Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking

Abstract

Circulating tumour DNA (ctDNA) allows tracking of the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies. However, exploiting ctDNA to determine how a patient's cancer is evolving in order to aid clinical decisions remains difficult. This is because ctDNA is a mix of fragmented alleles, and the contribution of different cancer deposits to ctDNA is largely unknown. Profiling ctDNA almost invariably requires prior knowledge of what genomic alterations to track. Here, we leverage on a rapid autopsy programme to demonstrate that unbiased genomic characterisation of several metastatic sites and concomitant ctDNA profiling at whole-genome resolution reveals the extent to which ctDNA is representative of widespread disease. We also present a methylation profiling method that allows tracking evolutionary changes in ctDNA at single-molecule resolution without prior knowledge. These results have critical implications for the use of liquid biopsies to monitor cancer evolution in humans and guide treatment.

Fig. 1. Genomic profiling analysis of LEGACY patient 1.

a Multiple samples from distinct metastatic deposits in different organs and the primary tumour were collected from this patient, together with blood germline reference (buffy coat) and plasma. b Copy number alterations analysis highlights genome-wide copy neutral LOH and overall homogeneous copy number profiles. Median total copy number in 1 Mb bins with a minimum mappability score of 0.8. c Single-nucleotide variant analysis identified a clonal PIK3CA driver mutation and convergent evolution for drug resistant ESR1 mutants. SNVs detected in more than one sample were clustered with sciClone (colour bar reported to the right of heatmap, see sciClone Cluster legend). d Diagram of inferred genome-wide copy neutral LOH event that can be explained by haploidisation followed by convergent re-diploidisation after a few cell divisions.

Fig. 2. Clonal evolution analysis of LEGACY patient 1.

a Subclonal decomposition allows construction of the tumour clone tree from the sample tree, thus revealing the evolutionary history of the metastatic cascade. b Only 2/12 samples were found to be polyclonal (Liver Met R3 and Ovary Met R2) and indeed exchanged clones between each other. The same clones went on to seed distinct metastatic deposits in a monoclonal fashion. For example, the pink subclone in Liver Met R3 appears clonal in Liver Met R4. The turquoise subclone in Ovary Met R2 appears clonal in Diaphragm Met R1. The two clones remain subclonal but at distinct proportions in the two polyclonal samples Liver Met R3 and Ovary Met R2. Other clones are greyed for clarity. c Reconstructed metastatic cascade in breast cancer LEGACY patient 1 shows that the primary tumour (green) spread early to the lung (grey) and subsequently spread independently to draining lymph nodes (blue). From the lung, the cancer spread to Liver Met R3 (red) and Ovary Met R2 (pink) in two independent waves. Liver Met R3 then spread to Ovary Met R3 and subsequently to diaphragm (orange). Ovary Met R2 independently re-seeded back to the liver, giving rise to a large clone (Liver Met R1, R2, and R4). Hence, polyclonal seeding was evident in one liver and one ovary sample, but otherwise early monoclonal seeding was the dominant pattern of metastatic spread.

Fig. 3. Genomic profiling and clonal evolution analysis of LEGACY patient 2.

a Multiple samples from distinct metastatic deposits in different organs were collected from this patient, together with blood germline reference (buffy coat) and plasma. b Copy number alterations analysis highlights largely homogeneous copy number profiles. c Single-nucleotide variants analysis identified clonal PIK3CA and ERBB2 putative driver mutations and multiple subclonal truncating mutations in DNA repair genes that seem to be responsible for the higher number of private mutations in Liver Met R2 and R3. d Mutational signature analysis highlights aging (signature 1A) but also APOBEC (signatures 2 and 13) as dominant mutational processes. e Clone tree shows early divergence for liver and lung metastases but largely monoclonal seeding of all lesions.

Fig. 4. Tracking the metastatic cascade in the plasma of LEGACY patients.

Contribution of different tumour samples to ctDNA in LEGACY patient 1 (a) and patient 2 (b) using multiple linear regression analysis of somatic mutations. Similarly, contribution of tumour sites to ctDNA can be measured using methylation clock analysis (epimutations instead of nucleotide substitutions), as presented here for LEGACY patient 1 using two clock-like methylation regions that have been previously validated as subject to methylation drift, namely ZNF454 (c) and IRX2 (d). e With the same data we can also reconstruct the tumour phylogenetic tree using methylation clock haplotypes for ZNF454, corroborating the overall phylogenetic structure revealed by whole-genome sequencing. f This is because epimutations contain phylogenetic information about diverging cell lineages. g The same can be done for IRX2, leading to the construction of a consistent tree. Notably, methylation clock analysis can detect clones down to 1% prevalence and is >1000 less costly than whole-genome sequencing. h–k The same methylation clock analysis has been applied to an independent cohort of early breast cancer patients with matched primary, lymph node and ctDNA. High correlation (Pearson) between frequency of methylation haplotypes in the primary tumour of patient 6 was found for both clocks ZNF454 (p = 4.94 × 10⁻⁶³) (h) and IRX2 (p = 3.11 × 10⁻¹³) (i). Also Patient 16 demonstrated high correlation between methylation patterns in ctDNA and a specific lymph nodal lesion, again for both ZNF454 (p = 7.86 × 10⁻⁶⁰) (j) and IRX2 (p = 7.51 × 10⁻⁵⁹) (k). P values refer to tests of no correlation.