CN117136308A - Novel biomarker - Google Patents
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Abstract
The present invention relates to an in vitro method for predicting the response to immunotherapy or for prognostic diagnosis of the survival time of a subject suffering from cancer, the method comprising measuring in a tissue affected by said cancer a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for both CD68 and CD163, determining the relationship between D1 and D2; and comparing the determined relationship to at least one predetermined reference value that predicts a response of the subject to immunotherapy or that is indicative of survival time of the subject.
Description
Technical Field
The present invention relates to the field of detection and analysis of cell populations for the purpose of prognosis of disease progression and in particular for the purpose of predicting the response of cancer patients to immunotherapy and evaluating survival time of cancer patients.
Background
Cancer is the leading cause of death worldwide, and it is estimated that about 960 tens of thousands die from cancer in 2017. The number of cancer cases has grown slowly with the increase in life expectancy due to the development of other death therapies. Thus, there is a continuing need for new methods for assessing cancer to inform both patients and caregivers of the status and future survival prospects of the individual disease of the patient.
The TNM classification system for malignant tumors (Brierly et al, 2017) provides an international unified standard describing and staging cancers, which is published by the International anticancer Union (UICC).
Has been proposed under the trade name(sometimes abbreviated as "IS" in this disclosure) a commercially available immune scoring system that evaluates CD3 in tumor center and at the invasion margin in conventionally resected tumors + And CD8 + Abundance of T cells (Galon et al, 2006). It has recently been validated as an independent prognostic factor in colon cancer stages I-III in addition to other clinical parameters, including T and N (Pag. Et al, 2018). Although->The effectiveness in colorectal cancer is demonstrated, but there is still a lack of strong evidence of its prognostic significance in other tumor types.
Has been shown to be CD163 + Tumor infiltrating macrophages and CD8 + Cells are key prognostic biomarkers in osteosarcoma (Gomez-Brouchet et al,2017). The presence of CD68 and CD163 staining has been found to be highly correlated, which has been found to indicate the possible presence of a common subset of macrophages. The interpretation results indicate that high levels of CD163 and CD68 are associated with better overall survival and no metastasis development survival. The authors also found CD8 between patient samples + The staining level was low, with a median staining of 1%. Although CD8 is detected in more than half of the patient samples + Cells, however their presence is significantly associated with lower metastasis rates at the time of diagnosis. CD8 has not been studied + 、CD163 + And CD68 + Relationship between quantitative measurements of cells.
WO2016/134416 discloses a method of providing a prognosis of a subject suffering from diffuse large B-cell lymphoma in response to a treatment regimen, the method comprising: determining an immune score for the subject based on a ratio of the level of any one or more of CD137, CD4, CD8, CD56, tnfa (α), and LMO2 in the subject to the level of any one or more of PD-1, PD-L1, CD163, CD68, PD-L2, LAG3, TIM3, and SCYA3 (CCL 3) in the subject, and comparing the immune score to a reference score; wherein an immune score compared to the reference score is indicative of the prognosis of the subject's response to the treatment regimen. The specific immunity scores disclosed in WO2016/134416 all define a ratio that includes multiple marker levels in the molecule, and all of the indicated immunity scores introduce PD-1 or PD-L1.
Disclosure of Invention
It is an object of the present invention to provide alternative and improved biomarkers for assessing various forms of cancer, and in particular to provide methods for prognostic diagnosis of survival time of a subject suffering from cancer.
Thus, in a first aspect, the invention relates to an in vitro method for predicting the response to immunotherapy or the survival time of a subject diagnosed with cancer, comprising:
-measuring, in a tissue affected by said cancer, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of the following: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2; and
-comparing the determined relationship with at least one predetermined reference value predicting the response of the subject to immunotherapy or indicative of the survival time of the subject.
In one embodiment, the method comprises
-obtaining a cancer tissue sample from the subject;
-in the tissue sample, measuring a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with at least one predetermined reference value indicative of the survival time of the subject; and
-determining a prognosis of the subject's response to the immunotherapy based on the comparison.
In one embodiment, a predetermined reference value for predicting a subject's response to immunotherapy is determined by:
-measuring, in a cancer tissue sample from each subject in a group of subjects diagnosed with said cancer and having a known response to immunotherapy, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining a reference value to predict the response of a subject diagnosed with cancer to immunotherapy.
In one embodiment, the predetermined reference value for the survival time of the prognosis subject is determined by:
-measuring, in a cancer tissue sample from each subject in a group of subjects diagnosed with said cancer and having a known survival time, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining a reference value to indicate the survival time of a subject diagnosed with cancer.
In one aspect, a method of measuring relative cell density in a tissue sample affected by cancer comprises the steps of: measuring a first density D1 of a first cell class consisting of cells positive for CD8 in the tissue sample and a second density D2 of a second cell class consisting of cells positive for at least one of the following in the tissue sample: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q, and calculating the relationship between D1 and D2.
In some embodiments, the second cell class consists of cells positive for both CD68 and CD 163.
In some embodiments, the second cell class consists of cells positive for at least one of C1qA, C1qB, and C1qC, and optionally CD 68.
In some embodiments, the determination of the relationship between D1 and D2 includes calculating the ratio D1/(D1+D2) or D1/D2 or their inverse.
In some embodiments, the cancer is selected from colorectal cancer, bladder cancer, lung cancer, melanoma, and gastroesophageal adenocarcinoma.
In some embodiments, the calculated proportion is combined with at least one clinical risk factor that determines the prognosis of survival time of the subject.
In some embodiments, the at least one clinical risk factor is selected from the group consisting of: sex, microsatellite instability status, tumor lateral, T-phase, N-phase, tumor differentiation of the subject.
In some embodiments, the measurement of cell density is performed by analyzing gene expression.
In some embodiments, the measurement of cell density is performed by: cells positive for CD8 in the tissue area analyzed were counted for cells positive for at least one of the following: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q, and optionally normalized to the size of the tissue region analyzed.
In some embodiments, the tissue region analyzed includes both tumor centers and invasion edges.
In some embodiments, cell counting is aided by staining the tissue with a detectable antibody specific for CD8, CD68, CD163, C1q, C1qA, C1qB, or C1qC to be detected.
