Today, the latest issue of Nature is on the cover of “The Whole Genome of Cancer” as the subject of this issue: In this issue of Nature, six related papers have been published. A series of papers have also been published in sub-journals such as Nature Genetics, Nature Biotechnology, and Nature Communications. Click on “Read the original/Read More” at the end of the article to access the list of all articles under the topic Nature.
So what kind of research project has attracted so much attention? In today’s article, let’s learn about the information. Specifically, the pan-cancer whole genome analysis collaboration led these studies is the Pan-Cancer Analysis Project, and “whole genome” is the key word. In the past, many studies have looked at which genes that encode proteins can cause cancer after mutations. But in fact, the genetic sequence of proteins encodes only about 2% of the human genome. We don’t know much about what the remaining 98% of the variations may be related to cancer. And that’s what whole-genome analysis is all about.
Led by the partnership, researchers from 744 organizations on four continents sequenced 2,658 cancer samples across the genome, covering 38 different types of cancer. In addition, non-cancerous samples from the same individual were also used for control. From a range of processes, such as obtaining samples, protecting patient privacy, obtaining sequencing data, and analyzing sequencing data, this research has a huge workload. And these efforts have finally paid off. We have a new understanding of the whole genome of cancer.
On average, there are about four to five mutations in each tumor genome sample that drive cancer development, giving these cancer cells a survival-selective advantage. A concluding article in Nature also notes that many of the tumor samples used in the study showed complex DNA rearrangements (17.8 percent chromoplexy and 22.3 percent chromoripsis, the two words explained below). By contrast, only 5% of the samples did not detect a driving mutation.
Image-based interpretation of Chromoplexy and chromothripsis (Photo: Resources)
In the second article, the researchers tried to look at cancer drivers from non-coding DNA. The study found that some new cancer-driven mutations, such as a non-coding region of the anti-cancer gene TP53, repeatedly appeared in a non-coding region, while the non-coding region of the gene TERT, which encodes telomerase, also had mutations that caused excessive expression. This may promote abnormal division of cancer cells.
The third and fourth articles focus on some characteristic genomic variations. What does that mean? It turns out that when DNA repair goes wrong, or when it comes to mutagenic agents in the environment, it leaves some characteristic changes in the DNA, but these changes are not easy to find. The researchers, backed by a large amount of data, found 97 similar characteristic changes that greatly expanded our understanding.
The fifth paper focuses on the evolution of cancer cells. By comparing mutations in different cell subgroups, we can reverse the sequence in which these mutations occur. The analysis found that in the early stages of cancer, the most common occurrence is drive-type mutations. This can even occur years before diagnosis, so it may have implications for early diagnosis of cancer or the development of biomarkers. Over time, the environmental impact will be smaller and the impact of DNA repair defects will increase.
In the final paper, researchers analyzed data from 1,188 transcription groups and found that copy-number variations remained a major driver of gene expression changes in cancer cells, but hundreds of single nucleotide mutations also affected the expression of surrounding genes. In addition, some mutations in cancer cells can cause changes in transcription information, such as the production of new protein coding sequences.
These breakthroughs, brought together by thousands of scientists, have given us a new insight into the cancer genome. A report in Nature at the same time also noted that we still lack clinically relevant data related to patient treatment and prognosis. This data can help us better understand the correlation between information about these genomes and cancer treatment.