Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer.

BACKGROUND: This study aimed to explore potential indicators associated with the neoadjuvant efficacy of TCbHP regimen (taxane, carboplatin, trastuzumab, and pertuzumab) in HER2 + breast cancer (BrCa) patients. METHODS: A total of 120 plasma samples from 40 patients with HER2 + BrCa were prospectively collected at three treatment times of neoadjuvant therapy (NAT) with TCbHP regimen. Serum metabolites were analyzed based on LC-MS and GC-MS data. Random forest was used to establish predictive models based on pre-therapeutic differentially expressed metabolites. Time series analysis was used to obtain potential monitors for treatment response. Transcriptome analysis was performed in nine available pre­therapeutic specimens of core needle biopsies. Integrated analyses of metabolomics and transcriptomics were also performed in these nine patients. qRT-PCR was used to detect altered genes in trastuzumab-sensitive and trastuzumab-resistant cell lines. RESULTS: Twenty-one patients achieved pCR, and 19 patients achieved non-pCR. There were significant differences in plasma metabolic profiles before and during treatment. A total of 100 differential metabolites were identified between pCR patients and non-pCR patients at baseline; these metabolites were markedly enriched in 40 metabolic pathways. The area under the curve (AUC) values for discriminating the pCR and non-PCR groups from the NAT of the single potential metabolite [sophorose, N-(2-acetamido) iminodiacetic acid, taurine and 6-hydroxy-2-aminohexanoic acid] or combined panel of these metabolites were greater than 0.910. Eighteen metabolites exhibited potential for monitoring efficacy. Several validated genes might be associated with trastuzumab resistance. Thirty-nine altered pathways were found to be abnormally expressed at both the transcriptional and metabolic levels. CONCLUSION: Serum-metabolomics could be used as a powerful tool for exploring informative biomarkers for predicting or monitoring treatment efficacy. Metabolomics integrated with transcriptomics analysis could assist in obtaining new insights into biochemical pathophysiology and might facilitate the development of new treatment targets for insensitive patients.

Quantification of Hepatic Steatosis on Dual-Energy CT in Comparison With MRI mDIXON-Quant Sequence in Breast Cancer.

OBJECTIVE: The study aimed to evaluate the correlation and diagnostic value of liver fat quantification in unenhanced dual-energy CT (DECT) using quantitative magnetic resonance imaging (MRI) mDIXON-Quant sequence as reference standard in patients with breast cancer. METHODS: Patients with breast cancer were prospectively recruited between June 2018 and April 2020. Each patient underwent liver DECT and MRI mDIXON-Quant examination. The DECT-fat volume fraction (FVF) and liver-spleen attenuation differences were compared with the MRI-proton density fat fraction using scatterplots, Bland-Altman plots, and concordance correlation coefficient. Receiver operating characteristic curves were established to determine the diagnostic accuracy of hepatic steatosis by DECT. RESULTS: A total of 216 patients with breast cancer (mean age, 50.08 ± 9.33 years) were evaluated. The DECT-FVF correlated well with MRI-proton density fat fraction ( r2 = 0.902; P < 0.001), which was higher than the difference in liver-spleen attenuation ( r2 = 0.728; P < 0.001). Bland-Altman analysis revealed slight positive bias; the mean difference was 3.986. The DECT-FVF yielded an average concordance correlation coefficient of 0.677, which was higher than the difference of liver-spleen attenuation (-0.544). The DECT-FVF and the difference in liver-spleen attenuation both lead to mild overestimation of hepatic steatosis. The areas under the curve of DECT-FVF (0.956) were higher than the difference in liver-spleen attenuation (0.807) in identifying hepatic steatosis ( P < 0.001). CONCLUSIONS: Dual-energy CT-FVF may serve as a reliable screening and quantitative tool for hepatic steatosis in patients with breast cancer.

PTX promotes breast cancer migration and invasion by recruiting ATF4 to upregulate FGF19.

BACKGROUND: Widely-spread among women, breast cancer is a malignancy with fatalities, and chemotherapy is a vital treatment option for it. Recent studies have underscored the potential of chemotherapeutic agents such as paclitaxel, adriamycin, cyclophosphamide, and gemcitabine, among others, in facilitating tumor metastasis, with paclitaxel being extensively researched in this context. The molecular mechanism of these genes and their potential relevance to breast cancer is noteworthy. METHOD: Clinical tissue specimens were used to analyze the expression and clinical significance of FGF19 or P-FGFR4 in patients with breast cancer before and after chemotherapy. qRT-PCR, ELISA, immunofluorescence and Western blotting were used to detect the expression level of FGF19 in breast cancer cells. The biological impacts of paclitaxel, FGF19, and ATF4 on breast cancer cells were assessed through CCK8, Transwell, and Western blot assays. The expression of ATF4 in breast cancer cells was determined through database analysis, Western blot analysis, qRT-PCR, and immunofluorescence. The direct interaction between FGF19 and ATF4 was confirmed by a luciferase assay, and Western blotting was used to assess the levels of key proteins in the stress response pathway. To confirm the effects of PTX and FGF19 in vivo, we established a lung metastasis model in nude mice. RESULTS: FGF19 expression was increased in breast cancer patients after chemotherapy. Paclitaxel can boost the migration and invasion of breast cancer cells, accompanied by an increase in FGF19 expression. ATF4 might be involved in facilitating the enhancing effect of FGF19 on breast cancer cell migration. Finally, stimulation during paclitaxel treatment could trigger a stress response, influencing the expression of FGF19 and the migration of breast cancer cells. CONCLUSION: These data suggest that paclitaxel regulates FGF19 expression through ATF4 and thus promotes breast cancer cell migration and invasion.

