169P - Integration of metabolomics and transcriptomics to reveal potential biomarkers associated with treatment response of neoadjuvant therapy in HER2+ breast cancer

Background

This study aimed to explore potential indicators associated with neoadjuvant efficacy of TCbHP regime (taxane, carboplatin, trastuzumab and pertuzumab) in HER2+ breast cancer.

Methods

LC-MS and GC-MS platform-based untargeted metabolomics were performed to determine the metabolic profiles of plasma samples from HER2+ breast cancer patients, who received and completed neoadjuvant therapy (NAT) with TCbHP regime and subsequent surgery at Chongqing University Cancer Hospital. Random forest (RF) was used to establish predictive models based on pre-therapeutic metabolic traits. Transcriptome analysis was performed in available samples to identify differentially expressed genes (DEGs) before treatment. Metabolic and transcriptomic data were integrated to uncover significantly perturbed pathways potentially involved in drug resistance.

Results

From July 20, 2020 to May 28, 2021, a total of 40 HER2+ breast cancer patients with 120 plasma samples were eligible and recruited for this study. Of whom 21 (52.5%) patients achieved pCR and 19 (47.5%) achieved non-pCR. There were significant differences in plasma metabolic profiles between non-pCR and pCR groups before and during treatment. A total of 100 differential metabolites were identified between pCR and non-pCR patients at pre-therapeutic period, which were significantly enriched in 40 metabolic pathways. Four key metabolites [sophorose, N-(2-acetamido)iminodiacetic acid, taurine and 6-hydroxy-2-aminohexanoic acid] were selected by RF analysis. The AUC value to discriminate pCR and non-PCR group to NAT of the single potential metabolite or combined panel of these metabolites were more than 0.910. 18 metabolites had potential value in monitoring effect. A total of 163 DEGs were identified between biopsy tissue samples from pCR and non-pCR patients. 39 altered pathways aberrantly expressed at both metabolic and transcriptional levels.

Conclusions

Metabolomics integrated with transcriptomics analysis could assist for gaining more insights into biochemical pathophysiology and facilitate the development of novel therapeutic targets for insensitive patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202000104); Chongqing Science and Health Joint Medical Research Project (Grant No.2021MSXM085, 2021MSXM291, 2022MSXM004); Talent Program of Chongqing (Grant No. CQYC20200303137). Beijing Science and Technology Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202000104); Chongqing Science and Health Joint Medical Research Project (Grant No.2021MSXM085, 2021MSXM291, 2022MSXM004); Talent Program of Chongqing (Grant No. CQYC20200303137); Beijing Science and Technology Innovation Medical Development Foundation (Grant No. KC2021-JF-0167-05).

Disclosure

All authors have declared no conflicts of interest.