Fei Xie

Metformin is an antidiabetic drug that has been tested widely as an anti-cancer agent. However, data from clinical trials have been mixed. Evidence for metformin’s efficacy in HER2+ breast cancer exists. Hence, we evaluated whether WBP2, a HER2-coamplified gene, can regulate the response of HER2+ breast cancer to metformin. Identification of biomarkers for predicting metformin response has implications in repurposing metformin for precision oncology. The effect of WBP2 on breast cancer response to metformin was studied using in vitro and mouse models. The mechanism of WBP2 on metformin-induced AMPK activation was elucidated, and its co-expression with p-AMPK was examined in clinical specimens using IHC. RNA-seq analyses were performed to elucidate WBP2’s mechanism in energy metabolism. WBP2 inhibited the metformin response of HER2+ breast cancer in vitro and in vivo. These effects were concomitant with WBP2-mediated repression of metformin-induced AMPK activation and mTOR inhibition in HER2+ breast cancer cells, a lower AMP:ATP ratio state, and enhanced glycolytic capacity and mitochondria respiration. Analysis of HER2-positive breast cancer samples supports the negative correlation between WBP2 expression and activated AMPK observed in vitro. RNA-seq analysis revealed the potential mechanism of WBP2 in regulating ATP production processes and preferential effect of WBP2 on metformin response in HER2+ breast cancer. This study reported a novel role of WBP2 in cancer metabolism and energetics that contributes new insights into the molecular etiology of cancer. WBP2 may be a biomarker for patient stratification, paving the way towards repurposing metformin for precision oncology.

Ductal carcinoma in situ (DCIS) and breast cancer are common female breast diseases and pose a serious health threat to women. Early diagnosis of breast cancer and DCIS can help to develop targeted treatment plans in time. In this paper, we investigated the feasibility of using Raman spectroscopy combined with convolutional neural network (CNN) to discriminate between healthy volunteers, breast cancer and DCIS patients. Raman spectra were collected from the sera of 241 healthy volunteers, 463 breast cancer and 100 DCIS patients, and a total of 804 spectra were recorded. The pre-processed Raman spectra were used as the input of CNN to establish a model to classify the three different spectra. After using cross-validation to optimize its hyperparameters, the model’s final classification performance was assessed using an unknown test set. For comparison with other machine learning algorithms, we additionally built models using support vector machine (SVM), random forest (RF) and k-nearest neighbor (KNN) methods. The final accuracies for CNN, SVM, RF and KNN were 98.76%, 94.63%, 80.99% and 78.93%, respectively. The values for area under curve (AUC) were 0.999, 0.994, 0.931 and 0.900, respectively. Therefore, our study results demonstrate that CNN outperforms three traditional algorithms in terms of classification performance for Raman spectral data and can be a useful auxiliary diagnostic tool of breast cancer and DCIS.

Breast cancer (BC) is associated with hereditary components, and some deleterious germline variants have been regarded as effective therapeutic targets. We conducted a clinic-based, observational study to better understand the distribution of deleterious germline variants and assess any clinicopathological predictors related to the variants among Chinese BC patients using a 32 cancer-related genes next-generation sequencing panel. Between November 2020 and February 2022, a total of 700 BC patients were recruited, and 13.1% (92/700) of them carried deleterious germline variants in 15 cancer-related genes, including 37 (37/700, 5.3%) in BRCA2, 29 (29/700, 4.1%) in BRCA1, 8 (8/700, 1.1%) in PALB2, 4 (4/700, 0.6%) in NBN, 3 (3/700, 0.4%) in MRE11A, 3 (3/700, 0.4%) in TP53 and 12 (12/700, 1.7%) in other genes. There were 28 novel variants detected: 5 in BRCA1, 14 in BRCA2, and 9 in non-BRCA1/2 genes. The variants in panel genes, HRR (homologous recombination repair)-related genes, and BRCA1/2 were significantly associated with the following clinicopathological factors: age at the initial diagnosis of BC, family history of any cancer, molecular subtype, Ki-67 index, and hereditary risk. In conclusion, we further expanded the spectrum of germline deleterious variants in Chinese BC patients, and the clinicopathological predictors of variants were identified to facilitate clinical genetic testing and counseling for appropriate individuals.