In one aspect, the invention relates to an in vitro method for predicting the response to immunotherapy or the survival time of a subject diagnosed with cancer, comprising:
a) Measuring, in a tissue affected by the cancer, a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them;
b) Determining a relationship between C1 and C2; and
c) The determined relationship is compared to at least one predetermined reference value that predicts a response of the subject to immunotherapy or is indicative of the survival time of the subject.
In some embodiments, the method comprises
-obtaining a cancer tissue sample from the subject;
-performing steps a) -c) of the method according to claim 16; and
-determining a prognosis of the subject's response to the immunotherapy based on the comparison.
In some embodiments, wherein the method is for predicting a subject's response to immunotherapy, the predetermined reference value is determined by:
-measuring, in a tissue affected by said cancer, a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them;
-determining a relationship between C1 and C2; and
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining a reference value to predict the response of a subject diagnosed with cancer to immunotherapy.
In one aspect, the method relates to a method of measuring the relative molecular concentration in a tissue sample affected by cancer, the method comprising the steps of: measuring in the tissue sample a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them; and calculating the relationship between C1 and C2.
In some embodiments, the second set of molecules consists of CD68 and CD163 or RNA molecules encoding them.
In some embodiments, the second set of molecules consists of at least one of C1qA, C1qB, and C1qC, and optionally CD68 or RNA molecules encoding them.
In some embodiments, the determination of the relationship between C1 and C2 includes calculating the ratio C1/(C1+C2) or C1/C2 or their reciprocal.
In some embodiments, the cancer is selected from colorectal cancer, breast cancer, pancreatic-duodenal cancer, bladder cancer, lung cancer, melanoma, and gastroesophageal adenocarcinoma.
In some embodiments, the determined relationship is combined with at least one clinical risk factor that determines the prognosis of survival time of the subject.
In some embodiments, the at least one clinical risk factor is selected from the group consisting of: sex, microsatellite instability status, tumor lateral, T-phase, N-phase, tumor differentiation of the subject.
In some embodiments, the measurement of concentration is performed by whole-body RNA sequencing.
Drawings
Fig. 1: forest plots of univariate associations of immune cell subclasses, (cell density assessment as classification values converted to three levels) with OS in non-therapeutic treated stage I-III colon cancer patients. The filled squares represent risk ratio (HR) and the whiskers (whisker) represent 95% Confidence Interval (CI). Cox regression was used for statistical analysis. Asterisks indicate statistically significant associations (p < 0.05).
Fig. 2: kaplan-Meier survival curve in patients with stage I-III colon cancer not treated with therapy (n=286). Overall survival (a) and relapse free survival (B) of patient groups divided by SIA divided into three categories, with SIA-low used as reference group. The overall survival (C) and relapse free survival (D) of the patient group were divided by IS.
Fig. 3: prediction accuracy of SIA, IS and clinical parameters for OS (a) and RFS (B) by 1000-fold bootstrap resampling using integrated time-dependent AUC analysis (iAUC).
Fig. 4: for patients with stage II colon cancer (a) and metastatic colorectal cancer (B) divided by SIA divided into three categories, kaplan-Meier curve of OS and number at risk.
Fig. 5: overall survival, divided by SIA, among 6 tumor types (bladder urothelial carcinoma (BUC), gastroesophageal Adenocarcinoma (GA), lung Cancer (LC), melanoma, endometrial cancer (UCEC) and Ovarian Cancer (OC)). Patients in BUC, GA and LC cohorts were divided in percentiles according to SIA levels. In two groups separated by median, melanoma patients were divided. SIA is prognostic in bladder cancer, gastroesophageal junction cancer, lung cancer and melanoma. In UCEC (p-value=0.996) and OC (p-value=0.383), overall survival divided by SIA and analyzed by log-rank showed no statistically significant correlation.
Fig. 6: of the 7 tumor types (3 upper groups), the group was divided into two classes by CD8A and C1q complement subunits: overall survival divided by the ratio between the overall RNA expression levels of each of C1QA, C1QB and C1QC, and overall survival divided by the average overall RNA expression levels of CD8A and CD3E (IS-like metrics) divided into two classes. Gene expression and survival data implemented from KM plotter database.
Fig. 7: SIA values were generated from the whole RNA data by calculating the ratio between counts of CD8A and C1QA-C expression in melanoma treated with 26 immune checkpoint inhibitors from patients grouped according to response.
Detailed Description
Definition of the definition
In the present disclosure the term "immune activation profile" or "SIA" is used to represent, on the one hand, a biomarker for the whole cd8+ cell population and, on the other hand, a biomarker for the macrophage subtype expressing both CD68 and CD163, which biomarkers comprise a calculated score based on the relationship between cell densities in tissue sections of cancer tissue.
"OS" is an abbreviation for overall survival.
"RFS" is an abbreviation for relapse free survival.
The term colon cancer is used to denote cancer of the colon (classified as anatomical site C18 in the TNM classification), whereas rectal cancer is used to denote cancer of the rectum (classified as anatomical site C20 in the TNM classification). The term colorectal cancer (or CRC) is used to refer to cancers of the colon or rectum.
The term "gastroesophageal adenocarcinoma" refers to adenocarcinoma of the esophagus or stomach region.
C1q or complement component 1q is a protein complex of 400kDa formed by three subunits, each comprising 6 peptide chains and a total of 18 peptide chains. Of these 18 peptide chains, 6 are A-chains (C1 qA), 6 are B-chains (C1 qB) and 6 are C-chains (C1 qC). In the context of the present disclosure, the term "C1q" refers to any of C1qA, C1qB, and C1qC, as well as the intact protein complexes and subunits thereof, as well as DNA/RNA encoding these, as provided by context. In the context of the present disclosure, the terms "C1qA", "C1qB" and "C1qC" refer to the individual peptide chains as well as DNA/RNA encoding these, as provided by context.
Detailed description of the invention
The invention is based on the following unexpected findings: measurements of two specifically defined cell classes in a tumor microenvironment and their calculation of relative densities can be used to predict the response of cancer patients to immunotherapy and survival of cancer patients. This ratio between cell classes can distinguish between responsive cells (responders) to immune checkpoint inhibitor therapy and also predicts survival in colon cancer better than prior art scoring methods and has the highest relative contribution to survival prediction compared to established clinical parameters. This ratio is prognostic in other cancers with high mutational burden, such as those of the lung, bladder, esophagus and melanoma.
