Advance bioinformatics tool tests approaches for identification of cancer genes

Being a complex genetic disease, cancer is resulted predominantly due to mutations in somatic cells of the genome. Mutations in the somatic cells may imbalance definite cell cycle process causing the attainment of cellular susceptibilities that convert a healthy cell to a cancerous cell. These changes drive cancer instigation and development. Additionally, they comprise of inactivating growth suppressors, counterattacking cell death, and further irregular phenotypes. As cancer is related with the genome disorder, hence it is practical that treatment approaches must be established on changes in genomic structure.

Numerous changes in somatic cells which are considered as mutation induce as well as promote tumor growth, and most of these genetic changes are being detected by the scientists of cancer genomics.

Traditional experimental approaches

Numeroussomatic mutations are being reported applying low‐throughput approaches, including systematic mutagenesis, cytogenetic techniques, genetic linkage analysis, and targeted gene sequencing. Conversely, all of the outdated methods are tiresome, cumbersome, and expensive.

Next-generation sequencing (NGS) in cancer genomics analysis

Next-generation sequencing detects the driver‐genes, with mutations leading the resistance to drugs. Furthermore, it can be applied to sense hypermutation that is likely to be important biomarker for immune checkpoint inhibitors. This also defines changes in double‐strand break repair pathway of DNA that is an evolving objective for novel treatments, and like wise-diligently linked to genetic genomic changes. Certain biological pathways along with a group of cancer‐associated genes (mutated genes) are being compared to pre-established biological pathways. Methods which are based on network search for cancer genes and biological pathways that characterize the interactions between cellular molecules. Methods learning pathways de novo do not utilize any previous information about the genes (interactions or pathways) and deduce cancer genes and pathways based on the forms of co‐occurrence or mutual individuality between the genetic abnormalities.

Methods for detecting novel cancer genes

Identification of unusual cancerous genes initiates with tumor samples sequenced. The major step is to distinguish genetic mutations using variant callers from sequencing data. The easiest methods to achieve this are by noticing recurring variations and by forecasting the practical influence of every mutation. The approaches can be classified mainly within 3kinds: (a) that utilize established paths, (b) those are founded on current biological system facts, and (c) envisaging cancer pathways de novo founded on the combinatorial forms of incidence in tumors group.

Limitations

There are numerous issues to be addressed so as to perform efficiently in future cases.  High cost is the major issue of NGS. The NGS‐based gene panel experiments charge several thousand US dollars per sample, and the whole genome sequencing costs more than NGS.

After the analysis in NGS, the further task is to understand the data and choose the therapeutic agent which will mostly be appropriate by linking gene alteration information along with scientific data. Presently the work is in progress in Japan and other developed countries. There are huge, online accessible genomic databases; but, the quantity of the data associated with medical cure outcomes is as yet inadequate.

New software tool uses AI to identify cancer cells

A team of researchers from UT Southwestern has developed a software that employs Artificial Intelligence to recognize cancer cells from digital pathology images. The software named as ConvPath, identify cells based on their appearance in the pathology images using an AI algorithm that learns from human pathologists. The algorithm effectively converts a pathology image into a “map” that displays the three-dimensional distributions and interactions of tumor cells, stromal cells, and lymphocytes in tumor tissue. The researchers believe that such information can help doctors customize treatment plans and pinpoint the right immunotherapy.

Reference Linkhttps://medicalxpress.com/news/2019-12-software-tool-ai-doctors-cancer.html