Detection system for judging survival prognosis of breast cancer patient based on immunomodulatory typing and application of detection system

Abstract

The invention discloses a detection system for judging survival prognosis of breast cancer patients based on immunomodulatory typing. The detection system comprises a data acquisition module, a data processing module, a prediction module and a result output module. According to the detection system, firstly, a to-be-detected breast cancer patient is subjected to immunoregulation typing, and the to-be-detected breast cancer patient with the immunoregulation subtype is judged to be good in survival prognosis; for a non-immunomodulatory subtype breast cancer patient to be detected, the homologous recombination repair state of the breast cancer patient is judged based on a positive threshold value of a homologous recombination repair defect score, and then survival prognosis is judged by combining the proportion of somatic cell mutation characteristics of the breast cancer patient in total somatic cell mutation. When the method is used for judging the survival prognosis of the breast cancer patient, the accuracy reaches 81% and is improved by 28% compared with the accuracy of judging the survival prognosis of the breast cancer patient through immunomodulatory typing, accurate judgment on the survival prognosis of the breast cancer patient can be achieved, and then guidance opinions are provided for clinical decision making.

Description

Technical Field

[0001] This invention relates to the field of survival prognosis technology, and more specifically, to a detection system for determining the survival prognosis of breast cancer patients based on immune regulation typing and its application. Background Technology

[0002] Breast cancer (BC) is the most common cancer among women, with an age-standardized rate (ASR) of 21.6 cases per 100,000 people, according to the global cancer epidemiology database GLOBOCAN (https: / / gco.iarc.fr / ). Thanks to the establishment of a categorized treatment model for breast cancer, the overall prognosis has significantly improved in recent years.

[0003] Current clinical classification models used for breast cancer prognosis include those based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), classifying patients as hormone receptor-positive / HER2-positive. 2 + / - Triple-negative breast cancer classification includes the "Fudan Classification" based on luminal surface features, and genomic classification based on somatic or germline mutations in breast cancer susceptibility genes such as BRCA1, BRCA2, p53, PTEM, ATM, and ATR. These classifications can differentiate the heterogeneity of breast cancer in terms of invasion, recurrence, and drug resistance to varying degrees, and provide important guidance for precision and personalized treatment.

[0004] Currently, the "Fudan Classification" is receiving widespread attention. It further classifies triple-negative breast cancer patients into luminal androgen receptor (LAR) subtypes, mesenchymal-like (MES) subtypes, basal-like immune-suppressed (BLIS) subtypes, and immunomodulatory (IM) subtypes. Among these, there is a significant difference in survival prognosis between the immunomodulatory subtype and the non-immunomodulatory subtypes (i.e., the other three subtypes). Therefore, immunomodulatory classification (classifying breast cancer into immunomodulatory and non-immunomodulatory subtypes) is considered to be a relatively effective predictor of breast cancer patient survival. However, its accuracy in predicting survival prognosis for non-immunomodulatory breast cancer subtypes is lower, and its predictive effect is poor.

[0005] Therefore, there is a need for a method that can more accurately predict the survival of breast cancer patients based on their immune regulatory subtypes, thereby providing guidance for clinical decision-making. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned shortcomings of the prior art and provide a detection system for determining the survival prognosis of breast cancer patients based on immune regulation typing and its application.

[0007] The first objective of this invention is to provide a diagnostic reagent for use in the preparation of products for determining the survival prognosis of breast cancer patients based on immune regulatory typing.

[0008] A second objective of this invention is to provide a detection reagent for use in the preparation of products for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and negative homologous recombination repair defects.

[0009] A third objective of this invention is to provide a detection reagent for use in the preparation of a product for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and positive homologous recombination repair deficiency.

[0010] The fourth objective of this invention is to provide a detection system for determining the survival prognosis of breast cancer patients with non-immunoregulatory subtypes and negative homologous recombination repair deficiency.

[0011] The fifth objective of this invention is to provide a detection system for determining the survival prognosis of breast cancer patients with non-immune regulatory subtypes and positive homologous recombination repair deficiency.

[0012] The sixth objective of this invention is to provide a detection system for determining the survival prognosis of breast cancer patients based on immune regulation typing.

[0013] A seventh objective of this invention is to provide the role of the above-described detection system in determining the survival prognosis of breast cancer patients based on immune regulation subtyping for non-diagnostic purposes.

[0014] To achieve the above objectives, the present invention is implemented through the following solution: The use of a diagnostic reagent in the preparation of a product for determining the survival prognosis of breast cancer patients based on immunomodulatory typing, said diagnostic reagent being a reagent for detecting the proportion of the following somatic mutational features among all somatic mutations: SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS10b, SBS12, SBS13, SBS17a, SBS17b, SBS19, SBS20, SBS24, SBS29, SBS30, SBS33, SBS36, SBS41, SBS48, SBS51, SBS54, SBS55, SBS57, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID5, ID6, ID9, ID11, ID13, and ID18.

[0015] The immunomodulatory subtype of breast cancer patients includes patients with immunomodulatory subtypes and patients with non-immunomodulatory subtypes. Patients with non-immunomodulatory subtypes include patients with luminal androgen receptor subtypes, mesenchymal subtypes, and basal-like immunosuppressive subtypes. Somatic mutations are recorded in the COSMIC database.

[0016] The present invention also claims protection for the use of a diagnostic reagent in the preparation of a product for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and negative homologous recombination repair deficiency, said diagnostic reagent being a diagnostic reagent for detecting the proportion of the following somatic mutation characteristics to total somatic mutations; said somatic mutation characteristics include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11.

[0017] Preferably, the threshold for determining a negative homologous recombination repair defect is 40 to 44.

[0018] More preferably, the threshold for determining a negative result of homologous recombination repair defect is 40.

[0019] The present invention also claims protection for the use of a detection reagent in the preparation of a product for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and positive homologous recombination repair deficiency, characterized in that the detection reagent is a detection reagent for detecting the proportion of the following somatic mutation features to total somatic mutations; the somatic mutation features include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18.

[0020] Preferably, the threshold for determining a positive result for homologous recombination repair defect is 40 to 44.

[0021] More preferably, the threshold for determining a positive result for homologous recombination repair defects is 40.

