Cancer biomarkers, especially those linked to genetic or epigenetic changes, frequently provide a quantifiable means to ascertain when individuals are susceptible to specific diseases. Some well-known examples of potentially prognostic cancer biomarkers include mutations in the genes KRAS, p53, EGFR, and erbB2 for colorectal, oesophageal, liver, and pancreatic cancer; mutations in the genes BRCA1 and BRCA2 for breast and ovarian cancer; abnormal methylation of the tumour suppressor genes p16, CDKN2B, and p14ARF for brain cancer; hypermethyl.
The precise diagnosis of cancer can also derive from the use of cancer biomarkers. This is certainly relevant when it’s essential to identify between primary and metastatic tumours. Researchers can compare the chromosomal changes in cells identified in the primary cancer location to those discovered in the secondary site to draw this differentiation. If the changes are the same, the secondary tumour can be classified as metastatic; if the changes are different, the secondary tumour can be classified as a different primary tumour.
People with tumours, for instance, have high quantities of circulating tumour DNA (ctDNA) as a result of tumour cells that have undergone apoptosis. The blood, saliva, or urine can all contain this tumour marker. Given the immense molecular heterogeneity of tumours discovered by next-generation sequencing research, it has lately been questioned whether it is possible to find a useful biomarker for early cancer diagnosis.
Prognosis and treatment predictions
The disease prognosis process, which begins after a person has been diagnosed with cancer, is another application of biomarkers in cancer treatment. Here, biomarkers can help figure out how aggressive a cancer is discovered as well as how likely it is to respond to a certain treatment. This is due in part to the possibility that specific medicines may be effective in treating tumours that express or contain certain biomarkers.
High levels of metallopeptidase inhibitor 1 (TIMP1), a marker linked to more severe forms of multiple myeloma, and/or high levels of oestrogen receptor (ER) and/or progesterone receptor (PR), markers linked to improved overall survival in breast cancer patients, are two examples of these prognostic biomarkers; Amplification of the HER2/neu gene, a sign that a patient’s breast cancer will probably respond to trastuzumab treatment; a mutation in exon 11 of the proto-oncogene c-KIT, a sign that a patient’s gastrointestinal stromal tumour (GIST) will probably respond to imatinib treatment; and mutations in the tyrosine kinase domain of EGFR1, a sign that a patient’
Pharmacodynamics and pharmacokinetics
Malignancy biomarkers can also be used to choose the best course of therapy for a certain patient’s cancer. Some people metabolise or alter the molecular structure of medications in different ways due to variations in each person’s genetic make-up. In some cases, a drug’s slower metabolism might lead to harmful situations where large concentrations of the drug build up in the body.
As a result, screening for such biomarkers can help with drug dose recommendations, particularly for cancer treatments. A gene that produces the enzyme thiopurine methyl-transferase is one example (TPMPT). Large doses of the leukaemia medication mercaptopurine cannot be metabolised by people with TPMT gene mutations, potentially leading to a catastrophic decrease in white blood cell count. Therefore, it is advised, for safety reasons, to administer mercaptopurine at a reduced dose to patients who have TPMT mutations.
Monitoring treatment response
Cancer biomarkers have also proven useful for tracking a treatment’s effectiveness over time. Since good biomarkers could significantly lower the cost of patient care, much research is being done in this field. This is because image-based procedures like CT and MRI, which are now used to monitor the status of tumours, are quite expensive.
The protein biomarker S100-beta in monitoring the response of malignant melanoma is one prominent biomarker gaining significant attention. The amount of cancer cells in these melanomas causes melanocytes, the cells that produce colour in the skin, to produce excessive concentrations of the protein S100-beta. Thus, S100-beta levels in the blood of these individuals are lowered in response to treatment.
Additionally, laboratory studies have demonstrated that tumour cells going through apoptosis can discharge biological components like cytochrome c, nucleosomes, cleaved cytokeratin-18, and E-cadherin. These macromolecules, as well as others, have been identified to circulate during cancer therapy, offering a potential source of clinical metrics for treatment monitoring.
Cancer biomarkers can be useful for predicting or keeping track of cancer recurrence. One such test used to predict the possibility of breast cancer recurrence is the Oncotype DX® breast cancer assay. Women who will receive hormone therapy for early-stage (Stage I or II), node-negative, oestrogen receptor-positive (ER+) invasive breast cancer are the target audience for this test. A panel of 21 genes are examined by Oncotype DX in cells collected after a tumour biopsy. The test’s results are presented as a recurrence score, which represents the likelihood of a recurrence in 10 years.