Understanding metabolic reprogramming in tumor microenvironment

Genomics based approaches to studies of cancer cells and cancer microenvironment

Recent genomic studies of cancer have greatly advanced our understanding of the underlining basis of this common disease, thus having led to the improvement of cancer treatment and public health over the past few decades. A major force of these discoveries has relied largely on the development of new sequencing technologies and novel computational approaches. In this issue, we have covered reviews in a wide range of such approaches to studies of cancer genomics, including next generation sequencing (NGS), third-generation sequencing (TGS), basic sequence alignment algorithm, and genome-wide association studies (GWAS).

The next-generation sequencing has made DNA sequencing dramatically simpler and faster. The sequencing process uses various platforms, such as DNA sequencing, RNA sequencing (RNA-Seq), single-cell RNA and DNA sequencing. To date, NGS has become a standard tool for many basic biology as well as for clinical and agronomical studies [6]. However, the read length in NGS data remains a bottleneck for biological research. Chen et al. give a brief description of NGS in cancer research, outline the limitations of NGS and then present a comprehensive review of key applications of third-generation sequencing (TGS) in cancer research including the identification of complex structure variation, alternatively splicing, gene fusion, and exogenous RNA [7]. With the technical improvement in reducing sequence errors and the development of novel bioinformatic algorithms, TGS is becoming a complementary sequencing technology alongside with NGS in cancer studies [8, 9].

Genomic signature profiling represented by k-mers is a critical approach to decoding important information from sequencing data. Existing fundamental techniques such as sequence alignment, RNA-seq transcriptome quantification, and microbial community profiling [10] predominantly rely on fixed-size k-mers while many research studies have shown the benefits of considering variable-length k-mers. Ju et al. present a novel genomic signature profiling approach, TahcoRoll, by extending the Aho-Corasick algorithm for profiling variable-length k-mers [11]. This new algorithm can potentially improve the current fixed k-mer-based computational pipelines.

Large-scale genome-wide association studies (GWAS) have identified hundreds of single-nucleotide polymorphisms (SNPs) associated with susceptibility to common cancers [12, 13]. Wang et al., having reviewed the major discoveries from cancer GWAS, describe methodologies of leveraging genetic risk factors and review the steps for the development and evaluation of risk prediction models [14].

Intratumor heterogeneity is a confirmed major cause of treatment failure and drug resistance in cancer. Single-cell sequencing has the advantages to addresses this issue by identifying subpopulations of cancer cells and immune cells within a single patient.

Metabolic reprogramming is the key interaction between cancer cells and tumor microenvironment

Efforts to uncover the link between tumor cell genetic mutations and tumor progression have been ongoing for several decades. Recently, scientists have discovered that in addition to gene mutations, tumor progression is also regulated by cancer microenvironment. How cancer cells sense and respond to microenvironmental changes is still a key question. It has been identified that cellular metabolism is an important modifier of various epigenetic modifications, such as DNA methylation, histone methylation and acetylation and RNA N6-methyladenosine (m6A) methylation [15]. In cancer cells, metabolic reprogramming driven by the oncogenic alterations can contribute to altered epigenetic modifications. The metabolites can serve as substrates, cofactors or regulators for epigenetic enzymes, thereby leading to changes in DNA, RNA and histone methylation and acetylation, which then promote the development of cancer [16], [17], [18]. Wang et al. summarized the abundance of key metabolites affected by metabolic reprogramming plays an important role in dynamically regulating epigenetic modifications in cancer [19]. Tan et al. further predict that most of the cancerous behaviors are the results of the metabolic reprogramming. Cancer cells have simplified their cellular system by mutating and repressing certain cell-polarity genes to revive a lost capability, namely cell cycle activation and progression driven by nutrient concentrations, for survival [20].

TME is highly hierarchical, and consists of tumor cells, endothelial cells, stromal cells and various types of immune cells. In these tumor-infiltrating immune cells, T cells, as well as NK cells, are tumor killing executors and the fundamental components for cancer immunotherapy. Increasingly more evidence has shown that myeloid cells that infiltrate into the tumor microenvironment are important regulator for both tumor cells and anti-tumor immune cells [21]. A better understanding of the diversity and functional roles of different myeloid cell subtypes and of how they are associated with TME remodeling may help to improve cancer therapy. In this issue, Shen et al. summarized the biological functions and therapeutic relevance of tumor-infiltrating myeloid cells, including tumor-associated macrophages, dendritic cells and neutrophils [22]. Specifically, they discussed how the tumor-derived factors and metabolites reprogram myeloid cells and what the functional interactions are between myeloid cells and other cells in the tumor microenvironment [23]. Understanding of the complexity of myeloid biology will pave the path for the development of novel and more effective therapeutics. A commentary in this issue further addressed the heterogenicity of myeloid cells. Wei et al. discussed the recent research article in Cell, that made a pan-cancer single-cell transcriptional atlas of human tumor-infiltrating myeloid cells. The single-cell transcriptome work provides valuable insights for understanding the functional heterogeneity and exploring new therapeutic targets [24, 25].

Targeting metabolic reprogramming for cancer therapy

Several bioenergetic metabolic pathways altered in cancer, including glycolysis, TCA cycle, glutaminolysis and fatty acid oxidation, have been investigated as potential drug targets. Drugs that target a variety of metabolic processes have been tested in preclinical cancer models [26]. Wang et al. summarized that several drugs can directly target metabolic enzymes, and others may have a role in inhibiting pathways that are crucial for supplying nutrients to cancer cells. Furthermore, the regulation of immunometabolism as a means of enhancing anti-tumor immunity is a growing area of active research in the field of cancer therapy. Shen et al. summarized that interfering with lipid metabolism represents a particularly attractive approach to enhancing tumor associate dendritic cells-mediated anti-cancer immunity.

In summary, it is now clear that metabolic reprogramming plays an important role in epigenetic modifications, thereby significantly contributing to cancer development. The advances of cutting edge high-throughput platform such as single-cell sequence, third-generation sequencing and computational methods will play important roles in completely uncovering the complexity of metabolic reprogramming interaction in cancer cells and with their microenvironment. Understanding these interactions will greatly promote the development of novel and more effective cancer therapies.