Palmitate Enhances the Efficacy of Cisplatin and Doxorubicin against Human Endometrial Carcinoma Cells

Endometrial cancer is the most common gynecological cancer worldwide. At present there is no effective screening test for its early detection and no curative treatment for women with advanced-stage or recurrent disease. Overexpression of fatty acid synthase is a common molecular feature of a subgroup of sex steroid-related cancers associated with poor prognoses, including endometrial cancers. Disruption of this fatty acid synthesis leads to cell apoptosis, making it a potential therapeutic target. The saturated fatty acid palmitate reportedly induces lipotoxicity and cell death by inducing oxidative stress in many cell types. Here, we explored the effects of palmitate combined with doxorubicin or cisplatin in the HEC-1-A and RL95-2 human endometrial cancer cell lines. The results showed that physiological concentrations of exogenous palmitate significantly increased cell cycle arrest, DNA damage, autophagy, and apoptosis in both RL95-2 and HEC-1-A cells. It also increased the chemosensitivity of both cell types. Notably, we did not observe that palmitate lipotoxicity reflected increased levels of reactive oxygen species, suggesting palmitate acts via a different mechanism in endometrial cancer. This study thus provides a potential therapeutic strategy in which palmitate is used as an adjuvant in the treatment of endometrial cancer.

Clinicopathological and molecular genomic features of monomorphic epitheliotropic intestinal T-cell lymphoma in the Chinese population: a study of 20 cases

Background Monomorphic epitheliotropic T-cell lymphoma (MEITL) is an aggressive non-Hodgkin lymphoma with a high fatality rate. This study was aimed to explore the clinicopathological and molecular genetic features of MEITL in the Chinese population. Methods A retrospective analysis was performed based on the clinical manifestations and pathological features of 20 Chinese MEITL. 9 cases with paired diseased-normal tissues were also analyzed for molecular information by whole-exome sequencing. Results There were 14 men and 6 women with a median age of 58.5 (28-81) years. 17(17/20) lesions were located in the jejunum or ileum; 13(13/20) cases had ulcers or perforations. Microscopically, except for 1(1/20) case of pleomorphic cells, the monomorphic, middle-sized tumor cells infiltrating into the intestinal epithelial and peripheral intestinal mucosa recess could be seen in the other 19 cases. Immunohistochemistry showed that most of the tumor cells in MEITL were positive for CD3(20/20), CD8(17/20), CD43(19/20), and CD56(15/20), but negative for CD5(20/20). The most frequently mutated genes of these Chinese cases were STAT5B (4/9) and TP53 (4/9), not SETD2(2/9). JAK3 mutations (3/9) were also detected with a high mutated frequency. We demonstrated that mutations of JAK-STAT pathway-related genes and the amplification of Chromosome 9q appeared at the same time in most cases(5/9). Conclusions The clinicopathological features were consistent with that in previous western studies, but a special case with pleomorphic cells was found in this study. The co-occurrence of JAK-STAT pathway-related gene mutations and the amplification of Chr9q is a molecular feature of MEITL.

Machine learning analysis of volatolomic profiles in breath can identify non-invasive biomarkers of liver disease: A pilot study

Disease-related effects on hepatic metabolism can alter the composition of chemicals in the circulation and subsequently in breath. The presence of disease related alterations in exhaled volatile organic compounds could therefore provide a basis for non-invasive biomarkers of hepatic disease. This study examined the feasibility of using global volatolomic profiles from breath analysis in combination with supervised machine learning to develop signature pattern-based biomarkers for cirrhosis. Breath samples were analyzed using thermal desorption-gas chromatography-field asymmetric ion mobility spectroscopy to generate breathomic profiles. A standardized collection protocol and analysis pipeline was used to collect samples from 35 persons with cirrhosis, 4 with non-cirrhotic portal hypertension, and 11 healthy participants. Molecular features of interest were identified to determine their ability to classify cirrhosis or portal hypertension. A molecular feature score was derived that increased with the stage of cirrhosis and had an AUC of 0.78 for detection. Chromatographic breath profiles were utilized to generate machine learning-based classifiers. Algorithmic models could discriminate presence or stage of cirrhosis with a sensitivity of 88–92% and specificity of 75%. These results demonstrate the feasibility of volatolomic profiling to classify clinical phenotypes using global breath output. These studies will pave the way for the development of non-invasive biomarkers of liver disease based on volatolomic signatures found in breath.

Palmitate Enhances The Efficacy of Cisplatin And Doxorubicin Against Human Endometrial Carcinoma Cells

Background: Endometrial cancer is the most common gynecological cancer worldwide. Overexpression of fatty acid synthase is a common molecular feature of a subgroup of sex steroid-related cancers associated with poor prognoses, including endometrial cancers. The saturated fatty acid palmitate reportedly induces lipotoxicity and cell death by inducing oxidative stress in many cell types. Here, we explored the effects of palmitate combined with doxorubicin or cisplatin in the HEC-1-A and RL95-2 human endometrial cancer cell lines. Methods: Endometrial cancer cells were cultured with in vitro and treated with palmitate, doxorubicin, and cisplatin. Cell metabolic activity and combination index was measured using MTT assay. Protein expression was assessed with western blotting. Flow cytometry was used to examine the cell cycle profiles, cell proliferation, apoptosis, ROS, mitochondria membrane potential, and mitochondrial mass. Immunocytochemistry was used to investigate the mitochondrial morphology.Results: Physiological concentrations of exogenous palmitate significantly increased cell cycle arrest, DNA damage, autophagy and apoptosis in both RL95-2 and HEC-1-A cells. It also increased the chemosensitivity of both cell types. Notably, we did not observe that palmitate lipotoxicity reflected increased levels of reactive oxygen species, suggesting palmitate acts via a different mechanism in endometrial cancer.Conclusion: This study provides a potential therapeutic strategy in which palmitate is used as an adjuvant in the treatment of endometrial cancer.

