage of the integration of data obtained by immunological evaluation constituted by the measurement of the values of the above-mentioned parameters with those derived from IMDC classification, whereby patients can obtain the maximum benefit from the therapy administered.

[0060] As already explained, for example, the class of "c-intermediate" patients classified using only IMDC classification includes patients identified as "good" by the method according to the present invention despite that. Similarly, after IMDC classification alone, several patients are classified as "c-poor" or "c-good", but there will also be patients who are found to be defined as "intermediate" by the method according to the present invention despite that.

[0061] Compared with the methods different from the standard IMDC classification that have been seen in the literature so far and already described in the background art section, the method of the present invention does not propose immunological parameters as an alternative to what has already been provided in the standard method. Instead, the reclassification first used and obtained by the parameters of IMDC, as already described and as systematized in Figure 2, is based on the serum VEGF concentration and CD8 obtained from the analysis of serum and / or blood samples from patients + CD137 + is proposed to complement with the percentage of T lymphocytes

[0062] Therefore, the method according to the present invention is not related to the use of modified IMDC classification, as in the cases described in the already cited literature. Instead, the patients already classified in the first step by the IMDC model can be re - subdivided as a more accurate prediction regarding the prognosis of the patients and the treatment required by the patients, characterized by an analysis at a level that enables this to be done.

[0063] Thanks to the method of the present invention that enables a more accurate classification to be carried out in the target population, by re - defining the patients based on their clinical and immunological characteristics, the said patients can avoid undergoing unnecessary highly toxic treatments and unsuccessful therapies and can obtain a higher treatment benefit.

[0064] A further advantage beyond the current state of the art brought about by the present invention is that, for its implementation, it uses two easily measurable parameters in the blood samples taken from patients. The patients only need to undergo a single evaluation by means of a single minimal venous sampling, in combination with a given clinical test, before the start of the therapy. Furthermore, the evaluation of the said immunological parameters is based on inexpensive, rapid and standardized methods commonly used in the clinical practice of many medical facilities, such as ELISA assays and flow cytometry assays.

[0065] These aspects are also beneficial from an economic perspective and do not impose a significant burden on patients or the National Health Service.

[0066] Next, in the following [Experimental Section], the present invention will be described in a more limited manner. [Experimental Section] Evaluation of a method for classifying mRCC patients into risk classes according to the present invention Twenty-three metastatic renal cancer patients who received TKI treatment were classified according to the IMDC score as follows: Seven patients were classified as having a favorable prognosis (''c-favorable'') Nine patients were classified as having an intermediate prognosis (''c-intermediate'') Seven patients were classified as having a poor prognosis (''c-poor'').

[0067] Before the start of the therapy, PBMCs (peripheral blood mononuclear cells) were collected from blood samples taken from these patients. For the analysis of the expression of CD137 on T lymphocytes, especially CD8 lymphocytes, the PBMCs were subjected to flow cytometry analysis. + CD137 on lymphocytes + For the analysis of the expression of CD137 on T lymphocytes, especially CD8 lymphocytes, the PBMCs were subjected to flow cytometry analysis.

[0068] For the patient population considered, the values corresponding to the quartiles (as already specified) for this parameter were: ''T lympho-favorable'': CD8 + CD137 + % value ≧ 2.620% (≧ 75th percentile); ''T lympho-intermediate'': CD8 + CD137 + % value falling within the range of 0.8 - 2.620% (0.8% ≦ CD8 + CD137 + < 2.620%) (i.e., falling within the interquartile range (IQR)); ''T lympho-poor'': CD8 + CD137 + % value < 0.8% (< 25th percentile) is.

[0069] In contrast, circulating CD8 + CD137+ The median of T lymphocytes% is 1%, CD8 + CD137 + % ≥ 1% (median): Score = 1; CD8 + CD137 + % < 1% (median): Score = 0 is.

[0070] At the same time, serum was collected from the patients and used as isolated samples to analyze the serum VEGF concentration by ELISA assay (Human VEGF Quantikine ELISA Kit, R&D System, cat.no. DVE00).

[0071] For the patient population considered, the values corresponding to the quartiles (as already specified) for these parameters are: "VEGF - good": Serum VEGF value < 192.6 pg / ml (< 25th percentile); "VEGF - intermediate": Serum VEGF values falling within the range of 192.6 - 472.5 pg / ml (192.6 pg / ml ≤ VEGF < 472.5 pg / ml; thus falling within the interquartile range (IQR)); "VEGF - poor": VEGF value ≥ 472.5 pg / ml (≥ 75th percentile) is.

[0072] In contrast, the median of serum VEGF concentration is 334 pg / ml, VEGF ≥ 334 pg / ml (median): Score = 0; VEGF < 334 pg / ml (median): Score = 1 is.