The predicted and prognostic biomarkers according to the invention confirm CD8 + The prognosis of cell infiltration affects and provides a prognostic subtype of macrophages that is undetectable using a single marker approach. Unlike some prior art methods, the present invention does not require independent assessment of tumor center and invasion margin. As shown below, the biomarkers according to the invention and known biomarkersCan be used as independent variables in multivariate analysis. These two measures are not redundant and it is speculated that different aspects of tumor immunity are obtained.
Modern in situ analytical techniques such as multi-marker immunohistochemistry and multispectral imaging are capable of sub-classifying immune cells into different phenotypes and functional groups through multiple labeling of markers. For acquired (adaptive) and innate immune cell visualization, the inventors developed two such panels, each consisting of antibodies to five immune markers. The co-expression pattern of these markers enabled the sub-classification of immune cells after cell segmentation of digitized tissue sections as described in the experimental section of the disclosure (table 1).
Table 1: the immune marker combinations in the two groups define classes and subclasses of immune cells.
The major immune cell lineages were defined by single marker expression (CD 4, CD8, CD45RO, CD68 and CD 163). In addition, cells are divided into subclasses based on marker co-expression. Thus, we identified memory cd4 (cd4+cd45ro+) and cd8 (cd8+cd45ro+) lymphocytes, classical regulatory T (cd4+foxp3+) and cd8+ Treg (cd8+) cells. Since markers of Natural Killer (NK) cells are less specific, we need co-expression of both markers (CD 56 and NKp 46) to classify cells as NK. Similarly, NK T (NKT) cells are defined as those expressing both NK markers and CD 3. Finally, the monocyte/macrophage lineage was subdivided into M1-like macrophages (CD68+CD163-), M2-like macrophages (CD68+CD163+) and CD68-CD163+ cells.
The prognostic impact of the density of different immune cells in colon cancer (n=286) untreated with TNM I-III therapy was assessed. Only two immune cell classes showed a correlation of cell density with Overall Survival (OS), namely CD 8-positive T lymphocytes (positive correlation, p=0.042) and M2-like macrophages (negative correlation, p=0.004) (fig. 1). Interestingly, only for the entire CD8 + The cell population showed positive association with OS, but for any sub-classification of CD8 + The subtype did not show a positive association with OS. Furthermore, the pan-macrophage marker CD68 alone and CD163 alone (which is considered a marker of M2 macrophage differentiation) are both unassociated with survival, whereas the more strictly defined M2-like macrophage subtype expressing both CD68 and CD163 is associated with survival. This M2-like cell subtype only accounts for tumor CD68 + Macrophage 5% and CD163 + 23% of cells.
Based on this unexpected finding, immune activation Signatures (SIA) based on both immune cell types were generated and form the basis of a part of the present invention. Without being bound by theory, it is hypothesized that CD8 + The relative infiltration levels of cells and M2-like macrophages cause interactions between the antitumor and pro-tumor properties of the immune microenvironment.
Furthermore, it was determined that the expression of the C1q component defines M2-like macrophages in malignant tissue as well as normal tissue. It was also found that a high ratio of CD8 to C1q gene expression was prognostic for overall survival in bladder, esophageal and rectal cancers, and lung adenocarcinoma, but not in ovarian and endometrial cancers, and lung squamous cell carcinoma, largely confirming the results from immunohistochemical-based SIA scores defined above.
It has also been found that immune activation characteristics can distinguish between cells that respond to immune checkpoint inhibitor therapy.
Thus, in a first aspect, the invention relates to an in vitro method for the prognostic diagnosis of survival time of a subject suffering from cancer, the method comprising
Measuring a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for both CD68 and CD163 in a tissue affected by said cancer,
-determining the relationship between D1 and D2; and
-comparing the determined relationship with at least one predetermined reference value indicative of the survival time of the subject.
In one embodiment, the method comprises
-obtaining a cancer tissue sample from the subject;
measuring in the tissue sample a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for both CD68 and CD163,
-determining the relationship between D1 and D2;
-comparing the determined relationship with at least one predetermined reference value indicative of the survival time of the subject; and
-determining a prognosis of survival time of the subject based on the comparison.
In one aspect, the invention relates to a method of measuring relative cell density in a tissue sample affected by cancer, the method comprising the steps of: measuring a first density D1 of a first cell class consisting of cells positive for CD8 in the tissue sample and a second density D2 of a second cell class consisting of cells positive for both CD68 and CD163 in the tissue sample, and calculating a relationship between D1 and D2.
In one aspect, the invention relates to a method generally as described herein, wherein the second cell class is defined not as cells positive for both CD68 and CD163, but as cells positive for at least two cell markers selected from the group consisting of: CD206, CD200R, CD, CD204, macrophage activating protein (MAF) and CD86, and the second density D2 is the density of this cell class. In one embodiment, the second cell class is defined as for at least CD206 and CD200R; CD206 and CD36; CD206 and CD204; CD206 and MAF; CD206 and CD86; CD200R and CD36, CD200R and CD204; CD200R and MAF; CD200R and CD86; CD36 and CD204; CD36 and MAF, CD36 and CD86; CD204 and MAF; CD204 and CD86; and/or cells positive for MAF and CD 86.
As is known in the art, a sample of tissue affected by cancer, i.e., cancerous tissue, may be obtained by surgical excision, biopsy, and the like.
In one embodiment, the predetermined reference value is determined by:
measuring a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for both CD68 and CD163 in a cancer tissue sample from each subject in a group of subjects diagnosed with said cancer and having a known survival time,
-determining the relationship between D1 and D2;
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining a reference value to indicate the survival time of a subject diagnosed with cancer.
The relationship between D1 and D2 can be calculated in many ways, such as a simple ratio between cell densities (i.e., D1/D2 or D2/D1) or in a relationship of one of the cell densities to the sum of the cell densities of the two cell categories (e.g., D1/(D1 +D2) or D2/(D1 +D2) or their inverse).
The reference value may be determined in a number of ways to correlate the relationship between D1 and D2 with the predicted immunotherapy response of the subject. The reference value may be determined by determining the relationship between D1 and D2 in samples from a reference group of patients diagnosed with the relevant cancer form, wherein for each patient in the reference group the actual immunotherapy response is known. These samples may be obtained from an existing collection of tissue samples (e.g., a "bio-pool"), or a new collection of samples collected from specifically selected, diagnosed, and/or categorized patients, wherein the samples are evaluated as useful for establishing a relevant reference group.
