What does CGA mean in HUMAN GENOME

Cancer Genome Analysis (CGA) is a scientific process that involves the systematic study and comparison of genetic characteristics of cancer cells. It helps to identify genetic mutations, chromosomal alterations, epigenetic modifications and gene expression patterns associated with cancer development and progression. CGA also assists in predicting drug responses, determining clinical outcomes, and pathogenesis of cancers.

CGA uses information from various sources such as the patient's medical history, genetic sequencing data, and high-throughput laboratory results. Additionally, it also takes into account large-scale genomic datasets from public repositories such as The Cancer Genome Atlas (TCGA).

By studying the genetic makeup of a person's tumor, CGA helps identify gene abnormalities or mutation that are associated with certain types of cancer. This information can be used to diagnose cancer earlier and more accurately by differentiating between healthy cells and those infected with cancer-causing genes.

Depending on the type of analysis needed for a particular case, prices vary significantly for different technologies employed in cancer genome analysis. However, there are many cost-effective solutions for performing routine analyses or testing multiple variants at once.

The detailed understanding obtained through CGA can be used to develop better treatment protocols tailored to each individual patient’s unique circumstances. It helps doctors determine the best combination of therapies with minimal risk to the patient and fewer toxicity issues has been developed that employ multi-omics approaches integrating genomic data with findings from proteomics or other ‘omics platforms..

Examples include drugs used to target specific genes found in tumors such as anti-angiogenic agents which target vascularization to prevent tumor growth; targeted radiation therapy using gold nanoparticles; small molecules inhibiting pathways involved in tumorigenesis; antineoplastic agents preventing proliferation or destroying malignant cells; immunotherapeutic agents activating the body’s immune system against tumors; protein inhibitors blocking specific pathways responsible for tumor growth; hormones such as aromatase inhibitors blocking estrogen receptors in cancers driven by hormones; and monoclonal antibodies targeting proteins expressed on certain types of cancer cells.

By identifying which disease related pathways are affected by particular mutations, healthcare professionals can use this data to develop more efficient treatments with increased accuracy. Furthermore, machine learning algorithms can analyze large datasets quickly making it easier for researchers to find critical patterns and insights and accelerate drug development cycles.