Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer

Abstract

Background: The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. Here, we aimed to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations.

Methods: We followed 3 patients with resectable disease and 4 patients with unresectable disease, including 4 patients with ≥ 3 serial follow-up samples, of whom 2 were rare long survivors (> 5 years). We performed whole exome sequencing of tumour gDNA and plasma ctDNA (n = 20) collected over a ~ 2-year period from diagnosis through treatment to death or final follow-up. Plasma from 3 chronic pancreatitis cases was used as a comparison for analysis of ctDNA mutations.

Results: We detected > 55% concordance between somatic mutations in tumour tissues and matched serial plasma. Mutations in ctDNA were detected within known PDAC driver genes (KRAS, TP53, SMAD4, CDKN2A), in addition to patient-specific variants within alternative cancer drivers (NRAS, HRAS, MTOR, ERBB2, EGFR, PBRM1), with a trend towards higher overall mutation loads in advanced disease. ctDNA alterations with potential for therapeutic actionability were identified in all 7 patients, including DNA damage response (DDR) variants co-occurring with hypermutation signatures predictive of response to platinum chemotherapy. Longitudinal tracking in 4 patients with follow-up > 2 years demonstrated that ctDNA mutant allele fractions and clonal trends were consistent with CA19-9 measurements and/or clinically reported disease burden. The estimated prevalence of 'stem clones' was highest in an unresectable patient where changes in ctDNA dynamics preceded CA19-9 levels. Longitudinal evolutionary trajectories revealed ongoing subclonal evolution following chemotherapy.

Conclusion: These results provide proof-of-concept for the use of exome sequencing of serial plasma to characterise patient-specific ctDNA profiles, and demonstrate the sensitivity of ctDNA in monitoring disease burden in PDAC even in unresectable cases without matched tumour genotyping. They reveal the value of tracking clonal evolution in serial ctDNA to monitor treatment response, establishing the potential of applied precision medicine to guide stratified care by identifying and evaluating actionable opportunities for intervention aimed at optimising patient outcomes for an otherwise intractable disease.

Keywords: Biomarkers; Circulating tumour DNA; Liquid biopsy; Monitoring.

Fig. 1

Summary of patients and samples for sequencing. A Outline of samples available for exome sequencing from PDAC and chronic pancreatitis (CP) control cases. B Clinical timelines including survival and treatment periods for sequenced PDAC patients. 5FU, 5-Fluorouracil; CAP, Capecitabine; Chemorad, Chemoradiation; GEM, Gemcitabine

Fig. 2

Comparison between somatic mutations in tumour and matched plasma from patients 45 and 95. Overlaps between somatic mutation calls in tumour and baseline pre-treatment (P1) plasma (top), and combined plasma (P1-P5/P4) from baseline plus follow-up sampling (bottom) in each patient, are shown in A and B. Comparisons were used to inform the development of our custom analysis pipeline, for the identification of candidate ctDNA mutations in plasma. Enriched gene signalling pathways (Reactome) observed in tumour tissues and ctDNA variants from combined plasma samples are shown in C and D

Fig. 3

Analysis of somatic copy number alterations and localised hypermutation in tumour and plasma. Absolute copy number calls from tumour and plasma samples are shown in A. Gains in overall copy number are highlighted in red and losses of copy number are shown in blue. Genome-wide somatic copy number calls in tumour (left) and matched baseline (pre-treatment) plasma (right) from patient 45 are displayed in B. Amplifications and copy number gains at the ERBB2 locus on chromosome 17 were observed in both tumour and plasma from this patient. C, D Rainfall plots showing the distribution of single somatic substitutions in tumour (C) and combined plasma (D) from patient 45, with arrows highlighting the presence of a unique kataegis region on chromosome 17 co-localising with ERBB2 amplification. This region was enriched for T > G substitutions and contained ERBB2 driver mutations in tumour, which were also detected in ctDNA. Inter-mutation distance is presented on the vertical axis and the number of mutations in each sample on the horizontal axis

Fig. 4

Identification of longitudinally trackable driver mutations in ctDNA. A Oncoprint showing patients with ctDNA mutations in PDAC drivers (KRAS, TP53, SMAD4, CDKN2A) and known RAS family genes (NRAS, HRAS) in plasma. The percentage of altered cases is displayed to the right. Lollipop plots displaying the mutations detected in ctDNA are shown alongside the oncoprint. B-F In patients with multiple plasma samples, the mean mutant allele fraction (MAF) was calculated for all mutation loci in ctDNA (patient-specific plus ctDNA variants in known PDAC drivers), at each timepoint. Available measurements of CA19-9 across serial timepoints for each patient are also shown. Examples of patient-specific ctDNA mutations observed in each case are displayed on the right (missense variants (circles), nonsense variants (triangles), CdsStartCNV variants (squares)). In two patients, temporal heterogeneity between ctDNA mutations in RAS and IDH genes was detected E, F. CdsStartCNV; single nucleotide variant at coding start; CAP, Capecitabine; CHEMORAD (CAP), Chemoradiation (with Capecitabine); GEM, Gemcitabine

Fig. 5

Identification of ctDNA variants with potential therapeutic actionability. Oncoprint showing mutated DNA damage repair (DDR) genes in ctDNA that were either predicted to confer response to platinum chemotherapy and/or PARP inhibition through in silico predictions (Cancer Genome Interpreter) (Biomarkers) or were identified within known DDR signalling pathways (Reactome) (Pathways). The percentage of altered cases is displayed to the right. Clinical characteristics of the cohort and enrichments for COSMIC mutational signatures associated with DDR, are shown on the bottom panels. Post-treatment plasma samples collected following platinum or other chemotherapies and/or radiation therapy, are indicated. DSBR, double strand break repair; MMR, mismatch repair; POLN, polymerase ν (nu) hypermutation

Fig. 6

Analysis of clonal evolutionary trajectories in ctDNA from patient 45. A Clinical timeline for patient 45 showing treatment dates for primary tumour resection, adjuvant chemotherapy (gemcitabine) and sampling timepoints, as days from initial diagnosis. B Scatterplot showing the estimated prevalence of inferred clones in ctDNA, across sampled timepoints. C Longitudinally observed phylogenetic tree showing the predicted evolutionary trajectories of individual ctDNA clones. Coloured triangles represent mutations unique to each respective clone. Examples of unique driver mutations acquired in individual clones are shown on the tree. D Clonal diagram of the tree structure from (D) showing differences between estimated clonal proportions across sampled timepoints. GEM, Gemcitabine