The reference value may also be determined in a number of ways to correlate the relationship between D1 and D2 with the expected survival time of the subject. The reference value may be determined by determining the relationship between D1 and D2 in samples from a reference group of patients diagnosed with the relevant cancer form, wherein the actual survival time is known for each patient in the reference group. These samples may be obtained from an existing collection of tissue samples (e.g., a "bio-pool"), or a new collection of samples collected from specifically selected, diagnosed, and/or categorized patients, wherein the samples are evaluated as useful for establishing a relevant reference group.
In one embodiment, the reference value is determined by: the relationship between D1 and D2 for each sample in the reference group is obtained and the obtained relationship is converted into a classification variable having a set number of levels/categories, such as "high" and "low" (two levels) or "high", "medium" and "low" (three levels), wherein the number of samples in each category is substantially equal.
The reference value may also be obtained by: for each subject providing a sample to the reference group, an expected time to live level (e.g., "> X weeks" and "+.x weeks" in the case of two categories) is assigned, each obtained relationship between D1 and D2 is assigned to the relevant time to live category and a cutoff value for statistical correlation between the categories is calculated.
To verify the prognostic value of the biomarkers according to the invention with respect to Overall Survival (OS) and Relapse Free Survival (RFS) in colon cancer, the immune activation profile ("SIA") was calculated as D1/(d1+d2) for each sample in the colon cancer reference group.
The SIA was then converted to three levels of classification variables, high, medium and low, using an unbiased method with 33.3% and 66.6% as cut-offs. For comparison, we generated-a sample measurement (IS) which quantifies CD3 at the tumor center and invasion margin + And CD8 + Cell density and similarly translated into a tertiary score (Pag tes et al, 2018). IS-low, IS-medium, and IS-high groups are defined as described, and IS-low IS used as a reference group. Both IS and SIA demonstrated strong association with OS and RFS in stage I-III colon cancer (fig. 2).
Interestingly, in the multivariate Cox model adjusted for pT period, pN period, patient age, gender, and MSI status, both SIA and IS are significantly independent predictors for OS and RFS (table 2).
Table 2: univariate and multivariate analysis of SIA and IS in untreated stage I-III colon cancer. MSI: microsatellite instability; MMR: mismatch repair; * Wald p value.
In addition, the predictive capabilities of SIA and IS and well known clinical risk factors were compared. The integrated time-dependent AUC analysis (iAUC) identified phase T as the strongest clinical predictor of OS (median iAUC 0.58) and phase N as the strongest clinical predictor of RFS (median iAUC 0.58) (fig. 3).
However, both clinical risk factors and IS are inferior to SIA (median iaauc 0.59 for OS and RFS). Addition of SIA to the model with combined clinical parameters further improved predictive power (median iaauc of OS and RFS was 0.66 and 0.67). Finally, when the clinical parameters IS and SIA were integrated into one model, the iacs of OS and RFS were 0.68 and 0.69, respectively. The relative contribution of SIA to OS predictions was higher than those for T and N phases (table 3).
| Covariates | Chi-square | Relative chi square value | P-value |
| SIA | 12.21 | 0.4395 | 0.00223 |
| pT phase | 10.37 | 0.3732 | 0.01567 |
| pN phase | 3.67 | 0.1320 | 0.05549 |
| Differentiation | 0.39 | 0.0140 | 0.82377 |
| Lateral deviation | 1.09 | 0.0391 | 0.29747 |
| Sex (sex) | 0.07 | 0.0024 | 0.79708 |
Table 3: use χ 2 The relative contributions of SIA and clinical parameters determined by the scale test to OS predictions.
When IS included in the model, the relative contribution of SIA and IS exceeded 50% and significantly exceeded the known clinical factor (table 4).
| Covariates | Chi-square | Relative chi square value | P-value |
| SIA | 10.80 | 0.2733 | 0.00452 |
| IS-sample | 10.64 | 0.2693 | 0.00488 |
| pT phase | 12.71 | 0.3217 | 0.00530 |
| pN phase | 3.98 | 0.1008 | 0.04599 |
| Differentiation | 0.43 | 0.0109 | 0.80683 |
| Lateral deviation | 0.95 | 0.0240 | 0.32983 |
| Sex (sex) | 0.00059 | 0.000015 | 0.98067 |
Table 4: use χ 2 The relative contributions of SIA, IS-like and clinical parameters determined to OS predictions were examined in proportion.
The analysis was repeated for stage II colon cancer patients (n=117) and similar results were observed with SIA dividing high and low risk diseases (fig. 4A). In addition, CRC patients with metastatic disease (n=66) were evaluated and divided into three percentile sites of equal size according to SIA. Again, longer OS were seen in SIA-high group (fig. 4B).
Thus, SIA shows independent prognostic performance superior to the strongest known clinical predictors (T and N phases), increasing significant value for the multivariate predictive model in stage I-III colon cancer patients and prognostic power in stage II colon cancer and in metastatic colorectal cancer patients.
It was further investigated whether SIA could also be used as prognostic factor in other tumor types. Since SIA appears to reflect a balance between anti-tumor and pro-tumor cell types in the tumor microenvironment, its usefulness in tumor types with significant immunogenic properties would likely be highest. Thus, we analyzed the data obtained from the TCIA program (charong et al, 2017) and categorized (rank, ranking) different tumor types according to mutation and number of neoantigens. We then analyzed four independent tumor groups characterized by high mutation and neoantigen counts, namely melanoma (n=94) (stronberg et al, 2009), lung cancer (n=251) (mick et al, 2016), bladder urothelial cancer (n=224) (Hemdan et al, 2014) and gastroesophageal adenocarcinoma (n=121) (jermemiasen et al, 2020). We also included two tumor groups with lower mutation and neoantigen density, endometrial cancer (n=295) (Huvila et al, 2018) and ovarian cancer (n=141) (Nodin et al, 2010).
Patients were divided according to SIA in the third column, except for melanoma where 41% of patients had the highest possible SIA value, and thus the median was used as the cutoff instead. When evaluated in the Cox regression model, high SIA was significantly associated with long survival in four tumor types with high mutation and neoantigen counts (p-value range 0.001-0.037), whereas no association was seen in endometrial and ovarian cancers (p-value 0.996 and 0.399, respectively) (fig. 5 and tables 5-8).
The relative risk estimated in the univariate Cox proportional hazards model using overall survival as the endpoint is shown in tables 3-6.
