A device for predicting sensitivity of neoadjuvant chemotherapy in breast cancer patients

Abstract

The present invention provides a prediction device for the sensitivity of breast cancer patients to neoadjuvant chemotherapy. Specifically, it includes a detection unit and a data analysis unit, wherein: the detection unit extracts characteristic gene sets of various immune cells based on high-resolution single-cell transcriptome sequencing data of breast cancer samples; the data analysis unit constructs a data model for predicting the sensitivity to neoadjuvant chemotherapy based on the above-mentioned characteristic gene sets of immune cells and the common transcriptome sequencing data of breast cancer patients. The immune cells identified based on single-cell data and their weight coefficients included in the model are: SPP1_mac cells (0.5684), plasma cells (0.6221), pDC cells (-0.9062), mDC cells (1.6405), Tn cells (0.9224), NKT cells (-0.6079), Tprf cells (1.1259).

Description

Technical Field

[0001] The invention belongs to the technical field of gene detection, and in particular relates to a device for predicting the sensitivity of breast cancer patients to neoadjuvant chemotherapy. Background Art

[0002] In recent years, tumors have become one of the major diseases that threaten the health of all mankind. Among them, breast cancer is the type of tumor with the highest incidence, which has caused a serious burden on families and society. In the current treatment of breast cancer, neoadjuvant chemotherapy (NAC) is one of the standard treatments for patients with advanced breast cancer. Its purpose is to reduce the tumor volume and reduce the pathological stage of the tumor in order to meet the standard of surgical resection. However, due to tumor heterogeneity, the sensitivity of different breast cancer patients to neoadjuvant chemotherapy varies greatly. Clinically, only some patients have a gradual shrinkage of the tumor after receiving neoadjuvant chemotherapy, and even no residual tumor cells in the surgical pathological specimens, that is, pathological complete response (pCR). Patients who are insensitive to chemotherapy may not shrink their tumors after neoadjuvant chemotherapy, or even increase their tumors. These chemotherapy-insensitive patients not only cannot benefit from neoadjuvant chemotherapy, but also have to bear the side effects of chemotherapy. Therefore, it is of great clinical significance to identify markers that can predict the efficacy of neoadjuvant chemotherapy in breast cancer patients and to divide patients who are sensitive to neoadjuvant chemotherapy and those who are insensitive.

[0003] Early studies have shown that the status of estrogen receptor (ER) and Ki-67 has a significant impact on the efficacy of neoadjuvant chemotherapy in breast cancer patients. A model has been developed to predict the pCR rate of ER-positive breast cancer patients after neoadjuvant chemotherapy. In the model evaluation, the area under the curve was 0.785. [1] ; ROC curve analysis of Ki-67 positive expression rate in predicting the efficacy of neoadjuvant chemotherapy showed that the AUC for predicting whether pCR can be achieved after neoadjuvant chemotherapy was 0.766 [2] In addition, the AUCs of the progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) models for predicting breast cancer neoadjuvant chemotherapy sensitivity were 0.673 and 0.695, respectively. [3]. Although the above model has certain predictive value, it can be seen that its predictive accuracy is poor, and the number of cases included in the model is small. It has not been verified with a large data set and lacks universality. In recent years, the accuracy of radiomics in predicting the efficacy of neoadjuvant chemotherapy has gradually improved. Radiomics scores are constructed after extracting and screening radiomics features from ultrasound images. The prediction model constructed based on multivariate logistic regression analysis has an AUC of 0.84 in the training set and an AUC of 0.82 in the validation set. Among them, the three variables that contribute most to the model prediction results are radiomics score, HER2, and ER. [4] Although its prediction accuracy has improved compared with the past, its image feature extraction and machine decision-making process lacks transparency and explainability, and is prone to selection bias. Its accuracy and repeatability are challenged, making it difficult to truly apply in clinical practice.

[0004] Tumor immune microenvironment has always been a hot topic in tumor research. A large number of studies have shown that the prognosis of breast cancer patients is closely related to the type and number of immune cell infiltration in the tumor microenvironment. [5,6] . Breast cancers of different pathological types respond differently to chemotherapy and neoadjuvant chemotherapy due to different immune microenvironments. A prospective clinical study has shown that the pCR rate of patients with lymphocyte-dominant breast cancer after neoadjuvant chemotherapy is significantly improved. Tumor infiltrating lymphocytes (TILs) can be used as a predictor to evaluate the response of breast cancer patients to neoadjuvant chemotherapy. [7] Since there are a variety of immune cells with different functions in the tumor's immune microenvironment, including but not limited to TILs, it is too one-sided to use TILs alone to predict the efficacy of neoadjuvant chemotherapy.

