Genomic Landscape of Cell-Free DNA in Patients with Colorectal Cancer

Abstract

"Liquid biopsy" approaches analyzing cell-free DNA (cfDNA) from the blood of patients with cancer are increasingly utilized in clinical practice. However, it is not yet known whether cfDNA sequencing from large cohorts of patients with cancer can detect genomic alterations at frequencies similar to those observed by direct tumor sequencing, and whether this approach can generate novel insights. Here, we report next-generation sequencing data from cfDNA of 1,397 patients with colorectal cancer. Overall, frequencies of genomic alterations detected in cfDNA were comparable to those observed in three independent tissue-based colorectal cancer sequencing compendia. Our analysis also identified a novel cluster of extracellular domain (ECD) mutations in EGFR, mediating resistance by blocking binding of anti-EGFR antibodies. Patients with EGFR ECD mutations displayed striking tumor heterogeneity, with 91% harboring multiple distinct resistance alterations (range, 1-13; median, 4). These results suggest that cfDNA profiling can effectively define the genomic landscape of cancer and yield important biological insights.Significance: This study provides one of the first examples of how large-scale genomic profiling of cfDNA from patients with colorectal cancer can detect genomic alterations at frequencies comparable to those observed by direct tumor sequencing. Sequencing of cfDNA also generated insights into tumor heterogeneity and therapeutic resistance and identified novel EGFR ectodomain mutations. Cancer Discov; 8(2); 164-73. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Figure 1. Genomic profiling by cfDNA or tumor tissue sequencing in CRC cohorts

A.) Comparison of mutation frequencies in cfDNA and tissue cohorts (SNVs only). Top 20 gene mutations in cfDNA listed. Includes missense and nonsense mutations only (splice site mutations, insertions, and deletions excluded). B.) Correlation between mutation frequencies in cfDNA versus tissue (top 20 genes in cfDNA listed). C.) Comparison of JAK2 V617F mutation frequency in cfDNA and tumor tissue databases. D.) Comparison of age between all patients with cfDNA profiling vs. patients with detectable JAK2 V617F mutation in blood.

Figure 2. Clonality of common gene mutations in cfDNA from patients with CRC

A.) Proportion of clonal vs. subclonal mutations in commonly mutated genes in cfDNA (top 20 genes in cfDNA listed). B.) Impact of variant functional significance on clonality of the alteration. C.) Scatter plot of KRAS mutant allele frequency versus APC mutant allele frequency. Patients with EGFR ECD mutations are labelled in red. D.) Scatter plot of EGFR mutant allele frequency versus APC mutant allele frequency. Patients with EGFR ECD mutations are labelled in red.

Figure 3. EGFR ECD mutations in cfDNA

A.) EGFR ECD mutations occurring more than once in cfDNA cohort. Three dominant clusters of amino acid substitutions in the binding domain of anti-EGFR antibodies are shown. B.) Molecular model of cetuximab bound to wild type EGFR V441 and EGFR V441D. C.) Molecular model of cetuximab bound to wild type EGFR V441 and EGFR V441G. D.) Binding assay of cetuximab and panitumumab to wild type, V441D, and V441G EGFR. ** indicates p<0.01 by one-way ANOVA with Tukey post-hoc test for cetuximab and panitumumab.

Figure 4. Heterogeneity of anti-EGFR resistance alterations in patients with EGFR ECD mutations

A.) (top) Known anti-EGFR antibody resistance alterations identified in cfDNA for patients with EGFR ECD mutations, with each row representing an individual patient. (bottom) Percentage of cases with alteration, with each bar representing mutational frequency B.) Case examples of patients with multiple EGFR pathway alterations. C.) Number of ECD (blue) and non-ECD (red) resistance alterations identified in cfDNA for each patient. Arrows indicate patients with EGFR ECD mutations only.