[0073] Based on the clinical classification (IMDC), the overall survival (OS) of patients was calculated by Kaplan Meyers analysis (p = 0.0005). In this classification, patients with "c - good" are classified as better than patients with "c - poor", but the survival curves of patients between "c - intermediate" and "c - good" are not statistically significant ((p = 0.1987). This major problem is overcome by the present invention, namely, calculating an immunological score for patients with "c - intermediate"; combining the IMDC classification with an immunological classification for patients with "c - poor" and "c - good" through.

[0074] The final stratification resulting from the method according to the present invention overcomes the major problem by improving the final survival curve between "good" and "intermediate" from a p - value = 0.1987 to a p - value = 0.0206.

Claims

1. A method for classifying patients with metastatic renal cell carcinoma (mRCC) into risk classes called "favorable", "intermediate", and "unfavorable" according to the IMDC model, wherein the model is based on the serum VEGF concentration value and circulating CD8 + CD137 + T lymphocyte percentage, and is complemented by the measurement results of two immunological parameters, and is characterized by immune-IMDC classification.

2. The method according to claim 1, characterized in that, if the serum VEGF value in a patient classified as "c-intermediate" according to the IMDC classification is equal to or greater than the median, a score of 0 is assigned to the patient, and if it is less than the median, a score of 1 is assigned to the patient.

3. Circulating CD8 in patients classified as "c-intermediate" according to the IMDC classification + CD137 + The method according to claim 1, characterized in that if the percentage of CD137 T lymphocytes is greater than or equal to the median, a score = 1 is assigned to the patient, and if it is less than the median, a score = 0 is assigned to the patient.

4. Patients classified as "c-intermediate" according to the IMDC classification, in the immune-IMDC classification a. Serum VEGF value and circulating CD8 + CD137 + When the score is one or two from the arithmetic sum of the scores derived from the CD137 T lymphocyte percentage value, it is "good" b. Serum VEGF value and circulating CD8 + CD137 + When the score derived from the arithmetic sum of the scores of the CD137 T lymphocyte percentage value is set to a score of 0, "intermediate" The method according to claim 2 or 3, characterized by classifying into

5. The serum VEGF value in a patient classified as "c-favorable" or "c-poor" according to the IMDC classification enables patients with a serum VEGF value < 25th percentile to be classified as "VEGF-favorable", patients with a serum VEGF value within the interquartile range between Q1 and Q3, i.e., Q1 ≤ VEGF < Q3, to be classified as "VEGF-intermediate", and patients with a VEGF value ≥ 75th percentile to be classified as "poor". The method according to claim 1, characterized by this.

6. In patients classified as "c - good" or "c - poor" according to the IMDC classification, circulating CD8 + CD137 + T lymphocyte percentage values enable patients with circulating CD8 + CD137 + T lymphocyte % values ≥ 75th percentile to be classified as "T lympho - good", and circulating CD8 within the interquartile range between Q1 and Q3 + CD137 + T lymphocyte % values, i.e., Q1 ≤ CD8 + CD137 + < Q3 to be classified as "T lympho - intermediate", and circulating CD8 + CD137 + The method according to claim 1, characterized in that it enables patients with circulating CD8 T lymphocyte percentage values < 25th percentile to be classified as "T lympho - poor".

7. Patients classified as "c-favorable" or "c-poor" according to the IMDC classification, in the immunological classification (i) a. Serum VEGF value and circulating CD8 + CD137 + When classified as "VEGF - good" and "T lympho - good", "VEGF - good" and "T lympho - intermediate", or "VEGF - intermediate" and "T lympho - good" according to the serum VEGF value and the percentage value of circulating CD8 + CD137 + T lymphocytes, respectively, it is "i - good"; b. Serum VEGF value and circulating CD8 + CD137 + When classified as "VEGF - good" and "T lympho - poor", "VEGF - intermediate" and "T lympho - intermediate", or "VEGF - poor" and "T lympho - good" according to the serum VEGF value and the percentage value of circulating CD8+ CD137+ T lymphocytes, respectively, "i - intermediate"; c. Serum VEGF value and circulating CD8 + CD137 + When classified as "VEGF-poor" and "T lympho-intermediate", "VEGF-poor" and "T lympho-poor", or "VEGF-intermediate" and "T lympho-poor" according to the serum VEGF value and the percentage value of circulating CD8+CD137+ T lymphocytes, respectively, it is "i-good". The method according to claim 5 or 6, characterized by classifying into

8. Patients classified as "c-favorable" or "c-poor" according to the IMDC classification, in the immune-IMDC classification a. If classified as "c-favorable" and "i-favorable", or "i-favorable" and "i-intermediate" respectively according to the IMDC model and the immunological classification (i), it is "favorable"; b. If classified as "c-favorable" and "i-poor", or "c-poor" and "i-favorable" respectively according to the IMDC model and the immunological classification (i), it is "intermediate"; c. If classified as "c-poor" and "i-intermediate", or "c-poor" and "i-poor" respectively according to the IMDC model and the immunological classification (i), it is "poor" The method according to claim 7, characterized by classifying into

9. Use of the method according to claim 1 for identifying a treatment course required by an mRCC patient.

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