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, 20 (5), 548-54

An Ultrasensitive Method for Quantitating Circulating Tumor DNA With Broad Patient Coverage

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An Ultrasensitive Method for Quantitating Circulating Tumor DNA With Broad Patient Coverage

Aaron M Newman et al. Nat Med.

Abstract

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II-IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to ∼0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

Figures

Figure 1
Figure 1. Development of CAncer Personalized Profiling by deep Sequencing (CAPP-Seq)
(a) Schematic depicting design of CAPP-Seq selectors and their application for assessing circulating tumor DNA. (b) Multi-phase design of the NSCLC selector. Phase 1: Genomic regions harboring known and suspected driver mutations in NSCLC are captured. Phases 2–4: Addition of exons containing recurrent SNVs using WES data from lung adenocarcinomas and squamous cell carcinomas from TCGA (n = 407). Regions were selected iteratively to maximize the number of mutations per tumor while minimizing selector size. Recurrence index = total unique patients with mutations covered per kb of exon. Phases 5,6: Exons of predicted NSCLC drivers, and introns and exons harboring breakpoints in rearrangements involving ALK, ROS1, and RET were added. Bottom: increase of selector length during each design phase. (c) Analysis of the number of SNVs per lung adenocarcinoma covered by the NSCLC selector in the TCGA WES cohort (Training; n = 229) and an independent lung adenocarcinoma WES data set (Validation; n = 183). Results are compared to selectors randomly sampled from the exome (P < 1.0 × 10−6 for the difference between random selectors and the NSCLC selector). (d) Analytical modeling of CAPP-Seq, whole exome and whole genome sequencing for different detection limits of tumor circulating DNA in plasma. Calculations are based on the median number of mutations detected per NSCLC for CAPP-Seq (i.e., 4) and the reported number of mutations in NSCLC exomes and genomes. Additional details are described in Methods. The vertical dotted line represents the median fraction of tumor-derived circulating DNA detected in plasma from patients in this study.
Figure 2
Figure 2. Analytical performance
(ac) Quality parameters from a representative CAPP-Seq analysis of plasma DNA, including length distribution of sequenced circulating DNA fragments (a), and depth of sequencing coverage across all genomic regions in the selector (b). (c) Variation in sequencing depth across plasma DNA samples from four patients. Orange envelope represents s.e.m. (d) Analysis of background rate for 40 plasma DNA samples collected from 13 patients with NSCLC and five healthy individuals (Supplementary Methods). (e) Analysis of biological background in d focusing on 107 recurrent somatic mutations from a previously reported SNaPshot panel. Mutations found in a given patient’s tumor were excluded. The mean frequency over all subjects was ~0.01%. A single outlier mutation (TP53 R175H) is indicated by an orange diamond. (f) Individual mutations from e ranked by most to least recurrent, according to mean frequency across the 40 plasma DNA samples. The p-value threshold of 0.01 (horizontal line) corresponds to the 99th percentile of global selector background in d. (g) Dilution series analysis of expected versus observed frequencies of mutant alleles using CAPP-Seq. Dilution series were generated by spiking fragmented HCC78 DNA into control circulating DNA. (h) Analysis of the effect of the number of SNVs considered on the estimates of fractional abundance (95% confidence intervals shown in gray). (i) Analysis of the effect of the number of SNVs considered on the mean correlation coefficient between expected and observed cancer fractions (blue dashed line) using data from panel h. 95% confidence intervals are shown for e,f. Statistical variation for g is shown as s.e.m.
Figure 3
Figure 3. Sensitivity and specificity analysis
(a) Receiver Operating Characteristic (ROC) analysis of plasma DNA samples from pre-treatment samples and healthy controls, divided into all stages (n = 13 patients) and stages II–IV (n = 9 patients). Area Under the Curve (AUC) values are significant at P < 0.0001. Sn, sensitivity; Sp, specificity. (b) Raw data related to a. TP, true positive; FP, false positive; TN, true negative; FN, false negative. (c) Concordance between tumor volume, measured by CT or PET/CT, and pg mL−1 of ctDNA from pretreatment samples (n = 9), measured by CAPP-Seq. Patients P6 and P9 were excluded due to inability to accurately assess tumor volume and differences related to the capture of fusions, respectively (see Supplementary Methods). Of note, linear regression was performed in non-log space; the log-log axes and dashed diagonal line are for display purposes only.
Figure 4
Figure 4. Noninvasive detection and monitoring of circulating tumor DNA
(ah) Disease monitoring using CAPP-Seq. (a,b) Disease burden changes in response to treatment in a patient with stage III NSCLC using SNVs and an indel (a), and a patient with stage IV NSCLC using three rearrangement breakpoints (b). (c) Concordance between different reporters (SNVs and a fusion) in a patient with stage IV NSCLC. (d) Detection of a subclonal EGFR T790M resistance mutation in a patient with stage IV NSCLC. The fractional abundance of the dominant clone and T790M-containing clone are shown in the primary tumor (left) and plasma samples (right). (e,f) CAPP-Seq results from post-treatment plasma DNA samples are predictive of clinical outcomes in a patient with stage IIB NSCLC (e) and a patient with stage IIIB NSCLC (f). (g,h) Monitoring of tumor burden following complete tumor resection (g) and Stereotactic Ablative Radiotherapy (SABR) (h) for two patients with stage IB NSCLC. (i) Exploratory analysis of the potential application of CAPP-Seq for biopsy-free tumor genotyping or cancer screening. All plasma DNA samples from patients in Table 1 were examined for the presence of mutant allele outliers without knowledge of the primary tumor mutations (see Supplementary Methods); samples with detectable mutations are shown, along with three samples assumed to be cancer-negative (P1-2, P1-3 and P16-3; Supplementary Methods). The lowest fraction of ctDNA among positive samples was ~0.4% (dashed horizontal line). Error bars in d represent s.e.m. (a,b,eh) Scale bars, 10 cm. Tu, tumor; Ef, pleural effusion; SD, stable disease; PD, progressive disease; PR, partial response; CR, complete response; DOD, dead of disease.

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