Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach

Machine learning identification of NK cell immune characteristics in hepatocellular carcinoma based on single-cell sequencing and bulk RNA sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant tumor; however, its immune microenvironment and mechanisms remain elusive. Single-cell sequencing allows for the exploration of immune characteristics within tumor at the cellular level. However, current knowledge regarding the roles of different immune cell populations in liver cancer progression is limited.

OBJECTIVE

The main objective of this study is to identify molecular markers with NK cell immune characteristics in hepatocellular carcinoma using various machine learning methods based on Single-Cell Sequencing and Bulk RNA Sequencing.

METHODS

We collected samples from eight normal liver tissues and eight HCC tumor tissues and performed single-cell RNA sequencing for immune cell clustering and expression profile analysis. Using various bioinformatic approaches, we investigated the immune phenotype associated with natural killer (NK) cells expressing high CD7 level. In addition, we verified the role of CD7 in the growth of HCC after NK cell and HCC cells cocultured by RT-qPCR, MTS and Flow cytometer experiments. Finally, we constructed a machine learning model to develop a prognostic prediction system for HCC based on NK cell-related genes.

RESULTS

Through single-cell typing, we found that the proportions of hepatocytes and NK cells were significantly elevated in the tumor samples. Moreover, we found that the expression of CD7 was high in HCC and correlated with prognosis. More importantly, Overexpression of CD7 in NK cells significantly inhibited the activity of MHCC97 cells and increased the number of apoptosis of HCC cells (p < 0.05). Furthermore, we observed that NK cells with high CD7 expression were associated with an activated immune phenotype.

CONCLUSION

Our study found that CD7 is an important biomarker for assessing immune status and predicting survival of HCC patients; hence, it is a potential target for immune therapy against HCC.

Stroma-infiltrating T cell spatiotypes define immunotherapy outcomes in adolescent and young adult patients with melanoma

The biological underpinnings of therapeutic resistance to immune checkpoint inhibitors (ICI) in adolescent and young adult (AYA) melanoma patients are incompletely understood. Here, we characterize the immunogenomic profile and spatial architecture of the tumor microenvironment (TME) in AYA (aged ≤ 30 years) and older adult (aged 31-84 years) patients with melanoma, to determine the AYA-specific features associated with ICI treatment outcomes. We identify two ICI-resistant spatiotypes in AYA patients with melanoma showing stroma-infiltrating lymphocytes (SILs) that are distinct from the adult TME. The SILhigh subtype was enriched in regulatory T cells in the peritumoral space and showed upregulated expression of immune checkpoint molecules, while the SILlow subtype showed a lack of immune activation. We establish a young immunosuppressive melanoma score that can predict ICI responsiveness in AYA patients and propose personalized therapeutic strategies for the ICI-resistant subgroups. These findings highlight the distinct immunogenomic profile of AYA patients, and individualized TME features in ICI-resistant AYA melanoma that require patient-specific treatment strategies.

Machine Learning Methods for Gene Selection in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.