[0005] Single-cell RNA-seq (scRNA-seq) can use high-resolution omics technology to detect gene expression levels at the level of individual cells, providing accurate information on cell types and states. In the analysis of the tumor immune microenvironment, scRNA-seq can provide transcriptome characteristics of cell diversity and heterogeneity. [8] It is currently one of the most accurate methods for analyzing the tumor immune microenvironment, but scRNA-seq is expensive and has extremely high sample requirements, making it difficult to popularize in clinical applications.

[0006] References:

[0007] [1] Tang Lingfeng, Shu Xiujie, Yan Ping, et al. Influencing factors and efficacy prediction of neoadjuvant chemotherapy for estrogen receptor-positive breast cancer[J]. Journal of the Third Military Medical University, 2020, 42(23): 2341-2349.

[0008] [2] Wang Le, Zheng Hongmei, Wu Xinhong, et al. Predictive value of clinical pathological characteristics and molecular classification of breast cancer on the efficacy and prognosis of neoadjuvant chemotherapy[J]. Chinese Journal of Practical Diagnosis and Treatment, 2019, 33(08): 739-743.

[0009] [3] Fang Y, Zhang Q, Wu Y, Wu J. HER2-positive is an independent indicator for predicting pathological complete response to neoadjuvanttherapy and Ki67-changed after neoadjuvant chemotherapy predicts favorable prognosis in Chinese women with locally advanced breast cancer. Medicine(Baltimore). 2024 Feb 9;103(6): e37170.

[0010] [4] Feng Xiaodan, Liu Feifei, Du Yao, et al. Prediction of the efficacy of neoadjuvant chemotherapy for breast cancer based on interpretable machine learning model based on ultrasound imaging and clinical characteristics[J]. Chinese Journal of Ultrasound in Medicine. 2024, 40(03): 278-282.

[0011] [5] Dieci MV, Miglietta F, Guarneri V. Immune Infiltrates in BreastCancer: Recent Updates and Clinical Implications. Cells. 2021 Jan 23; 10(2):223.

[0012] [6] Badr NM, Berditchevski F, Shaaban AM. The Immune Microenvironmentin Breast Carcinoma: Predictive and Prognostic Role in the NeoadjuvantSetting. Pathobiology. 2020; 87(2): 61-74.

[0013] [7] Issa-Nummer Y, Darb-Esfahani S, Loibl S, et al. Prospectivevalidation of immunological infiltrate for prediction of response toneoadjuvant chemotherapy in HER2-negative breast cancer-a substudy of theneoadjuvant GeparQuinto trial. PLoS One. 2013 Dec; 8(12): e79775.

[0014] [8] Mei Y, Xiao W, Hu H, et al. Single-cell analyzes reveals uppressive tumor microenvironment of human colorectal cancer. Clin TranslMed. 2021 Jun; 11(6): e422. Summary of the invention

[0015] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a single cell immune model for predicting whether pCR can be achieved after neoadjuvant chemotherapy for breast cancer.

[0016] The present invention accurately extracts characteristic gene sets of various immune cells based on patient scRNA-seq, and then infers the abundance of various immune cells in the sample based on the common transcriptome sequencing data and single-cell immune gene set of clinical samples, and uses Logistic regression to construct a prediction model for whether pCR can be achieved after neoadjuvant chemotherapy. This model can combine the accuracy of scRNA-seq with the low price and low sample requirements of common transcriptome sequencing. The construction of this model can comprehensively consider the advantages and disadvantages and guide the clinical treatment of patients.

[0017] In a first aspect, the present invention provides the use of a reagent for detecting the expression level of characteristic genes of immune cells in the preparation of a product for predicting the sensitivity of breast cancer to neoadjuvant chemotherapy, wherein the immune cells include SPP1_mac cells, plasma cells, pDC cells, mDC cells, Tn cells, NKT cells and / or Tprf cells.