Table 5: bladder urothelial cancer
| Covariates | HR(95％CI) | p value |
| SIA, three types | ||
| High relative low | 0.64(0.42-0.85) | p＝0.038 |
| Low to medium | 0.88(0.67-1.09) | p＝0.548 |
Table 6: gastroesophageal adenocarcinoma
| Covariates | HR(95％CI) | p value |
| SIA, three types | ||
| High relative low | 0.53(0.29-0.78) | p＝0.011 |
| Low to medium | 0.69(0.44-0.93) | p＝0.128 |
Table 7: lung cancer
| Covariates | HR(95％CI) | p value |
| SIA, three types | ||
| High relative low | 0.54(0.34-0.74) | p＝0.002 |
| Low to medium | 1.02(0.84-1.19) | p＝0.931 |
Table 8: melanoma (HEI)
| Covariates | HR(95％CI) | p value |
| SIA, two types | ||
| High relative low | 0.39(0.075-0.71) | p＝0.003 |
No correlation was observed in endometrial and ovarian cancers (p-values 0.996 and 0.399, respectively).
Furthermore, based on the iaauc analysis in the 4 groups, SIA exceeded IS for OS predictions, which showed a median iaauc ranging from 0.55 in bladder cancer to 0.61 in melanoma (table 9). Interestingly, the time-dependent distinguishing properties of SIA in colon cancer were higher than the recently published validated IS performance (iAUC 0.57 (Pag us et al, 2018)).
Table 9: the accuracy of SIA and IS predictions for OS in 4 cancer groups was resampled using integration time dependent AUC analysis (iAUC) with 1000-fold bootstrapping.
Thus, SIA is a prognostic factor in a variety of cancer tumor types.
Thus, in one embodiment of the invention, the cancer is a cancer with a median of mutation and/or neoantigen greater than 100. The median of mutations and neoantigens can be obtained from the cancer immunomic profile (Cancer Immunome Atlas) (TCIA) program @tcia.at/home) (Charoentong et al, 2017).
Single cell RNA sequencing data from 9 colorectal tumors were analyzed (Lee et al, 2020). 6520 macrophages of three subclasses defined by CD68 and CD163 gene expression were identified, including 17% M1-like macrophages, 79% M2-like macrophages and 4% CD68-cd163+ cells. Analysis of the differentially expressed genes in these macrophages indicated that cells of the M2-like subset overexpressed C1QA, C1QB, and C1QC, as well as the subfractions encoding the C1q, C1 complement complex. This was also observed in two other data sets, where C1QA-C was in the top up-regulated gene in M2-like macrophages in lung cancer (n=13286 cells) (Lambrechts et al, 2018) and uveal melanoma (n= 25413 cells) (Durante et al, 2020). In CRC and lung cancer, high expression of APOE encoding apolipoprotein E, limited to M2-like macrophages, was also observed. However, this is not the case in uveal melanoma, where APOE is also highly expressed in M1-like macrophages. By analysis of the complete dataset from three single cell collections of cancer tissue (54259 cells in CRC, 32439 cells in lung cancer and 97550 cells in uveal melanoma), it was observed that C1QA, C1QB and C1QC were almost exclusively expressed in macrophages, while APOE was also expressed in other cell types, consistent with previous findings.
It was further investigated whether there is a difference in the transcriptional profile of macrophages in tumors, adjacent tissues and in non-diseased organs. First, macrophages from tumor and peri-tumor tissues in CRC and lung cancer were compared and the expression levels of C1QA-C and APOE were found to be the same in macrophages from both sites. Then, scRNAseq data (He et al 2020) from 15 different non-malignant organs of the same individual were studied to determine if C1QA-C and APOE producing cells were also present in normal organs. Only a small fraction of the cells expressed C1QA-C (average 4% across all organs ranging from 0.12 in lymph nodes to 17-19% in liver), whereas the larger fraction expressed APOE (average 17%, from 0% in blood to 64% in skin). Most of the cells expressing C1QA-C are macrophages (defined positively by CD68 and/or CD 163) ranging from 45-56% positive cells in lymph nodes to 91-93% in liver.
When analyzing the macrophage subclass, C1QA-C expression is characteristic of M2-like macrophages, but is very low in M1-like cells, whereas APOE expression in macrophages is low and lacks the association with differentiation. Overall, expression of the C1q component defines M2-like macrophages in malignant as well as normal tissues.
Since C1QA-C expression in cancer is predominantly detected in M2-like macrophages, in one embodiment of the invention, the synthesis of the complement C1q component analyzed at the whole RNA level is used to define a second cell class according to the invention. Overall RNA expression data were extracted from KM plotter database (Nagy et al, 2021), the ratio between CD8A and expression levels of C1QA, C1QB or C1QC was divided into two classes, and survival analysis was performed for bladder cancer, esophageal cancer, rectal cancer, endometrial cancer and ovarian cancer, lung adenocarcinoma and lung squamous cell carcinoma. In addition to lung squamous cell carcinoma and ovarian carcinoma, high ratios were associated with improved survival in all tumor types analyzed (fig. 6), largely confirming the results from the immunohistochemical-based SIA scores (see fig. 5). Importantly, the Immunoscore-like metric generated from the whole RNA dataset had poor performance in addition to endometrial cancer (fig. 6). Thus, the ratio between CD8A and C1QA, C1QB or C1QC in the overall tumor gene expression data is prognostic among at least five tumor types.
Finally, it was investigated whether SIA could differentiate between cells responding to immune checkpoint inhibitor therapy. Integral RNA from melanoma in patients treated with anti-PD-1 therapy was analyzed (Hugo et al 2016). Then, the ratio between CD8A and C1QA, C1QB or C1QC gene expression was calculated. Interestingly, the responding cells (n=4) had higher SIA values compared to the partially responding cells (n=10) and the non-responding cells (n=12) (fig. 7A).
To enable more accurate feature estimation, single cell sequencing data from melanoma patients treated with anti-PD 1 and/or anti-CTLA 4 (n=48) (Sade Feldman et al, 2018) were also analyzed, and single cell gene expression levels of CD8A were used to define cd8+ cells, and combinations of expression of CD68 and any of CD163, C1QA, C1QB or C1QC were used to define M2-like macrophages to calculate SIA. With SIA derived from CD8A and cd68+cd163 (p=0.001), cd68+c1qa (p=0.026), cd68+c1qb (p=0.017) or cd68+c1qc (p=0.012), respectively, a clear correlation between high SIA score and response to immune checkpoint inhibitor therapy was observed (fig. 7B).
