Release date: 2014-09-10
Recently, British scientists have developed a new tool that can divide breast cancer into ten different subtypes, which is expected to improve the treatment of this disease. The results of the study were published in the August 28, 2014 issue of Genome Biology.
Cancer occurs because genetic changes cause normal cells to develop into tumors. As the understanding of breast cancer increases, we know that this is not a single disease – the genetic mutations that cause different types of cancer are different, which is why tumors respond differently to treatment, why they are different Speed ​​growth. Currently, clinicians have two key indicators for predicting treatment outcomes.
It is a major goal of cancer scientists to discover the research dynamics of tumor genetics and to create a system to diagnose tumor types. To this end, researchers at the British Cancer Institute and the University of Cambridge have developed the IntClust system, which uses genomic technology to create a classification system with sufficient detail to more accurately indicate which patient is getting Types of breast cancer to determine which treatment method will be most appropriate.
To test the system, the scientists analyzed 997 tumor samples used in their development of the system, as well as 7544 samples from public databases, along with genomic and clinical data, including data from The Cancer Genome Atlas. They use the IntClust system and the two main systems used today: PAM50, which divides cancer into five types; and SCMGENE, which classifies cancer into four types and classifies these data.
They found that IntClust is no different from existing systems, at least in predicting patient outcomes and treatment outcomes. However, this system identified only a subgroup that was previously unnoticed among 3.1% of women with poor survival, and these patients appeared to be resistant to treatment. Identifying the genomic signature of this patient group will enable early labeling of these high-risk cancers, and obtaining genomic data from this group of patients may help to study new ways to treat this type of cancer.
Currently, the use of this system to classify tumors is expensive for most clinicians, and interpreting these results requires training that is not available in many clinical settings. However, the details and precision of this system may be of great use to breast cancer researchers who will be able to investigate the reasons why certain types of cancer respond better to certain treatments in order to find clinical markers of breast cancer, or to find New therapeutic targets.
Source: Biopass
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