Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets

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Abstract

Background: We established a radiogenomic model to predict pathological complete response (pCR) in triple-negative breast cancer (TNBC) and explored the association between high-frequency mutations and drug resistance. Methods: From April 2018 to September 2019, 112 patients who had received neoadjuvant chemotherapy were included. We randomly split the study population into training and validation sets (2:1 ratio). Contrast-enhanced magnetic resonance imaging scans were obtained at baseline and after two cycles of treatment and were used to extract quantitative radiomic features and to construct two radiomics-only models using a light gradient boosting machine. By incorporating the variant allele frequency features obtained from baseline core tissues, a radiogenomic model was constructed to predict pCR. Additionally, we explored the association between recurrent mutations and drug resistance. Results: The two radiomics-only models showed similar performance with AUCs of 0.71 and 0.73 (p = 0.55). The radiogenomic model had a higher predictive ability than the radiomics-only model in the validation set (p = 0.04), with a corresponding AUC of 0.87 (0.73–0.91). Two highly frequent mutations were selected after comparing the mutation sites of pCR and non-pCR populations. The MED23 mutation p.P394H caused epirubicin resistance in vitro (p < 0.01). The expression levels of γ-H2A.X, p-ATM and p-CHK2 in MED23 p.P394H cells were significantly lower than those in wild type cells (p < 0.01). In the HR repair system, the GFP positivity rate of MED23 p.P394H cells was higher than that in wild-type cells (p < 0.01). Conclusions: The proposed radiogenomic model has the potential to accurately predict pCR in TNBC patients. Epirubicin resistance after MED23 p.P394H mutation might be affected by HR repair through regulation of the p-ATM-γ-H2A.X-p-CHK2 pathway.

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Zhang, Y., You, C., Pei, Y., Yang, F., Li, D., Jiang, Y. zhou, & Shao, Z. (2022). Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets. Journal of Translational Medicine, 20(1). https://doi.org/10.1186/s12967-022-03452-1

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