[0022] The present invention also claims protection for a detection system for determining the survival prognosis of breast cancer patients with non-immune regulatory subtypes and negative homologous recombination repair deficiency, including a prediction module and a result output module; The prediction module predicts survival based on the proportion of the following somatic mutation characteristics to the total somatic mutations in breast cancer patients with non-immune regulatory subtypes and negative homologous recombination repair defects, and obtains the survival prognosis prediction results for breast cancer patients. The somatic mutation signatures include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11; The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

[0023] The present invention also claims protection for a detection system for determining the survival prognosis of breast cancer patients with non-immune regulatory subtypes and positive homologous recombination repair deficiency, including a prediction module and a result output module; The prediction module predicts survival based on the proportion of the following somatic mutation characteristics to the total somatic mutations in breast cancer patients with non-immune regulatory subtypes and positive homologous recombination repair defects, and obtains the survival prognosis prediction results for breast cancer patients. The somatic mutation signatures include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18. The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

[0024] This invention also claims protection for a detection system for determining the survival prognosis of breast cancer patients based on immune regulation typing, including a data acquisition module, a data processing module, a prediction module, and a result output module; The data acquisition module is used to acquire whole exome sequencing data and transcriptome sequencing data of breast cancer patients; The data processing module performs immune regulation subtyping on breast cancer patients based on the transcriptome sequencing data of breast cancer patients obtained by the data acquisition module; the immune regulation subtyping is either an immune regulation subtype or a non-immune regulation subtype. For breast cancer patients with non-immunomodulatory subtypes, homologous recombination repair deficiency scores are calculated based on the whole-exome sequencing data of breast cancer patients acquired by the data acquisition module, and the homologous recombination repair deficiency status of breast cancer patients is determined based on the positive threshold of the homologous recombination repair deficiency score; the homologous recombination repair deficiency status is either homologous recombination repair deficiency positive or homologous recombination repair deficiency negative. The prediction module is used to predict the survival prognosis of breast cancer patients. For breast cancer patients identified as having an immune-regulatory subtype in the data processing module, the prognosis is considered to be good. For non-immunomodulatory subtype breast cancer patients identified by the data processing module as having homologous recombination repair deficiency and negative, survival prediction is performed based on the proportion of each of the following somatic mutation characteristics to the total somatic mutations, and the survival prognosis prediction results for breast cancer patients are obtained. The somatic mutation signatures include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11; For non-immunomodulatory subtype breast cancer patients identified by the data processing module as having the same recombination repair defect, survival prediction is performed based on the proportion of each somatic mutation feature to the total somatic mutations, resulting in the survival prognosis prediction of breast cancer patients. The somatic mutation signatures include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18. The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

[0025] Preferably, the homologous recombination repair defect score in the data processing module is an unweighted sum of the heterozygous deletion score (LOH score), the telomere genotype imbalance score (TAI score), and the large-scale structural transfer score (LST score).

[0026] Preferably, the positive threshold for homologous recombination repair defect scoring in the data processing module is 40-44.

[0027] More preferably, the positive threshold for homologous recombination repair of defects is 40.

[0028] Preferably, in the prediction module, for non-immunomodulatory subtype breast cancer patients identified by the data processing module as having homologous recombination repair deficiency, the survival prognosis of breast cancer patients is determined using Formula I; Formula I: F1=A0+A1×SBS3+A2×SBS10b+A3×SBS12+A4×SBS13+A5×SBS17b+A6×SBS20+A7×SBS29+A8×SBS33+A9×SBS48+A 10 ×SBS51+A 11 ×SBS55+A 12 ×SBS58+A 13 ×SBS59+A 14 ×SBS86+A 15 ×SBS88+A 16 ×ID1+A 17 ×ID2+A 18 ×ID6+A 19 ×ID9+A 20 ×ID11; Where A0~A 20 The results were obtained through LASSO regression analysis model; in Formula I, SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 are the rounded values ​​of the percentage of the corresponding somatic mutation features in the total somatic mutations that are less than 5%.

[0029] More preferably, A0~A 20 The regularization parameter λ1 of the LASSO regression analysis model is 0.05.

[0030] More preferably, A0 to A1 are obtained through a LASSO regression analysis model. 20At the same time, LASSO regression analysis was performed on the survival prognosis of samples in sample set 1 by combining the rounded values ​​of the percentage of somatic mutation characteristics of SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 in total somatic mutations that are less than 5%. The samples in sample set 1 were breast cancer patients with non-immunoregulatory subtypes and negative homologous recombination repair deficiency.

[0031] As a specific embodiment, the samples in sample set 1 are breast cancer patients identified as having a non-immune regulatory subtype and negative homologous recombination repair deficiency from the NODE database dataset (index number OEX00001583).

[0032] In sample set 1, the survival prognosis of the samples is defined as good or poor. Good survival prognosis is defined as recurrence-free survival (RFS) > 39 months, and poor survival prognosis is defined as recurrence-free survival (RFS) ≤ 39 months.

[0033] More preferably, the samples in sample set 1 are samples of non-immune regulatory subtypes with homologous recombination repair defect scores ≤40-44.

[0034] More preferably, the samples in sample set 1 are non-immune regulatory subtypes and samples with a homologous recombination repair defect score ≤40.

[0035] More preferably, Formula I is: F1=0.529025032176067+0.230221235097482×SBS3-0.498932854715304 ×SBS10b-0.730727683810486×SBS12-0.169223420844788×SBS13+0.6047 92959534835×SBS17b-0.2501964844098×SBS20+0.250241479591993×SB S29+0.119145057406723×SBS33-1.00959048472923×SBS48+0.332362491 108694×SBS51-0.0175788074073378×SBS55+0.45317812694346×SBS58+0.0106953077538109×SBS59+0.0758827599059131×SBS86+0.049485550187784×SBS88-0.249129004889655×ID1+0.107209967629524×ID2+0.11121744635206×ID6-0.939426205399708×ID9+0.0188203627063938×ID11.

[0036] More preferably, for breast cancer patients who are determined by the data processing module to be non-immunomodulatory subtypes with negative homologous recombination repair, if the F1 calculated using Formula I is ≥0.5, the survival prognosis of the breast cancer patient is good; otherwise, the survival prognosis of the breast cancer patient is poor.