[0018] In the present invention, the SPP1_mac cell is SPP1 +Macrophages; the plasma cells are plasma cells; the pDC cells are plasmacytoid dendritic cells; the mDC cells are mature dendritic cells; the Tn cells are initial T cells; the NKT cells are T cells with natural killer cell (NK cell) characteristics; and the Tprf cells are proliferating T cells.

[0019] According to a specific embodiment of the present invention, the immune cells are immune cells extracted based on single-cell transcriptome sequencing.

[0020] According to a specific embodiment of the present invention, the present invention performs parameter setting and dimensionality reduction clustering for quality control of single-cell transcriptome sequencing data, and formulates a gene set specific for each immune cell.

[0021] According to a specific embodiment of the present invention, the characteristic genes of SPP1_mac cells include SPPI, MIF, S100A6, CSTB, NUPR1 and S100A10, the characteristic genes of plasma cells include JCHAIN, IGKC, IGKV4-1, IGKV3-20, IGLV2-14, IGHA1, CD38, IGHG1, IGHG4 and MZB1, the characteristic genes of pDC cells include IL3RA, LILRA4, CLEC4C, ITM2C, JCHAIN, MZB1 and SCT, and the characteristic genes of mDC cells include FSCN1, LAMP3, CCR7, I DO1, EBI3, TMEM176A, LAD1, CXCL9, ACHE, NCCRP1 and CD274, the characteristic genes of Tn cells include IL7R, RPS2, RPL39, CCR7, SELL, RPS21, RPS29 and PNRC1, the characteristic genes of NKT cells include FCGR3A, FGFBP2, GZMH, GZMB, NKG7, CCL3, PRF1, GZMA, CCL4, IFNG and GZMM, and the characteristic genes of Tprf cells include MKI67, TUBA1B, STMN1, HIST1H4C, TUBB, HMGN2 and H2AFZ.

[0022] As a preferred embodiment, the immune cells are a combination of SPP1_mac cells, plasma cells, pDC cells, mDC cells, Tn cells, NKT cells and Tprf cells.

[0023] According to a specific embodiment of the present invention, the reagent for detecting the expression level of the characteristic gene of the immune cell includes a reagent for detecting the expression level of the mRNA of the characteristic gene, or a reagent for detecting the expression level of the encoded protein of the characteristic gene.

[0024] According to a specific embodiment of the present invention, the reagent for detecting the expression level of the mRNA of the characteristic gene includes a reagent for detecting the expression level of the mRNA of the characteristic gene using common transcriptome sequencing.

[0025] According to a specific embodiment of the present invention, the reagent for detecting the expression level of the characteristic gene of the immune cell includes a primer, a probe, or an antibody.

[0026] According to a specific embodiment of the present invention, the reagent for detecting the expression level of characteristic genes of immune cells includes a reagent used for high-throughput sequencing.

[0027] As a preferred embodiment, the high-throughput sequencing is bulk RNA-seq sequencing.

[0028] According to a specific embodiment of the present invention, the product includes a chip, a test kit, a test paper, a high-throughput sequencing platform or a detection device.

[0029] In a second aspect, the present invention provides a device for detecting sensitivity of breast cancer patients to neoadjuvant chemotherapy, which comprises a detection unit and a data analysis unit, wherein:

[0030] The detection unit detects characteristic genes from immune cells of the individual to be tested to obtain a test result;

[0031] The data analysis unit is used to analyze and process the detection results of the detection unit.

[0032] Wherein, the characteristic genes of the immune cells are the same as the characteristic genes of the immune cells described in the first aspect of the present invention.

[0033] According to a specific embodiment of the present invention, the detection unit extracts characteristic genes of immune cells of the individual to be tested based on single-cell transcriptome sequencing.

[0034] According to a specific embodiment of the present invention, the test result includes an immune cell gene set score, and the gene set score is the average expression of the characteristic genes of the immune cells. In the present invention, the data analysis unit further substitutes the immune cell gene set score into the following detection model to calculate IM_points:

[0035] IM_points=62.5273+0.5684×SPP1_mac cell gene set score+0.6221×plasma cell gene set score-0.9062×pDC cell gene set score+1.6405×mDC cell gene set score+0.9224×Tn cell gene set score-0.6079×NKT cell gene set score+1.1259×Tprf cell gene set score.

[0036] IM_points represents the degree of immune activation of tumor samples. The higher the score, the more activated the immune system is and the more sensitive it is to neoadjuvant chemotherapy.

