| Breast cancer is the most common cancer among women in the world and the main cause of cancer-related death in women.According to GLOBOCAN 2018,breast cancer alone accounts for 24.2%of all cancer cases and 15.0%of all cancer deaths in women.At present,imaging examination and biopsy are common methods for breast cancer diagnosis.However,they also have limitations such as taking trauma and potential risk of developing cancer for women.These shortcomings limit its application as early screening measures for breast cancer.The autoantibodies(AAbs)to tumor-associated antigens(TAAs)in serum can exist stably and continuously,so it can be used as serological tumor marker.Protein microarray technology is used to screen a panel of autoantibodies with high sensitivity and specificity,which lays the foundation for finding an economic and applicable kit for breast cancer diagnosis.And it is of great significance for clinical detection of breast cancer.ObjectiveThis study aims to use the customized protein microarray made of 154 cancer driver gene-encoded proteins to screen the potential TAAs and verify the findings from protein microarray by the enzyme-linked immunosorbent assay(ELISA)method and further evaluate the diagnostic value of these autoantibodies in breast cancer.To obtain a good performance model,this study established a model with multiple autoantibodies combination and then validated the prediction model by using independent set.The prediction model can not only recognize breast cancer from normal individuals but also distinguish breast cancer from benign breast diseases.This study provides a theoretical basis and technical support for the establishment of a new noninvasive serological immunodiagnosis method in the future for early breast cancer screening.Methods1.Preliminary screening of TAAs based on protein microarray technologyCustomized protein microarray consisting of 154 cancer driver genes-encoded proteins was used to screen the potential TAAs in sera from 27 paients with breast cancer and age and gender-matched 27 normal controls2.Using ELISA to detect autoantibodies against potential TAAs in 2 datasets1)Using small sample size for detection of anti-TAAs autoantibodies:According to the design of case-control study,the autoantibodies against potential TAAs screened out from protein microarray was detected by indirect ELISA in sera from 120 patients with breast cancer and 120 age and gender-matched normal controls.The difference of anti-TAAs autoantibodies level between breast cancer group and control group was tested by nonparametric method.And the χ2 test was used to compare the positive rate of two groups.ROC curve was used to evaluate the diagnostic value of these anti-TAAs autoantibodies.2)Using large sample size for validation of anti-TAAs autoantibodies:Indirect ELISA method was used to further verify the level of meaningful anti-TAAs autoantibodies in 758 serum samples(279 breast cancer,279 normal controls and 200 breast benign diseases).Nonparametric test was used to compare the difference of autoantibody level among these three groups,χ2 test was used to compare the difference of positive rate among these three groups,and the Bonferroni correction method was used to correct the a value.When P<0.0167,it was considered that the difference among these three groups was statistically significant.ROC curve was used to evaluate the diagnostic value of single anti-TAA autoantibody in breast cancer.3.The level of autoantibodies against TAAs in the sera of breast cancer patients before and after the operation was compared by repeated measurement analysis of variance.4.The establishment and validation of breast cancer diagn osis model1)Model construction:In this study,558 subjects(279 breast cancer and 279 normal controls)used as large sample size as training set for validation,the level of anti-TAA autoantibody in the sera that had significance in the above validation step was taken as the independent variable,and the 10-fold cross validation was applied to the training set for evaluation of the two classifers which contain logistic regression and random forest(RF)respectively.The classifier with the highest accuracy was selected and modeled in the training set.Then ROC analysis was used to evaluate the diagnostic value of the model for breast cancer in the training set.2)Model validation:A total of 240 subjects was used for small sample detection as the testing set to validate the above-mentioned breast cancer diagnostic model.ROC curve analysis was performed to evaluate the diagnostic value of the model for breast cancer patients.3)The ability of the model to distinguish breast cancer