Gene tag for predicting breast cancer neoadjuvant chemotherapy sensitivity and application thereof

A breast cancer, sensitivity technology, applied in the direction of genomics, proteomics, microbial measurement/testing, etc., can solve the problem that the effect of neoadjuvant chemotherapy for breast cancer has not been developed, so as to reduce medical costs, avoid overtreatment, The effect of precision therapy

Active Publication Date: 2021-07-02
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But so far, no clinically available gene signatures have been developed to predict the effect of neoadjuvant chemotherapy for breast cancer, that is, to predict whether pathological complete response (pCR) can be obtained, and to guide breast cancer patients to choose chemotherapy regimens.

Method used

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  • Gene tag for predicting breast cancer neoadjuvant chemotherapy sensitivity and application thereof
  • Gene tag for predicting breast cancer neoadjuvant chemotherapy sensitivity and application thereof
  • Gene tag for predicting breast cancer neoadjuvant chemotherapy sensitivity and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] Example 1: Collection of case data sets and screening of differential genes

[0023] The inventor selected 744 samples of breast cancer patients undergoing neoadjuvant therapy. These patients were all treated with paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T / FAC) or paclitaxel and doxorubicin-cyclophosphamide (T / AC) neoadjuvant chemotherapy. Gene expression datasets GSE32646 (chip platform GPL570), GSE20271 (chip platform GPL96), GSE20194 (chip platform GPL570), GSE25055 (chip platform GPL96), GSE41998 (chip platform GPL571). Except for the GSE25055 dataset which only contains HER2-negative breast cancer, the other datasets contain all types of breast cancer.

[0024] Using adjusted P0.6 as the standard, the inventor screened 238 and 224 differential genes in the pCR group and RD group in the GSE32646 and GSE20271 data sets, respectively, and obtained 54 common differential genes by taking the intersection.

Embodiment 2

[0025] Example 2: Discovery of predictive markers for neoadjuvant chemotherapy sensitivity to paclitaxel and anthracycline in breast cancer

[0026] The LASSO method was used to select the best biomarkers for predicting pCR with neoadjuvant chemotherapy in T / FAC by the least standard partial likelihood deviation. The group classification is calculated by ten-fold cross-validation, and the AUC curve is obtained by binary logistic regression. Therefore, the LASSO method assigns a regression coefficient to each signature. On this basis, a scoring system is constructed using the regression coefficients to weight the values ​​of the selected signatures. The inventor took 54 common differential genes of the GSE32646 data set as the training set (Training set), a total of 115 patients, after T / FAC neoadjuvant chemotherapy, 27 patients with pCR, accounting for 23.48%, 88 patients with RD, accounting for 76.52% %. Use the R language "glmnet" software package to perform LASSO regressi...

Embodiment 3

[0031] Example 3: Validation of the predictive model

[0032] The inventors used 4 data sets from different platforms to verify the prediction model constructed by this set of gene signatures containing 25 genes. That is, the prediction score of each sample is calculated by the expression of 25 genes, and the ability to distinguish pCR and RD samples is evaluated by the indicators of the receiver operating characteristic ROC curve. The verification results are as follows:

[0033] There were 74 patients in GSE20271 verification group 1 (Test1 set). After T / FAC neoadjuvant chemotherapy, there were 17 patients with pCR (22.97%), 57 patients with RD (77.03%), and 14 patients with pCR (18.92%) predicted according to the model score. %), 60 cases of RD patients (81.08%), the model evaluation index AUC is 0.9071, AC is 0.9054, SE is 0.7059, SP is 0.9649, PPV is 0.8571, NPV is 0.9167, the accuracy is good.

[0034]Among the 207 patients in GSE20194 verification group 2 (Test2 set),...

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Abstract

The invention belongs to the technical field of tumor gene detection, and particularly relates to a gene tag for predicting breast cancer paclitaxel and anthracycline neoadjuvant chemotherapy sensitivity and application thereof. In the invention, based on LASSO logistic regression, a gene tag consisting of 25 genes related to breast cancer neoadjuvant chemotherapy sensitivity is obtained, and a score containing a gene expression quantity is calculated and predicted, so that the sensitivity of a breast cancer patient using paclitaxel and anthracycline neoadjuvant chemotherapy can be accurately predicted, the response of the patient to treatment is predicted, and whether the patient benefits from chemotherapy is discriminated, and thus selection of neoadjuvant chemotherapy regimens for breast cancer is guided, excessive treatment is avoided, and the medical cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of tumor gene detection, and specifically relates to a group of gene tags composed of 25 gene expression levels for predicting the sensitivity of breast cancer paclitaxel and anthracycline neoadjuvant chemotherapy and the application thereof. Background technique [0002] According to the latest global cancer data in 2020, breast cancer has replaced lung cancer as the largest cancer in the world. Data: cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020, 2020). Breast cancer is a malignant tumor with highly heterogeneous biological characteristics. It has different clinical features, treatment response and prognosis according to different molecular types (Rouzier R, Perou CM, Symmans WF, et al: breast cancer molecular subtypes respond differently to preoperative chemotherapy. clin cancer res 11:5678-85, 2005). Predicting the treatment sensitivity of breast cancer and select...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/6886G16B5/00G16B20/00
CPCC12Q1/6886G16B5/00G16B20/00C12Q2600/106C12Q2600/158
Inventor 杨武林傅昌芳刘雨戴海明王宏志
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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