[0037] Preferably, in the prediction module, for non-immunomodulatory subtype breast cancer patients identified by the data processing module as having homologous recombination repair deficiency, Formula II is used to determine the survival prognosis of the breast cancer patients; Formula II: F2=B0+B1×SBS2+B2×SBS3+B3×SBS6+B4×SBS7a+B5×SBS7d+B6×SBS17a+ B7×SBS17b+B8×SBS19+ B9×SBS24+B 10 ×SBS30+B 11 ×SBS36+B 12 ×SBS41+B 13 ×SBS54+B 14 ×SBS55+B 15 ×SBS57+B 16 ×ID5+B 17 ×ID6+B 18 ×ID13+B19 ×ID18; Where B0~B 19 The results were obtained through LASSO regression analysis model; in Formula I, SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18 are the rounded values ​​of the percentage of the corresponding somatic mutation characteristics in the total somatic mutations that are less than 5%.

[0038] More preferably, B0~B 19 The regularization parameter λ2 of the LASSO regression analysis model is 0.04993.

[0039] More preferably, B0 to B are obtained through a LASSO regression analysis model. 19 At the same time, LASSO regression analysis was performed on the survival prognosis of samples in sample set 2 by combining the rounded values ​​of the percentage of somatic mutation characteristics of SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18 in total somatic mutations that are less than 5%. The samples in sample set 2 were breast cancer patients with non-immunoregulatory subtypes and positive homologous recombination repair deficiency.

[0040] As a specific embodiment, the samples in sample set 1 are breast cancer patients identified as having a non-immune regulatory subtype and positive homologous recombination repair deficiency from the NODE database dataset (index number OEX00001583).

[0041] In sample set 2, the survival prognosis of the samples is defined as good or poor. Good survival prognosis is defined as recurrence-free survival (RFS) > 39 months, and poor survival prognosis is defined as recurrence-free survival (RFS) ≤ 39 months.

[0042] More preferably, the samples in sample set 2 are non-immune regulatory subtypes with homologous recombination repair defect scores >40 to 44.

[0043] More preferably, the samples in sample set 2 are non-immune regulatory subtypes and have a homologous recombination repair defect score >40.

[0044] More preferably, Formula II is: F2=0.191137821526338+0.37436790794174×SBS2+0.0180943002871771×SBS3-0.221872673793905×SBS6+0.299349845623579×SBS7a-1.8 081888615682×SBS7d-0.903174993637052×SBS17a-1.34694082133942×SBS17b-(3.99950886022513E-06)×SBS19+0.163837007222794×SBS 24+0.482164973995526×SBS30+0.470728584425237×SBS36+0.798726326342207×SBS41+1.15784834425311×SBS54-2.43791957191518×SBS55+0.0290198193313017×SBS57-0.0666280168520665×ID5+0.900939747020252×ID6+0.226204660707941×ID13+0.19421721416777×ID18.

[0045] More preferably, for breast cancer patients who are determined by the data processing module to be negative for homologous recombination repair, if F2 calculated using Formula II is ≥0.5, the survival prognosis of the breast cancer patient is good; otherwise, the survival prognosis of the breast cancer patient is poor.

[0046] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a detection system for determining the survival prognosis of breast cancer patients based on immune regulation subtyping, including a data acquisition module, a data processing module, a prediction module, and a result output module. The detection system first performs immune regulation subtyping on the breast cancer patients to be tested. Patients with immune regulation subtypes are considered to have a good survival prognosis. For patients with non-immune regulation subtypes, a homologous recombination repair deficiency score is obtained, and the homologous recombination repair status is determined based on a positive threshold for the score. For breast cancer patients with a negative homologous recombination repair status, the system combines the patient's SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, and SBS... The survival prognosis of breast cancer patients is determined by the proportion of somatic mutations in SBS55, SBS57, ID5, ID6, ID13, and ID18 relative to total somatic mutations. For breast cancer patients with positive homologous recombination repair, the survival prognosis is further determined by the proportion of somatic mutations in SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 relative to total somatic mutations. The method described in this invention achieves an accuracy of 81% in determining the survival prognosis of breast cancer patients based on immunomodulatory typing, which is 28% higher than the accuracy of directly using immunomodulatory typing. This allows for accurate assessment of the survival prognosis of breast cancer patients with immunomodulatory typing, thus providing guidance for clinical decision-making. Attached Figure Description

[0047] Figure 1 This is a comparison of the significant differences in survival prognosis among non-immunomodulatory subtypes of breast cancer at the positive threshold of homologous recombination repair defect scores in Example 1; Figure 2 This is the ROC curve for predicting the survival prognosis of breast cancer patients in the sample set when the positive threshold for homologous recombination repair defect score is 40 in Example 1. Figure 3The following are ROC curves used in Example 2 to predict survival prognosis: a) ROC curve for predicting survival prognosis of HRD-negative non-immune-regulatory breast cancer patients in training set 1 using Formula I in Example 2; b) ROC curve for predicting survival prognosis of HRD-negative non-immune-regulatory breast cancer patients in validation set 1 using Formula I in Example 2; c) ROC curve for predicting survival prognosis of HRD-positive non-immune-regulatory breast cancer patients in training set 2 using Formula II in Example 2; d) ROC curve for predicting survival prognosis of HRD-positive non-immune-regulatory breast cancer patients in validation set 2 using Formula II in Example 2. Figure 4 The ROC curve is shown in Example 3, which is used to determine the survival prognosis of breast cancer patients in the sample set (experimental group) according to the method shown in Example 2. Figure 5 This is the ROC curve for determining the survival prognosis of breast cancer patients in the sample set using the method shown in the control group of Example 3; Figure 6 This is a graph showing the comparison of survival prognosis when the method shown in the control group of Example 3 was used to determine the survival prognosis of breast cancer patients in the sample set; Figure 7 This is the ROC result graph for determining the survival prognosis of 55 breast cancer patients in the test set according to the method shown in Example 2 in Example 3. Detailed Implementation

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods; the materials and reagents used, unless otherwise specified, are commercially available.

[0049] The clinical samples used in the test set of this embodiment came from the applicant's institution, and the patients were informed and their consent was obtained before the clinical samples were tested.

[0050] Example 1: Determination of the positive threshold for homologous recombination repair defect scoring I. Determination of the positive threshold for homologous recombination repair defect mitigation 1. Experimental Methods (1) Sample set and sample set processing The sample set consisted of tumor samples and adjacent normal tissues from 232 triple-negative breast cancer patients recorded in the NODE database dataset (index number OEX00001583). Each sample in the set had detailed clinicopathological features and follow-up time, ranging from 34.4 to 59.8 months. The samples in the set were used for immunomodulatory typing of breast cancer patients based on transcriptome sequencing results; the immunomodulatory typing included 52 immunomodulatory subtypes and 180 non-immunomodulatory subtypes. Furthermore, each sample in the set contained whole-exome sequencing data.