To verify the accuracy of SIA to predict therapeutic response, ROC analysis was performed, which resulted in an area under the curve (AUC) ranging from 0.70 (for SIA derived from CD8A and cd68+c1qa) to 0.79 (for SIA derived from CD8A and cd68+cd163). Single cell RNA sequencing data from renal cell carcinoma was analyzed (Bi et al 2021), with four patients receiving immune checkpoint therapy and having objective response records. Compared to one patient with tumor progression, two patients with partial response had a higher SIA, which was derived from cell counts considering complement co-expression by M2-like macrophages (fig. 7C). Overall, these observations support that SIA can predict the response in melanoma and other tumor types to treatment with immune checkpoint inhibitors.
In one embodiment, the cancer is selected from colon cancer, colorectal cancer, bladder cancer, lung cancer, melanoma, and gastroesophageal adenocarcinoma.
In one embodiment, the determined relationship is combined with at least one clinical risk factor that determines the prognosis of survival time of the subject.
In one embodiment, the at least one clinical risk factor is selected from the group consisting of: sex, microsatellite instability status, tumor lateral, T-phase, N-phase, tumor differentiation of the subject.
In an embodiment of the invention according to the above aspect, the measurement of cell density is performed by counting cells positive for CD8 and cells positive for both CD68 and CD163 in the analyzed tissue region, and optionally normalizing the size of the analyzed tissue region.
In an embodiment of the invention according to the above aspect, the analyzed tissue region comprises both tumor centers and invasion edges.
In embodiments of the invention according to the above aspects, cell counting is aided by immunofluorescent staining of tissue with detectable antibodies specific for suitable cell markers (e.g., CD8, CD68, and CD 163). Cell counting can generally be performed by: binding a detectable compound (commonly referred to as an "affinity binding agent") capable of specifically affinity binding to a suitable cell marker to cells in a tissue section of the tissue of interest, detecting the amount of the detectable compound bound, and correlating the detected amount to the size of the tissue section or the amount of each cell marker detected to the total amount of all cell markers or cell marker subpopulations. Affinity binders include antibodies (monoclonal and polyclonal) and antibody fragments that include at least the variable regions of both heavy and light immunoglobulin chains held together (typically by disulfide bonds) to retain the antibody binding site. Types of antibody fragments include Fab, fab ', F (ab') 2, fv, igg, single chain variable fragment (scFv), scFv dimer (diabody), scFv fusion protein (e.g., scFv-Fc), affibody (affibody), and the like. Other types of affinity binding agents, such as molecularly imprinted polymers, may also be utilized. Detectable compounds are also known in the art and include, for example, fluorescent moieties, metals (e.g., gold nanoparticles), and moieties that can be used to bind other detectable compounds, such as streptavidin or biotin.
In one aspect, the invention relates to a kit of parts (kit of parts) comprising a set of reagents suitable for aiding the counting of cells positive for CD8 as well as cells positive for both CD68 and CD163, or other suitable markers defining the cell class of interest as disclosed herein. These agents may be selected from the agents listed in table 12 and agents having equivalent function in detecting cells expressing a cellular marker of interest.
The following examples are included to further illustrate and aid in the understanding of the present invention. They should not be considered as limiting the scope of the invention, which is the scope of the appended claims. All references cited herein are expressly incorporated by reference in their entirety.
Examples
Materials and methods
Study group and tissue microarray
Colorectal cancer (CRC) cohort consisted of pre-recruited CRC patients living in Uppsala, sweden, most of which were included in Uppsala-Comprehensive Cancer Consortium (U-CAN, U-CAN. Uu. Se). In the uppsala area, 937 patients were diagnosed with CRC from 2010 to 2014 total. 746 (80%) of them is included in TMA. For this study, only patients with TMA material from the primary tumor were selected. After the staining procedure and quality control, 497 patients had data from two immune panels, of which 286 had TNM I-III untreated colon cancer. Table 10 provides the clinical pathology of the included patients and their tumors.
All patients received standard care according to the staging of the swedish national guidelines in 2008. According to this guideline, if a recurrence risk factor is present, a colon tumor is recommended for primary surgery and adjuvant chemotherapy. If the colon tumor is considered to be non-eradicated/border resectable, a neoadjuvant chemotherapy is administered prior to surgery to shrink the tumor. Rectal cancer receives preoperative or neoadjuvant radiotherapy/chemotherapy according to local or systemic recurrence risk classification. Formalin-fixed paraffin-embedded tissue blocks of primary tumors and distant metastases were used to construct TMAs. Each case was shown on TMA whose core was derived from the central part of the tumor and from the invasion border. The study was performed within ethical approval from the ethical committee of uppsala, sweden.
Table 10
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Table 10: baseline clinical pathology in colorectal cancer cohorts. Patient data are shown in the subgroup with successful staining available from each of the two multiplex subgroups (left column TIL subgroup and middle column NK/MF subgroup) and in the subgroup where both subgroups are available (right column SIA subgroup). Values are shown as case numbers (percent) unless otherwise indicated. Due to rounding, the percentage addition may not be 100%. ( MSI-microsatellite instability; MMR-mismatch repair; RT-radiotherapy; scRT-short term radiotherapy; lcRT-long term radiotherapy; CT-chemotherapy; CRT-radiotherapy and chemotherapy )
The melanoma cohort covers the TMA core from 94 patients diagnosed with primary cutaneous malignant melanoma in the uppsala region of sweden from 1980 to 2004 (stronberg et al 2009). The research ethics committee of the university of uppsala, sweden approved the study.
The lung cancer cohort covers TMA cores from 251 patients diagnosed with non-small cell lung cancer undergoing surgical treatment at the university of uppsala hospital sweden from 2006 to 2010 (mick et al, 2016). The study was performed under the permission from the ethics committee of the uppsala area.
The gastroesophageal cancer group includes breast and Markov from 2006 to 2010TMA core of 121 patients with gastroesophageal adenocarcinoma untreated by chemotherapy who underwent surgery at college hospital (jersey et al 2020). The study was conducted under the permission from the ethics committee of the lode area.
The urothelial carcinoma group covers the TMA core of primary urothelial tumor collection from 224 patients undergoing surgery at the university of uppsala hospital in 1984 to 2005 (Hemdan et al, 2014). The study was performed under the permission from the ethics committee of the uppsala area.
