Oncogenomics: Applications in Modern Technology

Technologies

Genome sequencing

• Pyrosequencing-based sequencers for DNA analysis provide a comparatively cheap way to produce sequence data.
• Array Comparative Genome Hybridization: This method assesses the variations in DNA copy numbers between healthy and malignant genomes. It makes use of the fluorescence intensity of samples that have been fluorescently labelled and hybridised into well-known probes on a microarray.
• Using amplified genomic segments that have undergone restriction-digesting and are then hybridised into human oligonucleotides, representational oligonucleotide microarray analysis can detect copy number variation with a resolution of between 30 and 35 kbit/s.
• Using genomic tags produced from restriction enzyme digests, digital karyotyping measures copy number variation. To determine tag density, these tags are then joined into ditags, concatenated, cloned, sequenced, and mapped back to the reference genome.
• By creating a BAC library from a cancer genome and sequencing the ends of the BACs, a technique known as bacterial artificial chromosome (BAC)-end sequencing (also known as end-sequence profiling) can locate chromosomal breakpoints. The end sequences of the BAC clones with chromosome abnormalities do not map to a comparable area of the reference genome, indicating a chromosomal breakpoint.

Transcriptomes

• Assess transcript abundance using microarrays. useful for prognosis and categorization, opening the door to new treatment modalities and facilitating the detection of mutations in the coding areas of proteins. Cancer research has begun to emphasise the relative abundance of alternative transcripts. Unique cancer types correlate with specific alternative transcript forms.
• RNA-Seq

Bioinformatics and functional analysis of oncogenes

Genomic data can be statistically analysed owing to bioinformatics tools. Oncogenes’ functional properties are still being determined. Potential responsibilities include their ability to change tumour development and particular roles at each stage of cancer development.

Bioinformatics computational analyses can be performed to identify likely functional and likely driver mutations following the discovery of somatic cancer mutations across a cohort of cancer samples. Three basic methods are frequently employed for this identification: mapping mutations, evaluating the impact of the mutation on a protein’s or regulatory element’s function and looking for indications of positive selection among a cohort of tumours. There are significant relationships of precedence between elements from the various techniques, even though the approaches are not always consecutive. Each phase makes use of a different tool.

Operomics

To comprehend the molecular processes underlying the development of cancer, optogenetics integrates genomics, transcriptomics, and proteomics.

Comparative oncogenomics

Cross-species comparisons are used in comparative oncogenomics to find oncogenes. In this study, cancer genomes, transcriptomes, and proteomes are examined in model organisms like mice to discover putative oncogenes. These oncogenes are then compared to human cancer samples to see whether their homologs have a role in the development of human malignancies. Similar genetic changes are seen in mouse models of cancer as they are in human cancers. These models are created using techniques like malignant cell graft transplantation or retroviral insertion mutagenesis.

Limitations

Significant-scale somatic mutation screening in breast and colorectal tumours revealed that a large number of low-frequency mutations each impact just marginally cell survival. It seems improbable that genome sequencing will reveal a single “Achilles heel” target for anti-cancer medications if cell survival is determined by several mutations with minor effects. But somatic mutations frequently congregate in a small number of signalling pathways, which are potential targets for therapy.

The cell populations that makeup cancer are diverse. Information about the variations in sequence and expression pattern between cells is lost when sequence data is collected from a complete tumour. Single-cell analysis can help to address this difficulty.

Large-scale genomic rearrangements rather than single mutations are sometimes responsible for clinically important characteristics of tumours, such as treatment resistance. Information on single nucleotide variants won’t be very relevant in this context.

Clinically useful information can be obtained through cancer genome sequencing in patients with uncommon or novel tumour forms. It is extremely difficult, necessitates expertise from numerous domains, and does not always result in a successful treatment plan to translate sequence information into a treatment plan.

References 

1.   https://www.sciencedirect.com/science/article/abs/pii/S0959437X04001959?via%3Dihub

2.   https://www.ncbi.nlm.nih.gov/pmc/articles/PMC206480/

Author

 Yash Batra