[0051] Recurrence-free survival (RFS) was used as a survival prognostic indicator. A recurrence-free survival of >39 months was considered a good survival prognosis, otherwise it was considered a poor survival prognosis. Survival prognosis refers to the survival outcome of breast cancer patients who received any treatment, chemotherapy, and / or no treatment.

[0052] Based on the whole-exome sequencing results of each breast cancer patient in the sample set, the scarHRD software was used to calculate the loss of heterozygosity (LOH), telomere genotype imbalance (TAI), and extensive structural metastasis (LST) scores for each breast cancer patient. The LOH, TAI, and LST scores were then summed unweighted to obtain the homologous recombination repair defect scores for each breast cancer patient in the sample set.

[0053] (2) Screening of the scoring threshold for homologous recombination repair defects Breast cancer patients with a homologous recombination repair deficiency score ≤ the positive threshold for homologous recombination repair deficiency score are considered to be homologous recombination repair deficiency negative (HRD negative); breast cancer patients with a homologous recombination repair deficiency score > the positive threshold for homologous recombination repair deficiency score are considered to be homologous recombination repair deficiency positive (HRD positive).

[0054] Within the positive threshold range of 35–50 for homologous recombination repair deficiency (HCD) score, with a step size of 1, each integer value was taken as the positive threshold for HRD score. Survival prognosis was predicted for breast cancer patients in the sample set who were negative and positive for HRD. The significance of the difference in survival prognosis at each positive threshold for HRD score was calculated. Specifically, breast cancer patients with non-immunomodulatory subtypes and negative HRD were judged as having poor survival prognosis, while breast cancer patients with non-immunomodulatory subtypes and positive HRD were judged as having good survival prognosis.

[0055] 2. Experimental Results The results of the comparison of significant differences in survival prognosis among non-immunomodulatory subtypes of breast cancer at different positive thresholds for homologous recombination repair deficiency scores are as follows: Figure 1As shown, the results indicated that when the positive threshold for homologous recombination repair deficiency score was 40–44, the difference in survival prognosis between HRD-negative and HRD-positive patients in non-immunoregulatory breast cancer subtypes was statistically significant (p-Value < 0.05); and when the positive threshold for homologous recombination repair deficiency score was 40, the difference in survival prognosis between HRD-negative and HRD-positive patients in non-immunoregulatory breast cancer subtypes was the greatest (p-Value = 0.017); therefore, the optimal positive threshold for homologous recombination repair deficiency score was determined to be 40.

[0056] II. Using the positive threshold of homologous recombination repair deficiency score for survival prognosis prediction in non-immunomodulatory subtype breast cancer patients in the sample set. 1. Experimental Methods A positive threshold of 40 was set for homologous recombination repair deficiency score. Survival prognosis was predicted for non-immunomodulatory subtype breast cancer patients in the sample set, and the ACC (Adverse Cost of Survival) was calculated based on the actual survival prognosis records, as detailed below: For breast cancer patients with the immunomodulatory subtype in the sample set, all were judged to have a good survival prognosis; The homologous recombination repair deficiency status of non-immunomodulatory subtype breast cancer patients was determined based on the homologous recombination repair deficiency score. Among them, breast cancer patients with non-immunomodulatory subtype and HRD negative were judged to have poor survival prognosis, while breast cancer patients with non-immunomodulatory subtype and HRD positive were judged to have good prognosis.

[0057] 2. Experimental Results When the positive threshold for homologous recombination repair deficiency score is 40, the ROC curve for predicting survival prognosis in breast cancer patients in the sample set is shown in the figure below. Figure 2 As shown in Table 1, the survival prognosis results are as follows.

[0058] Table 1. Survival prognostic outcomes of breast cancer patients in the sample set with an immune-regulatory subtype when the positive threshold for homologous recombination repair deficiency score was 40.

[0059] The results showed that when the positive threshold for homologous recombination repair defect score was 40, the area under the ROC curve (AUC) for predicting survival prognosis of breast cancer patients in the sample set with immune regulation subtype was 0.649, and the ACC for predicting survival prognosis was (52+49+46) / (52+0+63+49+46+22)×100%=63.4%.

[0060] Example 2: A method for assessing the survival prognosis of breast cancer patients for non-diagnostic purposes I. Survival prognosis prediction for HRD-negative non-immune-regulatory subtype breast cancer patients 1. Experimental Methods Whole exome data of breast cancer patients in the sample set shown in Example 1 were obtained, and the somatic mutation characteristics of breast cancer patients in the sample set were analyzed and mapped to the somatic mutation database COSMIC based on the default parameters using the sigflow (v2.1) analysis tool. According to the somatic mutation type, which is either point mutation (SBS) or insertion / deletion mutation (ID), for each somatic mutation characteristic, the proportion (percentage) of each somatic mutation characteristic in the total somatic mutations of each breast cancer patient was obtained. Then, the proportion (percentage) was divided by 5% and the integer value was taken as the percentage level of each somatic mutation characteristic.

[0061] The 180 non-immune-regulatory subtype breast cancer patients in the sample shown in Example 1 were divided into 112 HRD-negative non-immune-regulatory subtype breast cancer patients and 68 HRD-positive non-immune-regulatory subtype breast cancer patients, with a positive threshold of 40 for homologous recombination repair defect score.

[0062] The 112 patients with HRD-negative non-immunoregulatory breast cancer were divided into training set and validation set, with a ratio of 7:3, resulting in training set 1 (78 patients with HRD-negative non-immunoregulatory breast cancer) and validation set 1 (34 patients with HRD-negative non-immunoregulatory breast cancer).

[0063] Using the proportion of each somatic mutation feature in breast cancer patients in training set 1 as the feature and the survival prognosis of breast cancer patients in training set 1 as the predictor, the regularization parameter λ1 of LASSO regression analysis was set to 0.05. Through LASSO regression analysis, the set of somatic mutation features and related weight coefficients associated with the survival prognosis of HRD-negative non-immune regulatory subtype breast cancer patients were obtained, and the risk factor calculation formula I was obtained.

[0064] 2. Experimental Results For HRD-negative, non-immunomodulatory subtype breast cancer patients, a set of somatic mutation signatures associated with survival prognosis was obtained based on LASSO regression analysis. These signatures included SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 (COSMIC database: https: / / cancer.sanger.ac.uk / signatures / ). The risk factor calculation formula I is shown in Formula I.

