Association of Clonal Hematopoiesis in DNA Repair Genes With Prostate Cancer Plasma Cell-free DNA Testing Interference | Cancer Biomarkers | JAMA Oncology | JAMA Network
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Figure.  Source of Variants Detected in Prostate Cancer cfDNA Studies
Source of Variants Detected in Prostate Cancer cfDNA Studies

A, The prevalence of CHIP variants increased with age. CHIP was particularly prevalent (71%) in the 81 to 90 year age range. B, Consecutive series of 69 cfDNA studies. The DNA repair genes associated with PARPi eligibility are depicted along with other genes in which CHIP was detected. Each column represents 1 unique patient sorted by age. Variants detected in plasma are color coded by source, red indicates CHIP interference, somatic; green indicates non-CHIP, somatic (prostate cancer); yellow indicates germline. cfDNA indicates cell-free DNA; CHIP, clonal hematopoiesis of indeterminate potential; PARPi, poly(ADP) ribose polymerase inhibitor.

Table.  CHIP Clones Detected in DNA Repair Genes Used for PARPi Eligibility
CHIP Clones Detected in DNA Repair Genes Used for PARPi Eligibility
1.
Goodall  J, Mateo  J, Yuan  W,  et al; TOPARP-A investigators.  Circulating cell-free DNA to guide prostate cancer treatment with PARP inhibition.   Cancer Discov. 2017;7(9):1006-1017. doi:10.1158/2159-8290.CD-17-0261PubMedGoogle ScholarCrossref
2.
de Bono  J, Mateo  J, Fizazi  K,  et al.  Olaparib for metastatic castration-resistant prostate cancer.   N Engl J Med. 2020;382(22):2091-2102. doi:10.1056/NEJMoa1911440PubMedGoogle ScholarCrossref
3.
Annala  M, Vandekerkhove  G, Khalaf  D,  et al.  Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer.   Cancer Discov. 2018;8(4):444-457. doi:10.1158/2159-8290.CD-17-0937PubMedGoogle ScholarCrossref
4.
Schweizer  MT, Gulati  R, Beightol  M,  et al.  Clinical determinants for successful circulating tumor DNA analysis in prostate cancer.   Prostate. 2019;79(7):701-708. doi:10.1002/pros.23778PubMedGoogle ScholarCrossref
5.
Weitzel  JN, Chao  EC, Nehoray  B,  et al.  Somatic TP53 variants frequently confound germ-line testing results.   Genet Med. 2018;20(8):809-816. doi:10.1038/gim.2017.196PubMedGoogle ScholarCrossref
6.
Razavi  P, Li  BT, Brown  DN,  et al.  High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants.   Nat Med. 2019;25(12):1928-1937. doi:10.1038/s41591-019-0652-7PubMedGoogle ScholarCrossref
7.
Kuo  AJ, Paulson  VA, Hempelmann  JA,  et al.  Validation and implementation of a modular targeted capture assay for the detection of clinically significant molecular oncology alterations.   Pract Lab Med. 2020;19:e00153. doi:10.1016/j.plabm.2020.e00153PubMedGoogle Scholar
8.
Genovese  G, Kähler  AK, Handsaker  RE,  et al.  Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.   N Engl J Med. 2014;371(26):2477-2487. doi:10.1056/NEJMoa1409405PubMedGoogle ScholarCrossref
9.
Jaiswal  S, Fontanillas  P, Flannick  J,  et al.  Age-related clonal hematopoiesis associated with adverse outcomes.   N Engl J Med. 2014;371(26):2488-2498. doi:10.1056/NEJMoa1408617PubMedGoogle ScholarCrossref
10.
Slavin  TP, Coffee  B, Bernhisel  R,  et al.  Prevalence and characteristics of likely-somatic variants in cancer susceptibility genes among individuals who had hereditary pan-cancer panel testing.   Cancer Genet. 2019;235-236:31-38. doi:10.1016/j.cancergen.2019.04.005PubMedGoogle ScholarCrossref
11.
Swisher  EM, Harrell  MI, Norquist  BM,  et al.  Somatic mosaic mutations in PPM1D and TP53 in the blood of women with ovarian carcinoma.   JAMA Oncol. 2016;2(3):370-372. doi:10.1001/jamaoncol.2015.6053PubMedGoogle ScholarCrossref
12.
Mateo  J, Carreira  S, Sandhu  S,  et al.  DNA-repair defects and olaparib in metastatic prostate cancer.   N Engl J Med. 2015;373(18):1697-1708. doi:10.1056/NEJMoa1506859PubMedGoogle ScholarCrossref
13.
Torga  G, Pienta  KJ.  Patient-paired sample congruence between 2 commercial liquid biopsy tests.   JAMA Oncol. 2018;4(6):868-870. doi:10.1001/jamaoncol.2017.4027PubMedGoogle ScholarCrossref
14.
Bacon  JVW, Annala  M, Soleimani  M,  et al.  Plasma circulating tumor DNA and clonal hematopoiesis in metastatic renal cell carcinoma.   Clin Genitourin Cancer. 2020;18(4):322-331.e2. doi:10.1016/j.clgc.2019.12.018PubMedGoogle ScholarCrossref
15.
Abida  W, Campbell  D, Patnaik  A,  et al.  Non-BRCA DNA damage repair gene alterations and response to the PARP inhibitor rucaparib in metastatic castration-resistant prostate cancer: analysis from the phase II TRITON2 study.   Clin Cancer Res. 2020;26(11):2487-2496. doi:10.1158/1078-0432.CCR-20-0394PubMedGoogle ScholarCrossref
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    Brief Report
    November 5, 2020

