A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomic profiling of plasma from glioma and meningioma patients based on two complementary mass spectrometry techniques

INTRODUCTION

Extracranial and intracranial tumors are a diverse group of malignant and benign neoplasms, influenced by multiple factors. Given the complex nature of these tumors and usually late or accidental diagnosis, minimally invasive, rapid, early, and accurate diagnostic methods are urgently required. Metabolomics offers promising insights into central nervous system tumors by uncovering distinctive metabolic changes linked to tumor development.

OBJECTIVES

This study aimed to elucidate the role of altered metabolites and the associated biological pathways implicated in the development of gliomas and meningiomas.

METHODS

The study was conducted on 95 patients with gliomas, 68 patients with meningiomas, and 71 subjects as a control group. The metabolic profiling of gliomas and meningiomas achieved by integrating untargeted metabolomic analysis based on GC-MS and targeted analysis performed using LC-MS/MS represents the first comprehensive study. Three comparisons (gliomas or meningiomas vs. controls as well as gliomas vs. meningiomas) were performed to reveal statistically significant metabolites.

RESULTS

Comparative analysis revealed 97, 56, and 27 significant metabolites for gliomas vs. controls, meningiomas vs. controls and gliomas vs. meningiomas comparison, respectively. Moreover, among above mentioned comparisons unique metabolites involved in arginine biosynthesis and metabolism, the Krebs cycle, and lysine degradation pathways were found. Notably, 2-aminoadipic acid has been identified as a metabolite that can be used in distinguishing two tumor types.

CONCLUSIONS

Our results provide a deeper understanding of the metabolic changes associated with brain tumor development and progression.

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor's biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth

BACKGROUND

Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selective EGFR-T790M inhibitors has proven challenging through traditional screening platforms. With great advances in computer algorithms, machine learning improved the screening rates of molecules at full chemical spaces, and these molecules will present higher biological activity and targeting efficiency.

METHODS

An integrated machine learning approach, integrated by Bayesian inference, was employed to screen a commercial dataset of 70,413 molecules, identifying candidates that selectively and efficiently bind with EGFR harboring T790M mutation. In vitro cellular assays and molecular dynamic simulations was used for validation. EGFR knockout cell line was generated for cross-validation. In vivo xenograft moues model was constructed to investigate the antitumor efficacy of CDDO-Me.

RESULTS

Our virtual screening and subsequent in vitro testing successfully identified CDDO-Me, an oleanolic acid derivative with anti-inflammatory activity, as a potent inhibitor of NSCLC cancer cells harboring the EGFR-T790M mutation. Cellular thermal shift assay and molecular dynamic simulation validated the selective binding of CDDO-Me to T790M-mutant EGFR. Further experimental results revealed that CDDO-Me induced cellular apoptosis and caused cell cycle arrest through inhibiting the PI3K-Akt-mTOR axis by directly targeting EGFR protein, cross-validated by sgEGFR silencing in H1975 cells. Additionally, CDDO-Me could dose-depended suppress the tumor growth in a H1975 xenograft mouse model.

CONCLUSION

CDDO-Me induced apoptosis and caused cell cycle arrest by inhibiting the PI3K-Akt-mTOR pathway, directly targeting the EGFR protein. In vivo studies in a H1975 xenograft mouse model demonstrated dose-dependent suppression of tumor growth. Our work highlights the application of machine learning-aided drug screening and provides a promising lead compound to conquer the drug resistance of NSCLC.

Dietary factors and their influence on immunotherapy strategies in oncology: a comprehensive review

Immunotherapy is emerging as a promising avenue in oncology, gaining increasing importance and offering substantial advantages when compared to chemotherapy or radiotherapy. However, in the context of immunotherapy, there is the potential for the immune system to either support or hinder the administered treatment. This review encompasses recent and pivotal studies that assess the influence of dietary elements, including vitamins, fatty acids, nutrients, small dietary molecules, dietary patterns, and caloric restriction, on the ability to modulate immune responses. Furthermore, the article underscores how these dietary factors have the potential to modify and enhance the effectiveness of anticancer immunotherapy. It emphasizes the necessity for additional research to comprehend the underlying mechanisms for optimizing the efficacy of anticancer therapy and defining dietary strategies that may reduce cancer-related morbidity and mortality. Persistent investigation in this field holds significant promise for improving cancer treatment outcomes and maximizing the benefits of immunotherapy.

FT-Raman spectra in combination with machine learning and multivariate analyses as a diagnostic tool in brain tumors

Brain tumors are one of the most dangerous, because the position of these are in the organ that governs all life processes. Moreover, a lot of brain tumor types were observed, but only one main diagnostic method was used - histopathology, for which preparation of sample was long. Consequently, a new, quicker diagnostic method is needed. In this paper, FT-Raman spectra of brain tissues were analyzed by Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), four different machine learning (ML) algorithms to show possibility of differentiating between glioblastoma G4 and meningiomas, as well as two different types of meningiomas (atypical and angiomatous). Obtained results showed that in meningiomas additional peak around 1503 cm-1 and higher level of amides was noticed in comparison with glioblastoma G4. In the case of meningiomas differentiation, in angiomatous meningiomas tissues lower level of lipids and polysaccharides were visible than in atypical meningiomas. Moreover, PCA analyses showed higher distinction between glioblastoma G4 and meningiomas in the FT-Raman range between 800 cm-1 and 1800 cm-1 and between two types of meningiomas in the range between 2700 cm-1 and 3000 cm-1. Decision trees showed, that the most important peaks to differentiate glioblastoma and meningiomas were at 1151 cm-1 and 2836 cm-1 while for angiomatous and atypical meningiomas - 1514 cm-1 and 2875 cm-1. Furthermore, the accuracy of obtained results for glioblastoma G4 and meningiomas was 88 %, while for meningiomas - 92 %. Consequently, obtained data showed possibility of using FT-Raman spectroscopy in diagnosis of different types of brain tumors.