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An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage.

Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, Liu CL, Neal JW, Wakelee HA, Merritt RE, Shrager JB, Loo BW, Alizadeh AA, Diehn M - Nat. Med. (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [3].

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.

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Analytical performance(a–c) 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 panel25. 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.
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Figure 2: Analytical performance(a–c) 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 panel25. 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.

Mentions: We performed deep sequencing with the NSCLC selector to achieve ~10,000x coverage (pre-duplication removal) based on considerations of sequencing depth, median number of reporters, and ctDNA detection limit (Fig. 1d). We profiled a total of 90 samples, including two NSCLC cell lines, 17 primary tumor samples and matched peripheral blood leukocytes (PBLs), and 40 plasma samples from 18 human subjects, including five healthy adults and 13 patients with NSCLC (Supplementary Table 2). To assess and optimize selector performance, we first applied it to circulating DNA purified from healthy control plasma, observing efficient and uniform capture of genomic DNA (Supplementary Table 2). Sequenced plasma DNA fragments had a median length of ~170 bp (Fig. 2a), closely corresponding to the length of DNA contained within a chromatosome24. By optimizing library preparation from small quantities of plasma DNA, we increased recovery efficiency by >300% and decreased bias for libraries constructed from as little as 4 ng (Supplementary Fig. 3). Consequently, fluctuations in sequencing depth were minimal (Fig. 2b,c).


An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage.

Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, Liu CL, Neal JW, Wakelee HA, Merritt RE, Shrager JB, Loo BW, Alizadeh AA, Diehn M - Nat. Med. (2014)

Analytical performance(a–c) 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 panel25. 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.
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Figure 2: Analytical performance(a–c) 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 panel25. 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.
Mentions: We performed deep sequencing with the NSCLC selector to achieve ~10,000x coverage (pre-duplication removal) based on considerations of sequencing depth, median number of reporters, and ctDNA detection limit (Fig. 1d). We profiled a total of 90 samples, including two NSCLC cell lines, 17 primary tumor samples and matched peripheral blood leukocytes (PBLs), and 40 plasma samples from 18 human subjects, including five healthy adults and 13 patients with NSCLC (Supplementary Table 2). To assess and optimize selector performance, we first applied it to circulating DNA purified from healthy control plasma, observing efficient and uniform capture of genomic DNA (Supplementary Table 2). Sequenced plasma DNA fragments had a median length of ~170 bp (Fig. 2a), closely corresponding to the length of DNA contained within a chromatosome24. By optimizing library preparation from small quantities of plasma DNA, we increased recovery efficiency by >300% and decreased bias for libraries constructed from as little as 4 ng (Supplementary Fig. 3). Consequently, fluctuations in sequencing depth were minimal (Fig. 2b,c).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [3].

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.

Show MeSH
Related in: MedlinePlus