[0037] In the present invention, the data analysis unit further predicts the probability of pCR after neoadjuvant chemotherapy according to the following formula:

[0038] P= sigma(IM_points) = frac{1}{1 + e^{- IM_points}}.

[0039] The probability value P ranges from 0 to 1. The larger the P value, the more sensitive it is to neoadjuvant chemotherapy.

[0040] In a third aspect, the present invention provides a computer storage medium storing computer program instructions, which, when executed, achieve: obtaining a breast cancer neoadjuvant chemotherapy sensitivity assessment result of the individual to be tested based on the characteristic genes of the immune cells of the individual to be tested; wherein the characteristic genes of the immune cells are the same as the characteristic genes of the immune cells described in the first aspect of the present invention.

[0041] In a fourth aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it is achieved: obtaining a result of evaluating the sensitivity of the individual to be tested to neoadjuvant chemotherapy for breast cancer based on the characteristic genes of the immune cells of the individual to be tested; wherein the characteristic genes of the immune cells are the same as the characteristic genes of the immune cells described in the first aspect of the present invention. Beneficial Effects

[0042] The present invention provides an immune single cell model for predicting whether a breast cancer patient can achieve pathological complete remission through neoadjuvant chemotherapy. Based on the single cell transcriptome sequencing data of breast cancer, the characteristic gene sets of various immune cells are extracted, and then the abundance of various immune cells in the sample is inferred based on the common transcriptome sequencing data of clinical samples and the single cell immune gene set. The correlation between the characteristic gene sets of different types of immune cells and the pCR of neoadjuvant chemotherapy is analyzed by Logistic regression to construct a prediction model. In clinical practice, the model can predict whether the patient can benefit from neoadjuvant chemotherapy based on the common transcriptome sequencing data of the puncture samples of breast cancer patients, thereby guiding the patient's treatment decision, and has the advantages of high accuracy, universality, and rapidity. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Scoring the effectiveness of immune gene sets in distinguishing different immune cell types;

[0044] Figure 2Screening immune cell combinations for predicting chemotherapy efficacy for Lasso regression;

[0045] Figure 3 Construct a nomogram for predictive modeling for logistic regression;

[0046] Figure 4 To evaluate the predictive effect of the model of the present invention on the pCR status;

[0047] Figure 5 This is an example demonstration of the model of the present invention in practical clinical application. DETAILED DESCRIPTION

[0048] Before further describing the specific embodiments of the present invention, it should be understood that the scope of protection of the present invention is not limited to the specific embodiments described below; it should also be understood that the terms used in the examples of the present invention are for describing the specific embodiments rather than for limiting the scope of protection of the present invention.

[0049] When the embodiments give numerical ranges, it should be understood that, unless otherwise specified in the present invention, both endpoints of each numerical range and any numerical value between the two endpoints can be selected. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those generally understood by those skilled in the art. In addition to the specific methods, equipment, and materials used in the embodiments, according to the grasp of the prior art by those skilled in the art and the record of the present invention, any methods, equipment, and materials of the prior art similar or equivalent to the methods, equipment, and materials described in the embodiments of the present invention can also be used to realize the present invention.

[0050] Unless otherwise stated, the experimental methods, detection methods, and preparation methods disclosed in the present invention all adopt conventional techniques in the technical field. Example 1

[0051] 1. Extract characteristic gene sets of various immune cells based on breast cancer single-cell transcriptome sequencing data

[0052] (1) Quality control filtering and dimensionality reduction clustering were performed on single-cell transcriptome sequencing data of 6 breast cancer patients. Using the Cell Ranger software of 10xGenomics, cells were quality controlled according to the following conditions: the total number of single-cell mRNAs was greater than or equal to 1000; the total number of single-cell genes was greater than or equal to 500 and less than or equal to 1000; the proportion of single-cell mitochondrial mRNA was greater than or equal to 20%. After quality control filtering, a total of 41,040 qualified cells were identified for subsequent analysis. Cells expressing the EPCAM gene were clustered into epithelial cells, cells expressing the PECAM1 and PDGFRB genes were clustered into stromal cells, and cells expressing the PTPRC gene were clustered into immune cells. All cells that passed quality control were identified as three cell subpopulations: epithelial cells, stromal cells, and immune cells after dimensionality reduction clustering.

