Abstract
Next-generation sequencing (NGS) of circulating tumor DNA (ctDNA) supports blood-based genomic profiling but is not yet routinely implemented in the setting of a phase I trials clinic. TARGET is a molecular profiling program with the primary aim to match patients with a broad range of advanced cancers to early phase clinical trials on the basis of analysis of both somatic mutations and copy number alterations (CNA) across a 641 cancer-associated-gene panel in a single ctDNA assay. For the first 100 TARGET patients, ctDNA data showed good concordance with matched tumor and results were turned round within a clinically acceptable timeframe for Molecular Tumor Board (MTB) review. When a 2.5% variant allele frequency (VAF) threshold was applied, actionable mutations were identified in 41 of 100 patients, and 11 of these patients received a matched therapy. These data support the application of ctDNA in this early phase trial setting where broad genomic profiling of contemporaneous tumor material enhances patient stratification to novel therapies and provides a practical template for bringing routinely applied blood-based analyses to the clinic.
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Data availability
All the data generated or analyzed during this study are included in this published article or are available from the corresponding author upon reasonable request. Genome data has been deposited at the European Genome-phenome Archive, which is hosted at the European Bioinformatics Institute and the Centre for Genomic Regulation, under accession number EGAS00001003407.
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Acknowledgements
This research was co-funded by The Christie Charitable Fund, by Cancer Research UK (CRUK) via core-funding to the CRUK Manchester Institute (grant no. A27412, R.M.), the CRUK Manchester Centre (grant no. A25254, R.M.), the CRUK Manchester Experimental Cancer Medicines Centre (grant no. A25146, R.M.) and the NIHR Manchester Biomedical Research Centre (C.D. and M.K.). This research was supported by the NIHR Manchester Clinical Research Facility, the Manchester Academic Health Science Centre, the AstraZeneca iDECIDE Programme (grant no. 119106, C.D.) awarded to Manchester Cancer Research Centre, PCRF 2012 Project Grant (C.D.), CRUK Precision Panc grant (no. C480/A25235, C.D.), the EU IMI consortium CANCER-ID (grant no. 115749-Cancer-ID, C.D.) and Roche Products, Ltd. through the provision of the Foundation Medicine tumor profiling service. The views expressed are those of the authors and not necessarily those of the funders, the NHS, the NIHR or the Department of Health.
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D.G.R., A.M.H., G.B., C.D. and M.G.K. developed the clinical study, performed data analysis and wrote the manuscript. M.A., A.C., D.W., K.N., S.M. and N.S. performed ctDNA analysis. S.F., B.K., S.G. and C.M. provided bioinformatics support for the study. N.C., F.T., L.C., E.D., J.D., H.F., M.H., A.G., D.G., C.K., S.A., R.M., N.T., A.J.V., S.V., C.O., J.C. and R.K. recruited patients and provided clinical support for the study. J.S., S.S. and D.L. developed eTARGET and undertook software evaluations for the MTB. N.H., H.E. and A.W. performed tumor tissue analysis. A.J., K.F. and R.M. supported the MTB. All authors read and approved the final manuscript.
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Extended data
Extended Data Figure 1 Validation of the ctDNA NGS workflow.
a, Dot-plot showing ctDNA yield (ng ml−1 of plasma) from duplicate 4-ml plasma samples (n = 20) extracted using the Qiagen CNA manual kit (dark and light red boxes) and QIASymphony automated platform (dark and light blue triangles). No significant difference in ctDNA yield was seen between duplicate extractions and isolation approaches (paired t-test). The mean yield (thick black line) and standard deviation (thin black lines) for each sample set are indicated. b, Dot-plot showing fold enrichment for three genes following pull-down using the 641-gene SureSelect panel. RNaseP and b2M were not included in the gene panel and showed no enrichment. KRAS was in the panel and showed significant enrichment compared with the control genes. The mean fold enrichment (thick black line) and standard deviation (thin black lines) for each sample set are indicated (n = 20). c, Table showing the 14 mutations known to be present within the five European Molecular Genetics Quality Network controls (QC1–5) with expected frequencies highlighted in green. Mutations detected in each control are shown in blue, with all mutations detected only in samples expected.
Extended Data Figure 2 Twenty-two different tumor types were included within the first 100 TARGET patients.
Other tumor types consist of single cases of mesothelioma, renal cell carcinoma (RCC), transitional cell carcinoma (TCC), neuroendocrine tumor (NET), cervical, spiroadenocarcinoma, thyroid, digital papillary adenocarcinoma, pseudomyxoma peritonei and ovarian cancer.
Extended Data Figure 3 Concordance between tumor and ctDNA somatic mutations across the 19- and 24-gene clinical panels.
Mutations seen in the tumor (red box) and ctDNA (black box) for the 55 patients with mutations detected are shown.
Extended Data Figure 4 Table showing comparison between ctDNA mutations and tumor mutations identified by FoundationOne testing in a subset of 39 TARGET patients.
The two panels contained 230 overlapping genes in which 74 mutations were identified in the ctDNA of the 39 patients, with 52 of these also being seen in the tumor analysis.
Extended Data Figure 5 Analysis of correlation between ctDNA yield, number of mutations detected, average VAF and tumor burden in 100 TARGET patients.
a,b Dot-plots showing correlation between ctDNA yield and number of mutations detected in the ctDNA (a), and ctDNA yield and average VAF per sample for the TARGET patient cohort (b). No significant correlation was seen by linear regression analysis for either parameter (n = 99). c, Analysis of association between number of metastatic sites and average VAF. For the first 100 TARGET patients the number of metastatic sites ranged from 1 to 8, with a median value of 2. The average VAF (%) for each group of patients is shown above. d, Linear regression analysis showed a significant correlation between ctDNA VAF and number of metastatic sites (n = 99).
Extended Data Figure 6 Dot-plot showing age of tumor material in which concordance (red circles, n = 70) and discordance (blue boxes, n = 24) was found in TARGET patients with successful tumor and ctDNA analysis.
Analysis by two-tailed unpaired t-test showed no significant association between age of biopsy and mutation discordance (P = 0.1993). The mean age of the biopsies (thick black line) and standard deviation (thin black lines) for each data set are indicated.
Extended Data Figure 7 Figure showing genes found to be mutated (SNV or Indel) in 100 TARGET patients, presence of a mutation is indicated by a black box.
Disease type is indicated on the left of the panel and average VAF% indicated on the right of the panel.
Extended Data Figure 8 Comparison of WGS low-pass CNA data and CNVkit data from the same ctDNA samples showing high concordance between both CNA calling approaches.
All regions of gain (red) and loss (blue) seen in the WGS low-pass analysis are present in the CNVkit pull-down analysis. Chromosome numbers are shown below each sample.
Extended Data Figure 9 Table showing concordance at the gene level between tumor and ctDNA CNA for 23 patients who have undergone FoundationOne testing.
The number of genes reported as amplified or deleted in ctDNA and tumor tissue DNA by FoundationOne is shown, along with the number of concordant gene-level alterations reported in the tumor that where also found in ctDNA. Average VAF is shown in the right-hand column.
Extended Data Figure 10 Screenshot of eTARGET interface for ctDNA NGS analysis.
Overview of significant mutations detected, total ctDNA input into NGS library (ng) and average de-duplicated read depth is displayed. Additional clinical and genomic data are available in real time within eTARGET by selection from the menu on the top left of the screen.
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Rothwell, D.G., Ayub, M., Cook, N. et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study. Nat Med 25, 738–743 (2019). https://doi.org/10.1038/s41591-019-0380-z
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DOI: https://doi.org/10.1038/s41591-019-0380-z
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