Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

BRCA1-associated protein 1: Tumor predisposition syndrome and Kury-Isidor syndrome, from genotype-phenotype correlation to clinical management.

The BAP1 tumor suppressor gene encodes a deubiquitinase enzyme involved in several cellular activities, including DNA repair and apoptosis. Germline pathogenic variants in BAP1 have been associated with heritable conditions including BAP1 tumor predisposition syndrome 1 (BAP1-TPDS1) and a neurodevelopmental disorder known as Kury-Isidor syndrome (KURIS). Both these conditions are caused by monoallelic, dominant alterations of BAP1 but have never been reported in the same subject or family, suggesting a mutually exclusive genotype-phenotype correlation. This distinction is extremely important considering the early onset and aggressive nature of the types of cancer reported in individuals with TPDS1. Genetic counseling in subjects with germline BAP1 variants is fundamental to predicting the effect of the variant and the expected phenotype, assessing the potential risk of developing cancer for the tested subject and the family members who may carry the same variant and providing the multidisciplinary clinical team with the proper information to establish precise surveillance and management protocols.

Metabolic Features of Brain Function with Relevance to Clinical Features of Alzheimer and Parkinson Diseases.

Brain metabolism is comprised in Alzheimer's disease (AD) and Parkinson's disease (PD). Since the brain primarily relies on metabolism of glucose, ketone bodies, and amino acids, aspects of these metabolic processes in these disorders-and particularly how these altered metabolic processes are related to oxidative and/or nitrosative stress and the resulting damaged targets-are reviewed in this paper. Greater understanding of the decreased functions in brain metabolism in AD and PD is posited to lead to potentially important therapeutic strategies to address both of these disorders, which cause relatively long-lasting decreased quality of life in patients.

The J2-Immortalized Murine Macrophage Cell Line Displays Phenotypical and Metabolic Features of Primary BMDMs in Their M1 and M2 Polarization State.

Macrophages are immune cells that are important for the development of the defensive front line of the innate immune system. Following signal recognition, macrophages undergo activation toward specific functional states, consisting not only in the acquisition of specific features but also of peculiar metabolic programs associated with each function. For these reasons, macrophages are often isolated from mice to perform cellular assays to study the mechanisms mediating immune cell activation. This requires expensive and time-consuming breeding and housing of mice strains. To overcome this issue, we analyzed an in-house J2-generated immortalized macrophage cell line from BMDMs, both from a functional and metabolic point of view. By assaying the intracellular and extracellular metabolism coupled with the phenotypic features of immortalized versus primary BMDMs, we concluded that classically and alternatively immortalized macrophages display similar phenotypical, metabolic and functional features compared to primary cells polarized in the same way. Our study validates the use of this immortalized cell line as a suitable model with which to evaluate in vitro how perturbations can influence the phenotypical and functional features of murine macrophages.

N-acetylaspartate release by glutaminolytic ovarian cancer cells sustains protumoral macrophages.

Glutaminolysis is known to correlate with ovarian cancer aggressiveness and invasion. However, how this affects the tumor microenvironment is elusive. Here, we show that ovarian cancer cells become addicted to extracellular glutamine when silenced for glutamine synthetase (GS), similar to naturally occurring GS-low, glutaminolysis-high ovarian cancer cells. Glutamine addiction elicits a crosstalk mechanism whereby cancer cells release N-acetylaspartate (NAA) which, through the inhibition of the NMDA receptor, and synergistically with IL-10, enforces GS expression in macrophages. In turn, GS-high macrophages acquire M2-like, tumorigenic features. Supporting this in␣vitro model, in silico data and the analysis of ascitic fluid isolated from ovarian cancer patients prove that an M2-like macrophage phenotype, IL-10 release, and NAA levels positively correlate with disease stage. Our study uncovers the unprecedented role of glutamine metabolism in modulating macrophage polarization in highly invasive ovarian cancer and highlights the anti-inflammatory, protumoral function of NAA.

Exhaled breath condensate biomarkers for lung cancer.

Lung cancer is the main cause of cancer incidence and mortality worldwide and the identification of clinically useful biomarkers for lung cancer detection at both early and metastatic stage is a pressing medical need. Although many improvements have been made in the treatment and in the early screening of this cancer, most diagnosis are made at a late stage, when a lot of genetic and epigenetic changes have occurred. A promising source of biomarkers reflective of the pathogenesis of lung cancer is exhaled breath condensate (EBC), a biological fluid and a natural matrix of the respiratory tract. Molecules such as DNAs, RNAs, proteins, metabolites and volatile compounds are present in EBC, and their presence/absence or their variation in concentrations can be used as biomarkers. The aims of this review are to briefly describe exhaled breath composition, firstly, and then to document some of the EBC candidate biomarkers for lung cancer by dividing them according to their origin (genome, transcriptome, epigenome, metabolome, proteome and microbiota) in order to demonstrate the potential use of EBC as a helpful tool in cancer diagnostics, molecular profiling, therapy monitoring and screening of high risk individuals.