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Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab

Nature Cancer volume 1, pages 873–881 (2020)Cite this article

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Abstract

Immune checkpoint blockade (ICB) provides clinical benefit to a subset of patients with cancer. However, existing biomarkers do not reliably predict treatment response across diverse cancer types. Limited data exist to show how serial circulating tumor DNA (ctDNA) testing may perform as a predictive biomarker in patients receiving ICB. We conducted a prospective phase II clinical trial to assess ctDNA in five distinct cohorts of patients with advanced solid tumors treated with pembrolizumab (NCT02644369). We applied bespoke ctDNA assays to 316 serial plasma samples obtained at baseline and every three cycles from 94 patients. Baseline ctDNA concentration correlated with progression-free survival, overall survival, clinical response and clinical benefit. This association became stronger when considering ctDNA kinetics during treatment. All 12 patients with ctDNA clearance during treatment were alive with median 25 months follow up. This study demonstrates the potential for broad clinical utility of ctDNA-based surveillance in patients treated with ICB.

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Fig. 1: Baseline ctDNA level is correlated with OS and PFS.
Fig. 2: The change in ctDNA levels from baseline to C3, clinical response and survival.
Fig. 3: The change in ctDNA levels from baseline to C3 is strongly correlated with OS and PFS.
Fig. 4: Risk groupings according to early clinical response and ctDNA kinetics.
Fig. 5: Objective clinical response to pembrolizumab is associated with ctDNA clearance.
Fig. 6: ctDNA clearance identifies patients with highly favorable survival.

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Data availability

Anonymized WES data of tumor and match normals are available with controlled access approval through the European Genome-Phenome Archive under accession number EGAS00001003280. Source data for Figs. 1–6 can be found in Source data Figs. 1–6 files, and Source data for Extended Data Fig. 2–8 can be found in Source data Extended Data Fig. 2–8 files. The supporting variant level data for all figures are available in Supplementary Table 4. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

Code necessary to reproduce the core analyses are available without restrictions at https://github.com/pughlab/inspire-ctdna.

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Acknowledgements

Major funding support for the project was made possible by the Princess Margaret Cancer Foundation, Ontario Institute for Cancer Research and Terry Fox Research Institute. We thank Merck for contributing the study drug for the clinical trial. S.V.B. and T.J.P. were supported by the Gattuso-Slaight Personalized Cancer Medicine Fund. S.Y.C.Y. was supported in part by the University of Toronto Medical Biophysics OSOTF Excellence Award. M.A.J.I. was supported in part by a fellowship through the BMO Chair in Precision Cancer Genomics. L.L.S. holds the BMO Chair in Precision Cancer Genomics. T.J.P. holds the Canada Research Chair in Translational Genomics and is supported by a Senior Investigator Award from the Ontario Institute for Cancer Research. We are grateful to R. Salari, H.-T. Wu and S. Dashner for their technical support and insightful discussions of the project. We gratefully acknowledge the individuals from the Princess Margaret Tumour Immunotherapy Program (https://www.pm-tumorimmunotherapyprogram.ca/), including the immune monitoring (M. Butler, D. Gray, V. Sotov and D. Liu) and tissue correlatives (V. Speers, A. Giesler and S. Hakgor) teams. We thank the staff of the Princess Margaret Genomics Centre (https://www.pmgenomics.ca), Bioinformatics and High-Performance Computing Core, and PM-OICR Translational Genomics Laboratory (https://labs.oicr.on.ca/translational-genomics-laboratory) for their expertise in generating the sequencing data used in this study. Additional infrastructure support from the Canada Foundation for Innovation, Leaders Opportunity Fund (32383); Ontario Ministry of Research and Innovation, Ontario Research Fund Small Infrastructure Program and Ontario Institute for Cancer Research (https://genomics.oicr.on.ca).

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Author notes

  1. These authors contributed equally: Scott V. Bratman, S. Y. Cindy Yang.

Authors and Affiliations

  1. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Scott V. Bratman, S. Y. Cindy Yang & Trevor J. Pugh

  2. Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada

    Scott V. Bratman

  3. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Scott V. Bratman, S. Y. Cindy Yang, Marco A. J. Iafolla, Zhihui Liu, Aaron R. Hansen, Philippe L. Bedard, Stephanie Lheureux, Anna Spreafico, Albiruni Abdul Razak, Iulia Cirlan, Youstina Hanna, Derek L. Clouthier, Scott C. Lien, Pamela S. Ohashi, Wei Xu, Lillian L. Siu & Trevor J. Pugh

  4. Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada

    Marco A. J. Iafolla, Aaron R. Hansen, Philippe L. Bedard, Stephanie Lheureux, Anna Spreafico, Albiruni Abdul Razak & Lillian L. Siu

