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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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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.
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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.

Mentions: Next, we assessed the sensitivity and specificity of CAPP-Seq for disease monitoring and minimal residual disease detection using plasma samples from five healthy controls and 35 samples collected from 13 patients with NSCLC (Table 1 and Supplementary Table 4). We integrated information content across multiple instances and classes of somatic mutations into a ctDNA detection index. This index is analogous to a false positive rate and is based on a decision tree in which fusion breakpoints take precedence due to their nonexistent background and in which p-values from multiple reporter types are integrated (Methods). Applying this approach in an ROC analysis, CAPP-Seq achieved an area under the curve (AUC) of 0.95, with maximal sensitivity and specificity of 85% and 96%, respectively, for all plasma DNA samples from pretreated patients and healthy controls. Sensitivity among stage I tumors was 50% and among stage II–IV patients was 100% with a specificity of 96% (Fig. 3a,b). Moreover, when considering both pre and post-treatment samples, CAPP–Seq exhibited robust performance, with AUC values of 0.89 for all stages and 0.91 for stages II–IV (P < 0.0001; Supplementary Fig. 6). Furthermore, by adjusting the ctDNA detection index, we could increase specificity up to 98% while still capturing 2/3 of all cancer-positive samples and 3/4 of stages II–IV cancer-positive samples (Supplementary Fig. 6). Thus, CAPP-Seq can achieve robust assessment of tumor burden and can be tuned to deliver a desired sensitivity and specificity.


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)

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.
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4016134&req=5

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.
Mentions: Next, we assessed the sensitivity and specificity of CAPP-Seq for disease monitoring and minimal residual disease detection using plasma samples from five healthy controls and 35 samples collected from 13 patients with NSCLC (Table 1 and Supplementary Table 4). We integrated information content across multiple instances and classes of somatic mutations into a ctDNA detection index. This index is analogous to a false positive rate and is based on a decision tree in which fusion breakpoints take precedence due to their nonexistent background and in which p-values from multiple reporter types are integrated (Methods). Applying this approach in an ROC analysis, CAPP-Seq achieved an area under the curve (AUC) of 0.95, with maximal sensitivity and specificity of 85% and 96%, respectively, for all plasma DNA samples from pretreated patients and healthy controls. Sensitivity among stage I tumors was 50% and among stage II–IV patients was 100% with a specificity of 96% (Fig. 3a,b). Moreover, when considering both pre and post-treatment samples, CAPP–Seq exhibited robust performance, with AUC values of 0.89 for all stages and 0.91 for stages II–IV (P < 0.0001; Supplementary Fig. 6). Furthermore, by adjusting the ctDNA detection index, we could increase specificity up to 98% while still capturing 2/3 of all cancer-positive samples and 3/4 of stages II–IV cancer-positive samples (Supplementary Fig. 6). Thus, CAPP-Seq can achieve robust assessment of tumor burden and can be tuned to deliver a desired sensitivity and specificity.

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