[0053] (2) Immune cell re-clustering divided into 19 immune cell subtypes. Taking into account the impact of immune cells on the efficacy of neoadjuvant chemotherapy, the present invention performs secondary clustering on immune cells, and subdivides immune cells into: T cells (CD3D); B cells (MS4A1); plasma cells (JCHAIN) and myeloid cells (CD68) according to the genes they express. Since B cells and plasma cells account for a small proportion, they are no longer subdivided. The present invention extracts T cell and myeloid cell subsets respectively for further dimensionality reduction clustering, among which T cells are subdivided into naive T cells (IL7R), regulatory T cells (FOXP3), memory T cells (GZMK), effector T cells (GNLY), central memory T cells (ANXA1), proliferative T cells (MKI67), exhausted T cells (PDCD1) and NKT cells (FCGR3A); myeloid cells are subdivided into 3 types of macrophages (TREM2 + 、SPP1 + FOLR2 + Macrophages), monocytes, 4 types of dendritic cells (classical type I, classical type II, mature and plasmacytoid dendritic cells) and mast cells. So far, the present invention has identified 19 immune cell subsets in the immune microenvironment, which can better understand the composition of each cell component in the immune microenvironment and its changes during tumor development and treatment.

[0054] (3) Find the characteristic gene sets of each immune cell (Table 1). Use the FindMarkers function in the Seurat package to perform differential gene analysis. Genes that meet the following conditions are defined as differential genes: expression percentage greater than 10%, fold change greater than 1.0, and Benjamini-Hochberg corrected p value less than 0.05. Select the top 5 genes with the highest expression levels of various immune cell types and combine them with their classic marker genes to form specific gene sets. These gene sets can well represent the abundance of the corresponding cells after verification by the AddModuleScore function. The average expression level of the genes in the gene set is defined as the expression score of the gene set. Figure 1 In the figure, different immune cell classifications are shown on the left, and the figure on the right shows the gene set scores of various immune cells. The lower the score, the grayer the color, and the higher the score, the redder the cell color, indicating that the expression score of a specific gene set can well distinguish this immune cell from other immune cells ( Figure 1 ). The development of this specific gene set requires a complex and rigorous bioinformatics data analysis process, combined with tumor immunity-related knowledge, and it is very difficult to obtain.

[0055] Table 1. Characteristic gene sets of each immune cell subset

[0056]

[0057] 2. Using immune cell gene sets in the neoadjuvant chemotherapy cohort to build a model that can predict the efficacy of neoadjuvant chemotherapy

[0058] (1) A total of 82 breast cancer patients who received neoadjuvant chemotherapy were enrolled. Breast tissue of the patients was punctured before neoadjuvant chemotherapy, and bulk RNA-seq sequencing was performed on the samples obtained by puncture.

[0059] (2) Based on the aforementioned immune cell characteristic gene sets and bulk RNA-seq data, the gene set scores of each type of immune cell in each sample were calculated, and the average expression level of genes in each type of immune cell gene set was defined as the gene set score of the immune cell;

[0060] (3) The gene set scores of various immune cells were used as the independent variable X, the pCR status was used as the dependent variable Y, pCR was defined as 1, and non-pCR was defined as 0. The lasso regression was used to screen the optimal independent variable combination for predicting the pCR status. The results showed that SPP1_mac cells, plasma cells, pDC cells, mDC cells, Tn cells, NKT cells, and Tprf cells were the optimal independent variable combination ( Figure 2 ). Figure 2In the figure, the left figure shows the coefficients of 19 immune cells in predicting pCR in lasso regression, and the right figure shows the optimal number of independent variables output by lasso regression. When there are 7 independent variables, the prediction effect of pCR is best.

[0061] 3. Build a prediction model

[0062] (1) The above 7 immune cell characteristic gene set scores were used as independent variables, and the pCR status after neoadjuvant chemotherapy was used as the dependent variable. pCR was defined as 1 and non-pCR was defined as 0. Logistic regression was used to construct a prediction model ( Figure 3 ), the regression coefficients of the respective variables in the model are: SPP1_mac cells (0.5684), plasma cells (0.6221), pDC cells (-0.9062), mDC cells (1.6405), Tn cells (0.9224), NKT cells (-0.6079), Tprf cells (1.1259). IM_points=62.5273+0.5684×SPP1_mac cell gene set score+0.6221×plasma cell gene set score-0.9062×pDC cell gene set score+1.6405×mDC cell gene set score+0.9224×Tn cell gene set score-0.6079×NKT cell gene set score+1.1259×Tprf cell gene set score. The model predicts the probability of pCR: P = sigma(IM_points) = frac{1}{1 + e^{- IM_points}}.

