Liquid versus tissue biopsy for detecting acquired resistance and tumor heterogeneity in gastrointestinal cancers

Abstract

During cancer therapy, tumor heterogeneity can drive the evolution of multiple tumor subclones harboring unique resistance mechanisms in an individual patient^1-3. Previous case reports and small case series have suggested that liquid biopsy (specifically, cell-free DNA (cfDNA)) may better capture the heterogeneity of acquired resistance^4-8. However, the effectiveness of cfDNA versus standard single-lesion tumor biopsies has not been directly compared in larger-scale prospective cohorts of patients following progression on targeted therapy. Here, in a prospective cohort of 42 patients with molecularly defined gastrointestinal cancers and acquired resistance to targeted therapy, direct comparison of postprogression cfDNA versus tumor biopsy revealed that cfDNA more frequently identified clinically relevant resistance alterations and multiple resistance mechanisms, detecting resistance alterations not found in the matched tumor biopsy in 78% of cases. Whole-exome sequencing of serial cfDNA, tumor biopsies and rapid autopsy specimens elucidated substantial geographic and evolutionary differences across lesions. Our data suggest that acquired resistance is frequently characterized by profound tumor heterogeneity, and that the emergence of multiple resistance alterations in an individual patient may represent the 'rule' rather than the 'exception'. These findings have profound therapeutic implications and highlight the potential advantages of cfDNA over tissue biopsy in the setting of acquired resistance.

Extended Data Figure 1:. Phylogenetic trees for paired tumor biopsies

Phylogenetic trees for patients with WES for pre- and post-treatment samples. The number of somatic alterations assigned to each cluster and detected events in known cancer genes appear on the branches. A 2-D plot showing the cancer cell fraction distribution of subclones in the pre- and post-treatment samples. Events in known cancer are shown next to their subclonal cluster.

Extended Data Figure 2:. Patients with multiple post-progression tumor biopsies.

Three of five patients with multiple post-progression tumor biopsies are shown. The other two patients (TPS037 and TPS177) are shown in Figures 2 and 3, respectively. For each patient, the location of each tumor biopsy and the resistance alterations (blue) and truncal alterations (orange) detected in cfDNA and in each tumor specimen are shown. TPS001 is a RAS wild type colorectal cancer patient who developed resistance to an anti-EGFR antibody. Biopsy of one liver lesion revealed an activating MEK1 (MAP2K1) mutation, and biopsy of a second liver lesion identified a KRAS mutation. Both resistance alterations were detected in post-progression cfDNA. TPS007 is an FGFR2-fusion positive cholangiocarcinoma patient who developed resistance to an FGFR inhibitor. Five FGFR2 resistance mutations were identified in post-progression cfDNA, and three of these alterations were identified in distinct liver lesions harvested through a rapid autopsy program, with one lesion harboring two FGFR2 alterations. TPS011 is a RAS WT colorectal cancer patient who developed resistance to an anti-EGFR antibody. A recurrent colon tumor harbored a KRAS mutation, and an EGFR extracellular domain mutation--known to interfere with antibody binding--was identified in an ovarian metastasis, whereas both alterations were detected in cfDNA. Importantly, in all patients, individual resistance mechanisms emerging in distinct metastatic lesions were detectable in cfDNA.

Extended Data Figure 3:. Biclusters of patients based on similar changes (δ) in somatic alteration.

Biclustering of four δ matrices reflecting changes in cancer cell fraction of mutations or copy-number in known cancer genes or genesets yielded significant biclusters (all empirical p-values<0.0001; Online Methods). The biclusters from all four δ matrices included at least one bicluster with patient TPS130, a patient with an unknown mechanism of resistance. Patient TPS130 consistently biclustered together with TPS021 and TPS037, patients with known mechanisms of resistance, across all matrices, highlighting the possibility that additional genomic alterations contribute to resistance beyond the identified resistance alterations. (a) The change in somatic alterations, δ, is calculated based on WES data of the samples closest to the start and end of therapy. We biclustered four δ matrices: ${\delta }_{cancergene}^{copy-number},{\delta }_{geneset}^{mutation},$ ${\delta }_{geneset}^{copy-number}$, and ${\delta }_{geneset}^{copy-numberandmutation}$ and assessed their significance by comparing the size of biclustersagainst n=10000 permuted matrices with a two-sided t-test (Online Methods). (b-e) Illustration of the biclustering results (using BiMax) of the four δ matrices (biclusters listed in Supplementary Table 6). Outlined in red are biclusters containing TPS130 observed in all four δ matrices.

