How Genomic Cancer Tests Work
Genomic cancer tests analyze the DNA or RNA of a tumor to identify genetic mutations or other alterations that may be driving the growth of the cancer. Identifying these genetic alterations can guide treatment decisions, as certain drugs may target specific mutations. Additionally, tumor genomic tests can help:
- Identify the type and stage of a tumor
- Determine whether a tumor is likely to respond to certain treatments
- Predict the prognosis of a tumor
- Identify potential targets for personalized therapy
- Detect certain genetic mutations that may increase the risk of developing certain types of cancer
- Select the most appropriate clinical trials to participate in
How are genomic cancer tests performed?
- DNA tests analyze the DNA of a tumor to identify changes in the sequence of nucleotides (the building blocks of DNA) or in the structure of chromosomes.
- RNA tests analyze the RNA (a molecule that is transcribed from DNA) of a tumor to identify changes in gene expression.
Tumor profiling is a comprehensive type of genomic cancer testing that can identify a wide range of genetic alterations, including mutations, amplifications (when a specific gene is present in multiple copies), deletions (when a section of DNA is missing), and translocations (when two or more chromosomes are rearranged).
Targeted cancer tests focus on a specific set of genes that are known to be frequently altered in a certain type of cancer, making them more cost-effective and faster than genomic tumor profiling.
How are the results of genomic cancer tests interpreted?
The results of genomic cancer tests are interpreted by a team of experts including molecular pathologists, geneticists, and oncologists. They compare the genetic alterations identified in a patient's tumor with existing databases of cancer genetic alterations to determine their significance and potential impact on treatment.
In conclusion, genomic cancer tests provide valuable information that can help guide treatment decisions, optimize clinical outcomes, predict prognosis, and detect potential risk factors. They are an essential tool in the era of personalized cancer medicine.