The endometrial cancer group of the uterus consists of 295 cases of TMA cores of uterine cancer from patients treated by surgery at the university of finland Turku (Turku) hospital between 2004-2007 (Huvila et al, 2018). The study was conducted under the permission from the helsinki ethics review board.
As a group from the previous population-based group derived from two mixes: markov diet and cancer researchDiet and Cancer Study) and a Markov preventive program (+.>Preventive Project) provides ovarian cancer groups. The study was conducted under the permission from the ethics committee of the lode area.
Table 11 provides the clinical pathological features of the patients included in the melanoma, lung, gastroesophageal, urothelial, UCEC, and ovarian cancer groups and their tumors.
TABLE 11
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Table 11: baseline clinical pathology characteristics of the cohort were validated. Patient data is displayed in the group in which SIA data is available. Values are shown as case numbers (percent) unless otherwise indicated. Due to rounding, the percentage addition may not be 100%.
a Median survival time was calculated using Kaplan-Meier method
b When median survival time cannot be calculated from the data, mean survival time is estimated
Multiplex immunofluorescent staining
For multiplex immunofluorescent staining, 4 μm thick TMA sections were dewaxed, rehydrated and distilled H 2 And (3) flushing in O. Two staining protocols were established for the two antibody panels: the lymphocyte panel, with CD4, CD8, CD20, foxP3, CD45RO and pan-Cytokeratin (CK), and the NK/macrophage panel, cover CD56, NKp46, CD3, CD68, CD163 and pan-CK. The staining procedure was performed as described previously (Mezheyeuski et al, 2018). Detailed staining conditions and reagent references are provided in table 12.
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* Antigen retrieval was performed in a microwave oven at 100℃for 15 min.
Use of an amplification System->HRP or Opal HRP: />HRP anti-mouse IgG (peroxidase) (catalog number: MP-7402-50) and anti-rabbit IgG (peroxidase) polymer detection kit, prepared in horses (catalog number: MP-7401-50) (Vector laboratories); opal TM Polymer anti-Rabbit + anti-mouse HRP kit (catalog number: ARH1001 EA) (Akoya).
# In melanoma, melan A was used to identify malignant tissue against the stroma instead of the cytokeratin/E-cadherin mixture.
Table 12: list of antibodies, dilution and amplification reagents for multiplex fluorescent IHC.
The relevance of the cell markers CD206, CD200R, CD, CD204, macrophage activating protein (MAF) and CD86 for classifying the cell population as M2-like macrophage population was investigated as alternatives to the cd68+, cd163+ populations using the corresponding methods and reagents specific for these cell markers.
Imaging, image analysis and thresholding (thresholding)
The stained TMA was imaged in multispectral mode using the Vectra polar system (Akoya) at a resolution of 2 pixels/. Mu.m. Each image was manually examined and selected by a pathologist to exclude artifacts, staining defects, and accumulation of immune cells in the necrotic area and glandular structures. Vendor-provided machine learning algorithms executing in the inForm software are trained to classify the organization into three categories: tumor compartment, stromal compartment or empty space. Each group was trained separately by providing a set of samples manually annotated by a pathologist. Cell segmentation was performed using DAPI nuclear staining as described (Mezheyeuski et al, 2018). The perinuclear region 3 μm (6 pixels) from the nuclear boundary was considered the cytoplasmic region. The cell typing function of the inForm software was used to manually define a representative subset of cells positive for expression of each marker and a subset of cells negative for all markers. The intensity of expression of the marker in the selected cells was used to set a marker positive threshold.
In the R programming environment [ R Core Team,2013 ] by GeneVia technology (Tampere, finland)]The intensity threshold of the marker is determined. Marker-specific thresholds are defined by the distribution of positive and negative cell intensities for the markers. The marker-specific probability density distribution is estimated by smoothing the intensity values using a density function of R packages stats with gaussian kernel estimation with automatic bandwidth detection. The intensity threshold for each marker is set to (1) the average of the highest intensity of the negative cells and the lowest intensity of the positive cells if the intensities of the positive and negative cells do not overlap, or (2) an intensity value that minimizes the overall classification error based on the probability density distribution if there is an overlap. For each established threshold, i.e. for each marker, false positive rate, true positive rate, false negative rate, true negative rate and overall classification error are calculated and controlled individually. For each tumor type, a threshold is set up separately and independently and applied to the raw output data for the complete group. Thus, for each marker in the panel, each cell was characterized as positive or negative. This data was used to classify cells and define their immune subtypes (table 1). Finally, cell counts were normalized for the size of the tissue region analyzed and were used as cell density (units/mm in further analysis 2 ) And (3) using.
Immune activation features
The immune activation profile (SIA) was calculated as the ratio of cd8+ cell density to the sum of cd8+ and M2-like cell density or sia= (CD 8 density)/(cd8 density+m2-like density). As described generates(IS) (Pag [ s ] et al, 2018). Each tumor in the CRC TMA group is represented by a TMA core derived from the center part of the tumor and the invasion margin. CD3 and CD 8-positive cells are defined in each region, thus resulting in four values for each case (i.e., CD3 density in the tumor center, CD8 density in the tumor center, CD3 density at the invasion margin, CD8 density at the invasion margin). IS generated as described by calculating the average of the four values. In other groups, TMA cores were obtained from the whole tumor area with no separation between the central part and the invasion border. Thus, for these tumors, two values (CD 3 and CD 8-positive cell densities) were obtained for each case, and IS was generated by calculating the average of the two values. Furthermore, IS IS classified into 3 groups, low (average percentile 0-25%), medium (25-70%) and high (70-100%) using average percentiles.
Mutant and novel antigens
The median of the number of mutations and neoantigens between 19 solid cancers was obtained from the cancer immune panel profile (Cancer Immunome Atlas) (TCIA) program tcia.at/home)。
Statistics of
Statistical analysis was performed using R (version 3.5.1) and SPSS V20 (SPSS inc., chicago, IL). In patients with CRC phase I-III, where curative surgery is performed, the Relapse Free Survival (RFS) is calculated as the time from surgery to first record of disease progression (including local relapse or distant metastasis or death due to any cause, whichever occurs first). Overall Survival (OS) is the time from surgery to death for any reason. To estimate the relative risk in both univariate and multivariate models, a Cox proportional hazards model was used. The prediction accuracy of the model was assessed by 1000-fold bootstrap resampling and by calculating the time-dependent area under the receiver operating characteristic curve (iAUC) for each bootstrap sample. Using chi-square ratio (χ) 2 ) The relative importance of the parameters used to estimate the risk of survival is calculated.