    Association of Clonal Hematopoiesis in DNA Repair Genes With Prostate Cancer Plasma Cell-free DNA Testing Interference

    Author Affiliations
    • 1Department of Laboratory Medicine, University of Washington, Seattle
    • 2Department of Medicine, Division of Medical Oncology, University of Washington, Seattle
    • 3Brotman Baty Institute for Precision Medicine, Seattle, Washington
    • 4Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
    • 5Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
    JAMA Oncol. 2021;7(1):107-110. doi:10.1001/jamaoncol.2020.5161
    Key Points

    Question  How often are cell-free DNA (cfDNA) studies in prostate cancer confounded by clonal hematopoiesis (CHIP) variants in genes used for poly(ADP) ribose polymerase inhibitor (PARPi) eligibility?

    Findings  In this case series study of 69 men with advanced prostate cancer, 7 (10%) had CHIP variants in genes used for US Food and Drug Administration-approved indications of PARPi treatment, most frequently in ATM.

    Meaning  Men with prostate cancer are at high risk of being misdiagnosed as being eligible for PARPi therapy using current cfDNA tests; assays should use a whole-blood control sample to distinguish CHIP variants from prostate cancer.

    Abstract

    Importance  Cell-free DNA (cfDNA) testing is increasingly used in the treatment of patients with advanced prostate cancer. Clonal hematopoiesis of indeterminate potential (CHIP) can interfere with cfDNA testing and cause incorrect interpretation of results. There is an urgent need to better understand this problem following recent US Food and Drug Administration approval of poly(ADP) ribose polymerase inhibitors (PARPi) for metastatic prostate cancer based on variants in DNA repair genes that can be affected by CHIP.

    Objective  To determine the prevalence of clinically relevant CHIP interference in prostate cancer cfDNA testing.

    Design, Setting, and Participants  We report a case series of 69 patients with advanced prostate cancer (metastatic disease or with rising PSA following localized therapy) who had cfDNA variant testing with a large panel cancer next generation sequencing assay (UW-OncoPlexCT). To determine the source of variants in plasma, we tested paired cfDNA and whole blood control samples. The study was carried out in an academic medical center system reference laboratory.

