How is single-cell RNA sequencing contributing to the advancement of cancer therapeutics?

Abstract

The study of Tang et al investigated the distribution and dynamic changes of cell populations in the tumor microenvironment of gastric cancer (GC) patients using single-cell RNA sequencing (scRNA-seq). This comprehensive analysis highlights key interactions within the tumor microenvironment across different GC stages. Discussing applications of scRNA-seq data in clinical settings could pave the way for developing promising and personalized therapeutic strategies for GC patients. Therefore, further exploration of selecting anticancer drug candidates through gene screening derived from scRNA-seq will provide deeper insights into GC care.

Key Words: Gastric cancer; Single-cell RNA sequencing; Tumor microenvironment; Clinical trial; Anticancer drug candidate

Core Tip: Tang et al explored the distribution and dynamic changes of cell populations within tumor and adjacent tissues of gastric cancer patients using single-cell RNA sequencing (scRNA-seq). In addition to this study, deeper insight into the potential of scRNA-seq will be possible by incorporating information about selecting anticancer drug candidates with gene screening identified by scRNA-seq and their clinical applications.

TO THE EDITOR

I am pleased to read the high-quality article titled "Landscape of four different stages of human gastric cancer revealed by single-cell sequencing" by Tang et al[1], published in the World Journal of Gastrointestinal Oncology. The study explored the distribution and dynamic changes of cell populations within tumor and adjacent tissues of gastric cancer (GC) patients using single-cell RNA sequencing (scRNA-seq). By classifying 73645 single cells into 25 distinct cell clusters representing 10 different cell types, they observed significant differences in cell type distribution according to the progression of GC. Increased CD4 and CD8 T cell numbers were observed in the cancer tissues, while epithelial cells showed a tendency to degenerate. Interaction analysis revealed increased interactions between B cells and other mast cells in the progression stage of GC. It provides a comprehensive analysis of cell dynamics across the GC stages, highlighting key interactions within the tumor microenvironment. Deeper insight into the potential of scRNA-seq will be possible by incorporating information about selecting anticancer drug candidates with gene screening identified by scRNA-seq and their clinical applications.

Gene screening with scRNA-seq for selecting anticancer drug candidates targeting GC

There are three main theoretical points to emphasize in gene screening with scRNA-seq for selecting anticancer drug candidates targeting GC. First, scRNA-seq can identify unique cell subpopulations within the tumor microenvironment by analyzing gene expression in detail at the single-cell level, helping to pinpoint specific targets for drug development. Second, by integrating scRNA-seq data with computational tools and databases, researchers can identify existing drugs that can be repurposed for GC treatment, accelerating the discovery of potential anticancer drugs by leveraging known safety profiles and mechanisms of action. Third, using network-based approaches, researchers can identify drugs that target specific cellular interactions and pathways crucial for tumor growth and survival. A study by Xu-Shan Tang and colleagues concretely characterized the transcriptome profiles and cell-to-cell interactions within tumors and adjacent tissues of GC patients[1]. Their findings would contribute to enable the identification of unique cellular features at the single-cell level within tumor microenvironment of GC. It utilized a network-based approach, the third aspect of the points aforementioned above, this helps in developing personalized treatment strategies tailored to individual patients' tumor profiles. In addition to this, integrating discussions on gene screening for selecting possible anticancer drug candidates using scRNA-seq and their clinical applications will provide readers with deeper insights. While there are not many cases of gene screening applicable to GC care using scRNA-seq, it is believed that a few clinical analysis cases will be sufficient to acknowledge the usefulness of scRNA-seq.

scRNA-seq has been clinically applied in gene screening for identifying metastatic characterization, therapeutic application, and prognosis prediction of GC patients. These features make it ideal for integrating highly useful information for selecting anticancer drug candidates. Recently, two studies have tried to elucidate metastatic characterization of GC. In a study by Qian et al[2], scRNA-seq was performed on primary tumor (PT) and metastatic lymph node (MLN) from GC patients. They identified heterogeneous compositions of immune cells and distinct cell-to-cell interactions in the PT and MLN. They characterized the dynamics of gene expression in cancer cells between the PT and MLN, identified two subtypes of GC cells with distinct potentials for malignant biological behavior, and found that repression of neutrophil polarization-related genes such as Lipocalin-2 contributed to lymph node metastasis[2]. Wang et al[3] used scRNA-seq to understand metastasis from an intra-tumoral perspective, analyzing primary and MLN cancer tissues from GC patients. They investigated distinct tumor profiles and significant intra-tumoral heterogeneity (ITH) in GC patients, discovering ERBB2, CLDN11, CDK12, and potential evolution-driving genes Fos Proto-Oncogene, AP-1 Transcription Factor Subunit (FOS) and Jun Proto-Oncogene, and AP-1 Transcription Factor Subunit (JUN) as metastatic markers for GC treatment (Table 1)[3].

