Gene Interactions: Developing a taxonomy for colorectal cancer
Analyzing how gene interaction networks are perturbed in individuals can help identify different types of colorectal cancers, paving the way towards personalized care.
- Guangzhou University of Chinese Medicine, China
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, China
- Department of Endocrinology, Shandong Provincial Hospital, Shandong Provincial Hospital, China
Colorectal cancer is challenging to treat because different patients have widely varying symptoms and the cancers display a broad range of molecular characteristics (Guinney et al., 2015). What can researchers and clinicians do to overcome these obstacles? First, it is necessary to understand the biological properties that make each type of colorectal cancer different, and to establish a precise molecular classification for the different subtypes based on these differences. This will help researchers to develop effective drug interventions. In the past decade, many diseases have been classified based on their transcriptome. However, this ignores the fact that levels of gene expression can vary widely in living patients (Wang et al., 2019).
Now, in eLife, Zhenqiang Sun, Xinwei Han and colleagues at the First Affiliated Hospital of Zhengzhou University – including with Zaoqu Liu as first author – report how they have developed a taxonomy for colorectal cancers based on the interactions between genes rather than on the levels of gene expression (Liu et al., 2022). This approach analyzes networks of gene interactions, which tend to be conserved in healthy samples, but are perturbed in cancer. This makes it possible to characterize the different types of tumors based on how their gene interaction networks are perturbed.
First, the researchers. used a dataset including tumors and healthy samples to generate a large-scale network that had genes as the nodes of the network, and the interactions between genes as the edges. Next, for both healthy and cancerous samples, they quantified the level of perturbation by measuring how the interactions between each pair of genes in the network had changed.
This analysis showed that the perturbations were much stronger in the tumors. Moreover, using a computational approach called clustering analysis, Liu et al. were able to establish six subtypes of colorectal cancers, which they called gene interaction-perturbation network subtypes (or GINS for short; Figure 1). Comparing this new taxonomy with previously published classifications of colorectal cancers revealed that there are notable differences between GINS and subtypes from other taxonomies (Figure 1). Liu et al. were able to confirm the stability and reproducibility of their proposed classification by validating it in independent datasets, which suggests that this taxonomy will be extremely useful for clinical applications.
Figure 1
A new taxonomy for colorectal cancer.
Liu et al. identified six subtypes of colorectal cancer: GINS1 is a proliferative subtype (green); GINS2 is a stromal-rich subtype (yellow); GINS3 is a KRAS-inactivated subtype (grey); GINS4 is a mixed subtype (red); GINS5 is an immune-activated subtype (bright blue); and GINS6 is a metabolic reprogramming subtype (dark blue). Liu et al. report that GINS4 is an intermediate state between the GINS2 and GINS5 subtypes, which suggests that converting GINS4 into GINS5, which has a better prognosis and may be more sensitive to immunotherapy (especially to PARP inhibitors), might be a good treatment strategy. The GINS6 subtype, on the other hand, may respond well to statins based on its characteristics.
A good molecular classification needs to convey clear biological interpretations of the underlying data, as this will help lay the ground for trials and treatments aimed at specific subtypes (Lin et al., 2021; Ten Hoorn et al., 2022; Singh et al., 2021). Along these lines, Liu et al. report distinguishing features for each of the six subtypes they have identified. For example, GINS1 is a proliferative subtype, whereas GINS2 is a stromal-rich subtype; Figure 1 has details on the other four subtypes.
Looking forward, the new taxonomy will need to be validated by further studies in different populations. Additionally, the classification could be improved by integrating multi-omics data – such as transcriptomic data and proteomic data – into the network. In recent years, the HuRI and BioPlex initiatives have provided comprehensive human interactomes at the proteome level, improving our understanding of genotype-phenotype relationships (Luck et al., 2020; Huttlin et al., 2021). These could be used to make the new perturbation network more robust. Finally, it might also be possible to apply the new network to other challenges, for example, to identify new therapeutic agents for colorectal cancer through network-based approaches (Fang et al., 2021).
The work of Liu et al. is another example of the use of network-based approaches to classify cancer subtypes, such as the recent molecular taxonomy developed for meningioma (Nassiri et al., 2021). The results present and validate a new classification system for colorectal cancer, providing more insights into the heterogeneity of the disease, and facilitating both translational research and personalized approaches to treatment.
References
-
The consensus molecular subtypes of colorectal cancerNature Medicine 21:1350–1356.https://doi.org/10.1038/nm.3967
-
Clinical value of consensus molecular subtypes in colorectal cancer: a systematic review and meta-analysisJournal of the National Cancer Institute 114:503–516.https://doi.org/10.1093/jnci/djab106
Article and author information
Author details
Publication history
Copyright
© 2022, Fang et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 461
- views
-
- 38
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Gene Interactions: Developing a taxonomy for colorectal cancer
eLife 11:e84025.
https://doi.org/10.7554/eLife.84025
Further reading
-
- Cancer Biology
- Computational and Systems Biology
Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.
-
- Cancer Biology
- Cell Biology
TIPE (TNFAIP8) has been identified as an oncogene and participates in tumor biology. However, how its role in the metabolism of tumor cells during melanoma development remains unclear. Here, we demonstrated that TIPE promoted glycolysis by interacting with pyruvate kinase M2 (PKM2) in melanoma. We found that TIPE-induced PKM2 dimerization, thereby facilitating its translocation from the cytoplasm to the nucleus. TIPE-mediated PKM2 dimerization consequently promoted HIF-1α activation and glycolysis, which contributed to melanoma progression and increased its stemness features. Notably, TIPE specifically phosphorylated PKM2 at Ser 37 in an extracellular signal-regulated kinase (ERK)-dependent manner. Consistently, the expression of TIPE was positively correlated with the levels of PKM2 Ser37 phosphorylation and cancer stem cell (CSC) markers in melanoma tissues from clinical samples and tumor bearing mice. In summary, our findings indicate that the TIPE/PKM2/HIF-1α signaling pathway plays a pivotal role in promoting CSC properties by facilitating the glycolysis, which would provide a promising therapeutic target for melanoma intervention.