    Main Outcomes and Measures  Prevalence and gene spectrum of CHIP interference in patients with prostate cancer undergoing cfDNA testing.

    Results  We detected CHIP variants at 2% or more variant fraction in cfDNA from 13 of 69 men with prostate cancer (19%; 95% CI, 10%-30%). Seven men (10%; 95% CI, 4%-20%) had CHIP variants in DNA repair genes used to determine PARPi candidacy, including ATM (n = 5), BRCA2 (n = 1), and CHEK2 (n = 1). Overall, CHIP variants accounted for almost half of the somatic DNA repair gene variants detected. Participant CHIP variants were exponentially correlated with older age (R2 = 0.82). CHIP interference variants could be distinguished from prostate cancer variants using a paired whole-blood control.

    Conclusions and Relevance  In this case series, approximately 10% of men with advanced prostate cancer had CHIP interference in plasma cfDNA in DNA repair genes that are used for eligibility of PARPi therapy, most frequently in ATM. Clinical cfDNA testing should include a paired whole-blood control to exclude CHIP variants and avoid misdiagnosis.

    Introduction

    Cell-free DNA (cfDNA) variant analysis is used to guide treatment decisions for men with metastatic prostate cancer (mPC) and to enroll patients on clinical trials.1 Two poly(ADP) ribose polymerase inhibitors (PARPi) were recently granted US Food and Drug Administration (FDA) approval for use in selected patients with mPC based on DNA repair gene status: rucaparib for patients with BRCA1 or BRCA2 variants and olaparib for patients with ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D or RAD51L variants.2 Following these biomarker-guided approvals we expect cfDNA testing will sharply increase for patients with mPC because it offers the convenience and simplicity of testing on a blood sample in the advanced disease setting.1,3,4 Thus, there is an urgent need to understand cfDNA testing performance and sources of test interferences.

    Clonal hematopoiesis of indeterminate potential (CHIP) is a known confounder of cfDNA testing.5,6 Clonal hematopoiesis of indeterminate potential variants are detected in both plasma and whole blood, whereas prostate cancer variants are detected in plasma only. Yet most commercial labs perform cfDNA testing using a plasma-only approach that cannot reliably distinguish variants derived from prostate cancer vs those arising from CHIP. To improve cfDNA assay performance, we developed an approach (UW-OncoPlexCT) that simultaneously analyzes plasma and paired whole-blood control samples.4 Using this paired testing approach we sought to determine to what degree CHIP interferes with the results of prostate cancer cfDNA testing.

    Methods

    We retrospectively reviewed cfDNA study results from 69 patients with advanced prostate cancer (metastatic disease or with rising PSA following localized therapy) sequenced by our Clinical Laboratory Improvement Amendments (CLIA)-certified and College of American Pathologists (CAP)-accredited clinical UW-OncoPlexCT protocol. Plasma cfDNA and a paired whole-blood control sample were tested in every patient.4,7 We defined CHIP interference as a pathogenic variant with variant allele fractions (VAFs) of at least 2% in both the whole blood and plasma. Germline variants were distinguished from CHIP clones by tumor sequencing. Sequencing data analysis and variant interpretation were performed by an expert molecular pathologist (C.C.P.). All data were manually reviewed in the integrated genomics viewer (IGV) to exclude sequencing artifacts. Data were generated and preprocessed by the University of Washington NGS Laboratory and Analytics group. This study was performed in accordance with the Declaration of Helsinki guidelines and approved by the University of Washington/Fred Hutchinson Cancer Consortium institutional review board and all patients provided written informed consent.