  5. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

    Zhihui Liu & Wei Xu

  6. Natera, Inc., San Carlos, CA, USA

    Svetlana Shchegrova, Maggie Louie, Paul Billings, Bernhard Zimmermann, Himanshu Sethi & Alexey Aleshin

  7. Ontario Institute of Cancer Research, Toronto, Ontario, Canada

    Dax Torti, Kayla Marsh, Jenna Eagles & Trevor J. Pugh

  8. Department of Immunology, University of Toronto, Toronto, Ontario, Canada

    Scott C. Lien & Pamela S. Ohashi

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  1. Scott V. Bratman
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Contributions

S.V.B., L.L.S. and T.J.P. conceived the study. S.V.B., P.S.O., L.L.S. and T.J.P. secured funding. S.Y.C.Y. performed data analysis. M.A.J.I. reviewed the clinical data and performed data analysis. A.R.H., P.L.B., S.L., A.S., A.A.R. and L.L.S. accrued patients and supervised biospecimen sample collection. T.J.P., P.S.O., D.L.C. and D.T. supervised biospecimen sample processing by S.Y.C.Y., S.C.L., Y.H., I.C., K.M. and J.E. W.X. and Z.L. performed statistical analysis. S.S., M.L., B.Z., P.B., H.S. and A.A. conducted the ctDNA assays and provided technical expertise and scientific feedback. S.V.B., S.Y.C.Y., L.L.S., W.X., Z.L., D.T. and T.J.P. wrote the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding authors

Correspondence to Lillian L. Siu or Trevor J. Pugh.

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Competing interests

S.V.B. provides consultation for Bristol-Myers Squibb, receives research support from Nektar Therapeutics and is a coinventor on a patent application relating to circulating tumor DNA mutation detection technology (PCT/US2014/02502, licensed to Roche Molecular Diagnostics) and on a patent application relating to ctDNA methylation analysis technology (PCT/CA2018/000203). L.L.S. provides consultation for Merck (compensated), Pfizer (compensated), Celgene (compensated), AstraZeneca (compensated), Morphosys (compensated), Roche (compensated), GeneSeeq (compensated), Loxo (compensated), Oncorus (compensated), Symphogen (compensated), Seattle Genetics (compensated), GlaxoSmithKline (compensated), Voronoi (compensated), Treadwell Therapeutics (compensated), Arvinas (compensated), Tessa (compensated), Navire (compensated); receives research support (institutional) from Novartis, Bristol-Myers Squibb, Pfizer, Boerhinger-Ingelheim, GlaxoSmithKline, Roche/Genentech, Karyopharm, AstraZeneca/Medimmune, Merck, Celgene, Astellas, Bayer, Abbvie, Amgen, Symphogen, Intensity Therapeutics, Mirati, Shattucks and Avid; and is a stockholder in Agios (spouse) and Treadwell Therapeutics (spouse). S.S., M.L., P.B., B.Z., H.S. and A.A. are employees of Natera, Inc. T.J.P. provides consultation for Merck, Chrysalis Biomedical Advisors and the Canadian Pension Plan Investment Board, and receives research support from Roche.

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Extended data

Extended Data Fig. 1 CONSORT diagram for the study.

The numbers of patients included in the study and reasons for exclusion are shown.

Extended Data Fig. 2 Factors affecting somatic mutation detection in tumor tissue and plasma cell-free DNA.

a, Somatic mutation burden per patient are grouped by INSPIRE cancer cohorts: Squamous cell cancer of head and neck (SCCHN, cohort A), triple negative breast cancer (TNBC, cohort B), high grade serous ovarian cancer (HGSOC, cohort C), malignant melanoma (MM, cohort D), and mixed solid tumors (MST, cohort E). The N (patients) for each group is shown in the top axis. Two-sided Kruskal-Wallis test p-value is shown. b, c, Comparison of 16 mutations vs 1 mutation from sequencing data for detecting ctDNA. To evaluate the impact of measuring 16 mutations on the likelihood of detecting ctDNA, we randomly selected one of the 16 mutations from each of the 94 baseline cell-free DNA samples and repeated this with 10,000 iterations. (b) An example of one of the 10,000 iteration shows that ctDNA levels (MTM/mL) are correlated (Spearman R = 0.87) among N = 94 baseline cell-free DNA samples. Two-sided Spearman correlation test p-value is shown. (c) Histogram of the detection likelihood (sensitivity for ctDNA) from 94 baseline cell-free DNA samples when using a randomly selected single mutation (N = 10,000 iterations). The mean sensitivity (red dashed line) is 84%. The sensitivity using all 16 mutations (black dashed line) is 98%. d, Influence of tumor tissue source used for bespoke assay design on detectability of ctDNA. The number of detected variants in baseline cell-free DNA samples are shown according to whether the bespoke assay design was conducted from FFPE vs fresh-frozen tumor exomes. Total number of samples in each group shown on the top axis. Two-sided Wilcoxon rank sum test p-value is shown. Box plots represent the median, upper and lower quartiles of each distribution with whiskers showing the limits of the distribution (1.5 times the interquartile range).

