Phosphoenolpyruvate carboxykinase-2 (PCK2) is a therapeutic target in triple-negative breast cancer.

PURPOSE: Metabolic rewiring in malignant transformation is often accompanied by altered expression of metabolic isozymes. Phosphoenolpyruvate carboxykinase-2 (PCK2) catalyzes the rate-limiting step of gluconeogenesis and is the dominant isoform in many cancers including triple-negative breast cancer (TNBC). Our goal was to identify small molecule inhibitors of PCK2 enzyme activity. METHODS: We assessed the impact of PCK2 down regulation with shRNA on TNBC cell growth in vitro and used AtomNet® deep convolutional neural network software to identify potential small molecule inhibitors of PCK2-based structure. We iteratively tested candidate compounds in an in vitro PCK-2 enzyme assay. The impact of the top hit on metabolic flux and cell viability was also assessed. RESULTS: PCK2 downregulation decreased growth of BT-549 and MDA-MB-231 cells and reduced metabolic flux through pyruvate carboxylase. The first AtomNet® in silico structural screen of 7 million compounds yielded 86 structures that were tested in PCK2 enzyme assay in vitro. The top hit (IC50 = 2.4 µM) was used to refine a second round of in silico screen that yielded 82 candidates to be tested in vitro, which resulted in 45 molecules with inhibition > 20%. In the second in vitro screen we also included 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, previously suggested to be PCK2 inhibitor based on structure, which emerged as the top hit. The specificity of this compound was tested in PCK1 and PCK2 enzymatic assays and showed IC50 of 500 nM and 3.5-27 nM for PCK1 and PCK2, respectively. CONCLUSION: 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate is a high affinity PCK2 enzyme inhibitor that also has significant growth inhibitory activity in breast cell lines in vitro and represents a potential therapeutic lead compound.

Peripheral blood immune parameters, response, and adverse events after neoadjuvant chemotherapy plus durvalumab in early-stage triple-negative breast cancer.

PURPOSE: We evaluated T- and B-cell receptor (TCR and BCR) repertoire diversity and 38 serum cytokines in pre- and post-treatment peripheral blood of 66 patients with triple-negative breast cancer (TNBC) who received neoadjuvant chemotherapy plus durvalumab and assessed associations with pathologic response and immune-related adverse events (irAEs) during treatment. METHODS: Genomic DNA was isolated from buffy coat for TCR and BCR clonotype profiling using the Immunoseq platform and diversity was quantified with Pielou's evenness index. MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel was used to measure serum cytokine levels, which were compared between groups using moderated t-statistic with Benjamini-Hochberg correction for multiple testing. RESULTS: TCR and BCR diversity was high (Pielou's index > 0.75) in all samples. Baseline receptor diversities and change in diversity pre- and post-treatment were not associated with pathologic response or irAE status, except for BCR diversity that was significantly lower post-treatment in patients who developed irAE (unadjusted p = 0.0321). Five cytokines increased after treatment in patients with pathologic complete response (pCR) but decreased in patients with RD, most prominently IL-8. IFNγ, IL-7, and GM-CSF levels were higher in pre-treatment than in post-treatment samples of patients who developed irAEs but were lower in those without irAEs. CONCLUSION: Baseline peripheral blood cytokine levels may predict irAEs in patients treated with immune checkpoint inhibitors and chemotherapy, and increased post-treatment B-cell clonal expansion might mediate irAEs.

Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts.

Introduction: Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor. Methods: Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of 137Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.4 Gy/h, were analyzed for radiation-induced short- and long-term effects. Two sample cohorts, i.e., discovery (n = 30) and validation (n = 12), were subjected to RNA sequencing. The pool of the results from those six experiments with shared conditions (1.6 mGy/h; 24 h), together with an earlier time point (0 h), constituted a third cohort (n = 12). Results: The 100 mGy-exposed cells at all abovementioned dose rates, harvested at 0/24 h and 21 days after exposure, showed no strong gene expression changes. DMXL2, involved in the regulation of the NOTCH signaling pathway, presented a consistent upregulation among both the discovery and validation cohorts, and was validated by qPCR. Gene set enrichment analysis revealed that the NOTCH pathway was upregulated in the pooled cohort (p = 0.76, normalized enrichment score (NES) = 0.86). Apart from upregulated apical junction and downregulated DNA repair, few pathways were consistently changed across exposed cohorts. Concurringly, cell viability assays, performed 1, 3, and 6 days post irradiation, and colony forming assay, seeded just after exposure, did not reveal any statistically significant early effects on cell growth or survival patterns. Tendencies of increased viability (day 6) and reduced colony size (day 21) were observed at 12 mGy/h and 23.4 Gy/min. Furthermore, no long-term changes were observed in cell growth curves generated up to 70 days after exposure. Discussion: In conclusion, low doses of gamma radiation given at low dose rates had no strong cytotoxic effects on radioresistant VH10 cells.

Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies.

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.