    Results

    We detected CHIP interference clones at least 2% variant fraction in 13 of 69 patients (19%; 95% CI, 10%-30%). Seven patients (10%; 95% CI, 4%-20%) had CHIP variants in DNA repair genes that are used for PARPi selection (ATM n = 5, BRCA2, n = 1 and CHEK2, n = 1) (Figure) (Table). The 6 remaining patients had CHIP interference in genes frequently impacted by CHIP: ASXL1, DNMT3A, PTEN, TET2, and TP53 (Figure) (eFigure in the Supplement).8,9

    We observed that CHIP interference correlated exponentially with increasing age (R2 = 0.82). We detected CHIP in 0% (0/6) of men aged 40 to 50 years, 12.5% (2/16) of men aged 51 to 60 years, 6.3% (1/16) of men aged 61 to 70 years, 20.8% (5/24) of men aged 71 to 80 years, and 71% (5/7) of men aged 81 to 90 years (Figure, A).

    In 20 patients with advanced prostate cancer, we detected a total of 23 pathogenic variants in DNA repair gene variants used for selection of PARPi therapy, from the following source(s): CHIP interference somatic (n = 8, 1 patient had 2), non-CHIP somatic (n = 9), germline (n = 6) (Figure, B). We considered germline variants and non-CHIP somatic variants as true positives (n = 15) and CHIP interference as false positives (n = 8). Restricting the assay to a plasma-only analysis, only 65% of DNA repair gene variants detected were true positives (15/23). When incorporating a paired whole-blood control to remove CHIP interference, all DNA repair gene variants were true positives (15/15, 100%).

    The patient with BRCA2 CHIP interference had cfDNA testing done in parallel by an outside commercial laboratory using a plasma-only assay, which was unknown to our laboratory at the time of testing. The BRCA2 CHIP clone was clinically reported by the commercial lab with the recommendation to use PARPi therapy.

    Discussion

    We found that a strikingly high proportion of DNA repair gene variants in the plasma of patients with advanced prostate cancer are attributable to CHIP. The CHIP variants were strongly correlated with increased age, and even higher than expected by age group. The high rate of CHIP may also be influenced by prior exposure to chemotherapy.10,11 We are concerned that CHIP interference is causing false-positive cfDNA biomarker assessments that may result in patient harm from inappropriate treatment, and delays in delivering alternative effective treatment options. Without performing a whole-blood control, 7 of 69 patients (10%) would have been misdiagnosed and incorrectly deemed eligible for PARP-inhibitor therapy based on CHIP interference in plasma. In fact, 1 patient in this series had a BRCA2 CHIP clone that had been previously reported by a commercial lab with the recommendation to use a PARPi. To mitigate these risks, cfDNA results should be compared to results from whole-blood control or tumor tissue.12

    Challenges of accurate cfDNA testing are beginning to be described. A recent report13 highlighted inaccuracies of commercial laboratory cfDNA testing in patients with prostate cancer. In that report, cfDNA samples from 40 patients were sent to 2 separate CLIA-certified laboratories and only 9 of 40 (23%) demonstrated congruence (complete or partial) of positive findings.13 The consistent findings included ATM and TP53 variants in patients with low PSA at the time of blood draw, raising suspicion that these may be CHIP clones. The CHIP interference in cfDNA testing has also been reported in other cancer types. In renal-cell carcinoma (RCC), for example, CHIP was found to affect cfDNA results in 43% of patients.14

    Overall, ATM accounted for the majority of clinically relevant CHIP interference in our series. The ATM gene has been described as a frequent CHIP clone in clinical cancer predisposition testing, along with CHEK2 and TP53.10 We speculate that CHIP interference in cfDNA testing could be affecting results of PARPi clinical studies of patients with metastatic prostate cancer. Trials allowing plasma-only cfDNA testing for enrollment may have included patients with false-positive results associated with CHIP in DNA repair genes, particularly in ATM.15 We speculate that this could be contributing to low PARPi response rates reported in patients with ATM variants, such as recently reported from the TRITON2 study.15

    Limitations

    This study has several limitations including relatively small sample size, the retrospective nature of the study, and heterogeneity in patient populations and prior therapies.

