NEW YORK (GenomeWeb News) – Using an array of technologies, researchers at the University of British Columbia, Cross Cancer Institute of Alberta, and Cancer Research UK have illustrated just how complex triple negative breast cancer tumors are and how their mutational patterns evolve throughout tumor progression.
The finding could help explain why this particular subtype of breast cancer is so good at resisting treatment, and could eventually lead to more effective therapies.
The researchers, who published their study today in Nature, used microarrays, transcriptome sequencing, exome sequencing, and whole-genome sequencing to profile 104 cases of triple negative breast cancer.
"Groups of mutations within individual cases have different clonal frequencies, indicative of distinct clonal genotypes," the researchers wrote. Even at diagnosis, triple negative breast cancers "already display a widely varying clonal evolution that mirrors the variation in mutational evolution."
To search for mutation enrichment patterns, the researchers evaluated single-gene mutation frequency over multiple cases, looked at mutation frequency of multiple genes in the same gene family, and correlated mutation status with gene expression networks.
The team found that each tumor displayed multiple "clonal genotypes," suggesting that the cancer would have to be treated as multiple diseases, rather than a single entity.
"Triple negative breast cancer is not just one uniform subtype of breast cancer," Sam Aparicio, professor of pathology and lab medicine at UBC and senior author of the study, said in a statement. "It's actually extremely complex, with each cancer at a different stage in the evolutionary process at the time of diagnosis." This finding could "help explain why patient responses to treatment differ greatly," he added.
The researchers found that some tumors harbored just a handful of somatic aberrations in a few pathways, while others contained hundreds of somatic mutations. Similar to other cancer sequencing studies, they found that p53 is the most frequently mutated gene, and that tumors in which the mutation occurs early on tend to harbor the greatest number of overall mutations and show the greatest amount of clonal evolution.
Additionally, they found recurrent somatic mutations to other known cancer genes, such as PIK3CA and PTEN. However, around 12 percent of cases did not contain somatic mutations in any of the frequent driver genes, suggesting that primary triple negative breast cancers are "mutationally heterogenous from the outset," the authors wrote.
About 20 percent of cases contained potentially clinically actionable mutations, including the BRAF V600E allele, EGFR amplifications, and ERBB2 and ERBB3 mutations.
When comparing somatic mutations found from exome sequencing, whole-genome sequencing, and the SNP arrays to the RNA-seq data, they found that only 36 percent of the mutations were expressed, a finding similar to what other groups have reported — and that could be important when using genomic information to make treatment decisions.
Looking at mutationally enriched gene families, researchers identified the p53-related pathway, chromatin remodeling, PIK3 signaling, ERBB signaling, integrin signaling and focal adhesion, WNT/cadherin signaling, growth hormone and nuclear receptor co-activators, and ATM/RB-related pathways.
Aside from p53 mutations that occurred early in tumor development, the basal subtype cancers were also more likely to have more clonal genotypes, as were tumors with mutated PIK3CA pathways.
Pathways with cytoskeletal genes tended to have lower clonal frequencies, suggesting that the mutations to those genes were acquired much later.
Knowing when in tumor progression certain mutations arose could impact treatment. As reported by GenomeWeb Daily News sister publication Clinical Sequencing News last week, targeting the clones that occur earlier in progression may be a more effective way of treating cancer, since it is those mutations that are likely enabling the disease.
According to Aparicio, the findings from this most recent study could be used to design clinical trials to "explore patient responses to treatment at the genetic level and look at ways to improve therapies and outcomes for patients."