Source data

Extended Data Fig. 3 Rationale for calculation of absolute ctDNA levels from multiple mutation targets.

Analytical validation of the ctDNA quantification approach was conducted using Seraseq ctDNA Mutation Mix v2 (SeraCare, Milford, MA, USA) at various concentrations. For a given sample containing 16 target mutations, the mean tumor molecules per mL of plasma (MTM/mL) was calculated using two approaches (y-axis) and compared to known concentrations (x-axis). The blue data points represent the per-sample measurements of mean MTM/mL calculated over detected targets (slope=0.86), while the green data points represent the per-sample measurements of mean MTM/mL calculated over all 16 tested targets (slope=0.98). The red (X=Y) dashed line illustrates the ideal (expected) scenario in which the calculated experimental ctDNA levels are equal to the expected MTM/mL. The numbers of technical replicates at each concentration are shown (n = 2-18). In addition, all 10 negative control samples had no detected mutations (not shown). Pearson correlation coefficients (R2) are shown. This analytical comparison indicates that mean MTM/mL calculated over all tested targets provides a more accurate representation of the true amount of ctDNA, particularly at low levels of ctDNA.

Source data

Extended Data Fig. 4 ctDNA levels in the INSPIRE sub-cohorts and correlation with RECIST tumor measurements.

ctDNA levels (MTM/mL) at baseline a, and at cycle 3 b, as well as the fold-change in ctDNA levels from baseline to cycle 3 (∆ctDNAC3) c, are shown for each of the sub-cohorts: Squamous cell cancer of head and neck (SCCHN, cohort A), triple negative breast cancer (TNBC, cohort B), high grade serous ovarian cancer (HGSOC, cohort C), malignant melanoma (MM, cohort D), and mixed solid tumors (MST, cohort E). Box plots represent the median, upper and lower quartiles of each distribution with whiskers showing the limits of the distribution (1.5 times the interquartile range). The N (patients) for each group is shown in the top axis. Two-sided Kruskal-Wallis test p-values are shown for (a–c). d, Relationship between ctDNAB and RECIST 1.1 target lesion sum of tumor measurements (N = 94 patients). Scatter plot with Spearman correlation coefficient (R) and two-sided p-value are shown. The poor correlation in this diverse cohort of advanced cancers suggests that ctDNA may provide complementary information to RECIST tumor measurements. Of note, RECIST target lesion sum of tumor measurements does not incorporate measurements from non-target lesions.

Source data

Extended Data Fig. 5 ∆ctDNAC3 for the INSPIRE sub-cohorts.

Clinical response is indicated by line color. The inset table summarizes ∆ctDNAC3 values among patients with distinct clinical responses in each sub-cohort. Closed circles indicate ctDNA positive samples and open circles indicate ctDNA negative samples; SD, stable disease; CR, complete response; PR, partial response; PD, progressive disease; B, baseline; C3, cycle 3; ND, not detected (below detection threshold).

Source data

Extended Data Fig. 6 Relationship between ctDNA, biomarkers, and prognosis.

a, Relationship between ctDNA metrics (ctDNAB or ∆ctDNAC3) and other candidate biomarkers (PD-L1 or TMB). Scatter plots with Spearman correlation coefficients (R) and p-values are shown. Top row: N = 94 patients with both ctDNAB and TMB levels; N = 93 patients with both ctDNAB and PD-L1 levels. Bottom row: N = 72 patients with ∆ctDNAC3, TMB, and PD-L1 levels. b, Performance of multivariable Cox models incorporating ctDNA metrics (ctDNAB and/or ∆ctDNAC3). Multivariable Cox models for OS (left) or PFS (right) were generated. The baseline model incorporates cohort, PD-L1, and TMB. C-statistic is plotted for each model. Statistically significant differences (two-sided likelihood ratio test) are indicated.

Source data

Extended Data Fig. 7 Definitions of ctDNA dynamic profiles.

Serial ctDNA measurements in patients grouped according ctDNA dynamics (clearance at any time point, no clearance with all on-treatment values below baseline, no clearance with any on-treatment value above baseline). Triangles indicate values off the displayed range of the y-axis.

Source data

Extended Data Fig. 8 Landmark analyses of ctDNA clearance.

Kaplan-Meier curves of OS among patients classified as one of 3 groups (Clearance; Decrease from baseline; Increase from baseline) based on ctDNA at each indicated landmark (that is, C3, C6, C9, or C12).

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Bratman, S.V., Yang, S.Y.C., Iafolla, M.A.J. et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer 1, 873–881 (2020). https://doi.org/10.1038/s43018-020-0096-5

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