    Conclusions

    Findings of this study suggest that CHIP substantially interferes with plasma cfDNA testing in patients with advanced prostate cancer. There is a risk for widespread misdiagnosis and overtreatment of men with PARPi using currently available commercial cfDNA assays. We recommend that all cfDNA testing in patients with prostate cancer include a whole-blood control to distinguish CHIP from prostate cancer variants.

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    Article Information

    Accepted for Publication: August 13, 2020.

    Published Online: November 5, 2020. doi:10.1001/jamaoncol.2020.5161

    Corresponding Author: Colin C. Pritchard, MD, PhD, University of Washington, 1959 NE Pacific St, PO Box 357110, Seattle, WA 98195 (cpritch@uw.edu).

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Jensen K et al. JAMA Oncology.

    Author Contributions: Dr Pritchard had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Jensen, Sokolova, Nelson, Pritchard.

    Acquisition, analysis, or interpretation of data: Jensen, Konnick, Schweizer, Grivas, Cheng, Klemfuss, Beightol, Yu, Montgomery, Pritchard.

    Drafting of the manuscript: Jensen, Konnick, Schweizer, Beightol, Nelson, Pritchard.

    Critical revision of the manuscript for important intellectual content: Jensen, Konnick, Schweizer, Sokolova, Grivas, Cheng, Klemfuss, Yu, Montgomery, Pritchard.

    Statistical analysis: Jensen, Pritchard.

    Obtained funding: Nelson, Pritchard.

    Administrative, technical, or material support: Konnick, Cheng, Klemfuss, Beightol, Yu, Nelson, Montgomery, Pritchard.

    Supervision: Konnick, Pritchard.

    Conflict of Interest Disclosures: Dr Pritchard consults for AstraZeneca and Promega. Dr Grivas consults for AstraZeneca, Bayer, Bristol Myers Squibb, Clovis Oncology, Driver, EMD Serono, Exelixis, Foundation Medicine, GlaxoSmithKline, Genentech, Genzyme, Heron Therapeutics, Janssen, Merck, Mirati Therapeutics, Pfizer, Roche, Seattle Genetics, and QED Therapeutics; he also reports participation in an educational program for Bristol Myers Squibb; and institutional research funding from AstraZeneca, Bavarian Nordic, Bayer, Bristol Myers Squibb, Clovis Oncology, Debiopharm, Genentech, Immunomedics, Kure It Cancer Research, Merck, Mirati Therapeutics, Oncogenex, Pfizer, and QED Therapeutics. No other conflicts were reported.

    Funding/Support: This work was supported by US Department of Defense awards W81XWH-18-1-0756 (PC170510) (design and conduct of the study), W81XWH-18-1-0356 (PC170503P2) (design and conduct of the study), W81XWH-18-1-0354 (design and conduct of the study), and W81XWH-16-1-0484 (design and conduct of the study), the Pacific Northwest Prostate Cancer SPORE CA097186, the Brotman Baty Institute for Precision Medicine (design and conduct of the study), the Prostate Cancer Foundation (design and conduct of the study), the UW/FHCRC Institute for Prostate Cancer Research (IPCR) (design and conduct of the study, collection, management, analysis, and interpretation of the data), and National Institutes of Health/National Cancer Institute Cancer Center Support Grant P30 CA015704 (design and conduct of the study).

