This approach was published recently in a study designed to compare the tumor genomes of patients with de novo
AML to their relapse genomes[21
]. After sequencing each genome (de novo
tumor and relapse tumor) and the matched normal from skin for each patient, somatic mutations and structural variants were identified. Some of these appeared to be unique to the relapse sample in each case. We then obtained high sequencing read depth at each somatic mutation site in the de novo
and relapse tumors, and characterized the reads that contained the mutated base(s) at each site to calculate an allele frequency of that variant in the tumor cell population. Using kernel density estimation, we then identified groups of mutations present at the same allele frequencies, indicative of their prevalence in the tumor cell population. This comparison of allele frequency groups between de novo
and relapse disease allowed us to model the relative numbers of tumor subclones at each disease presentation, and defined AML progression as a clonal process, as illustrated in . Namely, all subclones originate from a founder clone that shares all but the newest mutations, and relapse disease shares mutations with the founder clone as well as new mutations that portend its proliferative advantage in the relapse presentation.
Figure 2 Model of the clonal progression process that occurs between the initial (de novo) and relapse presentation in AML patients. At diagnosis, this patient has an oligoclonal disease characterized by four different subclones, each present at a specific proportion (more ...)
In a similar study, with a slightly different experimental design, we recently explored the differences between myelodysplastic syndrome (MDS) genomes and the genomes found in those patients’ secondary AML (sAML) tumors. MDS identifies a heterogeneous group of syndromes characterized by dysplasia and ineffective hematopoesis. Since about 1/3 of these patients progress to sAML for reasons that are not well understood at the genomic level, we characterized these genomes to understand novel somatic variants in the sAML cells. In our study, the results were quite different than the de novo to relapse AML study outlined above. Namely, we found that the sAML genomes were all oligoclonal (comprised of several related tumor cell subclones, each with unique sets of mutations), each containing a pre-existing MDS founder clone that was out-competed in the sAML tumor cell population in some cases. We hypothesized that the oligoclonal nature of the sAML presentation may contribute to the very poor response rates of these patients to conventional chemotherapies that often induce remission in de novo AML treatment (Graubert et al., accepted for publication).
Akin to de novo
leukemia and relapse is metastatic tumor occurrence in patients with a primary solid tumor presentation. Similarly, the question of genetic relatedness between primary tumor cells and metastatic tumor cells is of interest, although as before, solid tumors present challenges in that typically the metastatic tumor is not surgically removed and/or banked, once diagnosed. There are, however, exceptions and two published reports to-date have studied this genetic relatedness in primary breast tumors and subsequent metastases. The first study involved a patient with lobular breast cancer that was followed 9 years later by a recurrent tumor in the breast[22
]. The second manuscript described a “trio” of tumors from one patient, including a primary basal-like ductal breast tumor, a brain metastasis that developed 8 months after the primary tumor was diagnosed, and a xenograft-propagated tumor derived from the primary tumor after its surgical removal [23
]. Both studies established a genetic relatedness between the primary and the metastatic tumors, albeit one that becomes more distant with time between the primary and metastatic disease diagnoses. In the second example, the metastatic tumor appeared to be enriched for a specific subclone within the primary disease that was characterized by certain low allele frequency mutations in the primary tumor genome rising to much higher allele frequencies in the metastatic tumor genome. More studies of this type are needed to fully understand the potential for metastasis and the roles of specific mutations in the tendency for certain tumors to metastasize.
The use of different initial preparatory methods and post-sequencing computational data analyses has expanded the scope of cancer genomics inquiry to include expressed and non-coding RNA (“RNA-seq”), and DNA methylation (“methyl-seq”) comparisons of tumor and matched non-malignant tissues from the same patient. If anything, the wealth of genomic information that can be collected from each tumor case proves two things; our relatively primitive ability to integrate data from different “omes” and our inability to quickly characterize the impact of different types of genomic alterations on tumor biology. Nevertheless, these cataloguing efforts will undoubtedly be valuable when coupled with downstream efforts to investigate the impact of genomic alterations on protein and pathway function in cell-based systems. Data integration, similarly, provides a challenge for computational and systems biologists—and one set of efforts will inform the other, ultimately advancing our understanding of tumor biology.