[0063] 4. Verify the prediction effect of the model

[0064] (1) In the above-mentioned 82 breast cancer patients who received neoadjuvant chemotherapy (training set), 29 patients achieved pCR after chemotherapy, and 53 patients did not achieve pCR. The predicted probability of pCR after neoadjuvant chemotherapy was calculated based on the above model and compared with the actual pCR status of the patients. The prediction effect of the model in the training set was evaluated based on the area under the ROC curve (AUC) value. The area under the ROC curve was AUC value of 0.92 (95% CI 0.86-0.98). At the optimal cutoff value, the specificity of the model for predicting pCR was 0.91 and the sensitivity was 0.83. ( Figure 4 A in the figure).

[0065] (2) Using the previously published bulk RNA-seq data of 159 breast cancer neoadjuvant cohorts as an independent external validation set 1 (GSE133882), the above method was used to calculate the area under the ROC curve of the model for predicting pCR status in the validation set. The AUC value was 0.84 (95% CI 0.77-0.91). At the optimal cutoff value, the model had a specificity of 0.90 and a sensitivity of 0.63 for predicting pCR ( Figure 4 B in the figure).

[0066] (3) The predictive effect of this model was compared with previous similar models for predicting sensitivity to neoadjuvant chemotherapy, including: IRSN-23 model (19 immune genes, derived from the literature PMID24356621, genes include: CARD9, IDO1, CXCL9, PNP, CXCL11, CEBPB, CD83, IL6ST, CX3CR1, CD1D, CTSC, CXCL10, IGHG1, HFE, ZEB1, VEGFA, SEMA3C, CR2, EDA), TILGS model (22 immune genes, derived from the literature PMID28905250, genes include: ICOS, SEL1L3, IL7R, TCF7, CENPA, IDO1, PAPOLA, SELL, LAMP3, CD53, HSPA8, MSL3, CKS2, TTK, LCK, WARS, PSME2, ​​TCP1, MCM6, HLA-DOB, LCP1, TXN), Ascierto model (5 immune genes, derived from literature PMID21479927, genes include: GBP1, OCLN, STAT1, IGKC, IGLL5), Bianchini model (5 immune genes, derived from literature PMID20805453, genes include IGKC, IGHG3, IGL, IGHA1, IGHG3), Blaye model (8 immune genes, derived from literature PMID35759853, genes include: BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7). These models were established based only on expression chips or bulk RNA-seq data, without using high-resolution single-cell transcriptome data. The AUC values ​​of the models described in the present invention were significantly higher than those of previous immune models for predicting pCR, both in the training set and in the independent external validation set ( Figure 4 A and B in the figure).

[0067] Figure 4In the figure, the left and right figures are the evaluation of the effect of the model in predicting pCR in the training set and the external independent validation set, respectively. The curve is the ROC curve, and the area under the curve is the AUC value. The AUC value and 95% confidence interval of the model (model) of the present invention and the previous models (IRSN-23, TILGS, Ascierto, Bianchini, Blaye) for predicting pCR are indicated in the lower right corner. The black dot represents the optimal cutoff value defined by the model, and the specificity and sensitivity of the model in predicting pCR at the optimal cutoff value are in brackets. Example 2

[0068] The model established based on the present invention predicts the efficacy of neoadjuvant chemotherapy for clinical breast cancer patient Liu XX.

[0069] Patient Liu XX, 47 years old, was found to have a mass in the right breast during physical examination. Breast color Doppler ultrasound showed a 2.2×1.2cm mass at 10 o'clock in the right breast and enlarged axillary lymph nodes. Pathology of puncture biopsy: invasive breast cancer, non-special type, grade III (histological score: 3+2+3=8 points). Immunohistochemistry results: ER (+), PR (+), HER-2 (1+), Ki-67 (hot spot about 80%+). The patient hopes to evaluate whether he is sensitive to neoadjuvant chemotherapy to decide whether to undergo neoadjuvant chemotherapy first or surgery first.