Benchmarking molecular feature attribution methods with activity cliffs

Feature attribution techniques are popular choices within the explainable artificial intelligence toolbox, as they can help elucidate which parts of the provided inputs used by an underlying supervised-learning method are considered relevant for a specific prediction. In the context of molecular design, these approaches typically involve the coloring of molecular graphs, whose presentation to medicinal chemists can be useful for making a decision of which compounds to synthesize or prioritize. The consistency of the highlighted moieties alongside expert background knowledge is expected to contribute to the understanding of machine-learning models in drug design. Quantitative evaluation of such coloring approaches, however, has so far been limited to substructure identification tasks. We here present an approach that is based on maximum common substructure algorithms applied to experimentally-determined activity cliffs. Using the proposed benchmark, we found that molecule coloring approaches in conjunction with classical machine-learning models tend to outperform more modern, deep-learning-based alternatives. However, none of the tested feature attribution methods sufficiently and consistently generalized when confronted with unseen examples.

A general optimization protocol for molecular property prediction using a deep learning network

The key to generating the best deep learning model for predicting molecular property is to test and apply various optimization methods. While individual optimization methods from different past works outside the pharmaceutical domain each succeeded in improving the model performance, better improvement may be achieved when specific combinations of these methods and practices are applied. In this work, three high-performance optimization methods in the literature that have been shown to dramatically improve model performance from other fields are used and discussed, eventually resulting in a general procedure for generating optimized CNN models on different properties of molecules. The three techniques are the dynamic batch size strategy for different enumeration ratios of the SMILES representation of compounds, Bayesian optimization for selecting the hyperparameters of a model and feature learning using chemical features obtained by a feedforward neural network, which are concatenated with the learned molecular feature vector. A total of seven different molecular properties (water solubility, lipophilicity, hydration energy, electronic properties, blood–brain barrier permeability and inhibition) are used. We demonstrate how each of the three techniques can affect the model and how the best model can generally benefit from using Bayesian optimization combined with dynamic batch size tuning.

Comparative metabolomics with Metaboseek reveals functions of a conserved fat metabolism pathway in C. elegans

ABSTRACTUntargeted metabolomics via mass spectrometry (MS) can reveal several 100,000 molecular features in a single sample, most of which may represent unidentified metabolites, posing significant challenges to data analysis. We here introduce Metaboseek, an open-source analysis platform designed for untargeted comparative metabolomics, and demonstrate its utility to elucidate functions of a conserved fat metabolism pathway, α-oxidation, using C. elegans as a model. Metaboseek integrates modules for molecular feature detection, statistics, molecular formula prediction, and MS/MS analysis, which uncovered more than >200 previously uncharacterized α-oxidation-dependent metabolites in an untargeted comparison of wildtype and α-oxidation-defective hacl-1 mutants. The identified structures support the predicted enzymatic function of HACL-1 and revealed that α-oxidation participates in metabolism of endogenous β-methyl-branched fatty acids and food-derived cyclopropane lipids. Our results showcase compound discovery via untargeted comparative metabolomics applied to a conserved primary metabolic pathway and suggest a model for the metabolism of cyclopropane lipids that are also part of human diets.

GTF2I Mutation in Thymomas: Independence From Racial-Ethnic Backgrounds. An Indian/German Comparative Study

Thymomas are the most frequent adult mediastinal cancers. Their etiology is unknown and their pathogenesis poorly understood. Racial, ethnic and environmental factors influence tumorigenesis in many cancers, but their role in thymomas remains unclear to date. In this study that included pretreatment thymoma cases from India and Germany (n = 37 and n = 77, respectively) we compared i) the prevalence of the thymoma-specific chromosome 7 c.74146970T > A mutation of the GTF2I gene in type A and AB thymomas; ii) epidemiological features; and iii) the frequency of myasthenia gravis (MG). Due to a known predominance of GTF2I mutation in A and AB histotypes, we included only a marginal number of type B thymomas as a control group in both cohorts. While the distribution of histological types between the cohorts was similar (p = 0.1622), Indian patients were strikingly younger (p < 0.0001; median age 50 vs. 65 years) and showed significantly lower tumour stage (Masaoka-Koga stage I) at primary diagnosis (p = 0.0005) than the German patients. In patients with known MG status (n = 17 in Indian and n = 25 in German cohort), a clear trend towards more frequent MG was observed in the Indian group (p = 0.0504; 48 vs. 82%). The prevalence of the GTF2I mutation (analysed in n = 34 Indian and n = 77 German patients) was identical in the two cohorts. We conclude that racial-ethnic and environmental factors do not significantly influence the most common molecular feature of thymomas but may have an impact on the timing of clinical presentation.

Data-science driven autonomous process optimization

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.