Fig. 1. Schematic of the TEC-Seq method. cfDNA is extracted from the blood and converted to a genomic library through ligation of a pool containing a small number of dual-index barcode adapters. The resulting cfDNA library is captured and redundantly sequenced to produce multiple duplicates of each DNA fragment. Sequence reconciliation among duplicate fragments identifies alterations present in identical DNA molecules with the same start and end position and exogenous barcodes. Alignment to the reference genome of multiple distinct molecules containing identical redundant changes is used to identify bona fide alterations.
Fig. 2. TEC-Seq error correction. Sequencing error rates of conventional NGS and theoretical upper limit for TEC-Seq are indicated at each base in the captured regions of interest (P < 0.0001, paired t test). Error rates are determined by identifying the number of alterations at each base (or assuming one alteration per base if no error was identified) divided by the total coverage at each base among the 44 healthy individuals analyzed.
Fig. 3. cfDNA and ctDNA in healthy individuals and patients with cancer. Amount of cfDNA extracted from all healthy individuals and patients with different cancer types (A) and from cancer patients of different stages (B). Mutant allele fraction of ctDNA detected in healthy individuals and patients with different cancer types (C) and in cancer patients of different stages (D). Means for each group are represented by the black bars in the columns analyzed. In patients for whom multiple alterations were detected, the highest value is indicated. Clinical characteristics of patients and stages are indicated in table S3.
Fig. 4. ctDNA in patients with breast, colorectal, lung, and ovarian cancer. Patients (n = 194) are each represented by a tick mark. (Left) Bar chart shows the number of alterations detected for each case. (Middle) Stage, cancer type, and histopathological subtype are represented by colored vertical bars. (Right) Mutant allele fractions for each alteration detected per patient are indicated with an “x” at the mean. Alterations are colored on the basis of hotspot status and whether any alterations were detected in the case.
Fig. 5. Concordance between alterations in plasma and tissue. Mutant allele fractions observed in the plasma are indicated for each alteration identified with a black bar at the mean. The presence of alterations in matched tumor specimens is indicated with green dots, whereas nonconcordant alterations are indicated in orange, and those that are not assessed are indicated in gray. Stage and cancer type for each patient are plotted in the two horizontal tracks at the bottom of the figure.
Fig. 6. Preoperative ctDNA amounts and outcome in colorectal cancer patients. Kaplan-Meier curves depict PFS (A) (P < 0.0001, log-rank test) and OS (B) (P < 0.0001, log-rank test) of 31 colorectal cancer patients, stages I to IV, stratified on the basis of a ctDNA mutant allele fraction threshold of 2%. Kaplan-Meier analyses of the 27 patients with stage I to III disease for PFS (C), (P = 0.0006, log-rank test) and OS (D) (P < 0.0001, log-rank test) were performed using the same threshold to examine the association of ctDNA with outcome in patients without stage IV disease.
- Table 1. Cancer cases containing alterations in driver genes.
Tissue type Cases in COSMIC Detectable cases* Detectable fraction Breast 1002 719 72% Colorectal 1248 1071 86% Lung 1198 932 78% Ovarian 647 524 81% *Detectable cases indicate those with at least one alteration in the cancer driver genes analyzed (table S1).
- Table 2. Cancer patients detected using TEC-Seq.
NA, not applicable.
Cancer type Patients (n) Patients
with ctDNA
alterations (n)Fraction of patients
with ctDNA
alterations (%)Colorectal I 8 4 50 II 9 8 89 III 10 9 90 IV 15 14 93 I–IV 42 35 83 Lung I 29 13 45 II 32 23 72 III 4 3 75 IV 6 5 83 I–IV 71 44 62 Ovarian I 24 16 67 II 4 3 75 III 8 6 75 IV 6 5 83 I–IV 42 30 71 Breast I 3 2 67 II 29 17 59 III 13 6 46 IV 0 NA NA I–IV 45 25 56 All I and II 138 86 62 III and IV 62 48 77 I–IV 200 134 67
Supplementary Materials
www.sciencetranslationalmedicine.org/cgi/content/full/9/403/eaan2415/DC1
Fig. S1. Simulations using limited exogenous barcodes.