[0065] Formula I: F1=0.529025032176067+0.230221235097482×SBS3-0.498932854715 304×SBS10b-0.730727683810486×SBS12-0.169223420844788×SBS13+0.6 04792959534835×SBS17b-0.2501964844098×SBS20+0.250241479591993× SBS29+0.119145057406723×SBS33-1.00959048472923×SBS48+0.3323624 91108694×SBS51-0.0175788074073378×SBS55+0.45317812694346×SBS58+0.0106953077538109×SBS59+0.0758827599059131×SBS86+0.049485550187784×SBS88-0.249129004889655×ID1+0.107209967629524×ID2+0.11121744635206×ID6-0.939426205399708×ID9+0.0188203627063938×ID11.

[0066] In Formula I, SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 represent the percentage levels of corresponding somatic mutation features in breast cancer patients.

[0067] II. Survival Prognosis Prediction for HRD-Positive Non-Immune-Regulating Subtype Breast Cancer 1. Experimental Methods Whole exome data of breast cancer patients in the sample set shown in Example 1 were obtained, and the somatic mutation characteristics of breast cancer patients in the sample set were analyzed and mapped to the somatic mutation database COSMIC based on the default parameters using the sigflow (v2.1) analysis tool. According to the somatic mutation type, which is either point mutation (SBS) or insertion / deletion mutation (ID), for each somatic mutation characteristic, the proportion (percentage) of each somatic mutation characteristic in the total somatic mutations of each breast cancer patient was obtained. Then, the proportion (percentage) was divided by 5% and the integer value was taken as the percentage level of each somatic mutation characteristic.

[0068] The 180 non-immune-regulatory subtype breast cancer patients in the sample shown in Example 1 were divided into 112 HRD-negative non-immune-regulatory subtype breast cancer patients and 68 HRD-positive non-immune-regulatory subtype breast cancer patients, with a positive threshold of 40 for homologous recombination repair defect score.

[0069] The 68 HRD-positive non-immune-regulatory subtype breast cancer patients were divided into training set and validation set in a ratio of 7:3, resulting in training set 2 (48 HRD-positive non-immune-regulatory subtype breast cancer patients) and validation set 2 (20 HRD-positive non-immune-regulatory subtype breast cancer patients).

[0070] Using the proportion of each somatic mutation feature in breast cancer patients in training set 2 as the feature and the survival prognosis of breast cancer patients in training set 2 as the predictor, the regularization parameter λ2 of LASSO regression analysis was set to 0.04993. Through LASSO regression analysis, the set of somatic mutation features and related weight coefficients associated with the survival prognosis of HRD-positive non-immune-regulatory subtype breast cancer patients were obtained, and the risk factor calculation formula II was obtained.

[0071] 2. Experimental Results For HRD-positive non-immunomodulatory subtype breast cancer patients, a set of somatic mutation signatures associated with survival prognosis was obtained based on LASSO regression analysis, including SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18 (COSMIC database: https: / / cancer.sanger.ac.uk / signatures / ). The risk factor calculation formula II is shown in Formula II.

[0072] Formula II: F2 = 0.191137821526338 + 0.37436790794174 × SBS2 + 0.0180943002871771 × SBS3 - 0.221872673793905 × SBS6 + 0.299349845623579 × SBS7a - 1.8081888615682 × SBS7d - 0.903174993637052 × SBS17a - 1.34694082133942 × SBS17b - (3.99950886022513E-06) × SBS19 + 0.163837007222794 × S BS24+0.482164973995526×SBS30+0.470728584425237×SBS36+0.798726326342207×SBS41+1.15784834425311×SBS54-2.43791957191518×SB S55+0.0290198193313017×SBS57-0.0666280168520665×ID5+0.900939747020252×ID6+0.226204660707941×ID13+0.19421721416777×ID18.

[0073] In Formula II, SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18 represent the percentage levels of corresponding somatic mutation features in breast cancer patients.

[0074] III. Predicting Survival Prognosis of Breast Cancer Patients Using Risk Factor Calculation Formulas 1. Experimental Methods (1) Predicting the survival prognosis of breast cancer patients using risk factor calculation formula I. Based on the risk factor calculation formula I obtained in step one, the survival prognosis of HRD-negative non-immune regulatory subtype breast cancer patients in training set 1 in step one is predicted as follows: Substitute the proportion of somatic mutation characteristics of each breast cancer patient in training set 1 into Formula I above to calculate the predicted value F1 for each breast cancer patient in training set 1; when F1≥0.5, it indicates that the breast cancer patient is judged to have a good survival prognosis; otherwise, it is considered to have a poor survival prognosis.

[0075] Based on the predicted and actual survival prognoses of breast cancer patients in training set 1, the AUC value of the predicted survival prognoses of breast cancer patients in training set 1 was calculated.

[0076] Following the method described above, replace training set 1 with validation set 1 to obtain the AUC value of breast cancer patients' survival prognosis in validation set 1.

[0077] Simultaneously, by combining the risk factor calculation formula I obtained in step one, the survival prognosis of the 112 HRD-negative non-immunomodulatory subtype breast cancer patients shown in step one was determined, the results were recorded, and the ACC value was calculated.

[0078] (2) Predicting the survival prognosis of breast cancer patients using risk factor calculation formula II. Based on the risk factor calculation formula II obtained in step two, survival prognosis prediction is performed for HRD-positive non-immune-regulatory subtype breast cancer patients in training set 2 of step two, specifically as follows: Substitute the proportion of somatic mutation characteristics of each breast cancer patient in training set 2 into Formula II above to calculate the predicted value F2 for each breast cancer patient in training set 2; when F2≥0.5, it indicates that the breast cancer patient is judged to have a good survival prognosis; otherwise, it is considered to have a poor survival prognosis.

[0079] Based on the predicted and actual survival prognoses of breast cancer patients in training set 2, the AUC value of the predicted survival prognoses of breast cancer patients in training set 2 was calculated.

[0080] Following the method described above, training set 2 is replaced with validation set 2 to obtain the AUC value of breast cancer patients' survival prognosis in validation set 2.

[0081] Simultaneously, by combining the risk factor calculation formula II obtained in step two, the survival prognosis of the 68 HRD-positive non-immunomodulatory subtype breast cancer patients shown in step two was determined, the results were recorded, and the ACC value was calculated.

