Cancer is a dangerous disease which involves unregulated cell growth i.e,cells divide and grow uncontrollably forming tumors and attack nearby parts of the body.It can also spread to distant parts of the body through bloodstream.Studies show that there are over 200 different types of cancers that effect humans.
There is still a lot to know about cancer and find out the exact reasons for its causes.continuous efforts are being carried out to find a drug that cures cancer completely and to detect the disease in early stages.Since,the disease shows in our DNA,analyzing this resource, viz the human genome, can be beneficial.
In regard to this,researchers from the Cancer Genome Atlas Research Network have assembled the most complete genetic profile of a type of blood cancer known as acute myeloid leukemia using an algorithm which is specially designed to analyze the human genome for cancer alterations.
A human genome is nothing but the complete set of human genetic information stored as DNA sequences.This information is quite useful in the analysis of the disease.The algorithm developed is named as Dendrix++.
The algorithm takes the genetic information found in patient’s cancerous cells and compares it to the genome of patient’s healthy cells.The difference in the two data sets is analyzed and the algorithm can identify the handful of genes among millions that are causing cancer.
This identification helps doctors to focus on developing medicines that can rectify these specific genes.
Analyzing and comparing genetic information is not a cake walk,since genetic mutation i.e,a permanent change in the DNA sequence continues even after the occurrence of original cancer-causing mutation.So,the algorithm should be able to sort out the cancer causing mutations among others.
Using Dendrix++,scientists have identified one sequence of genetic mutations called “driver mutations” which are the main cause of acute myeloid leukemia.They also found that the patients with this type of cancer had an average of 13 mutations which are very less compared to mutations found in adult cancers.
Another algorithm,called HotNet is also developed by an interdisciplinary team of biologists and computer scientists at Brown University and it was used to identify various genetic sequences through which ovarian cancer can be treated.These algorithms have also been used in several cancer research studies.
“That’s an advantage of computing: You can design an algorithm that can apply to more than just one set of data,” says Professor Upfal, a computer scientist.
The research report has an huge amount of data that will help scientists to develop medicines to cure problematic genes.The whole report is made available in the New England Journal of Medicine.