[0070] Here’s how to do it:

[0071] 1. Perform common transcriptome sequencing on the patient's breast cancer puncture samples;

[0072] 2. Calculate the scores of 7 types of immune cell characteristic gene sets based on transcriptome data;

[0073] 3. Substituting the score into the prediction model, the model predicted that the probability of the patient achieving pCR after neoadjuvant chemotherapy was as high as 0.92, that is, the model predicted that the patient had a 92% chance of achieving pCR after neoadjuvant chemotherapy ( Figure 5 Therefore, the patient decided to receive neoadjuvant chemotherapy.

[0074] 4. After receiving 8 cycles of neoadjuvant chemotherapy, the patient underwent breast MRI enhanced examination, and the examination results showed that the tumor had significantly regressed. Pathological examination of the surgical resection specimen showed that there were no residual tumor cells, and pathological complete remission was achieved, which was consistent with the prediction results of the model. ( Figure 5 B)

[0075] Figure 5 In the figure, the left picture shows the seven immune cell scores (red dots) calculated based on the bulk RNA-seq data of the patient's biopsy sample and the single-cell immune gene set, as well as the total score and corresponding pCR probability calculated by the prediction model. The right picture shows the breast MRI comparison of the patient before and after neoadjuvant chemotherapy.

[0076] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. Use of a reagent for detecting the expression level of characteristic genes of immune cells in the preparation of a product for predicting the sensitivity of breast cancer to neoadjuvant chemotherapy, characterized in that: The immune cells include a combination of SPP1_mac cells, plasma cells, pDC cells, mDC cells, Tn cells, NKT cells and Tprf cells; The characteristic genes of SPP1_mac cells include SPPI, MIF, S100A6, CSTB, NUPR1 and S100A10, the characteristic genes of plasma cells include JCHAIN, IGKC, IGKV4-1, IGKV3-20, IGLV2-14, IGHA1, CD38, IGHG1, IGHG4 and MZB1, the characteristic genes of pDC cells include IL3RA, LILRA4, CLEC4C, ITM2C, JCHAIN, MZB1 and SCT, and the characteristic genes of mDC cells include FSCN1, LAMP3, CCR7, IDO1, EBI 3. TMEM176A, LAD1, CXCL9, ACHE, NCCRP1 and CD274. The characteristic genes of Tn cells include IL7R, RPS2, RPL39, CCR7, SELL, RPS21, RPS29 and PNRC1. The characteristic genes of NKT cells include FCGR3A, FGFBP2, GZMH, GZMB, NKG7, CCL3, PRF1, GZMA, CCL4, IFNG and GZMM. The characteristic genes of Tprf cells include MKI67, TUBA1B, STMN1, HIST1H4C, TUBB, HMGN2 and H2AFZ.

2. The use according to claim 1, characterized in that: The reagent for detecting the expression level of the characteristic gene of the immune cell includes a reagent for detecting the expression level of the mRNA of the characteristic gene, or a reagent for detecting the expression level of the encoded protein of the characteristic gene.

3. The use according to claim 1, characterized in that: The reagents for detecting the expression levels of characteristic genes of immune cells include reagents used for high-throughput sequencing.

4. The use according to claim 3, characterized in that The high-throughput sequencing is bulk RNA-seq sequencing.

5. The use according to any one of claims 1 to 4, characterized in that: The products include chips, test kits, test strips, high-throughput sequencing platforms or prediction devices.

6. A device for predicting the sensitivity of breast cancer patients to neoadjuvant chemotherapy, comprising a detection unit and a data analysis unit, characterized in that: The detection unit detects characteristic genes from immune cells of the individual to be tested to obtain the test results. The data analysis unit is used to analyze and process the detection results of the detection unit; Wherein, the characteristic genes of the immune cells are the same as the characteristic genes of the immune cells described in claim 1.

7. A computer storage medium storing computer program instructions, which, when executed, achieve: obtaining a prediction result of the sensitivity of the individual to be tested to neoadjuvant chemotherapy for breast cancer based on the characteristic genes of the immune cells of the individual to be tested; characterized in that: The characteristic genes of the immune cells are the same as the characteristic genes of the immune cells according to claim 1.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the following is achieved: based on the characteristic genes of the immune cells of the individual to be tested, a prediction result of the sensitivity of the individual to be tested to neoadjuvant chemotherapy for breast cancer is obtained; characterized in that the characteristic genes of the immune cells are the same as the characteristic genes of the immune cells according to claim 1.

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