A Single-Arm Phase II Clinical Trial of Fulvestrant Combined with Neoadjuvant Chemotherapy of ER+/HER2- Locally Advanced Breast Cancer: Integrated Analysis of 18F-FES PET-CT and Metabolites with Treatment Response.

Purpose: This Phase II trial was objected to evaluate the efficacy and safety of adding fulvestrant to NCT in patients with ER+/HER2- locally advanced breast cancer (LABC). Additionally, the study aimed to investigate the association of 18F-FES PET-CT and metabolites with efficacy. Materials and Methods: Fulvestrant and EC-T regimen were given to ER+/HER2- LABC patients before surgery. At baseline, patients received 18F-FES PET-CT scan, and plasma samples were taken for LC-MS analysis. The primary endpoint was ORR. Secondary endpoints included tpCR and safety. Results: Among the 36 patients enrolled, the ORR was 86.1%, the tpCR rate was 8.3%. The incidence of grade ≥3 TEAEs was 22%. The decrease in ER value in sensitive patients was larger than that in non-sensitive patients, as was Ki-67 (p<0.05). The SUVmax, SUVmean, TL-ER of 18F-FES PET-CT in sensitive patients were significantly higher than those in non-sensitive patients (p<0.05). Moreover, these parameters were significantly correlated with MP grade and the change in ER expression before and after treatment (p<0.05). Thirteen differential expressed metabolites were identified, which were markedly enriched in 19 metabolic pathways. Conclusion: This regimen demonstrated acceptable toxicity and encouraging antitumor efficacy. 18F-FES PET-CT might serve as a tool to predict the effectiveness of this therapy. Altered metabolites or metabolic pathways might be associated with treatment response.

Identification and Verification of Key Tumor Genes Associated with Diagnosis and Prognosis of Breast Cancer Based on Bioinformatics Analysis.

Breast cancer (BC) is the most common cancer and the most frequent cause of cancer death among women worldwide. The aim of the present study was to identify the critical genes for the diagnosis and prognosis of BC. Two mRNA expression data (GSE29431 and GSE42568) were acquired from the GEO database. The determination of differently expressed genes (DEGs) between BC specimens and nontumor specimens was completed via the LIMMA package of R. GO annotation and KEGG pathway enrichment analyses were applied to explore the function of DEGs. Kaplan-Meier methods were used to determine the prognostic value of DEGs in BC using TCGA datasets. The diagnostic value of the survival-related DGEs were confirmed using ROC assays in two GEO datasets. RT-PCR was used to examine the expression of the critical genes in BC cells and normal breast cells. CCK-8 experiments were applied to explore the function of the critical genes in BC cells. In this study, we identified 31 DEGs between BC specimens and nontumor specimens. KEGG analysis revealed 31 DEGs were involved in PPAR signal path, AMPK signal path, glycerolipid metabolism, adipocytokine signaling pathway, phenylalanine metabolism, tyrosine metabolic process, and glycine, serine, and threonine metabolic process. Four DEGs including CRYAB, DEFB132, MAOA, and RBP4 were observed to be associated with clinical outcome of BC patients. Their diagnostic values were also confirmed in both GSE29431 and GSE42568 datasets. In addition, we analyzed TCGA datasets and confirmed that the results were consistent with GEO datasets. Finally, the results of RT-PCR confirmed that the expression of CRYAB and RBP4 was distinctly downregulated in BC cells. CCK-8 analysis revealed that overexpression of CRYAB and RBP4 distinctly suppressed the proliferation of BC cells. Overall, our findings suggested CRYAB and RBP4 as critical genes for the diagnosis and prognosis of BC patients. They may be used as novel biomarkers for BC patients.

Association between serum lipids and breast cancer risk in premenopausal women: systematic review and meta-analysis.

OBJECTIVE: Associations between serum lipids and their individual components with premenopausal breast cancer risk are unclear. This meta-analysis summarized the literature on serum lipids and premenopausal breast cancer risk to elucidate their relationship. METHODS: Eligible studies were identified by searching the PubMed, Embase, China National Knowledge Infrastructure, and Wanfang databases until 31 December 2020. Standardized mean difference (SMD) scores with 95% confidence intervals (95%CIs) were used to assess the impact of serum lipids on premenopausal breast cancer risk. The I2 statistic was calculated to measure the percentage of heterogeneity, and Egger's test was performed to measure publication bias. RESULTS: Thirteen studies were included. The SMD scores of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) were 12.90 (95%CI: 7.19-18.61) and 31.43 (95%CI: 8.72-54.15), respectively. The SMD scores of total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) were not significantly different between the groups. The included studies were highly heterogeneous. There were no publication biases found in TC, LDL-C, or HDL-C analyses, whereas publication bias was present in the TG analysis. CONCLUSIONS: TG and LDL-C were higher in premenopausal breast cancer patients than in women without breast cancer. However, no significant differences were found in TC or HDL-C levels.