Figure 1:. Identification of acquired resistance mechanisms in liquid versus tumor biopsy.

A comparison of specific resistance alterations identified in plasma cfDNA (N=42) versus tumor biopsy (N=23) for each patient. Patients are grouped according to tumor type (N=3) (CRC = colorectal, GE = gastroesophageal, biliary) and molecular subtype (N=5) (FGFR2 = FGFR2 fusion, MET = MET amplification, HER2 = HER2 amplification, BRAF^V600E, RAS wild type. Red represents alterations identified in plasma, but not in the tissue biopsies; green represents alterations identified in tissue biopsies, but not in plasma; and purple represents alterations identified in both plasma and tissue biopsies. Pale purple represents alterations identified in plasma that were not detected in the post-progression tissue biopsy but were eventually detected in subsequent tissue biopsies from the same patient. The alterations detected in cfDNA versus tumor biopsy are quantified in a histogram across the top of the panel and are summarized graphically on the right, depicting specifically the percentage of patients with one, more than one, or no experimentally validated resistance alterations identified by cfDNA (top) or tumor biopsy (bottom).

Figure 2:. Comparison of multiple tumor biopsies versus liquid biopsy in a BRAF-mutant colorectal cancer patient.

(a) cfDNA (N=1) and tumor tissue biopsy specimens (N=4) included in the analysis for patient TPS037. For each specimen, the alterations and associated allelic fractions as determined by either ddPCR, targeted NGS, or WES are shown (Supplementary Tables 3–5). All ddPCR analyses were performed to a minimum coverage depth of 300X. Layered pie charts represent likely clonal composition of each specimen with the color of each subclone matching the color of the respective gene and branch in the phylogenetic tree. (b) Phylogenetic tree representing clonal architecture present in the specimens (using PhylogicNDT). The number of somatic alterations assigned to each cluster and detected events in known cancer genes appear on the branches. (c) Representative clonal and subclonal coding somatic alterations detected in each plasma or tissue specimen. Size of each square represents the estimated cancer cell fraction of each alteration with an empty box indicating no detection. Events in known cancer genes are highlighted in blue.

Figure 3:. Serial liquid biopsy and autopsy in an FGFR2-fusion positive gastric cancer patient.

(a) The allele fraction of specific alterations in cfDNA isolated from serial plasma specimens during therapy with FGFR inhibitor were assessed by ddPCR to a minimum coverage depth of 300X. A truncal RHOA^Y42C mutation is shown in green. Emergent candidate resistance alterations are also shown. (b) Phylogenetic tree representing clonal architecture across all specimens. The number of somatic alterations assigned to each cluster and detected events in known cancer genes appear on the branches. FGFR2-fusion and amplification events have likely occurred in the cyan subclone 2 (based on expression and copy-number patterns). FGFR2^L617V mutations are found at high levels in subclones 4 and 9. (c) Clonal and subclonal alterations detected in each plasma (N=2), tumor biopsy (N=1), or autopsy specimens (N=17). Size of each square represents the estimated cancer cell fraction of each alteration with an empty box indicating no detection. FGFR2 expression (TPM), copy number (CN), and the presence of supporting CD44-FGFR2 fusion reads as determined by WES and RNAseq are shown, along with the number of reads for the two major FGFR2 resistance mutations (blue = mutant, green = wild type). Boxes with diagonal lines indicate no RNAseq data available. FGFR2 fusions were confirmed in the cfDNA samples based on off-target WES reads. (d) Diagram of the locations of autopsy specimens included in the analysis with likely clonal migration patterns across lesions. Layered pie charts represent the estimated clonal composition of each specimen with the color of each subclone matching the respective gene color in the branch of phylogenetic tree.