[0082] 2. Experimental Results (1) Results of predicting survival prognosis of breast cancer patients using risk factor calculation formula I Combined with Formula I, the ROC results for predicting the survival prognosis of HRD-negative non-immunomodulatory subtype breast cancer patients in training set 1 are as follows: Figure 3 As shown in a, the ROC results for judging the survival prognosis of HRD-negative non-immunomodulatory subtype breast cancer patients in validation set 1, combined with Formula I, are as follows: Figure 3 As shown in b in the figure; the survival prognosis prediction results of 112 HRD-negative non-immunomodulatory subtype breast cancer patients in the sample shown in Example 2, combined with Formula I, are shown in Table 2.

[0083] Table 2. Survival prognoses of 112 patients with HRD-negative non-immunomodulatory subtype breast cancer.

[0084] The ACC for determining the survival prognosis of 112 HRD-negative non-immunomodulatory subtype breast cancer patients was (42+36) / (42+13+21+36)×100%=69.6%.

[0085] (2) Results of predicting survival prognosis of breast cancer patients using risk factor calculation formula II Combined with Formula II, the ROC results for predicting the survival prognosis of HRD-positive non-immune-regulatory subtype breast cancer patients in training set 2 are as follows: Figure 3 As shown in c, the ROC results for judging the survival prognosis of HRD-positive non-immunomodulatory subtype breast cancer patients in validation set 2, combined with Formula II, are as follows: Figure 3 As shown in d; the survival prognosis prediction results of 68 HRD-positive non-immunomodulatory subtype breast cancer patients in the sample shown in Example 2, combined with Formula II, are shown in Table 3.

[0086] Table 3. Survival prognosis of 68 patients with HRD-positive non-immunomodulatory subtype breast cancer.

[0087] The ACC for determining the survival prognosis of 68 HRD-positive non-immunomodulatory subtype breast cancer patients was (43+15) / (43+7+3+15)×100%=85.3%.

[0088] IV. A method for assessing the survival prognosis of breast cancer patients for non-diagnostic purposes A method for assessing the survival prognosis of patients with immunomodulatory subtypes of breast cancer for non-diagnostic purposes includes the following steps: Transcriptome and whole-exome sequencing were performed on breast cancer patients to be tested. The immunoregulatory subtype of the breast cancer patients to be tested was determined based on the transcriptome sequencing results. The immunoregulatory subtype is either an immunoregulatory subtype or a non-immune regulatory subtype. When the breast cancer patient to be tested is an immunomodulatory subtype, the patient is considered to have a good survival prognosis. When the breast cancer patient to be tested is a non-immunomodulatory subtype, based on the whole-exome sequencing results, the ScarHRD software is used to calculate the loss of heterozygosity (LOH), telomere genotype imbalance (TAI), and extensive structural metastasis (LST) scores for the breast cancer patient to be tested. The LOH, TAI, and LST scores are then summed unweighted to obtain the homologous recombination repair deficiency score for the breast cancer patient to be tested. A positive threshold of 40 is set for the homologous recombination repair deficiency score. For HRD-negative breast cancer patients, the F1 score is calculated using Formula I, a risk factor calculation method. An F1 ≥ 0.5 indicates a good survival prognosis for the breast cancer patient to be tested; otherwise, the survival prognosis is poor. For HRD-positive breast cancer patients, the F2 score is calculated using Formula II, a risk factor calculation method. An F2 ≥ 0.5 indicates a good survival prognosis for the breast cancer patient to be tested; otherwise, the survival prognosis is poor. The specific calculation method for the proportion level of somatic mutation features shown in Formulas I and II is as follows: The whole exome sequencing data of the breast cancer patients to be tested are analyzed using the sigflow (v2.1) analysis tool based on the default parameters to obtain the somatic mutation features of the breast cancer patients to be tested. The somatic mutation features of the breast cancer patients are then mapped to the somatic mutation database COSMIC. Based on whether the somatic mutation type is point mutation (SBS) or insertion / deletion mutation (ID), the proportion (percentage) of each somatic mutation feature of the breast cancer patients to the total somatic mutations is obtained. This proportion (percentage) is then divided by 5% and rounded down to the nearest integer as the proportion level of the somatic mutation feature.

[0089] Example 3: A method for assessing the survival prognosis of breast cancer patients with different immune regulatory subtypes for non-diagnostic purposes. I. Experimental Methods 1. Database sample validation Using the 232 triple-negative breast cancer patients shown in Example 1 as a sample set, including 52 patients with immunomodulatory subtype breast cancer and 180 patients with non-immunomodulatory subtype breast cancer, experimental and control groups were set up respectively to determine the survival prognosis of breast cancer patients in the sample set.

[0090] Experimental group: The survival prognosis of breast cancer patients in the sample set was determined according to the method for non-diagnostic purposes as shown in Example 2. Based on the actual survival prognosis of breast cancer patients in the sample set, the AUC value of the survival prognosis of breast cancer patients in the sample set was calculated according to the method shown in Example 2, and the determination results were recorded.

[0091] Control group: Breast cancer patients with the immunomodulatory subtype in the sample set were classified as having a good prognosis, while breast cancer patients with the non-immunomodulatory subtype in the sample set were classified as having a poor prognosis. AUC values ​​were calculated and the classification results were recorded. A comparison of the prognostic outcomes of the breast cancer patients in the sample set was plotted.

[0092] 2. Clinical sample validation An additional 55 breast cancer patients collected from clinical practice were used as the test set. All breast cancer patients in the test set underwent whole exome sequencing and transcriptome sequencing, and their clinical survival prognosis time was recorded.

[0093] Based on the transcriptome sequencing results of each breast cancer patient in the test set, the immunomodulatory subtype of each breast cancer patient in the test set was determined, and the immunomodulatory subtype was classified as either an immunomodulatory subtype or a non-immunomodulatory subtype.

[0094] Next, the survival prognosis of each breast cancer patient in the test set was determined according to the method for determining the survival prognosis of breast cancer patients for non-diagnostic purposes as shown in Example 2. Based on the actual survival prognosis of breast cancer patients in the test set, the AUC value of determining the survival prognosis of breast cancer patients in the test set according to the method shown in Example 2 was calculated and the determination results were recorded.

[0095] II. Experimental Results 1. Database sample validation results The ROC results for determining the survival prognosis of breast cancer patients in the sample set (experimental group) according to the method shown in Example 2 are as follows: Figure 4 As shown in Table 4, the survival prognosis results of breast cancer patients in the sample set (experimental group) were determined according to the method shown in Example 2.