Fig. S2. Validation of the TEC-Seq approach.
Fig. S3. Mutation frequencies in cancer genes.
Fig. S4. ctDNA mutant allele fractions in serial blood draws.
Fig. S5. Comparison of ctDNA mutant allele fractions measured by TEC-Seq and ddPCR.
Fig. S6. ctDNA and tumor heterogeneity.
Fig. S7. Preoperative ctDNA mutant allele fractions in colorectal cancer patients.
Fig. S8. Preoperative CEA in colorectal cancer patients.
Fig. S9. Conversion efficiency of cfDNA.
Table S1. Genes analyzed by TEC-Seq (provided in a separate Excel file).
Table S2. Summary of TEC-Seq validation (provided in a separate Excel file).
Table S3. Summary of patients analyzed (provided in a separate Excel file).
Table S4. Summary of genomic analyses (provided in a separate Excel file).
Table S5. Alterations in blood cell proliferation genes in healthy individuals and cancer patients (provided in a separate Excel file).
Table S6. Germline alterations identified in cfDNA (provided in a separate Excel file).
Table S7. Somatic alterations detected in cfDNA of cancer patients (provided in a separate Excel file).
Table S8. Summary of colorectal cancer patient outcomes (provided in a separate Excel file).
Additional Files
- Supplementary Material for:
Direct detection of early-stage cancers using circulating tumor DNA
Jillian Phallen, Mark Sausen, Vilmos Adleff, Alessandro Leal, Carolyn Hruban, James White, Valsamo Anagnostou, Jacob Fiksel, Stephen Cristiano, Eniko Papp, Savannah Speir, Thomas Reinert, Mai-Britt Worm Orntoft, Brian D. Woodward, Derek Murphy, Sonya Parpart-Li, David Riley, Monica Nesselbush, Naomi Sengamalay, Andrew Georgiadis, Qing Kay Li, Mogens Rørbæk Madsen, Frank Viborg Mortensen, Joost Huiskens, Cornelis Punt, Nicole van Grieken, Remond Fijneman, Gerrit Meijer, Hatim Husain, Robert B. Scharpf, Luis A. Diaz, Siân Jones, Sam Angiuoli, Torben Ørntoft, Hans Jørgen Nielsen, Claus Lindbjerg Andersen, Victor E. Velculescu*
*Corresponding author. Email: velculescu{at}jhmi.edu
Published 16 August 2017, Sci. Transl. Med. 9, eaan2415 (2017)
DOI: 10.1126/scitranslmed.aan2415This PDF file includes:
- Fig. S1. Simulations using limited exogenous barcodes.
- Fig. S2. Validation of the TEC-Seq approach.
- Fig. S3. Mutation frequencies in cancer genes.
- Fig. S4. ctDNA mutant allele fractions in serial blood draws.
- Fig. S5. Comparison of ctDNA mutant allele fractions measured by TEC-Seq and ddPCR.
- Fig. S6. ctDNA and tumor heterogeneity.
- Fig. S7. Preoperative ctDNA mutant allele fractions in colorectal cancer patients.
- Fig. S8. Preoperative CEA in colorectal cancer patients.
- Fig. S9. Conversion efficiency of cfDNA.
Other Supplementary Material for this manuscript includes the following:
- Table S1. Genes analyzed by TEC-Seq (provided in a separate Excel file).
- Table S2. Summary of TEC-Seq validation (provided in a separate Excel file).
- Table S3. Summary of patients analyzed (provided in a separate Excel file).
- Table S4. Summary of genomic analyses (provided in a separate Excel file).
- Table S5. Alterations in blood cell proliferation genes in healthy individuals and cancer patients (provided in a separate Excel file).
- Table S6. Germline alterations identified in cfDNA (provided in a separate Excel file).
- Table S7. Somatic alterations detected in cfDNA of cancer patients (provided in a separate Excel file).
- Table S8. Summary of colorectal cancer patient outcomes (provided in a separate Excel file).
