By Zsombor Gal
According to the World Health Organization, cancer is the second leading cause of death worldwide. Estimated at nearly 10 million deaths in 2018, fatalities due to cancer are expected to increasesubstantially in the next few decades due to an aging population. These data warrant the development of new therapeutic approaches and early detection methods for cancer.
Although oncologists, pharmaceutical companies, and academic researchers have been collectively contributing to the search for cancer treatments and preventative methods, their efforts are lacking in many respects. In this regard, one of the most pressing issues has been a lack of genetic data linking abnormalities in the somatic genome to oncogenesis. Until recently, mutational processes and single-nucleotide polymorphisms (SNPs) associated with cancer have been identified on a mostly individual basis with a lack of information regarding their pan-cancer prevalence. In February of this year, Nature published a groundbreaking articlefrom the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG), an initiative established by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA).
The collaborators describe the unified analysis of more than 2,000 whole-cancer genomes received from North American, European, Asian, and Australian organizations. The goal of this work is to identify common SNPs via genome-wide association studies, detect chromosomal aberrations and other genomic abnormalities in cancer cells, and define the evolutionary basis of oncogenesis. This feat, requiring an enormous amount of computational analysis, is now achieved via next-generation sequencing and cloud computing.
A vast majority of samples contain several driver mutations, which provide a selective advantage to somatic clones. These mutations, some of which are found in tumor suppressor or telomere maintenance genes, allow a cancer cell to outgrow other clones and promote unrestrained proliferation in the presence of tumor cell-targeting immune surveillance. Interestingly, about 5% of samples lack driver mutations, which may suggest the list of known drivers is not complete. The collaborators also investigated mutations in non-coding regions of the genome, which are mostly uncharted territory in cancer genomics.
Other genomic abnormalities, including chromosomal abnormalities, insertions, deletions, and gene duplications, were analyzed and elaborated upon in a collection of paperspublished at the same time. The data suggests chromosomal rearrangements such as chromoplexy and chromothripsis are common and occur early in tumorigenesis, as revealed through the generation of evolutionary timelines from subclones, a defining feature of tumor heterogeneity. This implies that cancer cell populations accumulate genomic aberrations throughout their evolution, emphasizing early detection as an important factor in cancer treatment.
The PCAWG presents crucial information for the pharmaceutical industry, academics, and clinicians alike. The breadth of the data presented in these papers provides potential drug targets, new research avenues, and novel therapeutic considerations. Despite the well-organized genomic analyses the PCAWG has given us, their findings lack clinical dataregarding treatment plans and other interventions. While bearing in mind that patient privacy is a necessity, this information could link genetic findings to patient outcomes. In the context of personalized medicine, this could revolutionize cancer treatment practices and redefine care for future cancer patients.
Image source: https://www.nature.com/collections/afdejfafdb