[0096] Table 4. Results of determining the survival prognosis of breast cancer patients in the sample set according to the method shown in Example 2.

[0097] According to the method shown in Example 2, the survival prognosis ACC of breast cancer patients in the sample set was determined to be (52+85+51) / (52+85+20+24+51)×100%=81.0%.

[0098] The ROC results for judging the survival prognosis of breast cancer patients in the sample set using the method shown in the control group are as follows: Figure 5 As shown, the survival prognosis comparison results are as follows: Figure 6 As shown.

[0099] The survival prognosis results of breast cancer patients in the sample set determined by the method shown in the control group are shown in Table 5.

[0100] Table 5 shows the survival prognosis of breast cancer patients in the sample set as determined by the method used in the control group.

[0101] The ACC for judging the survival prognosis of breast cancer patients in the sample set by the method shown in the control group was (52+71) / (52+109+71)×100%=53.0%.

[0102] The specific results of survival prognosis assessment for the experimental and control groups are shown in Table 6.

[0103] Table 6. Specific results of survival prognosis assessment for the experimental and control groups.

[0104] Results show that, according to the method shown in Example 2, when using the homologous recombination defect score and somatic mutation characteristics of breast cancer patients to jointly determine the survival prognosis of immune-regulated subtype breast cancer patients, the ACC was higher and >80%; which is higher than the accuracy ACC (63.4%) shown in Example 1 when only the homologous recombination defect score was used to determine the survival prognosis.

[0105] 2. Clinical sample validation results The ROC results for determining the survival prognosis of 55 breast cancer patients in the test set according to the method shown in Example 2 are as follows: Figure 7 As shown in Table 7, the survival prognosis of 55 breast cancer patients in the test set was determined according to the method shown in Example 2.

[0106] Table 7 Results of determining the survival prognosis of breast cancer patients in the test group according to the method shown in Example 2.

[0107] According to the method shown in Example 2, the survival prognosis ACC of breast cancer patients in the test set was determined as (10+24+10) / (10+24+6+5+10)×100%=80.0%.

[0108] Results show that the method for determining the survival prognosis of breast cancer patients for non-diagnostic purposes, as shown in Example 2, can accurately determine the survival prognosis, whether for breast cancer samples recorded in the database or clinical breast cancer samples.

[0109] Example 4: A predictive system for determining the survival prognosis of breast cancer patients with different immune regulatory subtypes. A predictive system for determining the survival prognosis of breast cancer patients with different immune regulatory subtypes includes a data acquisition module, a data processing module, a prediction module, and a result output module. The data acquisition module is used to acquire the whole exome sequencing results and transcriptome sequencing results of breast cancer patients; The data processing module determines the immunomodulatory subtype of breast cancer patients based on the transcriptome sequencing results obtained by the data acquisition module; the immunomodulatory subtype is either an immunomodulatory subtype or a non-immune-regulatory subtype. For patients with non-immunomodulatory subtype breast cancer, based on the whole-exome sequencing results obtained from the data acquisition module, the ScarHRD software was used to calculate the loss of heterozygosity (LOH), telomere genotype imbalance (TAI), and extensive structural metastasis (LST) scores. The LOH, TAI, and LST scores were then summed unweighted to obtain the homologous recombination repair deficiency score. The HRD status of patients with non-immunomodulatory subtype breast cancer was determined with a positive threshold of 40 for the homologous recombination repair deficiency score. The HRD status was defined as HRD positive or HRD negative. For breast cancer patients with non-immunomodulatory subtypes, the whole exome sequencing data of breast cancer patients obtained from the data acquisition module were analyzed using the sigflow (v2.1) analysis tool with default parameters to obtain the somatic mutation characteristics of the breast cancer patients to be tested. The somatic mutation characteristics of breast cancer patients were then mapped to the somatic mutation database COSMIC. Based on whether the somatic mutation type was point mutation (SBS) or insertion / deletion mutation (ID), the percentage of each somatic mutation characteristic in the total somatic mutations of breast cancer patients was obtained. This percentage was then divided by 5% and rounded to the nearest integer as the proportion level of that somatic mutation characteristic. The prediction module is used to predict the survival prognosis of breast cancer patients. For breast cancer patients whose data processing module determines that they are of the immune-regulatory subtype, the prognosis is considered to be good. For breast cancer patients with HRD-negative, non-immunomodulatory subtypes as determined by the data processing module, the F1 of breast cancer patients is calculated using Formula I, which is based on the risk factors shown in Formula I. When F1 ≥ 0.5, the survival prognosis of breast cancer patients is good; otherwise, the survival prognosis of breast cancer patients is poor. For breast cancer patients with HRD-positive non-immunomodulatory subtypes identified by the data processing module, the F2 of breast cancer patients is calculated using the risk factors shown in Formula II. When F2 ≥ 0.5, the survival prognosis of breast cancer patients is good; otherwise, the survival prognosis of breast cancer patients is poor. Formula I: F1=0.529025032176067+0.230221235097482×SBS3-0.498932854715 304×SBS10b-0.730727683810486×SBS12-0.169223420844788×SBS13+0.6 04792959534835×SBS17b-0.2501964844098×SBS20+0.250241479591993× SBS29+0.119145057406723×SBS33-1.00959048472923×SBS48+0.3323624 91108694×SBS51-0.0175788074073378×SBS55+0.45317812694346×SBS58+0.0106953077538109×SBS59+0.0758827599059131×SBS86+0.049485550187784×SBS88-0.249129004889655×ID1+0.107209967629524×ID2+0.11121744635206×ID6-0.939426205399708×ID9+0.0188203627063938×ID11; In Formula I, SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11 represent the percentage levels of the corresponding somatic mutation features in breast cancer patients. Formula II: F2 = 0.191137821526338 + 0.37436790794174 × SBS2 + 0.0180943002871771 × SBS3 - 0.221872673793905 × SBS6 + 0.299349845623579 × SBS7a - 1.8081888615682 × SBS7d - 0.903174993637052 × SBS17a - 1.34694082133942 × SBS17b - (3.99950886022513E-06) × SBS19 + 0.163837007222794 × S BS24+0.482164973995526×SBS30+0.470728584425237×SBS36+0.798726326342207×SBS41+1.15784834425311×SBS54-2.43791957191518×SB S55+0.0290198193313017×SBS57-0.0666280168520665×ID5+0.900939747020252×ID6+0.226204660707941×ID13+0.19421721416777×ID18; In Formula II, SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18 represent the percentage levels of corresponding somatic mutation features in breast cancer patients. The result output module is used to output the results obtained by the prediction module.

