Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer's disease

Background

Alzheimer's disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.

Methods

In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.

Results

Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.

Conclusion

This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.

Multifunctional Lipid Bilayer Nanocarriers for Cancer Immunotherapy in Heterogeneous Tumor Microenvironments, Combining Immunogenic Cell Death Stimuli with Immune Modulatory Drugs

In addition to the contribution of cancer cells, the solid tumor microenvironment (TME) has a critical role in determining tumor expansion, antitumor immunity, and the response to immunotherapy. Understanding the details of the complex interplay between cancer cells and components of the TME provides an unprecedented opportunity to explore combination therapy for intervening in the immune landscape to improve immunotherapy outcome. One approach is the introduction of multifunctional nanocarriers, capable of delivering drug combinations that provide immunogenic stimuli for improvement of tumor antigen presentation, contemporaneous with the delivery of coformulated drug or synthetic molecules that provide immune danger signals or interfere in immune-escape, immune-suppressive, and T-cell exclusion pathways. This forward-looking review will discuss the use of lipid-bilayer-encapsulated liposomes and mesoporous silica nanoparticles for combination immunotherapy of the heterogeneous immune landscapes in pancreatic ductal adenocarcinoma and triple-negative breast cancer. We describe how the combination of remote drug loading and lipid bilayer encapsulation is used for the synthesis of synergistic drug combinations that induce immunogenic cell death, interfere in the PD-1/PD-L1 axis, inhibit the indoleamine-pyrrole 2,3-dioxygenase (IDO-1) immune metabolic pathway, restore spatial access to activated T-cells to the cancer site, or reduce the impact of immunosuppressive stromal components. We show how an integration of current knowledge and future discovery can be used for a rational approach to nanoenabled cancer immunotherapy.

The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.