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Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets.
Zhang, Ying; You, Chao; Pei, Yuchen; Yang, Fan; Li, Daqiang; Jiang, Yi-Zhou; Shao, Zhimin.
Afiliação
  • Zhang Y; Department of Breast Surgery, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, People's Republic of China.
  • You C; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
  • Pei Y; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
  • Yang F; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
  • Li D; Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
  • Jiang YZ; Department of Breast Surgery, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, People's Republic of China.
  • Shao Z; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
J Transl Med ; 20(1): 256, 2022 06 07.
Article em En | MEDLINE | ID: mdl-35672824
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 (21 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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Neoplasias de Mama Triplo Negativas Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Neoplasias de Mama Triplo Negativas Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article