[0110] Comparative Example 1: A method for assessing the survival prognosis of breast cancer patients with different immune regulatory subtypes for non-diagnostic purposes. I. Experimental Methods Using 232 breast cancer patients from the sample set in Example 1 as the breast cancer samples to be tested, the regularization parameters λ1 and λ2 were adjusted according to the method shown in Example 2 to obtain the somatic mutation characteristics and the related weight coefficients of somatic mutation characteristics that are related to the survival prognosis of breast cancer patients. Using the 232 breast cancer patients in Example 1 as the sample set, the survival prognosis of breast cancer patients was judged and the accuracy (ACC) was calculated.

[0111] II. Experimental Results After adjusting the regularization parameters λ1 and λ2, the somatic mutation characteristics associated with the survival prognosis of patients with non-immunomodulatory subtype breast cancer and the ACC results for judging survival prognosis are shown in Table 8.

[0112] Table 8. Specific results of comparative assessment of survival prognosis in patients with non-immune-regulatory subtypes of breast cancer.

[0113] The results showed that, among all combinations of regularization parameters λ1 and λ2, the combination of somatic mutation feature sets selected in Example 2 had the highest ACC for judging the survival prognosis of non-immune regulatory subtypes.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description and ideas, and it is neither necessary nor possible to exhaustively describe all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. The use of a diagnostic reagent in the preparation of a product for determining the survival prognosis of breast cancer patients based on immune regulatory typing, characterized in that, The detection reagent is a reagent for detecting the proportion of the following somatic mutation features to the total somatic mutations; the somatic mutation features include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS10b, SBS12, SBS13, SBS17a, SBS17b, SBS19, SBS20, SBS24, SBS29, SBS30, SBS33, SBS36, SBS41, SBS48, SBS51, SBS54, SBS55, SBS57, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID5, ID6, ID9, ID11, ID13, and ID18.

2. The use of a detection reagent in the preparation of a product for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and negative homologous recombination repair deficiency, characterized in that, The detection reagent is a reagent for detecting the proportion of the following somatic mutation features to the total somatic mutations; the somatic mutation features include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9 and ID11.

3. The use of a detection reagent in the preparation of a product for determining the survival prognosis of breast cancer patients with non-immunomodulatory subtypes and positive homologous recombination repair deficiency, characterized in that, The detection reagent is a reagent for detecting the proportion of the following somatic mutation features to the total somatic mutations; the somatic mutation features include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13 and ID18.

4. A detection system for determining the survival prognosis of breast cancer patients with non-immune regulatory subtypes and negative homologous recombination repair deficiency, characterized in that, Includes a prediction module and a results output module; The prediction module predicts survival based on the proportion of the following somatic mutation characteristics to the total somatic mutations in breast cancer patients with non-immune regulatory subtypes and negative homologous recombination repair defects, and obtains the survival prognosis prediction results for breast cancer patients. The somatic mutation signatures include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11; The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

5. A detection system for determining the survival prognosis of breast cancer patients with non-immune regulatory subtypes and positive homologous recombination repair deficiency, characterized in that, Includes a prediction module and a results output module; The prediction module predicts survival based on the proportion of the following somatic mutation characteristics to the total somatic mutations in breast cancer patients with non-immune regulatory subtypes and positive homologous recombination repair defects, and obtains the survival prognosis prediction results for breast cancer patients. The somatic mutation signatures include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18. The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

6. A detection system for determining the survival prognosis of breast cancer patients based on immune regulation subtyping, characterized in that, It includes a data acquisition module, a data processing module, a prediction module, and a result output module; The data acquisition module is used to acquire whole exome sequencing data and transcriptome sequencing data of breast cancer patients; The data processing module performs immune regulation subtyping on breast cancer patients based on the transcriptome sequencing data of breast cancer patients obtained by the data acquisition module; the immune regulation subtyping is either an immune regulation subtype or a non-immune regulation subtype. For breast cancer patients with non-immunomodulatory subtypes, homologous recombination repair deficiency scores are calculated based on the whole-exome sequencing data of breast cancer patients acquired by the data acquisition module, and the homologous recombination repair deficiency status of breast cancer patients is determined based on the positive threshold of the homologous recombination repair deficiency score; the homologous recombination repair deficiency status is either homologous recombination repair deficiency positive or homologous recombination repair deficiency negative. The prediction module is used to predict the survival prognosis of breast cancer patients. For breast cancer patients identified as having an immune-regulatory subtype in the data processing module, the prognosis is considered to be good. For non-immunomodulatory subtype breast cancer patients identified by the data processing module as having homologous recombination repair deficiency and negative, survival prediction is performed based on the proportion of each of the following somatic mutation characteristics to the total somatic mutations, and the survival prognosis prediction results for breast cancer patients are obtained. The somatic mutation signatures include SBS3, SBS10b, SBS12, SBS13, SBS17b, SBS20, SBS29, SBS33, SBS48, SBS51, SBS55, SBS58, SBS59, SBS86, SBS88, ID1, ID2, ID6, ID9, and ID11; For non-immunomodulatory subtype breast cancer patients identified by the data processing module as having homologous recombination repair deficiency, survival prediction is performed based on the proportion of each of the following somatic mutation characteristics to the total somatic mutations, resulting in the survival prognosis prediction of breast cancer patients. The somatic mutation signatures include SBS2, SBS3, SBS6, SBS7a, SBS7d, SBS17a, SBS17b, SBS19, SBS24, SBS30, SBS36, SBS41, SBS54, SBS55, SBS57, ID5, ID6, ID13, and ID18. The result output module is used to output the survival prognosis prediction results obtained by the prediction module.

7. The detection system according to claim 6, characterized in that, The homologous recombination repair defect score in the data processing module is an unweighted sum of the heterozygous deletion score, the telomere genotype imbalance score, and the large-scale structural transfer score.

8. The detection system according to claim 6, characterized in that, The positive threshold for homologous recombination repair defect scoring in the data processing module is 40-44.

9. The detection system according to claim 8, characterized in that, The positive threshold for the homologous recombination repair defect score is 40.

10. The role of the detection system of claim 6 in determining the survival prognosis of breast cancer patients based on immune regulation subtyping for non-diagnostic purposes.

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