What's New in Gastric Cancer: The Therapeutic Implications of Molecular Classifications and Future Perspectives

Despite some remarkable innovations and the advent of novel molecular classifications the prognosis of patients with advanced gastric cancer (GC) remains overall poor and current clinical application of new advances is disappointing. During the last years only Trastuzumab and Ramucirumab have been approved and currently used as standard of care targeted therapies, but the systemic management of advanced disease did not radically change in contrast with the high number of molecular drivers identified. The Cancer Genome Atlas (TCGA) and Asian Cancer Research Group (ACRG) classifications paved the way, also for GC, to that more contemporary therapeutic approach called "precision medicine" even if tumor heterogeneity and a complex genetic landscape still represent a strong barrier. The identification of specific cancer subgroups is also making possible a better selection of patients that are most likely to respond to immunotherapy. This review aims to critically overview the available molecular classifications summarizing the main druggable molecular drivers and their possible therapeutic implications also taking advantage of new technologies and acquisitions.

Heterogeneity of mesencephalic dopaminergic neurons: From molecular classifications, electrophysiological properties to functional connectivity

The mesencephalic dopamine (DA) system is composed of neuronal subtypes that are molecularly and functionally distinct, are responsible for specific behaviors, and are closely associated with numerous brain disorders. Existing research has made significant advances in identifying the heterogeneity of mesencephalic DA neurons, which is necessary for understanding their diverse physiological functions and disease susceptibility. Moreover, there is a conflict regarding the electrophysiological properties of the distinct subsets of midbrain DA neurons. This review aimed to elucidate recent developments in the heterogeneity of midbrain DA neurons, including subpopulation categorization, electrophysiological characteristics, and functional connectivity to provide new strategies for accurately identifying distinct subtypes of midbrain DA neurons and investigating the underlying mechanisms of these neurons in various diseases.

Tumor Biology Hides Novel Therapeutic Approaches to Diffuse Large B-Cell Lymphoma: A Narrative Review

Diffuse large B-cell lymphoma (DLBCL) is a malignancy of immense biological and clinical heterogeneity. Based on the transcriptomic or genomic approach, several different classification schemes have evolved over the years to subdivide DLBCL into clinically (prognostically) relevant subsets, but each leaves unclassified samples. Herein, we outline the DLBCL tumor biology behind the actual and potential drug targets and address the challenges and drawbacks coupled with their (potential) use. Therapeutic modalities are discussed, including small-molecule inhibitors, naked antibodies, antibody-drug conjugates, chimeric antigen receptors, bispecific antibodies and T-cell engagers, and immune checkpoint inhibitors. Candidate drugs explored in ongoing clinical trials are coupled with diverse toxicity issues and refractoriness to drugs. According to the literature on DLBCL, the promise for new therapeutic targets lies in epigenetic alterations, B-cell receptor and NF-κB pathways. Herein, we present putative targets hiding in lipid pathways, ferroptosis, and the gut microbiome that could be used in addition to immuno-chemotherapy to improve the general health status of DLBCL patients, thus increasing the chance of being cured. It may be time to devote more effort to exploring DLBCL metabolism to discover novel druggable targets. We also performed a bibliometric and knowledge-map analysis of the literature on DLBCL published from 2014-2023.

A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy

Background

Hepatocellular carcinoma (HCC) is heterogeneous and refractory with multidimensional features. This study aims to investigate its molecular classifications based on multidimensional molecular features scores (FSs) and support classification-guided precision medicine.

Methods

Data of bulk RNA sequencing, single nucleotide variation, and single-cell RNA sequencing were collected. Feature scores (FSs) from hallmark pathways, regulatory cell death pathways, metabolism pathways, stemness index, immune scores, estimate scores, etc. were evaluated and screened. Then, the unsupervised clustering on the core FSs was performed and the characteristics of the resulting clusters were identified. Subsequently, machine learning algorithms were used to predict the classifications and prognoses. Additionally, the sensitivity to immune therapy and biological roles of classification-related prognostic genes were also evaluated.

Results

We identified four clusters with distinct characteristics. C1 is characterized by high TP53 mutations, immune suppression, and metabolic downregulation, with notable responsiveness to anti-PD1 therapy. C2 exhibited high tumor purity and metabolic activity, moderate TP53 mutations, and cold immunity. C3 represented an early phase with the most favorable prognosis, lower stemness and tumor mutations, upregulated stroma, and hypermetabolism. C4 represented a late phase with the poorest prognosis, highest stemness, higher TP53 mutations, cold immunity, and metabolic downregulation. We further developed practical software for prediction with good performance in the external validation. Additionally, FTCD was identified as a classification-specific prognostic gene with tumor-suppressing role and potential as a therapeutic target, particularly for C1 and C4 patients.

Conclusions

The four-layer classification scheme enhances the understanding of HCC heterogeneity, and we also provide robust predictive software for predicting classifications and prognoses. Notably, C1 is more sensitive to anti-PD1 therapies and FTCD is a promising therapeutic target, particularly for C1 and C4. These findings provide new insights into classification-guided precision medicine.

Meningioma: current updates on genetics, classification, and mouse modeling

Meningiomas, the most common primary brain tumors in adults, are often benign and curable by surgical resection. However, a subset is of higher grade, shows aggressive growth behavior as well as brain invasion, and often recurs even after several rounds of surgery. Increasing evidence suggests that tumor classification and grading primarily based on histopathology do not always accurately predict tumor aggressiveness and recurrence behavior. The underlying biology of aggressive treatment-resistant meningiomas and the impact of specific genetic aberrations present in these high-grade tumors is still only insufficiently understood. Therefore, an in-depth research into the biology of this tumor type is warranted. More recent studies based on large-scale molecular data such as whole exome/genome sequencing, DNA methylation sequencing, and RNA sequencing have provided new insights into the biology of meningiomas and have revealed new risk factors and prognostic subtypes. The most common genetic aberration in meningiomas is functional loss of NF2 and occurs in both low- and high-grade meningiomas, whereas NF2-wildtype meningiomas are enriched for recurrent mutations in TRAF7, KLF4, AKT1, PI3KCA, and SMO and are more frequently benign. Most meningioma mouse models are based on patient-derived xenografts and only recently have new genetically engineered mouse models of meningioma been developed that will aid in the systematic evaluation of specific mutations found in meningioma and their impact on tumor behavior. In this article, we review recent advances in the understanding of meningioma biology and classification and highlight the most common genetic mutations, as well as discuss new genetically engineered mouse models of meningioma.

Exploring the Complex and Multifaceted Interplay between Melanoma Cells and the Tumor Microenvironment

Malignant melanoma is a very aggressive skin cancer, characterized by a heterogeneous nature and high metastatic potential. The incidence of melanoma is continuously increasing worldwide, and it is one of the most common cancers in young adults. In the past twenty years, our understanding of melanoma biology has increased profoundly, and disease management for patients with disseminated disease has improved due to the emergence of immunotherapy and targeted therapy. However, a significant fraction of patients relapse or do not respond adequately to treatment. This can partly be explained by the complex signaling between the tumor and its microenvironment, giving rise to melanoma phenotypes with different patterns of disease progression. This review focuses on the key aspects and complex relationship between pathogenesis, genetic abnormalities, tumor microenvironment, cellular plasticity, and metabolic reprogramming in melanoma. By acquiring a deeper understanding of the multifaceted features of melanomagenesis, we can reach a point of more individualized and patient-centered disease management and reduced costs of ineffective treatments.