Back in May 2018 we reported the first output from the Pan-Cancer Atlas, a massive undertaking that evolved from The Cancer Genome Atlas, itself a huge project aiming to set up a genetic data-base for three cancer types: lung, ovarian, and glioblastoma.
The next instalment from the Pan-Cancer Analysis of Whole Genomes (PCAWG) has just appeared featuring the analysis of a staggering 2,658 whole-cancer genomes and their matching, normal tissues across 38 tumour types and it has reminded us, yet again, of nature’s capacity to surprise. The first finding was that, on average, cancer genomes contained four or five driver mutations when coding and non-coding genomic elements were combined. That’s roughly consistent with the accepted estimate over the last few decades. What was unexpected, however, was that in around 5% of cases no drivers were identified, suggesting that there are more of these mutations to be discovered. Also somewhat surprising is that chromothripsis, the single catastrophic event producing simultaneously many variants in DNA, is frequently an early event in tumour evolution.
The analyses also revealed several mechanisms by which the ends of chromosomes in cancer cells are protected from telomere attrition and that variants transmitted in the germline can affect subsequently acquired patterns of somatic mutation.
A glimpse of the data
The panorama of driver mutations includes the summary below of tumour-suppressor genes with biallelic inactivation (i.e., mutation of one allele (copy) followed by gene deletion of the remaining allele) in 10 or more patients. Familiar tumour suppressors are prominent on the left hand side, as expected. These include TP53 (the guardian of the genome) and the tumour suppressors CDKN2A and CDKN2B (cyclin-dependent kinase inhibitors 2A and 2B) that regulate the cell cycle.
Tumour-suppressor genes for which both copies of the gene (alleles) are inactivated in 10 or more patients. GR = genomic rearrangement, i.e. chromosome breakage. From The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium.
Aneuploidy in the genome of a tumour without known drivers. Each row is an individual tumour: the boxes show chromosome loss (blue) or gain (red). The cancer is a rare kidney tumour (chromophobe renal cell carcinoma). From The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium.
Two tumour types had a surprisingly high fraction of patients without identified driver mutations: 44% for a rare type of kidney cancer (chromophobe renal cell carcinoma) and 22% in a rare pancreatic neuroendocrine cancer. It turned out (as shown in the above figure) that there was a striking loss or gain of chromosomes — called aneuploidy — in the cells of these cancers. This suggests that wholesale loss of tumour suppressor genes or gain of oncogenic function was providing the ‘drivers’ for these cancers.
The genomic cancer message
We should first acknowledge the mind-boggling effort and organization involved in collecting thousands of paired samples, sequencing them and analyzing the output. However, the value of these massive projects is beginning to emerge — and the news is mixed.
One critical trend is that genomic analysis is re-defining the way cancers are classified. Traditionally they have been grouped on the basis of the tissue of origin (breast, bowel, etc.) but this will gradually be replaced by genetic grouping, reflecting the fact that seemingly unrelated cancers can be driven by common pathways.
Perhaps the most encouraging thing to come out of the genetic changes driving these tumours is that for about half of them potential treatments are already available. That’s quite a surprise but it doesn’t mean that hitting those targets will actually work as anti-cancer strategies. Nevertheless, it’s a cheering point that the output of this phenomenal project may, as one of the papers noted, serve as a launching pad for real benefit in the not too distant future.
On the other hand, the intention of precision medicine is to match patients to therapies on the basis of genomics and, notwithstanding the above point, the consortium notes that “A major barrier to evidence-based implementation is the daunting heterogeneity of cancer chronicled in these papers, from tumour type to tumour type, from patient to patient, from clone to clone and from cell to cell. Building meaningful clinical predictors from genomic data can be achieved, but will require knowledge banks comprising tens of thousands of patients with comprehensive clinical characterization. As these sample sizes will be too large for any single funding agency, pharmaceutical company or health system, international collaboration and data sharing will be required.”
See for yourself
The PCAWG landing page (http://docs.icgc.org/pcawg/) provides links to several data resources for interactive online browsing, analysis and download of PCAWG data.