Targeting nucleotide metabolism can not only inhibit tumor initiation and progression but also exert serious side effects. With in-depth studies of nucleotide metabolism, our understanding of nucleotide metabolism in tumors has revealed their non-proliferative effects on immune escape, indicating the potential effectiveness of nucleotide antimetabolites for enhancing immunotherapy. A growing body of evidence now supports the concept that targeting nucleotide metabolism can increase the antitumor immune response by (1) activating host immune systems via maintaining the concentrations of several important metabolites, such as adenosine and ATP, (2) promoting immunogenicity caused by increased mutability and genomic instability by disrupting the purine and pyrimidine pool, and (3) releasing nucleoside analogs via microbes to regulate immunity. Therapeutic approaches targeting nucleotide metabolism combined with immunotherapy have achieved exciting success in preclinical animal models. Here, we review how dysregulated nucleotide metabolism can promote tumor growth and interact with the host immune system, and we provide future insights into targeting nucleotide metabolism for immunotherapeutic treatment of various malignancies.

MORC family CW-type zinc finger 2 (MORC2) is a newly identified chromatin-remodeling enzyme involved in DNA damage response and gene transcription, and its dysregulation has been linked with Charcot-Marie-Tooth disease, neurodevelopmental disorder, and cancer. Despite its functional importance, how MORC2 is regulated remains enigmatic. Here, we report that MORC2 is O-GlcNAcylated by O-GlcNAc transferase (OGT) at threonine 556. Mutation of this site or pharmacological inhibition of OGT impairs MORC2-mediated breast cancer cell migration and invasion in vitro and lung colonization in vivo. Moreover, transforming growth factor-β1 (TGF-β1) induces MORC2 O-GlcNAcylation through enhancing the stability of glutamine-fructose-6-phosphate aminotransferase (GFAT), the rate-limiting enzyme for producing the sugar donor for OGT. O-GlcNAcylated MORC2 is required for transcriptional activation of TGF-β1 target genes connective tissue growth factor (CTGF) and snail family transcriptional repressor 1 (SNAIL). In support of these observations, knockdown of GFAT, SNAIL or CTGF compromises TGF-β1-induced, MORC2 O-GlcNAcylation-mediated breast cancer cell migration and invasion. Clinically, high expression of OGT, MORC2, SNAIL, and CTGF in breast tumors is associated with poor patient prognosis. Collectively, these findings uncover a previously unrecognized mechanistic role for MORC2 O-GlcNAcylation in breast cancer progression and provide evidence for targeting MORC2-dependent breast cancer through blocking its O-GlcNAcylation.

Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (¹⁸F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.

This study used a prospective cohort study to observe the effect of triple-negative breast cancer on the 2-year disease-free survival rate with or without "TCM formula". From November 1 st, 2016, the first patient was enrolled in the cohort study. A total of 356 patients were enrolled on January 30, 2019. Among them, 154 cases were followed up for 2 years. During the follow-up, there were 6 cases of shedding, so 6 cases were affected. A total of 148 cases were included in the analysis, including 73 in the exposed group and 75 in the non-exposed group. The exposed group was given "TCM formula" on the basis of standardized treatment, and the non-exposed group was treated with simple triple-negative breast cancer. The two groups visited each of the three months. The interview included safety examination (hematology and imaging). The endpoint was the difference in 2-year invasive disease-free survival between the exposed and non-exposed groups and the safety of the "TCM formula". There were 6 cases of shedding during the experiment and the shedding rate was 3.9 %. The 2-year rate of invasive disease-free survival in the exposed team was 88.7 % and the non-exposed group was 82.5 %. Logistic multivariate regression analysis predicted that "TCM formula" could reduce the disease-related recurrence and metastasis rate by 11 % (OR = 0.89, 95 % CI 0.37-0.956, P＜0.05). Through K-M survival analysis, TNBC patients with age ≤35 years and regional lymph node stage N1 may be the benefit group of "TCM formula"(P＜0.05). During the study, the incidence of total adverse events was 8.2 % in the exposed group, mainly manifested as stomach discomfort, diarrhea, and hepatocyte damage. 1. In the exposed group, the two-year rate of invasive disease-free survival increased by 6.2 % compared with the non-exposed group(P＞0.05). 2. According to K-M survival analysis, TNBC patients with age ≤35 years and regional lymph node metastasis to N1 may be potential beneficiaries of "TCM formula". 3. "TCM Formula" is safe and tolerable to most patients.

Background: The germline variant spectrum of triple-negative breast cancer (TNBC) is different from that of other subtypes and has demonstrated ethnic differences. However, the germline variants of TNBC among Chinese patients and its clinical significance remain unclear. Methods: Using our multi-omics TNBC cohort (n = 325), we determined the spectrum of germline variants in TNBC and aimed to illustrate their biological and clinical implications. Results: Overall, 16.0% (52 of 325) of TNBC patients harbored at least 1 pathogenic or likely pathogenic germline variant. These germline variants were associated with early onset of TNBC, the occurrence of contralateral breast cancer, the basal-like immune-suppressed mRNA subtype, and the homologous recombination deficiency (HRD) mutation subtype. Somatic allele-specific imbalance was observed in 54.1% of these germline variants, which was correlated with early onset of breast cancer and elevated HRD. The genes BRCA1 (7.4%), RAD51D (2.8%), and BRCA2 (2.2%) were those most frequently mutated. The RAD51D germline variants, especially K91fs, were enriched in Chinese patients with TNBC compared with Caucasian and African American patients. The Chinese-specific RAD51D germline variants were functionally associated with the instability of the RAD51D protein, HRD, and sensitivity to PARP inhibitors. Conclusions: Chinese TNBC patients have a distinct spectrum of germline variants, with a remarkable impact on the clinical and molecular characteristics of the tumor. Integrative germline-somatic analysis may help identify TNBC patients who are most likely to be affected by their germline variants and in performing clinical interventions more precisely. The RAD51D variants enriched in our cohort may serve as therapeutic targets and guide precision treatment of TNBC.