Novel high-resolution taxonomy facilitates effective management of CRC patients

Recent research discovered that bowel tumors can be classified into six clinically relevant subcategories based on patterns of gene interactions seen within tumor cells. The study was published in eLife.

Bowel Tumor

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This new bowel cancer taxonomy will help doctors comprehend the disease and may eventually be used for precision medicine, allowing them to adapt the best treatments to individual patients.

Currently, disease staging is used to guide clinical management of people with bowel cancer, but this is inadequate because there can be substantial differences between tumors at a similar stage.”

Zaoqu Lu, Study Lead Author and Master, The First Affiliated Hospital of Zhengzhou University

Zaoqu Lu adds, “More detailed molecular classification of bowel cancer is more effective, but it is still based on a snapshot of the gene activity levels within a tumor at one point in time (for example, at a biopsy), and does not reflect the changing dynamics of gene activity and how genes interact with each other.”

Biological interaction networks, on the other hand, which are maps of which genes typically interact with each other, stay rather stable throughout time and can more consistently characterize tissues.

Furthermore, gene connections are highly preserved in healthy tissue but greatly disrupted in illness. By quantifying an “interaction change” for distinct gene pairs, the total transition in gene interactions in diseased tissue can be compared to the background level in normal tissue.

The scientists used this gene interaction network (GIN) technique to build a large-scale network of alterations in gene interactions utilizing over 2,000 bowel cancer tissue samples and 308 normal bowel tissue samples.

Using the network, they were able to categorize bowel tumors into six subtypes, each with distinct clinical and molecular characteristics. Important tumor characteristics were the percentage of cancer cells to other cell types and whether the cancer cells were similar to stem cells, which can determine how likely a tumor is to grow and spread.

The immunogenicity of the bowel tumor tissue, or how likely it was to elicit an immune response, was also classified by the GIN classification. This information is critical for predicting whether a patient will react to immunotherapy.

A patient’s prognosis, resistance to radiation and immunotherapy, and propensity for sensitivity to various chemotherapy medications are all useful features that might help tailor treatment. The same six subgroups were repeatedly found when the GIN technique was applied to 19 additional colon cancer datasets.

The first GIN produced a classifier using 289 genes based on more than 2,000 interactions between about 1,400 genes. This would be difficult to test for in a clinical situation, thus the scientists simplified the classifier to a randomly chosen 14-gene mini-classifier to create an easily usable clinical tool.

The researchers discovered that the mini-classifier was just as reliable as the complete classifier at classifying the six subtypes of bowel cancer when they applied it to a subset of 214 bowel cancer samples.

Our study has identified and validated a high-resolution classification system which could serve as a tool for optimizing treatment decisions for patients with bowel cancer. The next step will be to confirm the biological and clinical interpretability of the six bowel cancer subtypes in a prospective clinical trial, but we believe that this new taxonomy could facilitate more effective personalized treatment of patients with bowel cancer.”

Xinwei Han, Study Senior Author, Professor and Director, Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University

Journal reference:

Liu, Z., et al. (2022) Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer. eLife.


The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of AZoLifeSciences.
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