Table 1 Gene profiling with single-cell RNA sequencing targeting gastric cancer in clinical settings[1-5].

Order	Clinical application targeting GC	Target
1	Metastatic characterization	Lipocalin-2, ERBB2, CLDN11, CDK12, FOS, and JUN ↑
2	Finding therapeutic targets	Treg and AKCR1 ↑; DCD1, CTLA4, HAVCR2, LAG-3, and TIGIT ↓; CDH1, RHOA, ERBB2, FGFR2, AXIN1, and PTCH1 mutation
3	Finding prognostic markers	17q, BTF3, CKB, VPS28, TM4SF1, EIF2E, GPX4, and TPM2 ↑

↑ indicates an increased expression; ↓ indicates a decreased expression. GC: Gastric cancer.

Next, two studies addressing therapeutic applications using scRNA-seq could be discussed. Li et al[4] profiled the transcriptomes of 47304 cells with scRNA-seq from 9 GC patients. They identified a significant enrichment of regulatory T cells (Treg) in tumor tissues and an increase in AKCR1, which is specifically expressed in tumor endothelial cells, as well as a low expression of PDCD1, CTLA4, HAVCR2, LAG-3, and T Cell Immunoreceptor with Ig and ITIM Domains (TIGIT). Kwon et al[5] conducted a phase II trial of pembrolizumab in patients with advanced microsatellite instability–high GC, including analysis of whole-exome sequencing and scRNA-seq. They identified sequence alterations in microsatellites and an elevated mutational burden in 20% of GC. CDH1, RHOA, ERBB2, FGFR2, AXIN1, and PTCH1 were the major alteration genes in GC patients who did not experience significant tumor reduction. Particularly, the identification of AXIN1 mutations provides a clue that combination therapy of immune checkpoint inhibitors and Wnt/β-catenin pathway inhibitors would be a promising therapeutic option (Table 1)[5].

Finally, a study could be discussed addressing the utility of scRNA-seq in exploring markers that contribute to prognostic prediction. Wang et al[6] explored the origins and implications of ITH in GC using scRNA-seq. Single-cell analysis of ITH revealed that patients with molecular features of colorectal-like cells survived significantly longer than those with gastric-dominant features. Further, they found that all patients whose tumors had 17q gain were short-term survivors, and fundamental to GC progression is related to a 12-gene signature. The results suggest that genes associated with poor prognosis may be inferred as follows: BTF3, CKB, VPS28, TM4SF1, EIF2E, GPX4, and TPM2 (Table 1)[6].

Based on three major theoretical points that should be emphasized in gene screening for selecting anticancer drug candidates, the latest scRNA-seq approach related to the progression of GC could be discussed into three categories: Characteristics of metastasis, application to treatment, and exploration of markers for predicting prognosis. These studies clearly suggest the possibility of identifying key genes involved in this process while providing a comprehensive picture of GC progression, thereby providing a basis for selecting anticancer drug candidates that could enable personalized treatment of GC. The addition of this information would shed light on the potential of scRNA-seq beyond the study of Tang et al[1] limited to immune cell interactions to a broader perspective as a tool for selecting anticancer drug candidates.

Now, researchers are starting to recognize that scRNA-seq holds significant potential for application in treating various types of cancer. By identifying similar gene expression patterns across cancer types, scRNA-seq could provide a strategic foundation for the development of personalized treatment. This approach would contribute to the advancement of precision medicine, considering the heterogeneity of cancer. Collecting data from diverse populations and clinical settings enhances the generalizability of findings and facilitates the identification of potential biological markers. This comprehensive approach significantly bolsters the reliability and validity of scRNA-seq. Further, to integrate scRNA-seq into clinical practice, standardized protocols for interpreting and utilizing scRNA-seq data should be developed through collaboration between clinicians and researchers, a data management system that effectively utilizes genetic information while protecting patient privacy should be established. These efforts will facilitate clinical trials to validate the efficacy of scRNA-seq-based diagnostic and therapeutic strategies, thereby it would be served as a valuable tool for treating other types of cancer, in addition to GC.

CONCLUSION

scRNA-seq has evolved to provide detailed insights into cellular heterogeneity, allowing researchers to study individual cells within tumors. Tang et al[1] investigated cell-to-cell interactions within the tumor microenvironment using scRNA-seq based on this background. Further discussion of how scRNA-seq can be used to develop personalized therapeutic strategies with gene screening would highlight the potential of scRNA-seq to provide a deeper understanding of tumor biology and finding more effective anticancer drug candidates for GC care.

ACKNOWLEDGEMENTS

I thank the editors and reviewers for their comments that helped to improve the manuscript.