Additional file 1 of Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets
Creators: name: Zhang, Ying
name: You, Chao
name: Pei, Yuchen
name: Yang, Fan
name: Li, Daqiang
name: Jiang, Yi-zhou
name: Shao, Zhimin
Publisher: name: figshare
Year: 2022
Subjects: subject: Space Science
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Plant Biology
subject: Virology

description: Additional file 1: Figure S1. Principal component analyses of radiomic features from 3 types of MRI machines. Figure S2. Details of radiomic feature extraction using LASSO (a) and XGBoost (b) after 2 cycles. Two feature selection steps were applied to the extracted radiomic features with the least absolute shrinkage and selection operator (LASSO) and XGBoost. (a) The LASSO model is a linear combination of the selected features weighted by their respective coefficients. The x-axis denotes LASSO coefficients. Features with nonzero coefficients denote greater contributions to the model and are selected. (b) Feature importance evaluates how valuable each feature was in the construction of the gradient boosted decision trees within the XGBoost model and is calculated by information gain. The x-axis measures the information gain. Figure S3. ROC curves of the radiomic model at baseline (green) and after 2 cycles (yellow). Figure S4. Mutation profile oncoplot of the study population. Figure S5. Selection of genomic features using XGBoost. For genomic feature selection, variables including mutation status (positive or negative), detected mutation counts and VAF of each gene were input. After standardization, feature importance evaluates how valuable each feature was in the construction of the gradient boosted decision trees within the XGBoost model and is calculated by information gain. The x-axis measures the information gain. Features with the top 10 information gain rankings are presented. The selection criterion was defined as an information gain over 75. Therefore, 5 VAF features were selected. Figure S6. IC50 and colony formation assay with epirubicin and paclitaxel treatment in stable cells expressing wild-type REL and D268E mutation. (a) REL mutations discovered in this cohort. REL p.D268E was identified as a recurrent mutation. (b) Stable shREL SUM-159 and MDA-MB-231 cells were further r transfected with wild-type REL or p.D268E mutation, respectively, and subjected to immunoblotting. (c-f) SUM-159 and MDA-MB-231 cells stably expressing wild-type REL and the D268E mutation were treated with increasing doses of epirubicin and subjected to colony formation survival assays. Representative images of the surviving colonies are shown in C and D, and the corresponding quantitative results are shown in E and F. (g-h) SUM-159 and MDA-MB-231 cells stably expressing wild-type REL and the D268E mutation were treated with increasing doses of epirubicin and subjected to IC50 assays. *p&lt; 0.05, **p&lt; 0.01, ***p&lt; 0.001 (i-l) SUM-159 and MDA-MB-231 cells stably expressing wild-type REL and D268E mutation were treated with increasing doses of paclitaxel and subjected to colony formation survival assays. Representative images of surviving colonies are shown in I and J, and the corresponding quantitative results are shown in K and L. (m-n) SUM-159 and MDA-MB-231 cells stably expressing wild-type REL and the D268E mutation were treated with increasing doses of paclitaxel and subjected to IC50 assays. *p&lt; 0.05, **p&lt; 0.01, ***p&lt; 0.001 Figure S7. Construction of MED23 (a) and REL (b) knockdown cell lines. (a) SUM-159 and BT-549 cells were transfected with shMED23 and shNC. After selection by puromycin, the cells were collected and subjected to immunoblotting. (b) SUM-159 and MDA-MB-231 cells were transfected with shREL and shNC. After 48 h of transfection, the cells were treated with puromycin for 7-10 days at a concentration of 5 μg/ml and then subjected to immunoblotting. Figure S8. IC50 and colony formation assay with paclitaxel treatment in stable cells expressing wild-type MED23 and P394H mutation. (a-d) MED23 was knocked down via shRNA. SUM-159 and BT-549 cells stably expressing wildtype MED23 and P394H mutation were treated with increasing doses of paclitaxel and subjected to colony formation survival assays. Representative images of surviving colonies are shown in A and C, and the corresponding quantitative results are shown in B and D. (e-f) SUM-159 and BT-549 cells stably expressing shNC, shMED23, wild-type MED23 and P394H mutation were treated with increasing doses of paclitaxel and subjected to IC50 assays. *p&lt; 0.05, **p&lt; 0.01, ***p&lt; 0.001. Figure S9. Apoptosis of SUM-159 and BT-549 cells stably expressing wild-type MED23 and p.P394H mutation after epirubicin treatment. The upper right quadrant represents dead/late apoptotic cells, whereas the lower right quadrant represents early apoptotic cells. After treatment with epirubicin (10 nM, 24 h), cells were stained with Annexin V/PE and 7AAD, and observed by flow cytometry. *p&lt; 0.05, **p&lt; 0.01, ***p&lt; 0.001. Figure S10. MED23 p.P394H promoted homologous recombination repair. (a) U2OS cells expressing wild-type MED23 and the P394H mutation were transfected with I-SceI. After 24 h, the cells were treated with 10 μM triamcinolone acetonide for another 48 h. The HR reporter assay was determined by flow cytometry. (b) The working model of the HR reporter system is presented. I-SceI endonuclease was introduced into GFP/U2OS cells. (c) Quantification of GFP-positive cells. **p&lt; 0.01 Table S1. Detailed information on the expression constructs (a), primers (b) and shRNA sequences (c). Table S2. The vendors and working concentrations of antibodies.