Organoid Culture And Personalized Treatment Of Giant Papillary Breast Cancer And Construction Of Prognostic Evaluation Model For Patients With Breast Cancer

Giant papillary breast cancer is a rare clinical pathological type of breast cancer.It is urgent to explore the biological characteristics of the disease and discover new therapeutic drugs.In our study,we collected a 63-year-old case of giant papillary From fresh tumor specimens of breast cancer cases,a patient-derived organoid model that can be passaged for more than 6 months was constructed in vitro.The results of HE staining showed that the organoids maintained the same histological characteristics as the primary tumor.The results of immunohistochemical analysis showed that the type The organ maintains a high fidelity.Then,we conducted drug susceptibility experiments.The experimental results showed that Fulvestrant(IC50=0.275umol),Pabocinil(IC50=2.21umol),BKM120(IC50=3.81umol),Iverolimus(IC50= 4.45umol),tamoxifen(IC50=19.13umol).The results show that our organoid model of giant papillary breast cancer can be used to explore its pathological characteristics and simulate feasible dosing regimens in vitro,which has certain reference value for clinical decision-making.In the second part,To screen the predictive factors affecting the prognosis of patients with breast cancer and to establish a model to evaluate the prognosis of patients with breast cancer.The clinical and follow-up data of 119779 breast cancer patients from2010 to 2015 were obtained from SEER database.All cases were randomly divided into70%(83854)training set and 30%(35925)validation set.Lasso regression was used to screen the predictive factors for predicting the prognosis of patients with breast cancer,and a Cox proportional hazard model was constructed.The(ROC)curve of the receiver working curve was used to evaluate the differential ability of the model in the training set and verification set,respectively,and compared with the accuracy of simply including TNM prognostic staging factors(primary tumor size,regional lymph nodes and distant metastasis)to predict the prognosis of patients.The calibration map is used to evaluate the accuracy of the model,and finally the results of the model are visualized in the way of nomogram.The results show that Age,race,tumor grade,tumor size,molecular classification,surgical treatment,lymph node status and distant metastasis were independent predictors of survival.In the training set,the areas under the working curve of the subjects in one year,three years and five years were 0.84,0.812 and 0.797 respectively,and 0.712,0.707 and 0.684 in the verification set.When the prognostic staging factors of TNM were included alone,the values were 0.738,0.714 and 0.693 respectively.One-year,three-year and five-year calibration maps show that the model has good prediction accuracy.Compared with relying solely on the traditional TNM prognostic staging factors to predict prognosis,this model can quantify and evaluate the prognosis of patients more accurately,and can provide intuitive and rational information for doctors,patients and medical policy makers.