from benign breast disease:ROC curve analysis was used to evaluate the performance of the model to distinguish breast cancer patients from benign breast disease.4)The diagnostic value of the model for the clinical subgroup of breast cancer:The χ2 test was used to compare the positive rates of the model in different clinical stages,different age,hormone levels,lymph node metastasis and histological types of breast cancer patients.The diagnostic value of the model for each subgroup of breast cancer patients was evaluated by calculating sensitivity,specificity,Youden index and coincidence rate.Results1.The results of customized protein microarray:based on customized protein microarray technology,16 potential TAAs were identified,including ALK,BRCA2,CDKN2A,CEBPA,CEP55,CSF1R,FGFR3,FUBP1,GATA3,GNAS,HIST1H3B,HRAS,PTCH1,p62(IMP2),RalA,SRSF2.The AUC range of the 16 TAAs was 0.613-0.734 and when the specificity was 92.6%,the range of sensitivity was 18.5%-48.2%.2.The results of detection of anti-TAAs autoantibodies by ELISA:based on the results of protein microarray,after the detection of the small sample and the validation of large sample,12 anti-TAAs autoantibodies(ALK,BRCA2,CDKN2A,CEBPA,CEP55,FUBP1,GATA3,HIST1H3B,HRAS,PTCH1,p62(Imp2),Ra1A)were finally identified.These identified anti-TAAs autoantibodies were evaluated in the sera of breast cancer patients compared to controls.With normal sera as control,the AUC range of 12 anti-TAAs autoantibodies was 0.593-0.769.If benign breast disease was defined as control,the AUC range was 0.530-0.736.The positive rates of the 12 anti-TAAs autoantibodies among breast cancer group,normal controls group and benign breast disease group were 15.8%-59.2%,9.7%-11.7%and 6.5%-27.0%respectively.3.The comparison of serum anti-TAAs autoantibodies in patients with breast cancer before and after operation:the relative concentration of 12 anti-TAAs autoantibodies in the serum of breast cancer patients without any treatment was 22.1ng/ml,18.1ng/ml,28.0ng/ml,27.1ng/ml,25.9ng/ml,28.9ng/ml,18.4ng/ml,20.6ng/ml,23.8ng/ml,28.8ng/ml,20.1ng/ml,33.1ng/ml respectively.However,within one month after operation the relative concentration of 12 anti-TAAs autoantibodies was 19.5ng/ml,11.7ng/ml,20.2ng/ml,18.4ng/ml,14.2ng/ml,16.7ng/ml,13.4ng/ml,17.5ng/ml,19.5ng/ml,16.2ng/ml,15.3ng/ml,23.7ng/ml.The level of 12 anti-TAAs autoantibodies were significantly lower than that before operation(P<0.05).4.Establishment and validation of a multiple diagnostic models:based on 12 anti-TAAs autoantibodies mentioned above,multiple diagnostic models of breast cancer were established and validated.Through a series of verifications and comparisons of these models,an optimal diagnostic model which was the combination of 6 anti-TAAs autoantibodies(BRCA2,CEBPA,CEP55,FUBP1,HRAS,and RalA)was identified.The AUC of the model for the diagnosis of breast cancer patients in the training set was 0.878(95%CI:0.846-0.915),with a sensitivity of 75.6%and a specificity of 90.0%.In the testing set,the AUC,sensitivity,and specificity were 0.858(95%CI:0.823-0.901),66.0%,and 91.4%,respectively.When breast cancer patients were compared with benign breast disease patients,the AUC was 0.860(95%CI:0.841-0,902),with the sensitivity of 70.5%and the specificity of 90.0%.The analysis results of each subgroup of breast cancer showed that there was no significant difference in the positive rate of the diagnosis prediction model among the subgroups of different clinical stages,different age stages,lymph node metastasis and histological types(P>0.05).But the positive rates between HER-2 positive and HER-2 negative(44.1%vs 69.8%)breast cancer patients were significantly-different(P=0.017).Conclusions1.Custom-made protein microarray technology was applied to identify potential TAAs in the sera from breast cancer patients.16 potential anti-TAAs autoantibodies identified from protein microarray were further verified and validated with a large sample size.Finally,12 out of 16 potential anti-TAA autoantibodies were identified to have a certain diagnostic value in breast cancer.2.The serum autoantibody level in postoperative breast cancer patients within one month significantly decreased compared to preoperative patients.3.A group of a combination of 6 anti-TAAs autoantibodies(BRCA2,CEBPA,CEP55,FUBP1,HRAS,and RalA)as an optimal predictive model was selected and identified by modeling methods.Compared with a single anti-TAAs autoantibody,the model improved the sensitivity and specificity of breast cancer diagnosis and had a certain ability to distinguish breast cancer from benign breast disease. |
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