Overview
Latest mechanistic insights gained from preclinical studies, as well as the approval of the first immunotherapies, have prompted a growing number of academic researchers and pharmaceutical/biotech companies to explore the effect of immunity in tumor pathogenesis and reconsider the role of immunotherapy. Furthermore, technological advancements such as next-generation sequencing are enabling unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and, as a result, individualized treatment. However, the increasing complexity of generated data, as well as the variety of bioinformatics methods and tools, present significant challenges for both tumor immunologists and clinical oncologists.
Data Types in Cancer Genomics
Whole Genome and Exome Sequence Data
- The sequence of a sample's entire DNA is obtained using whole-genome shotgun sequencing. The existence of numerous somatic alterations such as single nucleotide mutations, insertions and deletions, alterations in copy number, and large structural variations such as inversions, duplications, translocations, and gene fusions can be investigated using these sequence data.
Whole Transcriptome Sequence Data
- Expressed mutations such as SNVs and indels are identified using whole transcriptome sequencing, also known as RNA-seq. De novo transcriptome assembly can also be a useful tool for detecting events like novel transcripts, skipped exons, retained introns, and novel splicing events.
Epigenomic Data
- The study of the epigenome, the cell's transcriptional control, has also been made possible by next-generation sequencing technologies. Changes in the epigenome and the resulting alterations in the expressional profile of the cell have been found in a variety of cancers, implying that changes in the epigenome and the resulting alterations in the expressional profile of the cell could be the cause of many diseases, including cancers.
Analysis Algorithms and Tools for Cancer Genomics
Sequence Alignment and Assembly
- NGS technologies can generate a large number of short reads in a short amount of time. The ability to reconstruct the entire genome from these reads with great accuracy in an efficient manner is critical for their use in cancer genomics. In general, there are two options: aligning the reads to the reference genome or performing a de novo assembly.
Discovery of Point Mutation
- Following that, the alignment and/or assembly results are examined for any type of somatic mutation, including single nucleotide variants.
Structural Variation Detection
- Various cancer types have been linked to structural changes such as large insertions and deletions, duplications, inversions, translocations, and gene fusions. With the advent of next-generation sequencing technologies and associated analysis tools, various SVs, including copy-neutral events and their corresponding breakpoints, can now be detected at a much higher resolution and with greater accuracy.
Identification of Indels
- After SNVs, indels are the second most common type of variation in the human genome, with the ability to change or completely eliminate a protein's function. As a result, robust probabilistic algorithms for detecting somatic indels from paired cancer and normal samples are urgently needed.
Expression Analysis
- High-throughput sequencing and analysis of small non-coding transcripts, such as miRNAs, is also possible. Combining protein-coding gene expression profiles with miRNA expression, promoter methylation, and copy number variation data can reveal which genes are silenced and thus may play a role in tumor suppression, as well as which genes are overexpressed and may act as oncogenes.
References
- Anandaram H. A review on application of biomarkers in the field of bioinformatics & nanotechnology for individualized cancer treatment. MOJ Proteom Bioinform. 2017;5(6).
- Kasaian K, Li YY, Jones SJ. Bioinformatics for Cancer Genomics. InCancer Genomics 2014. Academic Press.
