Research ArticleCancer

Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer

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Science Translational Medicine  07 Aug 2019:
Vol. 11, Issue 504, eaax7392
DOI: 10.1126/scitranslmed.aax7392
  • Fig. 1 Development of a multiplexed assay for personalized ctDNA detection and monitoring.

    (A) Results of binomial sampling at varying input DNA amounts (bottom x axis) and corresponding plasma volumes (top x axis). Maximum theoretical sensitivity for 1 in 105 tumor fraction (y axis) was calculated as the probability of detecting at least one mutated DNA fragment for at least one targeted mutation. Each line shows the number of mutations tested (25 to 100, increments of 5). A plasma DNA concentration of 5 ng/ml of plasma (or 1500 haploid genome copies) and no molecular loss during library preparation are assumed. Sensitivity for detection of ctDNA at 0.001% tumor fraction is limited if only two to four mutations are assayed but can be improved with higher input of plasma DNA and increasing number of patient-specific mutations. (B) For TARDIS, sequencing library preparation includes linear pre-amplification to improve molecular conversion, single-stranded DNA ligation using hairpin oligonucleotides to allow error suppression using template fragment sizes and unique molecular identifiers (UMIs), and multiplexed PCR to enrich targeted genomic loci. (C) Schematic representation of read structure and error suppression. TARDIS uses UMIs (indicated by different read colors) and fragment sizes to group sequencing reads into RFs. We exclude PCR errors (red circle) by requiring consensus of all RF members and polymerase errors (yellow circles) introduced during linear pre-amplification by requiring support by at least two RFs. Additional description of error suppression strategies is provided in Materials and Methods.

  • Fig. 2 Analytical performance of TARDIS in reference samples.

    (A) Mutation-level sensitivity and specificity across 93 reference samples and 8 mutations, requiring each mutation to be supported by ≥2 RFs and an AF consistent with ≥0.5 mutant molecules. Each row corresponds to a targeted mutation, and each column corresponds to a single sample analyzed at the identified AF. (B) Sample-level sensitivity and specificity, requiring ≥2 RFs contributed by 1 mutation with multiple fragment sizes or >1 mutation, each with an AF consistent with ≥0.5 mutant molecules. (C) Comparison of variant AFs observed using TARDIS (y axis) with expected variant AFs measured using ddPCR (x axis, 48 data points). For each variant, mean observed AF across all replicates (at the same expected AF) is presented. Gray line is linear fit. (D) Comparison of sample AFs observed using TARDIS (mean for all eight mutations assayed in each replicate sample, 77 data points) with known sample AFs (mean of known variant AFs). Gray line is linear fit to the mean at each expected AF. (E) CVs of variant AFs decreased with increasing number of mutant molecules per mutation. CVs calculated across 7 to 16 replicates at each mutation fraction for each of eight mutations (48 data points). (F) CVs of sample-level AFs were lower than those for individual mutations, demonstrating the advantage of leveraging multiple mutations for ctDNA quantification. CVs calculated across 7 to 16 replicates for sample-level AFs across six mutation fractions.

  • Fig. 3 Evaluation of analytical performance in reference samples at 3 in 105 tumor fraction.

    (A) Variant-level sensitivity and specificity across 56 reference samples and 16 mutations, requiring each mutation to be supported by ≥2 RFs and an AF consistent with ≥0.5 mutant molecules. Twenty-two mutations were analyzed in this experiment. However, six mutations were inferred to contribute biological background because these were recurrently observed in a wild-type DNA sample sourced from immortalized cell lines. These included known hotspot variants in TP53 (n = 4 of 4 targets), APC (n = 1 of 2 targets), and GNAS (n = 1 of 1 targets). These mutations were dropped from further analysis. Each row corresponds to a targeted mutation, and each column corresponds to a single sample analyzed at the identified AF. (B) Sample-level sensitivity and specificity, requiring ≥2 RFs contributed by one mutation with multiple sizes or >1 mutations, each with an AF consistent with ≥0.5 mutant molecules. Although a mutation with two RFs was observed in one wild-type sample, this mutation was supported by a single size, and at the sample level, ctDNA was determined to be undetectable. (C) Accuracy evaluated by comparison of sample AFs observed using TARDIS (mean for all 16 mutations assayed in each replicate sample) with known sample AFs (mean of known variant AFs measured using digital PCR). Blue line is linear fit to the mean at each expected AF. (D) Precision evaluated using CVs of sample-level AFs, calculated across 8 to 32 replicates.

  • Fig. 4 ctDNA analysis in patients with early and locally advanced breast cancer before treatment and after completion of NAT.

    (A) Clinical characteristics of the cohort. (B) Summary of results, tumor stage, grade, mitotic rate, subtype, ctDNA detection before treatment and after NAT, and residual disease assessment. Pathological staging was performed after surgery and completion of NAT. NA, not available or not applicable; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; ypTis, in situ disease; ypT1-3 and ypN1-3, tumor and nodal stage upon pathological staging. (C) ctDNA fraction at baseline. (D) ctDNA fraction after completion of NAT, grouped by clinical response to treatment (residual disease versus pathological complete response). (E) Changes in pre- and posttreatment ctDNA fraction in patients with residual disease and pathCR.

  • Fig. 5 Receiver operating characteristic curve for predicting residual disease using ctDNA fraction after completion of NAT.

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/11/504/eaax7392/DC1

    Fig. S1. Schematic overview of TARDIS.

    Fig. S2. Comparison of raw and TARDIS-corrected background errors.

    Fig. S3. Comparison of total cfDNA concentration between plasma samples from patients and healthy volunteers.

    Fig. S4. Variant and tumor fractions in individual patients.

    Fig. S5. Receiver operating characteristic curve for predicting residual disease using ctDNA fraction after completion of NAT in subgroups.

    Table S1. Mutations targeted in reference samples.

    Table S2. Expected mutation fractions in reference samples analyzed.

    Table S3. Tumor and germline sequencing statistics.

    Table S4. Oligonucleotide sequences used for sequencing library preparation.

    Data file S1. Mutations detected in reference samples in Fig. 2.

    Data file S2. Mutations detected in reference samples in Fig. 3.

    Data file S3. Details of patient plasma samples and ctDNA tumor fraction.

    Data file S4. Mutations detected in patient plasma samples.

  • The PDF file includes:

    • Fig. S1. Schematic overview of TARDIS.
    • Fig. S2. Comparison of raw and TARDIS-corrected background errors.
    • Fig. S3. Comparison of total cfDNA concentration between plasma samples from patients and healthy volunteers.
    • Fig. S4. Variant and tumor fractions in individual patients.
    • Fig. S5. Receiver operating characteristic curve for predicting residual disease using ctDNA fraction after completion of NAT in subgroups.
    • Table S1. Mutations targeted in reference samples.
    • Table S2. Expected mutation fractions in reference samples analyzed.
    • Table S3. Tumor and germline sequencing statistics.
    • Table S4. Oligonucleotide sequences used for sequencing library preparation.
    • Legends for data files S1 to S4

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Data file S1 (Microsoft Excel format). Mutations detected in reference samples in Fig. 2.
    • Data file S2 (Microsoft Excel format). Mutations detected in reference samples in Fig. 3.
    • Data file S3 (Microsoft Excel format). Details of patient plasma samples and ctDNA tumor fraction.
    • Data file S4 (Microsoft Excel format). Mutations detected in patient plasma samples.

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