Fighting Cancer around the World: A Framework for Action

Exploring the Anticancer Potential of Furanpydone A: A Computational Study on its Inhibition of MTHFD2 Across Diverse Cancer Cell Lines

Cancer poses a significant global health challenge due to its high mortality rate and complex treatment strategies. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), which is notably overexpressed in various malignancies, represents a promising target for anticancer drug development. Furanpydone A, a new 4-hydroxy-2-pyridone alkaloid isolated from the endophytic fungus Arthrinium sp. GZWMJZ-606, has shown potent inhibitory activity against several cancer cell lines. This study provides the first computational evaluation of furanpydone A, focusing on its potential inhibition of MTHFD2 through molecular docking and 200 ns molecular dynamics (MD) simulations. Molecular docking revealed a binding free energy of -8.08 kcal/mol for furanpydone A, comparable to the control compound DS44960156 (-8.13 kcal/mol), indicating stable interactions with the MTHFD2 active site. MD simulations confirmed the structural stability of the furanpydone A-MTHFD2 complex, with RMSD values ranging from 1.5 to 2.9 Å, RMSF values below 4 Å, and a radius of gyration (Rg) of 26.7 Å. Furanpydone A maintained approximately four consistent hydrogen bonds throughout the simulation. Analysis of furanpydone A binding pose orientations and interactions with the MTHFD2 enzyme at 0 ns, 40 ns, 80 ns, 120 ns, 160 ns, and 200 ns revealed consistent and stable binding. MM-PBSA analysis showed a binding free energy (ΔGbind) of -23.57 ± 0.13 kcal/mol, with electrostatic and van der Waals interactions contributing significantly, suggesting competitive binding affinity to the control compound (-25.32 ± 0.11 kcal/mol). The contribution of individual amino acid residues, including key residues such as ARG43, TYR84, ASN87, LYS88, GLN132, and PRO314, indicated strong interactions that support the stability of the furanpydone A-MTHFD2 complex. ADMET predictions indicated that furanpydone A met key drug-likeness criteria and demonstrated good oral bioavailability, suitable distribution profile, minimal risk of drug-drug interactions, efficient elimination, and low toxicity potential. These findings suggest that furanpydone A is a promising candidate for cancer treatment, warranting further in vitro and in vivo validation, and highlighting its potential impact on the development of new anticancer therapies.

Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms

BACKGROUND/OBJECTIVES

Magnetic Resonance Imaging (MRI) plays a vital role in brain tumor diagnosis by providing clear visualization of soft tissues without the use of ionizing radiation. Given the increasing incidence of brain tumors, there is an urgent need for reliable diagnostic tools, as misdiagnoses can lead to harmful treatment decisions and poor outcomes. While machine learning has significantly advanced medical diagnostics, achieving both high accuracy and computational efficiency remains a critical challenge.

METHODS

This study proposes a hybrid model that integrates MobileNetV2 for feature extraction with a Support Vector Machine (SVM) classifier for the classification of brain tumors. The model was trained and validated using the Kaggle MRI brain tumor dataset, which includes 7023 images categorized into four types: glioma, meningioma, pituitary tumor, and no tumor. MobileNetV2's efficient architecture was leveraged for feature extraction, and SVM was used to enhance classification accuracy.

RESULTS

The proposed hybrid model showed excellent results, achieving Area Under the Curve (AUC) scores of 0.99 for glioma, 0.97 for meningioma, and 1.0 for both pituitary tumors and the no tumor class. These findings highlight that the MobileNetV2-SVM hybrid not only improves classification accuracy but also reduces computational overhead, making it suitable for broader clinical use.

CONCLUSIONS

The MobileNetV2-SVM hybrid model demonstrates substantial potential for enhancing brain tumor diagnostics by offering a balance of precision and computational efficiency. Its ability to maintain high accuracy while operating efficiently could lead to better outcomes in medical practice, particularly in resource limited settings.

Landscape of germline BRCA1/BRCA2 variants in breast and ovarian cancer in Peru

Background

There is an increasing amount of data from Latin America on the characterization of BRCA variants; however, there is limited information from Peru. We conducted a retrospective study to describe germline pathogenic/likely pathogenic(P/LP) variants and variants of uncertain/unknown significance (VUS) in the BRCA1 and BRCA2 genes in Peru, in patients with breast and ovarian cancer, candidates for treatment with poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors.

Methods

The patients were evaluated during the period 2019-2021. Genomic DNA was isolated from peripheral blood samples and targeted sequencing was performed using the Ampliseq BRCA panel. Genetic variant interpretation was carried out in accordance with the recommendations of the American College of Medical Genetics and ClinVar. During this period, 525 patients (143 with breast cancer and 382 with ovarian cancer) were studied.

Results

We found that 14.7% (21/143) of breast cancer patients and 20.7% (79/382) of ovarian cancer patients were carriers of P/LP variants in BRCA1/2. The most frequent pathogenic variants detected in BRCA1 were c.2105dupT (BIC: 2224insT, n=12, 18.75%), c.68_69delAG (BIC: 185delAG, n=6, 9.38%), c.140G>T and c.815_824dupAGCCATGTGG (n=5, 7.81%), while in BRCA2 were c.8023A>G (n=6, 16.67%), c.6024dupG (BIC: 6252insG, n=4, 11.11%), and c.9235delG (BIC: 9463delG, n=3, 8.33%). Regarding VUS, we found that 6.99% (10/143) of breast cancer patients and 7.33% (28/382) of ovarian cancer patients were carriers of a VUS in BRCA1/2. For BRCA1, the most frequent VUS was c.93C>G (n=2), and for BRCA2, c.5465A>T (n=4), c.3101T>C (n=3), c.205C>A and c.437T>C (n=2).

Conclusion

We found a frequency of 14.7% germline mutations in breast cancer patients and 20.7% in ovarian cancer patients. The most recurrent mutations were BRCA1 c.2105dupT and BRCA2 c.8023A>G. We found that BRCA2 c.8023A>G, c.6024dupG, and c.9235delG were not previously reported in Peruvian patients. BRCA1 c.2344dupA is a novel mutation that has not been previously reported in any database. The frequency of VUS in our cohort was 7.2%.

Cancer patients' return-to-work adaptation experience and coping resources: a grounded theory study

OBJECTIVE

To explore the return-to-work adaptation experience and coping resources used by cancer patients.

METHODS

With the help of the Nantong Cancer Friends Association, from June 2019 to January 2020, this study recruited 30 cancer patients who had returned to work using purpose sampling, snowball sampling and theoretical sampling. The researchers analyzed the data using initial-, focusing-, and theoretical coding.

RESULTS

The adaptation of cancer patients to return-to-work is a rebuilding process by taking advantage of the available personal and external coping resources. The adaptation experience includes: focusing on rehabilitation, rebuilding self-efficacy, and adjusting plans.

CONCLUSION

Medical staff should help patients mobilize coping resources to adapt to return to work.