When researchers examine intricate human diseases like cancer, a crucial step involves comparing the DNA sequence of a affected individual to a template of genetic information from a healthy individual known as the reference genome. This process helps identify changes in the DNA, referred to as variations. Researchers strive to label the disease accurately to uncover its causes and how it responds to various treatments.
Since the year 2000, the prevailing human reference genome has been incomplete due to technological limitations in accessing challenging genomic regions. Consequently, some changes detected by scientists were false positives, complicating the identification of variants responsible for tumor growth.
In 2022, the Society of Scientists heralded the advent of the first truly complete human genome, employing a new methodology that is less fragmented than prior techniques. Since then, numerous researchers have begun to explore the benefits of utilizing this new genome in lieu of older reference genomes for studying complex genetic diseases like cancer.
Recent hypotheses from researchers in Canada and the United States suggest that the complete human genome can more accurately detect substantial mutations, or structural variants, providing superior cancer detection compared to standard reference genomes. If our genome were a textbook, these mutations would manifest as missing, added, or reversed paragraphs or pages. Studies have shown that structural mutations can lead to cancer by amplifying cancer-promoting genes, causing abnormal gene fusions, and disabling genes that naturally suppress cancer growth.
The researchers validated their hypothesis using established cancer cell lines in combination with a cancer-free control known as COLO829. This cell line serves as a benchmark for analyzing structural mutation data. The research team scrutinized four independent cell line samples sequenced by different laboratories and analyzed three tumor samples from patients with blood cancer, brain cancer, and ovarian cancer to assess their findings in a real-world clinical context. Additionally, they compared the cancer’s DNA sequence to both reference genomes and employed four distinct computational tools to identify structural variations.
The new complete human reference genome contains approximately 200 million additional base pairs of DNA sequence, addressing gaps and completing regions missing from the standard reference genome. Upon manual inspection of the COLO829 sample results, researchers noted a significant reduction in incorrectly identified structural variants—down from 225 to only 83 when utilizing the complete reference genome. This indicates a marked enhancement in our capability to detect structural variations.
While the new human reference genome has improved the accuracy of DNA change identification, it lacks the extensive medical annotations present in older reference genomes used to associate DNA changes with diseases. To bridge this gap, the researchers employed a tool called Levio SAM2 to match and lift over results between the new and old genomes. This strategy allows researchers to leverage the enhanced accuracy of new genomes while retaining the detailed medical knowledge linked to older genomes, effectively yielding the best of both worlds.
The integrated approach was applied to three patient samples, revealing that fewer cancer-specific mutation candidates necessitated manual clinical review compared to analyses based solely on standard reference genomes. The fewer candidates streamline the challenging process of pinpointing cancer-causing mutations amidst a myriad of false alarms. One notable mutation, spanning 609,000 base pairs and affecting a gene previously associated with several cancers, was detected in a patient’s sample. This variant exhibited a weak signal in the older reference genome but strong evidence in the new reference genome.
In conclusion, the researchers assert that their method optimizes the detection of structural mutations in cancer by minimizing false positives, aiding physicians in prioritizing clinically significant mutations. They emphasized that reducing false positives is vital for analyzing patient samples, as filtering out errant mutations to isolate genuine cancer drivers requires both time and expertise. Although their lifting strategy extended analysis time by approximately 50% compared to using only the older reference genome, researchers deemed this trade-off acceptable due to the substantial accuracy improvements observed.
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Source: sciworthy.com