    Role of the Funder/Sponsor: The funding agencies had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Goodall  J, Mateo  J, Yuan  W,  et al; TOPARP-A investigators.  Circulating cell-free DNA to guide prostate cancer treatment with PARP inhibition.   Cancer Discov. 2017;7(9):1006-1017. doi:10.1158/2159-8290.CD-17-0261PubMedGoogle ScholarCrossref
    2.
    de Bono  J, Mateo  J, Fizazi  K,  et al.  Olaparib for metastatic castration-resistant prostate cancer.   N Engl J Med. 2020;382(22):2091-2102. doi:10.1056/NEJMoa1911440PubMedGoogle ScholarCrossref
    3.
    Annala  M, Vandekerkhove  G, Khalaf  D,  et al.  Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer.   Cancer Discov. 2018;8(4):444-457. doi:10.1158/2159-8290.CD-17-0937PubMedGoogle ScholarCrossref
    4.
    Schweizer  MT, Gulati  R, Beightol  M,  et al.  Clinical determinants for successful circulating tumor DNA analysis in prostate cancer.   Prostate. 2019;79(7):701-708. doi:10.1002/pros.23778PubMedGoogle ScholarCrossref
    5.
    Weitzel  JN, Chao  EC, Nehoray  B,  et al.  Somatic TP53 variants frequently confound germ-line testing results.   Genet Med. 2018;20(8):809-816. doi:10.1038/gim.2017.196PubMedGoogle ScholarCrossref
    6.
    Razavi  P, Li  BT, Brown  DN,  et al.  High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants.   Nat Med. 2019;25(12):1928-1937. doi:10.1038/s41591-019-0652-7PubMedGoogle ScholarCrossref
    7.
    Kuo  AJ, Paulson  VA, Hempelmann  JA,  et al.  Validation and implementation of a modular targeted capture assay for the detection of clinically significant molecular oncology alterations.   Pract Lab Med. 2020;19:e00153. doi:10.1016/j.plabm.2020.e00153PubMedGoogle Scholar
    8.
    Genovese  G, Kähler  AK, Handsaker  RE,  et al.  Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence.   N Engl J Med. 2014;371(26):2477-2487. doi:10.1056/NEJMoa1409405PubMedGoogle ScholarCrossref
    9.
    Jaiswal  S, Fontanillas  P, Flannick  J,  et al.  Age-related clonal hematopoiesis associated with adverse outcomes.   N Engl J Med. 2014;371(26):2488-2498. doi:10.1056/NEJMoa1408617PubMedGoogle ScholarCrossref
    10.
    Slavin  TP, Coffee  B, Bernhisel  R,  et al.  Prevalence and characteristics of likely-somatic variants in cancer susceptibility genes among individuals who had hereditary pan-cancer panel testing.   Cancer Genet. 2019;235-236:31-38. doi:10.1016/j.cancergen.2019.04.005PubMedGoogle ScholarCrossref
    11.
    Swisher  EM, Harrell  MI, Norquist  BM,  et al.  Somatic mosaic mutations in PPM1D and TP53 in the blood of women with ovarian carcinoma.   JAMA Oncol. 2016;2(3):370-372. doi:10.1001/jamaoncol.2015.6053PubMedGoogle ScholarCrossref
    12.
    Mateo  J, Carreira  S, Sandhu  S,  et al.  DNA-repair defects and olaparib in metastatic prostate cancer.   N Engl J Med. 2015;373(18):1697-1708. doi:10.1056/NEJMoa1506859PubMedGoogle ScholarCrossref
    13.
    Torga  G, Pienta  KJ.  Patient-paired sample congruence between 2 commercial liquid biopsy tests.   JAMA Oncol. 2018;4(6):868-870. doi:10.1001/jamaoncol.2017.4027PubMedGoogle ScholarCrossref
    14.
    Bacon  JVW, Annala  M, Soleimani  M,  et al.  Plasma circulating tumor DNA and clonal hematopoiesis in metastatic renal cell carcinoma.   Clin Genitourin Cancer. 2020;18(4):322-331.e2. doi:10.1016/j.clgc.2019.12.018PubMedGoogle ScholarCrossref
    15.
    Abida  W, Campbell  D, Patnaik  A,  et al.  Non-BRCA DNA damage repair gene alterations and response to the PARP inhibitor rucaparib in metastatic castration-resistant prostate cancer: analysis from the phase II TRITON2 study.   Clin Cancer Res. 2020;26(11):2487-2496. doi:10.1158/1078-0432.CCR-20-0394PubMedGoogle ScholarCrossref
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