Reference to the literature
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Claims (26)
1. An in vitro method for predicting the response to an immunotherapy or the survival time of a subject diagnosed with cancer, or a prognostic diagnosis of a subject with cancer, comprising:
-measuring, in a tissue affected by said cancer, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of the following: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2; and
-comparing the determined relationship with at least one predetermined reference value predicting the response of the subject to immunotherapy or indicative of the survival time of the subject.
2. The method of claim 1, wherein the method comprises
-obtaining a cancer tissue sample from the subject;
-in the tissue sample, measuring a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with at least one predetermined reference value indicative of the survival time of the subject; and
-determining a prognosis of the subject's response to immunotherapy based on the comparison.
3. A method for predicting a subject's response to immunotherapy according to any one of the preceding claims, wherein the predetermined reference value is determined by:
-measuring, in a cancer tissue sample from each subject in a group of subjects diagnosed with said cancer and having a known response to immunotherapy, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining the reference value to predict the response of a subject diagnosed with the cancer to immunotherapy.
4. A method for predicting survival time of a subject according to claims 1-3, wherein the predetermined reference value is determined by:
-measuring, in a cancer tissue sample from each subject in a group of subjects diagnosed with said cancer and having a known survival time, a first density D1 of a first cell class consisting of cells positive for CD8 and a second density D2 of a second cell class consisting of cells positive for at least one of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q,
-determining the relationship between D1 and D2;
-comparing the determined relationship with the known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining the reference value to indicate the survival time of a subject diagnosed with the cancer.
5. A method of measuring relative cell density in a tissue sample affected by cancer, comprising the steps of: measuring a first density D1 of a first cell class consisting of cells positive for CD8 in the tissue sample and a second density D2 of a second cell class consisting of cells positive for at least one of the following in the tissue sample: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q, and calculating the relationship between D1 and D2.
6. The method of any one of the preceding claims, wherein the second cell class consists of cells positive for both CD68 and CD 163.
7. The method of any one of claims 1-4, wherein the second cell class consists of cells positive for at least one of C1qA, C1qB, and C1qC, and optionally CD 68.
8. The method according to any of the preceding claims, wherein the determination of the relationship between D1 and D2 comprises calculating the ratio D1/(d1+d2) or D1/D2, or their reciprocal.
9. The method of any one of the preceding claims, wherein the cancer is selected from colorectal cancer, breast cancer, pancreatic-duodenal cancer, bladder cancer, lung cancer, melanoma, and gastroesophageal adenocarcinoma.
10. The method of any one of the preceding claims, wherein the determined relationship is combined with at least one clinical risk factor that determines a prognosis of survival time of the subject.
11. The method of claim 10, wherein the at least one clinical risk factor is selected from the group consisting of: sex, microsatellite instability status, tumor lateral, T-phase, N-phase, tumor differentiation of the subject.
12. The method according to any one of the preceding claims, wherein the measurement of cell density is performed by analysis of gene expression.
13. The method according to any of the preceding claims, wherein the measurement of cell density is performed by: cells positive for CD8 in the tissue area analyzed were counted for cells positive for at least one of the following: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q, and optionally normalizing for the size of the analyzed tissue region.
14. The method of claim 13, wherein the analyzed tissue region includes both tumor centers and invasion edges.
15. The method according to any one of claims 13 or 14, wherein cell counting is assisted by staining the tissue with a detectable antibody specific for CD8, CD68, CD163, C1q, C1qA, C1qB or C1qC to be detected.
16. An in vitro method for predicting the response to an immunotherapy or the survival time of a subject diagnosed with cancer, or a prognostic diagnosis of a subject with cancer, comprising:
a) Measuring, in a tissue affected by the cancer, a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them;
b) Determining a relationship between C1 and C2; and
c) The determined relationship is compared to at least one predetermined reference value that predicts a response of the subject to immunotherapy or is indicative of the survival time of the subject.
17. The method of claim 16, wherein the method comprises
-obtaining a cancer tissue sample from the subject;
-performing steps a) -c) of the method according to claim 16; and
-determining a prognosis of the subject's response to immunotherapy based on the comparison.
18. A method for predicting response of a subject to immunotherapy according to claim 16 or 17, wherein said predetermined reference value is determined by:
-measuring, in a tissue affected by said cancer, a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them;
-determining a relationship between C1 and C2; and
-comparing the determined relationship with a known survival time of the subject to obtain an association between the relationship and the survival time; and
-determining the reference value to predict the response of a subject diagnosed with the cancer to immunotherapy.
19. A method of measuring the relative molecular concentration in a tissue sample affected by cancer, comprising the steps of: measuring a first concentration C1 of a first set of molecules selected from the group consisting of CD8 and RNA molecules encoding it in the tissue sample and a second concentration C2 of a second set of molecules selected from the group consisting of: c1q; a combination of CD68 and CD 163; and combinations of CD68 and C1q; RNA molecules encoding them; and calculating the relationship between C1 and C2.
20. The method of any one of claims 16-19, wherein the second set of molecules consists of CD68 and CD163 or RNA molecules encoding them.
21. The method of any one of claims 16-20, wherein the second set of molecules consists of at least one of C1qA, C1qB, and C1qC, and optionally CD68 or an RNA molecule encoding the same.
22. The method according to any one of claims 16-21, wherein the determination of the relationship between C1 and C2 comprises calculating the ratio C1/(c1+c2) or C1/C2, or their reciprocal.
23. The method of any one of claims 16-22, wherein the cancer is selected from colorectal cancer, breast cancer, pancreatic-duodenal cancer, bladder cancer, lung cancer, melanoma, and gastroesophageal adenocarcinoma.
24. The method of any one of claims 16-23, wherein the determined relationship is combined with at least one clinical risk factor that determines a prognosis of survival time of the subject.
25. The method of claim 24, wherein the at least one clinical risk factor is selected from the group consisting of: sex, microsatellite instability status, tumor lateral, T-phase, N-phase, tumor differentiation of the subject.
26. The method of any one of claims 16-25, wherein the measuring of the concentration is performed by whole-body RNA sequencing.
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